[
    {
        "DOI": "10.1016/J.SCITOTENV.2026.181790",
        "Title": "Local and distal processes determine precipitation isotope records in the Great Plains USA",
        "Year": 2026,
        "Abstract": "Nuanced characterizations of moisture source dynamics and local hydrometeorological processes are essential for interpreting long-term records of stable isotopes in precipitation. Here, we analyze over two decades of stable isotope records from a site in the Great Plains of United States, revealing a distinct seasonal contrast in 18O variability between warm (MarchNovember) and cold (DecemberFebruary) periods. During the warm season, isotopic enrichment was largely driven by enhanced convective activity and sub-cloud evaporation under high VPD conditions. Back-trajectory diagnostics indicate that continental moisture sources dominate precipitation at the study site, while Gulf of Mexico transport via the Great Plains low-level jet exerts a disproportionate influence on 18O and d-excess variability. During extreme precipitation years, isotopic signatures reflect the combined effects of atmospheric circulation anomalies and local aridity. The 2012 drought year exhibited elevated 18O and reduced d-excess consistent with enhanced kinetic fractionation under dry conditions, whereas the wet year 2019 showed isotopic enrichment associated with intensified Gulf-sourced moisture transport under humid conditions. These findings demonstrate how precipitation 18O in the Great Plains integrates both local evaporative demand and large-scale moisture transport processes. Given ongoing challenges in representing humidity trends and regional hydroclimate dynamics in climate models, improved characterization of moisture sources and isotope variability is critical for evaluating model projections and interpreting long-term climate change in semi-arid continental regions."
    },
    {
        "DOI": "10.1016/J.ATMOSENV.2026.121995",
        "Title": "The impact of model retraining frequency on predictive performance in air pollution forecasting",
        "Year": 2026,
        "Abstract": "High-resolution PM 2.5 forecasts are increasingly produced with machine-learning models, yet practical guidance on how often these models should be retrained and validated remains limited. This study quantifies the impact of retraining frequency bias correction air-quality prediction skill across multiple cities contrasting emission sources meteorological regimes, using The Goddard Earth Observing System composition forecast (GEOS-CF) fused in-situ observations. Site-specific trained at least two years hourly data, alternative update schedules (618-month baselines 612-month cycles) evaluated RMSE, R 2 , SHAP-based feature importance. Bias-corrected consistently improves GEOS-CF by more than 107% in reduces RMSE over 75%, annual providing largest gains (13% increase 12% reduction RMSE) relative to frequent updates. SHAP analysis shows that dominant predictors their importance vary city, combinations boundary-layer height, aerosol optical depth, humidity, wind, nitrogen oxides driving levels, demonstrating a single global pre-trained model is inadequate locally tuned required. Together, results define minimum data requirements, preferred intervals, need for site-specific bias-corrected offering concrete design rules operational forecasting systems."
    },
    {
        "DOI": "10.1029/2025JD045335",
        "Title": "A Climatology of Mesoscale Convective System Hazards in the United States and Their Representation in a ConvectionPermitting Model",
        "Year": 2026,
        "Abstract": "Abstract Mesoscale convective systems (MCSs) are large, organized convective storms that frequently produce flash floods and other severe hazards such as damaging winds, hail, and tornadoes. Developing an observationally based MCS hazard climatology is important for establishing a baseline to evaluate the representation of these events in numerical models. This study constructs such a climatology using a 13year MCS data set, storm reports, and atmospheric reanalysis. MCSrelated and nearstorm environmental variables are extracted and used to train objectbased machine learning (ML) models. Three models are developed to predict flash floods, severe (including all wind, hail and tornado events), and significantsevere events, with the latter representing higherimpact hazards. The flash flood and severe models perform well in distinguishing hazardproducing MCSs from nonproducing ones, while the significantsevere model shows limited skill, likely due to sample size constraints. The flash flood and severe models are then applied to the full MCS archive to reconstruct a more complete warm season hazard climatology, addressing the potential underreporting in storm reports and gaps in flash flood reports during early years. The study also examines the application of these models to convectionpermitting model simulations. While the spatial distribution of simulated MCS hazards generally aligns with observations, event frequencies differ considerably. These discrepancies are attributed to biases in both the derived input variables and the representation of MCS properties within the model. , Plain Language Summary Mesoscale convective systems (MCSs) are large, organized thunderstorms that produce hazardous weather, including flash floods, damaging winds, hail, and tornadoes. This study creates a 13year climatology of MCSrelated hazards by developing machine learning models trained on observational and atmospheric data. Three separate models were built: one for flash floods, one for all types of severe weather (wind, hail, and tornadoes), and one for the most intense severe events. The models for flash floods and general severe weather worked well, while the model for the most intense events had more difficulty, likely because these cases are rarer. The study also tested whether the same prediction models could be used with output from highresolution model simulations. While the simulations showed similar storm patterns in space, the number of predicted hazardous events did not always match realworld observations. This mismatch was due to differences in both the simulated weather conditions and how the storms themselves were represented in the model. Overall, the study shows that machine learning can help fill gaps in severe weather records and improve our understanding of storm hazards. , Key Points Developed objectbased machine learning (ML) models to predict mesoscale convective system (MCS)produced hazards Created a more complete MCS hazard climatology for the warm season by correcting historical data gaps and storm underreporting ML applied to ConvectionPermitting Models reveals biases in simulated MCSs and predictors lead to inaccurate hazard frequency"
    },
    {
        "DOI": "10.1029/2025WR040312",
        "Title": "Integrating Satellite Retrievals, Numerical Models, and Machine Learning for Global Groundwater Recharge Estimation",
        "Year": 2026,
        "Abstract": "Abstract Knowledge of the groundwater recharge rate determines whether aquifer use is sustainable. However, accurately measuring recharge globally presents significant challenges due to the complexity of subsurface processes and the lack of direct observational methods. This study addresses these challenges by developing a methodology that integrates satellite data, numerical models, and machine learning to estimate groundwater recharge globally. The methodology involves two steps. First, we run a numerical model, Hydrus1D, to simulate soil moisture fluxes in the unsaturated zone by solving the Richards equation in the vertical direction for 235 different points representing various climates and soil types across the globe. Second, using Hydrus1D inputs and outputs, we train a supervised ensemble machinelearning model, specifically a Gaussian Process Regression model, as an emulator to mimic Hydrus1D. This enables us to process satellite observations efficiently to estimate annual recharge flux globally. Inputs for the model include NASA's SMAP soil moisture and GPM precipitation observations, ERA5 climate reanalysis data, and soil hydraulic properties. Rainfall, unsaturated hydraulic conductivity, and soil moisture are identified as the most significant predictors of groundwater recharge. The approach effectively captures global recharge patterns, particularly in regions with high rainfall, though it shows some limitations in arid areas with minimal recharge and heavily irrigated areas. We confirm the reasonableness of recharge estimates by comparing them with observed changes in subsurface water storage from the GRACE satellite mission. The method effectively captures the observed trends in water storage, demonstrating the model's capability to estimate recharge using largescale satellite and reanalysis data. , Plain Language Summary We need to know how much groundwater is replenished to ensure its sustainable use, but measuring the recharge that replenishes groundwater globally is difficult, if not impossible. This study develops a new method that combines satellite data, computer models, and machine learning to estimate groundwater recharge at a global scale. First, a numerical model simulates how water moves through the soil under a wide variety of climate and soil conditions. Then, a machine learning model is trained to mimic the numerical model and learn from data to estimate annual recharge rates efficiently. The model uses inputs like soil moisture and rainfall data from satellite observations, climate records, and soil properties. The key factors influencing recharge are rainfall, soil moisture, and metrics of how easily water moves through soil. The method accurately maps global recharge patterns but has some limitations in dry and irrigated areas. The results showed that the new method effectively captures realworld recharge patterns, making it a valuable tool for understanding groundwater sustainability. , Key Points A new method integrates satellite data, physicallybased models, and machine learning to estimate global groundwater recharge Rainfall, soil moisture, and unsaturated hydraulic conductivity are key predictors of groundwater recharge The method accurately captures global recharge patterns and water storage trends, aligning with independent studies"
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108937",
        "Title": "Widespread extreme precipitation events over Iran: Large-scale patterns and their associated global indices",
        "Year": 2026,
        "Abstract": "Widespread extreme precipitation events (WEPEs) have intensified globally in recent decades, leading to severe hydrological and socio-economic impacts. Iran has experienced several destructive WEPEs, particularly during 2019 2020. Using Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG-F) data 20002024, (EPs) were classified as WEPEs when their spatially connected affected area exceeded 10% of Iran's land area. A total 153 identified across all seasons. Owing the dominant contribution winter spring events, subsequent large-scale analyses focused on these two seasons, comprising 135 (80 55 spring). Evaluation against observations from 39 synoptic stations IMERG-identified WEPE days, indicates that IMERG-F reliably captures frequency intensity EPs supporting its use long-term analysis. Cluster analysis using Ward's method delineated three spatial sub-regions identified: R1 (western northwestern Iran), R2 (eastern, central southeastern Iran) R3 (southern Iran). exhibited highest while event duration contributed comparably R3, reaching up 500-h/25-year (~21-day/25-year). Composite reveal over R1, are associated with upper deep trough Red Sea, eastern Saudi Arabia, Sea extending toward respectively. Increased instability dynamical forcing substantially strengthen upwards motion, moisture transport is dominated by strong integrated vapor Persian Gulf. Lead-lag composite 200 hPa geopotential height anomalies a clear evolution preceding Iran. Furthermore, closely Circumglobal Wave Train (CGT) both North Atlantic Oscillation (NAO) influence indirectly modulating CGT variability."
    },
    {
        "DOI": "10.1029/2025JD045001",
        "Title": "Urban Impacts on Precipitation in the Greater Ho Chi Minh City Metropolitan Area, Vietnam",
        "Year": 2026,
        "Abstract": "Abstract Urban impacts on precipitation have been extensively studied in midlatitude developed countries. However, despite the recent rapid urban development, such studies focusing on Southeast Asian cities remain limited. This study evaluated urban impacts on precipitation in the Greater Ho Chi Minh City Metropolitan area (GHCM) by multicase numerical simulations using the Weather Research and Forecasting model (WRF) for 217 precipitation events during the rainy seasons from 2013 to 2022. We conducted each simulation with and without 2020 urban land use (URB and NO_URB), and under two different initial and boundary conditions to enhance the robustness of the results. Results show that daily precipitation over the central urban area was up to 30% greater in URB than in NO_URB, with statistical significance ( p < 0.05), especially between 15:00 and 22:00 LT. URB also exhibited higher sensible heat flux (+250 W m 2 ), higher convective boundary layer height (+350 m), and lower surface pressure (0.2 hPa) than NO_URB at 14:00 LT. These thermodynamic changes caused greater Convective Available Potential Energy and smaller Convective Inhibition than those of NO_URB, indicating intensified atmospheric instability due to urbanization. In addition, gridscale horizontal water vapor convergence was stronger in URB than in NO_URB. This convergence is caused by the lower surface pressure in URB than NO_URB. As a result, water vapor accumulated within the convective boundary layer. These results indicate that the urban area strengthened atmospheric instability and gridscale horizontal water vapor convergence, resulting in greater precipitation than the nonurban case. , Plain Language Summary Urban effects on rainfall have been well studied in developed countries in the midlatitudes. However, even though cities in Southeast Asia have grown rapidly in recent years, there are still only a few studies on this region. This study examined how urbanization affects rainfall in the Greater Ho Chi Minh City Metropolitan area (GHCM), using numerical simulations for 217 rainy events from 2013 to 2022. For each event, we ran two simulations: one that included the land use of urban areas as of 2020, and one that included urban areas removed. Our results showed that, in the urban scenario, daily rainfall in the central urban area was up to 30% greater than that in the nonurban scenario, especially between 15:00 and 22:00 LT. A key difference from cities in midlatitude regions is that this increase in rainfall lasted for a longer time in GHCM. We found two main reasons for the increased rainfall in the urban scenario. First, urbanization caused the atmosphere to become more unstable. Second, urbanization caused the horizontal moisture convergence in the urban area, and as a result, water vapor accumulated within the convective boundary layer. , Key Points Under synopticscale undisturbed conditions, rainfalls over the Greater Ho Chi Minh City Metropolitan Area increase because of urbanization Urbaninduced strong atmospheric instability and horizontal water vapor convergence were key drivers of the urbaninduced rainfall increase A set of 434 simulation cases enabled statistically robust assessment of urban impacts on precipitation"
    },
    {
        "DOI": "10.1029/2025JD045573",
        "Title": "MaddenJulian Oscillation and Atmospheric Rivers: New Insights on Water Source and Transport for Extreme Rainfall Over the Western U.S.",
        "Year": 2026,
        "Abstract": "Abstract Atmospheric rivers (ARs) were first documented by Zhu and Newell for transporting global water vapor. ARs contribute to extreme rainfall, especially over the Western United States. The primary water vapor source of ARs is from the tropical ocean, where convective systems bring the moist flux upward from the surface to the troposphere. Previous studies have investigated ARs in connection to the MaddenJulian Oscillation (MJO) using the Realtime Multivariate MJO (RMM) Index, which is based on Empirical Orthogonal Function (EOF) analysis of outgoing longwave radiation and upperlevel wind. The question of what is the physical mechanism connecting the MJO and ARs remains unclear. This study aims to provide new insights into the effect of MJO convection as a water vapor source for ARs by investigating the direct connection between the MJO convection and ARs using the Largescale Precipitation Tracking (LPT) developed by Kerns and Chen. We track the MJO largescale precipitation and ARs in time and space using satellite data and reanalysis data from 2000 to 2024. We find that largescale convection of the MJO serves as a major water vapor source for ARs during the boreal winter (DecemberMarch) when the MJO LPT systems extended further in the westcentral Pacific. During these months, ARs are twice as likely to occur when the MJO convection is active. ARs are stronger when they are physically connected to MJO convection. These stronger ARs are more likely to lead to increased extreme rainfall and flood risk along the U.S. West Coast. , Plain Language Summary Atmospheric rivers (ARs), vehicles of water vapor transport from the tropics to the midlatitudes, play a key role in extreme rainfall and flooding on the West Coast of the United States. Past research has linked the ARs to the MaddenJulian Oscillation (MJO) using common statistical indices, but these indices cannot capture the actual physical connection between the two, especially the intensity and duration of ARs to the MJO convection. To better understand the real connection of the two physical systems, we use a tracking method that follows the MJO largescale precipitation in connection to ARs from 2000 through 2024. We find that explicitly tracking the location of the MJO precipitation for individual MJO events provides new insights on the role of the MJO, as the main water source, in AR intensity and duration. In particular, we find that ARs that physically overlap with MJO rainfall last longer, are larger, and are more intense. These ARs are also more likely to lead to flooding events in California, Oregon, and Washington during the boreal winter (DecemberMarch). , Key Points Largescale convection linked to the MaddenJulian Oscillation (MJO) is a major water vapor source for wintertime atmospheric rivers (ARs) ARs are about twice as likely to occur when MJO convection is active. They tend to be stronger when physically connected to MJO convection These stronger ARs, associated with MJO convection, are linked to extreme rainfall and heightened flood risk along the U.S. West Coast"
    },
    {
        "DOI": "10.1029/2025GL120807",
        "Title": "Tropical TCV as a Process Diagnostic: Connecting Probability to Processes in kmScale Models Via Moisture Budget Statistics",
        "Year": 2026,
        "Abstract": "Abstract Observations show a bimodal frequency distribution in total column vapor (TCV) over tropical oceans, with convective rainfall predominantly produced on the moist side of the frequency minimum between two modal peaks. Here we show a kmscale model of the tropics with explicit convection produces a bimodal TCV distribution, whereas the same model with parameterized convection does not. The parameterized model also fails to realistically confine rainfall to a moist mode. Using concepts from statistical mechanics we relate TCV frequency and tendency, and isolate process contributions to tendency in TCV phasespace. Where bimodality is lacking, we find an incorrect relationship between moisture flux convergence and TCV in environments with little or no rainfall. The resulting lack of a strong gradient in TCV tendency with respect to TCV is inconsistent with that expected to maintain a TCV frequency minimum. Our results demonstrate value in the TCV distribution as a process diagnostic. , Plain Language Summary It has been observed that the tropical atmosphere at any given time is mainly split into two regions: a moist region where columns contain high quantities of water vapor, and a drier region with less vapor. These regions are separated by a narrow margin zone with moderate amounts of moisture. In other words, moist and dry columns are both common, but marginal columns are less frequent. Models of the tropical atmosphere need to capture this distribution because moisture affects cloud and rainfall patterns, with implications for weather forecasting and for climate simulation. For a model of the entire tropical atmosphere on a kmscale grid, we show that a realistic column moisture distribution can be limited by model subgrid physics. Failure to realistically separate moist and dry regions of the distribution is associated with rainfall biases and with weaker redistribution of moisture by winds. , Key Points The tropical moisture distribution is bimodal in observations, but reproducing the bimodality in kmscale models depends on model physics The lack of bimodality is associated with excessive probability of light rainfall, specifically in dry to moderately moist columns In columns with low rain, the model without bimodality has a weak relationship between moisture and flux contributions to moisture tendency"
    },
    {
        "DOI": "10.1029/2025JD044800",
        "Title": "Precipitation Efficiency by Storm Type in kmScale Climate Simulations and Satellite Observations",
        "Year": 2026,
        "Abstract": "Abstract Precipitation efficiency (PE), the fraction of cloud condensate converted into surface precipitation, is a key metric for understanding Earth's hydroclimate but has been challenging to observe and evaluate in models. Using satelliteretrieved ice water path (IWP) and precipitation estimates, we assess PE in a 4km convectionpermitting climate simulation across six North American storm types: smallscale, intermediate, and mesoscale convective systems, tropical cyclones, extratropical cyclones, and atmospheric rivers. The simulation reproduces observed PE characteristics and IWPprecipitation relationships across all storm types. The agreement between IWPbased and an alternative PE estimation method leveraging saturation adjustment is better for convective storms that produce a lot of cloud ice. PE varies by storm type, with tropical cyclones exhibiting the highest PE and least sample variability. PE also evolves over storm lifetimes, showing lower values during initiation and decay. Our results support the use of kmscale models for realistic PE analysis. , Plain Language Summary Precipitation efficiency (PE) measures how much condensate in clouds reaches the ground as rain or snow. Understanding PE is important because even if models produce the same amount of precipitation, differences in efficiency reflect different cloud and precipitation processes that affect weather and climate. However, PE has been difficult to measure, limiting how well weather and climate models can be evaluated. In this study, we use satellite data measuring ice in clouds and surface precipitation to estimate PE. We compare these estimates with results from a highresolution climate model that simulates weather at a grid spacing of 4 km. We examine six common storm types in North America, including thunderstorms, hurricanes, and atmospheric rivers. We find that PE varies by storm type. Hurricanes have the highest PE and show the least variability between cases. The model reproduces these patterns and the relationship between cloud ice and precipitation. We also compare two methods for estimating PE in the model: one based on cloud ice growth and another using detailed condensation physics. These methods agree best for convective storms producing lots of cloud ice. Our results show that finescale climate models can realistically simulate how clouds produce precipitation, improving prediction across scales. , Key Points Precipitation efficiency is accurately captured by kilometerscale model simulations when compared to satellite data Agreement between precipitation efficiency calculation methods is strongest for convective storms Precipitation efficiency varies with storm type and life stage, with tropical cyclones exhibiting the highest values"
    },
    {
        "DOI": "10.1002/JOC.70099",
        "Title": "La Nina Impacts on Southeastern African Climate: The Influence of Event Duration",
        "Year": 2025,
        "Abstract": "ABSTRACT The multiyear La Nina event of 20202023, which brought with it several climate disasters across the globe, sparked both mainstream and scientific interest in La Nina events, which typically have received less attention than El Nino. In southern Africa, there is a general expectation in the scientific community and among user groups that La Nina events result in cool and wet summers. However, such impacts do not always occur and the full diversity of La Nina impacts, including multiyear events, has not been systematically explored. Here, various temperature and rainfall characteristics occurring during three categories of La Nina eventssingle, double, and triple year events areinvestigated for the period 19702023. Spaceparameter bubble plots are used to display anomalies in midlate summer rainfall, heavy rain and dry spell frequencies, and in mean temperature and extreme heat days across four domains in southeastern Africa. Despite being relatively populated with important port cities and agriculture, not much work has focused on this region. Doubleyear La Nina summers were the least consistent in exhibiting expected wet characteristics. During tripleyear events, the subtropical domains, southern Mozambique and eastern South Africa, showed the greatest tendency towards wet conditions, although a few seasons deviated significantly in rainfall distribution from their multiyear counterparts. In contrast, the tropical domains, southern Tanzania and northern Mozambique, more consistently were cooler than average than the subtropical domains. These findings highlight the diversity of La Nina impacts on summer conditions in southeastern Africa and show that La Nina events are not necessarily associated with cooler and wetter than average summers over the region."
    },
    {
        "DOI": "10.1029/2025JD044655",
        "Title": "Increasing Temporal Variability of Global Tropical Cyclone NearStorm Rainfall Under Global Warming: Insights From CMIP6 HighResMIP Simulations",
        "Year": 2025,
        "Abstract": "Abstract Tropical cyclone (TC) rainfall poses a significant threat to coastal regions, particularly in the nearstorm region, where the inner core's strongest convection occurs. Temporal variability in the nearstorm rainfall rate over a TC's lifetime, including rapid increases and decreases in rainfall rate, impacts forecast accuracy and hazard preparedness. Using multiple HighResMIP CMIP6 simulations under the SSP58.5 scenario, we find that nearstorm rainfall rate temporal variability increases under pronounced anthropogenic warming, driven primarily by elevated atmospheric moisture. Analysis of relative rainfall rate changes suggests that this heightened variability aligns with rising nearstorm rainfall rate trends. These findings have critical implications for coastal communities, highlighting not only the increased risk of nearstorm rainfall but also the rapid intensification or decline of these rainfall events. , Plain Language Summary Tropical cyclones, also known as hurricanes or typhoons, bring heavy rainfall that can cause flooding in coastal areas. Accurately predicting these rainfall patterns is essential for keeping people safe. Our study uses advanced climate models (HighResMIP CMIP6, SSP58.5 scenario) to compare rainfall in today's climate with that in a future warmer climate. We found that rainfall near a storm's center will become more unpredictable, with more frequent sudden increases and decreases in rainfall intensity. This is mainly because warmer air can hold more moisture, leading to more variable rainfall. These findings highlight increased flood risks and challenges for forecasting, urging coastal communities to prepare for more unpredictable and intense rainfall events. , Key Points Tropical cyclone nearstorm rainfall rate temporal variability, including rapid increase and rapid decrease events, increases under warming The increased rainfall rate temporal variability is driven primarily by increased atmospheric moisture Rising tropical cyclone nearstorm rainfall rate likely drives greater temporal variability"
    },
    {
        "DOI": "10.1029/2024JD042828",
        "Title": "A European Hail and Lightning Climatology From an 11Year KilometerScale Regional Climate Simulation",
        "Year": 2025,
        "Abstract": "Abstract Hail and lightning, associated with severe convective storms, can cause extensive damage to infrastructure, agriculture, and ecosystems. Because of the small scale of these storms and the complexity of the involved processes, observing and modeling convective storms is challenging. The potential of online diagnostics in convectionpermitting models to simulate hail and lightning, especially over climatic time scales and extended regions, has not yet been fully exploited. To address this gap, we present a Europeanwide hail and lightning climatology (20112021) using the Consortium for Small Scale Modeling (COSMO) regional climate model with a horizontal grid spacing of 2.2 km, coupled with a hail growth model (HAILCAST) and the lightning potential index (LPI) diagnostics. We further developed a new Europeanwide hail product based on the Operational Program for the Exchange of Weather Radar Information (OPERA) composite. Model validation against observations demonstrates an overall good performance in simulating hail and lightning on spatial, seasonal, and diurnal scales. The highest hail frequencies occur during summer along the slopes of high mountain ridges, such as the Alps, Pyrenees, and the Carpathians, aligning with observed lightning hotspots in Europe. In autumn, hail and lightning occur predominantly over the Mediterranean and along the Adriatic coast. Severe hail events with a maximum hail diameter larger than 20 mm mainly occur in the Po Valley, western Spain, and Eastern Europe. This 11year simulation provides a Europeanwide data set of severe convective storms and their properties, serving as a basis for further studies of convective events and their impacts. , Plain Language Summary Severe convective storms often cause hail and lightning, which can lead to significant damage. Such events are difficult to observe and model due to their small scale and complexity. To address this, we performed an 11year long highresolution climate simulations (2.2 km) using COSMO with hail and lightning diagnostics. Additionally, we developed a Europeanwide hail data set based on the Operational Program for the Exchange of Weather Radar Information radar composite. The model was validated against radar and crowdsourced data, demonstrating good performance. Hail and lightning are most frequent along mountain ranges such as the Alps and Pyrenees in summer, and in autumn, storms shift to the Mediterranean. This 11year data set offers valuable insights into the characteristics of severe storms across Europe, supporting further research on factors driving these events and their impact in the context of climate change. , Key Points We present a Europeanwide climatology of hail and lightning based on a convectionpermitting climate simulation We introduce a new radarbased Europeanwide hail data set for the years 20132021"
    },
    {
        "DOI": "10.1029/2025GL117722",
        "Title": "Rapid Increase in Extreme Hourly Precipitation in Recent Two Decades in the Tibetan Plateau and Its LargeScale Drivers",
        "Year": 2025,
        "Abstract": "Abstract Although extreme hourly precipitation (EHP) over the Tibetan Plateau (TP) has received growing attention, its spatiotemporal variability and the underlying mechanisms governing regional responses remain unclear. This study explores the spatiotemporal characteristics of EHP over its eastern edge during the summers of 19882023. A trend reversal is identified: the amount, intensity and frequency of EHP all decreased during 19882003 but increased in 20042023, with the amount and frequency exhibiting significant rapid growth. These changes are linked to the coevolution of the South Asian High (SAH) and Western Pacific Subtropical High (WPSH). Their joint expansion enhances EHP via favorable dynamical and thermodynamic configurations, including intensified upperlevel divergence, lowerlevel convergence, deep ascending motion, convective instability, 500 hPa specific humidity, and moisture flux convergence. Spatially, SAH expansion primarily promotes EHP over the northeastern TP via thermodynamic processes, whereas WPSH expansion mainly intensifies EHP over the southeastern TP through dynamical forcing. , Plain Language Summary The Tibetan Plateau (TP) is crucial for global water balance, yet research on extreme hourly precipitation (EHP) here remains inadequate, especially on its eastern edge, where frequent geological disasters occur. Analyzing 19882023 summer data, this study found that EHP over the TP's eastern edge decreased before 2004, but rapidly increased afterward. This change is closely linked to the coevolution of the South Asian High (SAH) and the Western Pacific Subtropical High (WPSH). Their joint expansion creates favorable dynamical and thermodynamic conditions for EHP. These findings aid in guiding disaster prevention and reduction over the TP under global warming. , Key Points Summer extreme hourly precipitation (EHP) over Tibetan Plateau (TP)'s eastern edge declined in 19882003 then significantly rose in 20042023 The change of extreme hourly precipitation is linked to coevolution of the South Asian High and Western Pacific Subtropical High There are spatial differences in the effects of the South Asian High and the Western Pacific Subtropical High"
    },
    {
        "DOI": "10.1029/2024MS004840",
        "Title": "Extreme Precipitation Depiction in ConvectionPermitting Earth System Models Within the nextGEMS Project",
        "Year": 2025,
        "Abstract": "Abstract As extreme precipitation events become more frequent and intense, localscale climate services are increasingly needed to help communities adapt. We here evaluate two fully coupled convectionpermitting Earth System Models for their ability to resolve mesoscale extreme weather events. Using the Integrated Forecasting System (IFS) and Icosahedral Nonhydrostatic Weather and Climate Model (ICON) within the Next Generation Earth Modeling Systems (nextGEMS) project, we evaluate their depiction of extreme precipitation with a focus on the Mediterranean region through a comparison with high resolution reanalysis, gridded observations, a regional climate model, and two lowerresolution climate models. The results are then compared at a common, coarser resolution globally. For dry extremes, we find that the higher resolution and hybrid/explicit representation of convection of the nextGEMS models improve the representation of dry day fraction over land by about 5%7% points. Generally, the nextGEMS models concentrate dry spells into limited frequency yet overly long periods, although the lack of convection parameterization in ICON reduces maximum annual dry spell length over land by 45 days compared to a lowerresolution model version. For wet extremes, the nextGEMS models properly high intensities of heavy precipitation, aside from overestimation in ICON over mountainous terrain. ICON, with no convection scheme, tends to create overly intense, small, convective cells that are triggered without moisture convergence. Overall, the depiction of wet and dry precipitation extremes in the Mediterranean region are representative of the nextGEMS' models performance across the global midlatitudes demonstrating the models' value in simulating extreme weather systems. , Plain Language Summary The latest computational advancements have led to the development of climate models that have high spatial resolutions and treat atmospheric convection in a way that resembles current weather forecasting models. These convectionpermitting atmosphereocean coupled models offer to complement largeensemble coarser global climate models and provide localscale climate information to help local communities adapt to changes in extreme weather caused by climate change. Here we examine the depiction of extreme precipitation events in two convectionpermitting Earth System Models developed in the Next Generation Earth Modeling Systems (nextGEMS) project. Over the Mediterranean region, these models are shown to improve the simulation of both dry and wet extremes including the frequency of dry hours and the occurrence of extreme rainfall. The lack of a convection scheme in ICON appears to improve most extreme precipitation metrics but also creates some new issues. Overall, the progress made in developing these models should allow for a better representation of drought and floods. The results found here appear applicable to other midlatitude regions globally marking these models as a step forward in future extreme weather prediction and adaptation. , Key Points Fully coupled convectionpermitting Earth System Models improve the depiction of wet/dry precipitation extremes over the Mediterranean region The explicit resolution of convection helps simulate accurate dry spells over land but creates overly intense convective precipitation The improvement of precipitation extrema in convectionpermitting Earth System Models is a key step toward local climate risk information"
    },
    {
        "DOI": "10.3390/SU18094427",
        "Title": "Random Forest-Based Landslide Risk Assessment for Mountain Roads Under Extreme Rainfall: Implications for Infrastructure Resilience",
        "Year": 2026,
        "Abstract": "Extreme rainfall poses an increasing threat to mountainous transportation systems by frequently triggering landslides along road corridors. Most existing studies focus on long-term landslide susceptibility, whereas event-scale assessments remain limited, particularly in road environments. This study develops an event-scale framework for assessing landslide risk along mountain roads under extreme rainfall conditions, using the July 2023 237 rainfall event in Mentougou District, Beijing, as a case study. A Random Forest model was constructed by integrating multi-source geospatial data with an event-specific inventory of 8930 landslides. The model achieved high predictive performance, with ROCAUC values of 0.9187 and 0.9166 for the validation and test datasets, respectively. Feature importance analysis further indicates that landslide occurrence is controlled by the combined effects of rainfall, terrain conditions, vegetation cover, and anthropogenic disturbance, with rainfall acting as the primary trigger. High-risk road segments are mainly concentrated in the southeastern part of the study area, showing clear spatial clustering. These results highlight the value of event-scale analysis and demonstrate the effectiveness of the road-oriented framework for identifying hazardous segments under extreme rainfall conditions. The proposed approach provides practical support for landslide monitoring, risk mitigation, and resilient management of mountainous transportation infrastructure."
    },
    {
        "DOI": "10.1016/J.RSE.2026.115429",
        "Title": "Intercalibration of the newest Microwave Radiation Imagers onboard the Chinese Fengyun-3 satellites",
        "Year": 2026,
        "Abstract": "Accurate intercalibration of passive microwave (PMW) radiometers is a prerequisite for generating consistent Fundamental Climate Data Records (FCDR). However, reconciling the radiometric baselines of new-generation sensors, particularly those in drifting orbits (e.g., FY-3G) or observing heterogeneous land surfaces, remains a challenge due to severe spatiotemporal sampling mismatches and complex scene-dependent biases. To address these issues, we propose a novel Periodicity and Environmental Constraints based Multi-stage Inter-Calibration (PEC-MIC) framework to harmonize the FY-3G MWRI-RM and FY-3F MWRI-II observations with the GPM/GMI reference standard. The framework employs a reconstruction-then-correction strategy comprising three stages. First, a physically constrained interpolation utilizes Fourier harmonic modeling to reconstruct the diurnal temperature cycle, effectively bridging the sampling gaps caused by orbital drift. Second, a global non-linear correction based on Locally Weighted Scatterplot Smoothing (LOWESS) removes the intensity-dependent systematic errors of the sensors. Third, an environmentally-weighted PCA refinement isolates and eliminates residual scene-dependent biases (e.g., surface roughness artifacts) over complex terrain. Validation results demonstrate that the PEC-MIC significantly outperforms linear methods. The spatiotemporal interpolation is verified to achieve high accuracy, yielding RMSE of approximately 1.2 K even in data-sparse regions. Following the intercalibration process, the root mean square difference (RMSD) between the FY-3 sensors and the GMI is reduced by 50%70%, stabilizing within 0.81.5 K for most channels over land. The proposed method successfully decouples sensor-level drifts from geophysical variability, providing a robust solution for harmonizing diverse satellite constellations."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108965",
        "Title": "A climatology of hail in Europe (20142024) based on GPM-DPR sensor data",
        "Year": 2026,
        "Abstract": "This study presents a continental-scale hail climatology for Europe (20142024) using the Global Precipitation Measurement (GPM) Dual-frequency Radar (DPR). To address limitations of inhomogeneous surface observations, four detection algorithms were applied at radar-pixel level. Normalized frequency maps (01) generated AprilSeptember period, individual months, and subregions, to identify analyze European hotspots. The results main epicenter near Alpine range, with secondary axes extending over Balkan peninsula Apennines, confirming orography as dominant influence. analysis revealed clear seasonal evolution. That is, activity began in April scattered foci, shifted toward southeastern May, reached its maximum spatial extent heterogeneity June, consolidated system during summer peak (JulyAugust), finally moved Mediterranean Sea September. derived robustly validates documented patterns, provides new continental details, addresses observational gaps terrestrial networks, thereby value satellite radar studying extreme events. A is from GPM-DPR sensor data. Alps are control. Secondary high-frequency identified Balkans Apennines. Hail shifts southeast May"
    },
    {
        "DOI": "10.1016/J.ASTROPARTPHYS.2026.103252",
        "Title": "Monitoring the upper atmospheric temperature and interplanetary magnetic field with the GRAPES-3 muon telescope",
        "Year": 2026,
        "Abstract": "We study the influence of variations in the upper atmospheric temperature and interplanetary magnetic field on the cosmic ray induced atmospheric muon flux measured by the GRAPES-3 experiment over 22 years (20012022) of data; spanning three solar cycles: the declining phase of Solar Cycle 23, the full Cycle 24, and the rising and maximum phases of Cycle 25. Located in Ooty, India, the GRAPES-3 large area (560m2) muon telescope detects 4 billion muons daily above 1GeV, with an angular resolution of 4, enabling a statistical precision <0.01% on the hourly muon rate. After accounting for the effect of atmospheric pressure variations, we compare this data with the upper atmospheric temperature inferred from NASA's MERRA-2 dataset as well as magnetic field data from the ACE and WIND spacecraft at Lagrange point L1. A simultaneous iterative fitting method employing Fast Fourier Transforms and a narrow band-pass filter reveals the temperature and magnetic field coefficients to be T=0.22410.04(stat.)0.0220(syst.)%K1 and M=0.5740.027(stat.)0.011(syst.)%nT1, respectively, for an assumed hadronic attenuation length =120 g cm2, underscoring the potential of the GRAPES-3 muon telescope to serve as a real time monitor of the upper atmospheric temperature or interplanetary magnetic field."
    },
    {
        "DOI": "10.1029/2025JC023486",
        "Title": "Impacts of Salinity Stratification on SubSeasonal SST Warming in the Northern Indian Ocean",
        "Year": 2026,
        "Abstract": "Abstract The Northern Indian Ocean (NIO) experiences strong upper ocean warming during the spring intermonsoon season, with nearly 90% of the days showing net heat gain. However, observations reveal spatially heterogeneous Sea Surface Temperature (SST) trends [O(1C) differences] in these regions over intraseasonal timescales (1545 days) and mesoscale and smaller length scales (<100 km), coinciding with significant lateral variability in winds [O(2 m s 1 )] and salinity stratification [O(2 g kg 1 ) in surface salinity and O(20 m in mixed layer depth)]. This study investigates the role of salinitydriven mixed layers in driving these gradients in foundational SST warming using onedimensional modeling. Simulation results using realistic surface forcing show that lateral differences in stratification result in spatial differences in warming of foundational SST by about 0.20.5C over 1421 days, specifically for shallow mixed layers. However, the influence of stratification on foundational SST warming is nuanced and varies across the NIO, leading to either enhanced or reduced warming. Idealized simulations show that this contrast depends on net heat flux and water optical properties, with stratified cases warming more under high fluxes and turbid conditions. To generalize, we derive an analytical expression for the crossover heat flux , the threshold at which stratified and unstratified cases warm equally. depends on shortwave radiation, mixed layer depth and optical properties. For representative clearsky conditions, ranges from 103 to 136 W m 2 . These findings underscore the role of salinitydriven stratification and biooptical feedback in shaping SST gradients, with likely implications for subseasonal to seasonal monsoon forecasting. , Plain Language Summary The Northern Indian Ocean undergoes intense surface warming during the spring intermonsoon season, prior to Monsoons. Observations reveal that the surface warming trend is spatially uneven over intraseasonal timescales (1545 days) and shorter length scales (<100 km). This variability coincides with significant lateral differences in wind speeds and salinity stratification. Our study shows that in shallow mixed layers, riverinedriven salinity stratification strongly influences surface warming differences. Interestingly, the impact of salinity stratification on surface warming varies in the NIO, leading to either enhanced or reduced warming. Idealized simulations show that this difference is due to the sensitivity of surface warming nature to net heat flux and water optics. Under higher heat flux and more murkier conditions, stratified cases warm more. We also derive a theoretical dailyaveraged net heat flux value at which stratified and unstratified scenarios result in the same SST warming rate. This threshold ranges from 103 to 136 W m 2 under typical clearsky conditions and tropical openocean waters, depending on water clarity and mixed layer depth. These results highlight the importance of salinitydriven stratification and biooptical feedback in modulating regional SST evolution, factors that can influence tropical cyclone intensity and monsoon predictability. , Key Points Spatial variability of O(1C) is observed during the spring intermonsoon warming in the Northern Indian Ocean Salinity stratification can enhance or suppress surface warming by up to 0.5C, depending on heat fluxes and water optics The dailyaveraged heat flux value where salinity stratification begins to amplify surface warming is quantified ( Q cross )"
    },
    {
        "DOI": "10.1016/J.RSMA.2026.104944",
        "Title": "Policy-driven water quality trends in Qinhuangdao coastal waters, China (20032024): Nutrient controls and climate vulnerabilities via high-accuracy hue angle",
        "Year": 2026,
        "Abstract": "Coastal water color, as captured by the hue angle from satellite imagery, provides a comprehensive indicator of ecosystem dynamics influenced environmental and human factors. This study optimizes bias-correction model for deriving MODIS data using large in situ hyperspectral dataset, achieving improved accuracy ( R 2 = 0.71, MAPE 5.67%, RMSE 12.05). Applied to Qinhuangdao coastal waters over 20032024, uncovers nearshore-to-offshore gradient, V-shaped seasonal pattern, triphasic interannual trend: degradation (20032011), recovery (20122023), 2024 reversal. Phytoplankton chlorophyll-a (Chl-a) emerges dominant driver color variability 0.67), with dissolved inorganic nitrogen (DIN) key influence 0.54), underscoring nutrient-driven eutrophication. Policy measures, including 2008 Olympic pollution controls 2018 Bohai Sea remediation, correlate post-2012 improvements. The demonstrates greater sensitivity than Forel-Ule Index, offering valuable tool management."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108919",
        "Title": "Environments conducive to cloud-to-ground and ignited lightning in a boreal forest of Northeast China",
        "Year": 2026,
        "Abstract": "Cloud-to-ground (CG) lightning is the main ignition source for wildfires in boreal forests. Characterizing the environmental controls on both CG lightning occurrence and ignition success is therefore essential for developing effective early warning systems. However, previous analyses of conditions favorable for lightning often involved the incorporation of non-igniting intracloud (IC) lightning because of data limitations, creating a significant knowledge gap. This gap is particularly acute in the Greater Khingan Mountains (GKM) of China, which contain largest boreal forest and experience the highest frequency of lightning-ignited wildfires (LIWs). In this study, meteorological, atmospheric, aerosol, and land-surface datasets from 2019 to 2023, along with lightning records and LIW occurrences, are synthesized to distinguish igniting lightning and identify the key determinants of CG lightning occurrences and ignition success. The results indicated that CG lightning occurs preferentially in higher-elevation mountainous areas that are dominated by needle-leaved deciduous forests. Statistical analyses revealed significant (p < 0.01) associations between CG lightning occurrences and elevated aerosol loadings, higher temperatures, greater precipitation levels, and enhanced vertical atmospheric instability. Furthermore, compared with non-igniting CG lightning, igniting lightning is associated with greater atmospheric instability and stronger updrafts, along with significantly warmer and drier land-surface conditions that promote fuel aridity. Collectively, these findings establish a conceptual framework for identifying high-risk lightning events and achieving enhanced early targeted LIW warning capabilities."
    },
    {
        "DOI": "10.1016/J.AGWAT.2026.110408",
        "Title": "A coupled hydrologic-agroeconomic modeling framework to evaluate adaptive irrigation strategies under groundwater withdrawal restrictions",
        "Year": 2026,
        "Abstract": "Growing groundwater scarcity requires integrated tools to capture interactions among hydrology, agricultural production, markets, and land use. This study presents an iterative modeling framework that couples hydrologic, crop-yield, economic models two-way feedback water availability, market responses under constraints. The primary goal of this paper is describe the methodological development coupled demonstrate significance model interaction. Applied western United States, we evaluated adaptive restricting use beyond recharge levels, represented through changes in irrigation management expansion or shrinkage crop markets reallocation. Results coupling converges stable equilibrium within 10 iterations. At equilibrium, deficit emerges as dominant adaptation strategy California, with levels stabilizing at approximately 70% full demand, while Arizona New Mexico experience stronger yield sensitivities. Early iterations produce commodity price increases up 10% for fruit vegetable crops; however, these moderate allocation production patterns adjust across regions. Deficit spatial reallocation irrigated partially offset losses, variability observed different states: California maintains yields primarily via irrigation, whereas will rely mainly on reducing area absorb shock. By capturing between biophysical processes, approach highlights how strategies land-use decisions evolve stress provides a transferable platform evaluating policies."
    },
    {
        "DOI": "10.1029/2024RG000874",
        "Title": "Processes Driving DriftIce Evolution and Its Interaction With the Oceanic and Atmospheric Boundary Layers",
        "Year": 2026,
        "Abstract": "Abstract Sea ice is a crucial component of polar climate systems and is undergoing substantial changes in both hemispheres due to evolving climatic conditions. Arctic sea ice is transitioning from perennial to seasonal cover, and the Southern Ocean sea ice is exhibiting recent minima and enhanced seasonality. As global warming continues, the role of sea ice in polar climate systems is expected to transform further. However, many theoretical frameworks and parameterizations in current seaice models are based on observations from an earlier era dominated by thicker multiyear ice. Here, we synthesize the physical processes governing the dynamics and thermodynamics of drift icethe mobile pack iceand its coupling with the atmospheric and oceanic boundary layers. Our goal is to provide a coherent theoretical framework of the seaice evolution equations and to summarize parameterizations of subgrid processes used across models of varying complexity. These include representations of momentum and scalar fluxes, icethickness distribution and redistribution, snow and meltpond processes, waveice interactions, and physicalbiogeochemical feedbacks. We also examine how sea ice impacts ocean stratification and mixing, as well as the atmospheric boundary layer and clouds. Finally, we highlight recent observational findings and outline priorities for improving the representation of driftice processes, particularly in light of the changing climate and ice state. , Plain Language Summary Polar regions are warming faster than the rest of the planet. This has caused substantial seaice loss and changes in the physical properties of the drifting seaice cover. As these trends continue, it is critical to understand the processes that govern the evolving seaice conditions, how their relative importance may shift, and how they are represented in models. This article reviews how drift icethe mobile, floating packforms, melts, moves, and interacts with the ocean below and the atmosphere above. We bring together the key ideas and equations behind these processes and explain how they are typically included in models (parameterizations). Because many commonly used formulations were developed from observations during an era when the ice was thicker and older, we highlight the processes most affected by the shift toward thinner, more seasonal ice in both hemispheres. Finally, we outline key uncertainties and priorities for improvement. , Key Points This review provides an uptodate theoretical framework of oceanseaiceatmosphere interactions We summarize governing equations and parametrizations commonly used in seaice models and propose improvements based on recent observations Focus is on processes affected by the shift in seaice characteristics from multiyear ice to younger, thinner, and more dynamic ice types"
    },
    {
        "DOI": "10.1029/2025JD045676",
        "Title": "First Detection of FireworkGenerated Infrasound in the Upper MesosphereLower Thermosphere With a RocketBorne Sensor: Results From the MOMO3 Sounding Rocket Experiment",
        "Year": 2026,
        "Abstract": "Abstract We present the first direct detection of fireworkgenerated infrasound in the upper mesosphere and lower thermosphere using a rocketborne sensor. A wideband capacitive differential pressure sensor (INF03D) was installed on board the MOMO3 sounding rocket, a privately developed vehicle launched from Taiki, Hokkaido, Japan, in May 2019, which reached a maximum altitude of 113 km. Ten spherical fireworks were launched from the ground before and after liftoff to provide temporally tagged acoustic sources. Here, denotes the rocket launch time ( s), and all time stamps are given relative to launch unless otherwise stated. Threedimensional raytracing simulations were performed with four atmospheric profile sets (NRLMSISE00/HWM14, JAWARA, ERA5, and MERRA2) to evaluate spatiotemporal intersections between acoustic ray paths and the rocket trajectory. Four distinct pressure transients with Hilbertenvelope amplitudes of 1.02.8 Pa were detected at 5076 km during ascent, falling within the modeled arrival windows for the T30 s and T90 s shots. In addition, two weak peaks (3.8 mPa each) were observed at 106109 km during descent, coinciding with a predicted intersection window and classified as marginal hits due to their limited signaltonoise ratios (SNR). Additional ascentphase transients around T+170T+185 s lacked corresponding rayrocket intersections, suggesting unmodeled propagation or nonfirework sources, and telemetry loss after T+282 s prevented evaluation of latedescent signals. These findings demonstrate that 0.12 Hz infrasound from nearsurface explosions can propagate to over 100 km altitude and that rocketborne sensors provide an effective platform for in situ acoustic measurements in the middle and upper atmosphere. , Plain Language Summary Sound from fireworks and other loud events can travel hundreds of kilometers upward into the lowdensity air near the edge of space. We installed a lightweight pressure sensor on a Japanese sounding rocket to directly listen for these very lowfrequency waves during a suborbital flight. During ascent between 45 and 109km altitude, the instrument recorded several brief pressure pulses. Using records of fireworks and a computer model of how sound travels through changing winds and temperatures, we found that multiple pulses lined up in both time and location with fireworks on the ground. The strongest signals had frequencies from 0.1 to 2 Hz, which can survive to high altitudes because lowfrequency waves are absorbed less by the atmosphere. These results show that rocketborne measurements can fill a gap between ground stations and balloons and can be used to test how the atmosphere guides infrasound from the surface to the lower thermosphere. , Key Points Rocketborne sensors detected infrasound between 45 km and the lower thermosphere, with a marginal signal near 109 km Ray tracing links multiple pressure peaks along the ascent to timestamped fireworks on the ground Lowfrequency (0.12 Hz) waves persist to high altitudes despite expected absorption"
    },
    {
        "DOI": "10.1016/J.JEEM.2026.103348",
        "Title": "The mental health toll of heat stress in India",
        "Year": 2026,
        "Abstract": "The literature on the relationship between extreme heat and mental health in low- middle-income countries has primarily focused long-term indirect channels, such as harvest failures their impacts health. In contrast, this study assesses direct, short-term effects of stress explores potential underlying physiological mechanisms. Moreover, by using a measure that accounts for non-linear interactions relative humidity (wet bulb temperature) comparing it with conventional temperature measures (dry temperature), I show neglecting interaction may underestimate true effect. combine data self-reported depression anxiety symptoms from four waves Indian WHO-SAGE survey high-resolution climate data, exploiting quasi-random variation exposure driven timing location implementation. results to increases reporting depressive symptoms, but not symptoms. are consistently smaller insignificant when dry is used together linear control variable. evidence points during agricultural work, increased cognitive difficulties, sleeping disturbances pathways linking among rural populations, while urban populations affected. Finally, shows access District Mental Health Program plays protective role, mitigating negative impact These underscore need integrate into assessments adaptation policies."
    },
    {
        "DOI": "10.1016/J.APGEOG.2026.104008",
        "Title": "Disentangling the impact of climate and ecological restoration measures on ecosystem service: A case study in Yimeng mountains",
        "Year": 2026,
        "Abstract": "Ecological restoration project (ERP) plays a pivotal role in enhancing ecosystem services (ES) and promoting regional sustainable development, yet a quantitative understanding of how multiple restoration measures (ERMs) affect ES under interacting influence of climatic conditions remains limited. In this study, we developed an analytical framework based on explainable machine learning to disentangle the independent contribution and interactive effects of nine ERMs and climatic variables on changes in five ESs in the Yimeng Mountains ERP. Results suggest that: within the short-term assessment period (2020-2023), (1) ERMs exhibited heterogeneous impacts on ES provision, with afforestation measure showing the greatest overall enhancement of ES, whereas forest tending measure exhibited the strongest independent contribution to ES variation and pronounced interactions with climatic factors; (2) the implementation of ERMs was associated with intensified both synergies and trade-offs among individual ES without altering their original relationship directions, with the strongest trade-off between wind erosion prevention and water conservation strengthening from 0.32 to 0.78; and (3) Climatic drivers explained 6788% of the observed variability in ES indicators, highlighting the important role of climateERMs interactions in shaping restoration outcomes. This study introduces a fine-scale analytical perspective at the measure level and an explainable framework for disentangling climate-restoration interactions, supporting iterative refinement of restoration strategies and climate-adaptive management."
    },
    {
        "DOI": "10.3390/RS17183238",
        "Title": "Emission Control and Sensitivity Regime Shifts Drive the Decline in Extreme Ozone Concentration in the Sichuan Basin During 20152024",
        "Year": 2025,
        "Abstract": "In recent years, ozone (O3) pollution has become a prominent air quality concern in the Sichuan Basin (SCB). Based on surface O3 measurements from 22 cities between 2015 and 2024, this study investigates the evolution of extreme O3 pollution events and their underlying causes. While the average O3 concentration, the number of affected cities, and the total O3 pollution hours have all increased during the past decade, extreme O3 concentrations have shown a significant decline since 2020. These trends suggest that O3 pollution in the SCB has become more spatially extensive and less intense. Decomposition analysis attributed ~75% of the post-2020 decline in extreme O3 concentrations to precursor emission reductions, with meteorological variability explaining the remaining ~25%. Satellite observations of formaldehyde (HCHO) and nitrogen dioxide (NO2) column densities indicate a regional shift in O3 formation regimes across the SCB, with many areas transitioning from VOC (volatile organic compound)-limited to transitional or NOx (nitrogen oxide)-limited conditions. This shift likely contributed to the broader spatial extent and longer duration of O3 pollution in recent years. Model sensitivity simulations and Integrated Reaction Rate (IRR) analysis demonstrate that reductions in precursor emissions, particularly NOx, directly weakened daytime photochemical O3 production and disrupted NOx-driven radical propagation under transition and NOx-limited conditions, collectively driving the observed decline in extreme O3 concentrations."
    },
    {
        "DOI": "10.1029/2025JD044565",
        "Title": "Hourly Nitrogen Oxides Emissions Estimated From TEMPO and Comparison With FacilityLevel Monitoring Data",
        "Year": 2025,
        "Abstract": "Abstract We use the directional derivative approach to estimate hourly emissions from measured by the Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument. The hourly scanning capacity of TEMPO enables the inclusion of the tendency term, relaxing the steadystate assumption that limits previous satellitebased methods. The directional derivative emission estimator is calculated on the local coordinates of TEMPO Level2 pixels and consequently oversampled to 1km grids. These innovations enable integration of emission estimators over km elliptical footprints centered at 14 power plants in the US, resulting in over 15,500 hr of emission rates in the first 17 months of TEMPO operation. TEMPObased emission rates are compared with measurements from stackmounted Continuous Emissions Monitoring Systems (CEMS). Inclusion of the tendency term improves correlations in 10 out of 14 power plants and the overall correlations at hourly, daily, and monthly scales. The TEMPObased emission rates are around four times lower than CEMS, highlighting the challenges to accurately retrieve vertical column and to quantify / ratio near strong point sources. , Plain Language Summary Nitrogen oxides, primarily emitted by power plants and vehicles, are major air pollutants that create smog, contribute to acid rain, and affect climate. Thus, monitoring nitrogen oxides emission in real time is crucial for environmental protection and human health. This study uses data from a geostationary satellite instrument, Tropospheric Emissions: Monitoring of Pollution (TEMPO), which measures the vertically integrated column amount of nitrogen dioxide every hour across North America. We applied directional derivative approach, a method to estimate hourly nitrogen oxides emissions directly from the TEMPO observations. Our method accounts for how nitrogen oxides emissions change throughout the day, something previous satellitedatadriven approaches couldn't capture. We compared the estimated nitrogen oxides emissions with smokestack measurements from power plants across the United States. Our results showed that considering the hourly changes of nitrogen dioxide significantly improved the emission estimation. We demonstrate that the unprecedented spatiotemporal resolution of TEMPO measurements coupling with our method can be a powerful tool to track emission with near real time capability, supporting more effective environmental monitoring and policy decisions. , Key Points First demonstration of hourly nitrogen oxides emission from Tropospheric Emissions: Monitoring of Pollution (TEMPO) measurements compared against facilitylevel continuous emission data Incorporating nitrogen dioxide column tendency significantly improves emission estimation and enhances correlation with facilitylevel data Nitrogen oxides emission is estimated using the directional derivative approach from Level 2 data preserving TEMPO's native spatial pattern"
    },
    {
        "DOI": "10.1016/J.RSASE.2024.101423",
        "Title": "Seasonal variations and trends in solar UV spectral irradiances based on data from the Ozone Monitoring Instrument at solar noon in Southern Amazonas, Brazil",
        "Year": 2025,
        "Abstract": "Ultraviolet (UV) radiation has significant implications for public health and the environment, making it crucial to understand the dynamics of UV irradiances, particularly in sensitive regions such as the southern mesoregion of Amazonas. This study aimed to analyze the seasonal variations and trends in UV irradiances (305, 310, 324, and 380 nm) in the mentioned region using remote sensing data. The data were derived from satellite-mounted sensors, covering the period from January 2005 to December 2022. The results indicate a well-defined seasonality of UV irradiances, with intensity peaks in summer and spring. The largest and smallest monthly variations in UV irradiances (305 and 310 nm) occurred in February and September, respectively, while for UV irradiances (324 and 380 nm), these variations were observed in November and September. As for the trends, the most significant findings included substantial increases in UV irradiances (324 and 380 nm) and a reduction in Cloud Optical Thickness (COT). A significant negative correlation between ozone and UV irradiance (305 nm) was also observed, along with a strong correlation between COT and UV irradiances (324 and 380 nm). The study revealed a critical situation in July, emphasizing the need for additional precautions regarding UV exposure. While the results indicate concerning behaviors in irradiances and COT, the lack of spectral UV sensors on the ground in the southern Amazon region highlights the urgent need for investment in advanced monitoring technologies so that further studies can describe these dynamics more precisely."
    },
    {
        "DOI": "10.1016/J.AEAOA.2025.100324",
        "Title": "Cement and brick factories contribute elevated levels of NO2 pollution in Nepal: Evidence of high-resolution view from space",
        "Year": 2025,
        "Abstract": "An upsurge in the pollution level areas with a high concentration of brick and cement factories Nepal is concerning. Nitrogen dioxide (NO 2 ), key air quality indicator, can be effectively monitored from space. This study utilizes high-resolution satellite observations NO TROPOspheric Monitoring Instrument (TROPOMI). It examines distribution over emerging sources nitrogen oxides 2018 to 2021. Rapid growth has turned Lumbini-Butwal-Palpa corridor, Midwest Nepal, more polluted than capital city Kathmandu. Between 2019 2021, levels this corridor rose considerably, while it remained steady Kathmandu Valley. TROPOMI-derived inferred x emissions nearly doubled span three years. Conversely, Valley exhibited no significant changes except 2020 when declined. drop coincided COVID-19-related travel restrictions other reduced activities. recorded by Ozone (OMI) 2005 shows an annual increase 3.5 % both regions. A comparison between EDGAR inventory estimates for reveal comparable values but around 35 higher discrepancy city, as well rapid rise due large-scale development industries, highlights need timely updates bottom-up emission inventory."
    },
    {
        "DOI": "10.1016/J.AOSL.2021.100035",
        "Title": "The exceptionally strong and persistent Arctic stratospheric polar vortex in the winter of 20192020: 2019-2020",
        "Year": 2021,
        "Abstract": "The Arctic stratospheric polar vortex was exceptional strong, cold and persistent in the winter and spring of 20192020. Based on reanalysis data from the National Centers for Environmental Prediction/National Center for Atmospheric Research and ozone observations from the Ozone Monitoring Instrument, the authors investigated the dynamical variation of the stratospheric polar vortex during winter 20192020 and its influence on surface weather and ozone depletion. This strong stratospheric polar vortex was affected by the less active upward propagation of planetary waves. The seasonal transition of the stratosphere during the stratospheric final warming event in spring 2020 occurred late due to the persistence of the polar vortex. A positive Northern Annular Mode index propagated from the stratosphere to the surface, where it was consistent with the Arctic Oscillation and North Atlantic Oscillation indices. As a result, the surface temperature in Eurasia and North America was generally warmer than the climatology. In some places of Eurasia, the surface temperature was about 10 K warmer during the period from January to February 2020. The most serious Arctic ozone depletion since 2004 has been observed since February 2020. The mean total column ozone within 6090N from March to 15 April was about 80 DU less than the climatology. 2019-2020,.NCEPOMI, ..2020.NAM, AONAO, , , 20201210K.202022004, 20203-46090N80DU."
    },
    {
        "DOI": "10.3390/LAND14030482",
        "Title": "Assessing the Impact of Amazonian Fires on Atmospheric NO2 Using Satellite Data",
        "Year": 2025,
        "Abstract": "In the Amazon region, the impact of fire on the regions biogeochemical processes remains poorly understood. In this study, we examined the relationship of seasonal fire on greenhouse gas (GHG) emissions over the study region during the last two decades of the 21st century by integrating calibrated and validated satellite-derived products of estimations of burned biomass area, land cover, vegetation greenness, rainfall, carbon monoxide (CO), and nitrogen dioxide (NO2) through geospatial techniques. Results revealed a strong impact of fire activity on GHG emissions, with abrupt changes in CO and NO2 emission factors between early- and middle-dry-season fires (JulySeptember). We found a strong positive correlation (r2 = 0.71) between NO2 and burned biomass when a small percentage of burned biomass (around 0.5%) is released during a fire. However, in the event of a large percentage of burned biomass (>0.8%), the correlation coefficient between NO2 and burned biomass was weak (r2 = 0.41). New models need to be developed that incorporate the substantial existing knowledge on the seasonal dynamics of fire-derived GHG emissions. This information should be utilized to make effective decisions about how to manage fire in the Amazon ecosystems and to drive further data collection campaigns and modelling initiatives."
    },
    {
        "DOI": "10.1002/2016JD024927",
        "Title": "Satelliteenhanced dynamical downscaling for the analysis of extreme events",
        "Year": 2016,
        "Abstract": "Abstract The use of regional models in the downscaling of general circulation models provides a strategy to generate more detailed climate information. In that case, boundaryforcing techniques can be useful to maintain the largescale features from the coarseresolution global models in agreement with the inner modes of the higherresolution regional models. Although those procedures might improve dynamics, downscaling via regional modeling still aims for better representation of physical processes. With the purpose of improving dynamics and physical processes in regional downscaling of global reanalysis, the Regional Spectral Modeloriginally developed at the National Centers for Environmental Predictionemploys a newly reformulated scaleselective bias correction, together with the 3hourly assimilation of the satellitebased precipitation estimates constructed from the Climate Prediction Center morphing technique. The twoscheme technique for the dynamical downscaling of global reanalysis can be applied in analyses of environmental disasters and risk assessment, with hourly outputs, and resolution of about 25 km. Here the satelliteenhanced dynamical downscaling added value is demonstrated in simulations of the first reported hurricane in the western South Atlantic Ocean basin through comparisons with global reanalyses and satellite products available in ocean areas. , Key Points Blending two boundaryforcing techniques to improve dynamical downscaling Assimilation of satellitebased precipitation estimates for extremeevent analysis Dynamical downscaling applied in regional hydroclimate reconstructions"
    },
    {
        "DOI": "10.1016/J.RSASE.2026.102055",
        "Title": "Land use change-driven amplification of AOD over the Indo-Gangetic Plain",
        "Year": 2026,
        "Abstract": "The Indo-Gangetic Plain (IGP) is a critical region for agricultural productivity, but its densely populated landscape faces significant challenges from aerosol pollution, largely driven by land use changes and agricultural practices. Understanding the spatiotemporal variation in Aerosol Optical Depth (AOD) is crucial for assessing its environmental and health impacts amid evolving agricultural practices. This study examines AOD trends across the upper, central, and lower IGP from 2001 to 2022, hypothesizing that changes in Land Use and Land Cover (LULC), particularly cropland expansion, along with associated agricultural practices of crop residue burning (CRB), are primary contributors. MODIS AOD and LULC datasets were used to assess seasonal trends alongside fire events and land use changes. Findings reveal significant AOD increases, particularly in the lower IGP, where vegetation loss and cropland expansion intensified CRB and aerosol emissions. Seasonal trends showed consistent AOD growth, with the lower IGP exhibiting the most pronounced increases in both AOD and fire counts. In contrast, the upper IGP exhibited reduced CRB activity and declining AOD trends during 2012 to 2022, indicating improved air quality. Analysis of Angstrom Exponent (AE) and Single Scattering Albedo (SSA) confirms the strong influence of biomass burning on AOD. Multivariate regression analysis further indicates that CRB is the dominant driver of AOD variability, while meteorological factors primarily modulate aerosol dispersion. These findings underscore the strong link between LULC changes, CRB, and AOD trends, highlighting the urgency of implementing sustainable agricultural practices and emission controls to mitigate pollution and enhance air quality across the IGP."
    },
    {
        "DOI": "10.1016/J.UCLIM.2025.102756",
        "Title": "A multi-scale investigation of rainfall drivers over Metro Manila, Philippines using empirical orthogonal function analysis",
        "Year": 2026,
        "Abstract": "The Philippines is a country vulnerable to worsening climate, especially its rapidly urbanizing capital, Metro Manila (MM). However, the understanding of rainfall in this region still poor due complex interplay synoptic and local factors, alongside data scarcity methodological limitations. This research aims identify key drivers behind observed long-term trends over MM. Empirical orthogonal function analysis was applied daily ERA5 geopotential recurring patterns between 1991 2021. For each pattern, annual precipitation amount (PA), frequency (PF), intensity (PI) values were calculated from available PAGASA station Mann-Kendall trend test used PA, PF, PI for pattern. These then correlated MM urban expansion recent decades. A similar made variables linked formation (i.e. surface temperature, CAPE, vertical moisture advection, evaporation.) Results reveal that primarily driven by conditions, particularly southwest monsoon (SWM) passing low pressure systems. occurring more frequently days leading up SWM season when there high system present northern Philippines. expansion, which seems create ideal conditions initiation. results study may inform decisions policy makers stakeholders involved disaster risk management planning. tropical cyclones drive Manila, Rainfall occurrence increasing significantly calm area. Temperature, evaporation are as well. show significant correlation expansion."
    },
    {
        "DOI": "10.1029/2024JB030689",
        "Title": "A Decadal Survey of the NearSurface Seismic Velocity Response to Hydrological Variations in Utah, United States",
        "Year": 2026,
        "Abstract": "Abstract Ongoing climate change is leading to an increase in prolonged droughts and severe weather events, which are particularly pronounced in semiarid regions, such as the western United States. These extremes could have lasting social and environmental impacts. Continuous monitoring of nearsurface hydrological processes and groundwater resources provides helpful information for effective water resource management. The seismological signature of groundwater fluctuations is clear in the temporal variations in seismic velocities, dv/v. To this end, developing a proxy for groundwater level using dv/v represents an opportunity, but further understanding of the relation between dv/v and subsurface hydrology is required. In this study, we apply singlestation crosscomponent correlation analysis to 28 broadband seismic stations in Utah between January 2006 and March 2023 and analyze the dv/v in the 24 Hz frequency band. To explain dv/v, we linearly superimpose thermoelastic stresses, soil moisture estimated from remote sensing data products, and a longterm deep water table pore pressure. We find that the relative contributions of each depend on the location. Still, adding a longterm water table decline, which is not systematically observed in soil moisture, better fits our data. We conclude that soil moisture alone does not explain the variations in total water storage when subsurface moisture is decoupled from the deepwater table. We also conclude that dv/v can be used as a proxy for water storage. , Plain Language Summary Climate change makes droughts and water shortages more common in the western United States. Tracking how water moves and is stored underground is essential for understanding these changes, but traditional tools such as monitoring wells and satellites cover too little ground or lack enough detail. In this study, we used small changes in the speed of seismic waves, dv/v, recorded at 28 seismic stations across Utah over more than a decade. These subtle changes help reveal how water flows and accumulates below the surface. By combining dv/v data with estimates of soil moisture and longterm lake level changes (used as a proxy for groundwater), we built a model to separate the effects of shallow and deepwater processes. We found that dv/v can detect seasonal and longterm shifts in subsurface water, and deep groundwater changes are significant in some areas. This seismic method offers a powerful new way to monitor underground water across large regions, bridging the gap between scattered well data and coarse satellite data. Our approach may help scientists and water managers better understand and adapt to changing water availability in a warming climate. , Key Points Multiyear wetdry water cycles are closely consistent with dv/v observations, notably at stations within the Great Salt Lake watershed The annual dv/v variations and their peak times closely correspond to expected water cycle patterns in Utah Using longterm lake level as a groundwater proxy in modeling reveals regional recharge timing differences driven by elevation and snowmelt"
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2025.108645",
        "Title": "On the dynamic link between summer inner-continental warming and the outer-continental weakened precipitation extreme ascent in East Asia",
        "Year": 2026,
        "Abstract": "This study elucidates the mechanism by which Arctic-cooling-induced inner-continental warming over East Asia suppressed July precipitation extremes during 20132019 along the quasi-stationary rainy front in the outer-continental region encompassing eastern China, the Yellow Sea, and Korea (eCYK). Spatiotemporal variations in precipitation were analyzed using IMERG satellite data. At the same time, large-scale circulation, coupled with microphysical aerosolcloud interactions and cloud properties, was investigated using ERA5 reanalysis, Terra-MODIS observations, and WRF-Chem simulations. Despite a general increase in summer precipitation and more frequent extreme events across the eCYK region, a marked weakening of extreme ascent was observed from 2013 to 2019, contributing to widespread drought conditions associated with pronounced Arctic cooling and intensified inner-continental warming. The warming-induced expansion of the upper-tropospheric Tibetan High strengthened the East Asian jet along its northern flank, while the southward intrusion of polar air contrasted with it. The resulting anomalous northwesterly flow over the eCYK region, situated near the East Asian jet exit, was further amplified by indirect circulation, which disrupted the transport of moisture from the tropical oceans surrounding Southeast Asia toward the rainy front. Terra-MODIS cloud observations revealed reduced convective cloud formation, which directly led to weaker precipitation extremes. Furthermore, WRF-Chem simulations showed that microphysical processes driven by anthropogenic aerosol indirect effects reduced precipitation by approximately 5.6 % across the eCYK region in July 2018. The combined impact of inner-continental warming and enhanced aerosol formation suppressed convective cloud development, thereby delaying the onset and reducing the intensity of extreme precipitation events."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2025.108726",
        "Title": "Tropical Cyclone Intensity Sensitivity to Sea Surface Temperature and Mixed Layer Depth",
        "Year": 2026,
        "Abstract": "Rapidly intensifying tropical cyclones (TCs) are among the most dangerous and least predictable weather systems. In this work, we focus on two intense Atlantic TCs that showed rapid intensification (RI, defined as at least 36 hPa deepening in 24 h) and which impacted the coast of the Gulf of Mexico. This study focused on how sea surface temperature (SST) and ocean mixed-layer depth (OMLD) modulate the rapid intensification (RI) and maximum intensity (MI) of two hurricanes, Wilma and Rita (2005). Using the Weather Research and Forecasting (WRF) model coupled with a simplified 1D ocean model, 20 simulations were performed to systematically vary SST (13 C) and OMLD (doubled or halved), as well as remove observed SST anomalies (SSTA). Results show that SST dominates TC intensity changes from around 1520 hPa C1, and correspondingly higher deepening rates. Warmer simulations also exhibit an increased energy transfer from the ocean, supporting increased near-surface equivalent potential temperature, a more symmetrical wind field and elevated accumulated cyclone energy (ACE). By contrast, deeper OMLD enhances thermodynamic support but is constrained by SST conditions, limiting the total surface heat flux in the storm's vicinity. Results also stress that remote heat fluxes across a broader region, not just beneath the storm core, play a crucial role in determining maximum intensity and deepening. Westward landfall shifts up to 300 km are observed in +3 C scenarios, emphasizing the potential of ocean heat in altering trajectories. These findings highlight the critical importance of accurately representing upper-ocean thermal structures to improve predictions of TC intensity and trajectories, particularly as warmer future SSTs may lead to more frequent and dynamically evolving hurricanes."
    },
    {
        "DOI": "10.1029/2025MS005289",
        "Title": "How Convective Mass Flux Responds to Environmental Humidity",
        "Year": 2026,
        "Abstract": "Abstract Our goal in this study is to characterize the relationship between lower tropospheric environmental humidity and convective mass flux in the tropics. To do so, we have created gridded convective mass flux data sets from five global stormresolving models (GSRMs). We have three principal findings. First, in humid environments, mass flux increases with height from the surface through the depth of the lower free troposphere, forming a deepinflow. In dry environments, mass flux does not increase with height in the lower free troposphere. Second, midtropospheric mass flux increases nonlinearly with increasing lower tropospheric humidity, resembling a widely reported pickup in tropical precipitation. Third, increased lower tropospheric humidity is associated with reduced updraft buoyancy. To interpret these findings, we employ a simple threeequation parcel model with stochastic entrainment. The parcel model suggests that the response of convective mass flux to lower tropospheric humidity is governed by two effects: (a) survival, in which a greater share of entraining parcels ascend rather than detrain with greater humidity; and (b) dilution, in which the average entrainment rate among surviving parcels increases with environmental humidity. Together, survival and dilution account for the three mass flux responses to humidity. , Plain Language Summary This study aims to quantify and understand the rate at which air mass ascends within cumulus and cumulonimbus clouds in the tropics. This rate is known as convective mass flux . Using finescale supercomputer simulations of Earth's atmosphere, we find that convective mass flux is extremely sensitive to humidity in the lowest few kilometers of the atmosphere. Greater humidity leads to greater convective mass flux. We then test whether environmental humidity increases mass flux by making cloudy air more buoyant (i.e., less dense relative to its surroundings). We reject this hypothesis, finding instead that the opposite occurs: Greater humidity is associated with cloudy air which is less buoyant. To make sense of these results, we use a simple set of equations to simulate cloudy air as it rises and ingests dry environmental air. As the environment becomes more humid, cloudy air may absorb a greater mass of environmental air without drying out and halting its ascent. This causes mass flux to increase with humidity and causes convective clouds' average density to become more like that of their environment. , Key Points Convective mass flux transitions to deep inflow and increases quasiexponentially as lower tropospheric relative humidity increases Greater lower tropospheric humidity is not associated with greater populationmean buoyancy in convective updrafts These phenomena are the result of increasing survival and dilution among convectively active parcels of air"
    },
    {
        "DOI": "10.1029/2025GL119833",
        "Title": "The Atmosphere's Substantial Role in Interannual Variability of Earth's Energy Imbalance",
        "Year": 2026,
        "Abstract": "Abstract Earth's Energy Imbalance (EEI) is a key metric to quantify climate change. While the ocean absorbs most excess heat, the atmosphere contributes only 1%2% to the longterm mean of EEI. However, our analysis of observational data demonstrates that variations in the atmosphere's energy content play a much larger role in interannual variations of EEI, especially in recent years. Including atmospheric energy uptake substantially improves agreement between observed variations in global net radiative flux at topofatmosphere (TOA) and ocean heat uptake interannually over 20052024. It also reconciles a delay between variability of these two quantities, with oceanic storage variability leading net TOA flux anomalies by 2 months. The phase shift can be explained by the atmosphere's important role in buffering and redistributing energy during El Nino Southern Oscillation. The ability to robustly diagnose these relationships is owing to continuous efforts to monitor and improve estimates of the different EEI components. , Plain Language Summary The increase of humanmade greenhouse gases in the atmosphere results in a net global energy flux at the top of the atmosphere. In the longterm mean, most of this excess heat is absorbed by the ocean due to its large thermal capacity. A comparatively small fraction warms the land, melts ice, and warms and moistens the atmosphere. However, here we show that atmospheric storage plays a nontrivial role on shorter timescales. We investigate the balance among variations in the global flux at the top of the atmosphere, the rate of atmospheric warming, and the rate of oceanic warming from year to year over the past 2 decades. We find that changes in ocean warming lead the net energy flux at the top of the atmosphere by 2 months, and these two timeseries are fairly well correlated on these interannual time scales, but the sum of atmospheric and oceanic rates of energy uptake are better correlated with a maximum correspondence at zero time lag. Hence the atmosphere is playing an important role in buffering and redistributing yeartoyear energy uptake by the climate system, most notably during El Nino and La Nina events, but especially in 2023, when surface temperatures increased remarkably. , Key Points Atmospheric and oceanic energy storage change sums correlate better to topoftheatmospheric energy fluxes than do ocean changes alone Atmospheric energy storage inclusion reconciles a 2months phase shift between topoftheatmospheric energy fluxes versus ocean changes Over the past 2 decades, interannual atmospheric energy storage variations are correlated with El Nino Southern Oscillation, but are especially large in 2023"
    },
    {
        "DOI": "10.1016/J.EJRH.2026.103245",
        "Title": "Evaporative Stress Index as a flash drought tool in the southeastern U.S.",
        "Year": 2026,
        "Abstract": ": This study focuses on the state of Alabama in southeastern United States, a humid subtropical region characterized by strong seasonal variability precipitation, vegetation growth, and atmospheric demand. These conditions complicate drought monitoring, particularly detection rapid-onset (flash) droughts driven abrupt soilmoisture depletion during growing season. The AtmosphereLand Exchange Inverse (ALEXI) model provides daily evapotranspiration estimates at 4-km resolution across from which Evaporative Stress Index (ESI) is derived as standardized anomaly actual-to-potential ratio (AET/PET). Because evaporative stress reflects water use, ESI often linked to root-zone soil moisture, though this relationship varies with season land cover. evaluates statistical between ALEXI-based in-situ moisture observations examines behavior change anomalies ( ) soilmoisture-defined flash events. Results show that correlations are modest when evaluated year-round but strengthen substantially late spring through fall, peaking SeptemberNovember. frequently becomes negative one two weeks prior rapid decline, coinciding approximately 60% events average up 90% fall. findings demonstrate that, despite limitations, provide valuable early information soilmoisture-driven onset systems offer complementary insight for monitoring warning States. Evaluates over SCAN stations. Assesses ESIsoil depth, season, cover, soil. Develops soilmoisture-based climatology 20012020. Demonstrates skill identifying droughts. Shows identifies 60%90% droughts, strongest signals"
    },
    {
        "DOI": "10.1016/J.APR.2026.103014",
        "Title": "Analysis of urban greenhouse gas dispersion for downtown Montreal using computational fluid dynamics and field measurements",
        "Year": 2026,
        "Abstract": "Cities and urban regions are responsible for significant amounts of greenhouse gas (GHG) emissions, which, along with associated air pollution byproducts, then dispersed locally globally. An investigation the complex dispersion dynamics is carried out downtown region Montreal, Canada, using computational fluid models. These models initialized validated measurements from a multiplatform field campaign day 21 February 2024. Measurements recorded atmospheric conditions aircraft flights radiosondes, Doppler lidar. Carbon dioxide (CO 2 ) methane (CH 4 concentrations were measured by aircraft, drone, Picarro GasScouter in fixed, location. This showed presence low-level jet, characteristics which investigated. The most suitable initialization determined, after thermal stability effects investigated comparing incompressible compressible numerical GHGs simulated considering emissions vehicles, McGill University's Ferrier building power plant, large government buildings. model predictions CO compare well rooftop measurements, while larger differences found CH comparisons drone profiles. findings suggest that traffic contribute significantly to local GHG point towards emission sources missing model. also used investigate across region, discussion their vertical distribution comparison against satellite measurements."
    },
    {
        "DOI": "10.29303/JPPIPA.V12I3.14532",
        "Title": "Calibration and Evaluation of GPM Satellite Rainfall for Estimating Annual Maximum Rainfall at Ungauged Sites in the Rea Watershed",
        "Year": 2026,
        "Abstract": "Limited rain gauge coverage and uneven station distribution remain major challenges for hydrological analysis in ungauged watersheds. This study calibrated and evaluated GPM IMERG satellite rainfall for estimating annual maximum daily rainfall (AMS) in the Rea Watershed, Indonesia. AMS data from four rain gauge stations (Tepas, Taliwang, Seteluk, and Pototano) were used as ground observations, while nine GPM grid cells represented spatial rainfall over the watershed. A rainfall-range-based multiplicative correction was applied using gaugesatellite AMS pairs. Performance was evaluated using Percent Bias (PBIAS), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Sum of Squared Errors (SSE), followed by leave-one-station-out (LOSO) validation. Uncorrected GPM data showed systematic underestimation, with PBIAS of -17.7%, RMSE of 66.64 mm, and MAE of 48.67 mm. After optimized correction, PBIAS shifted to +13.8% and MAE slightly improved to 47.97 mm, but RMSE increased to 75.82 mm and SSE to 201,213 mm2. These results indicate that the correction reduced bias but did not improve overall accuracy. LOSO validation produced RMSE values of 50.83-72.13 mm, indicating sensitivity to station omission. Overall, GPM rainfall can support preliminary AMS estimation in ungauged areas, but its application should be treated cautiously because of persistent uncertainty in extreme rainfall estimation."
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135429",
        "Title": "Accounting for the uncertainty of precipitation forecasts and its impacts on probabilistic flood inundation mapping skill",
        "Year": 2026,
        "Abstract": "Uncertainty in operational weather forecasts, particularly in predicting storm location and precipitation patterns, presents challenges for flood inundation mapping. As hurricane forecasts evolve, spatial discrepancies in precipitation estimates lead to misalignment between forecasted and observed rainfall, affecting flood prediction accuracy. This study implements a storm-positionconditioned Quantitative Precipitation Forecast (QPF) displacement ensemble using HRRR forecasts and propagates those precipitation placement perturbations through a 2D hydrodynamic model (SFINCS) to quantify impacts on deterministic and probabilistic flood inundation mapping. The analysis, focused on Hurricane Beryl (July 2024), evaluates the impact of multiple storm location scenarios over 24-hour forecast periods for cycles generated in 6-hour intervals. QPF fields are used as input to the Super-Fast INundations of CoastS (SFINCS) model, applied to the highly urbanized central Houston area, to generate forecast maps of flood extent and water surface elevations. Results show that incorporating HRRR forecasts and spatial displacement of QPF fields improves the correlation with in-situ meteorological and water elevation observations, reduces biases in predicted flood depths, and better represents the spatial variability of flood impacts across the study area. The probabilistic flood inundation mapping generated through this approach highlights the benefits of ensemble-based forecast integration, providing decision-makers with a range of plausible flooding scenarios. By accounting for uncertainties in storm position, rainfall intensity, and timing, the methodology enhances the reliability and operational value of short-term flood forecasts in urban environments, supporting improved flood risk management and emergency preparedness."
    },
    {
        "DOI": "10.1016/J.SRS.2026.100415",
        "Title": "Quantifying the impact of vegetation on carbon monoxide reduction using multi-source remote sensing in China",
        "Year": 2026,
        "Abstract": "With the intensification of climate change and environmental pollution, reducing carbon monoxide (CO) emissions has become a focal point global governance. Existing research often relies on site-specific observational data, limiting continuous spatiotemporal analysis while failing to quantify impacts complex factors, such as vegetation structure. This study integrated passive active satellite remote sensing sensors with aerodynamic models analyze distribution contributions CO reduction across China from 2013 2022. extends traditional site-scale spatial scale, enhancing model accuracy through high-resolution data. Our findings indicate that in regions east Hu-line primarily contributes dry deposition, cumulative deposition 5.2854 million tons, improving air quality by 0.00216% yielding economic benefits 7.436 billion USD. Forest-type nearly ten times more than non-forest types, significant differences. Evergreen broadleaf trees contribute most, 2.7826 tons. Wavelet transform coherence identifies leaf area index (LAI) parameter highest all time-frequency scales, averaging wavelet 0.79. The SHapley Additive exPlanations (SHAP) indicates temperature is main factor influencing temporal fluctuations. These results provide deeper understanding vegetation's role cycle, offering implications for policies, management, ecosystem service enhancement globally."
    },
    {
        "DOI": "10.1029/2025JH001216",
        "Title": "Transfer Learning for TransformerBased Drought Forecasting Across Diverse Precipitation Products in India",
        "Year": 2026,
        "Abstract": "Abstract Effective drought management is critical for agriculturebased economies like India. This study examines whether a transformerbased architecture can function as a robust pretrained backbone for operational drought forecasting across India. We utilized a pretrained transformer model, which was trained on the Indian Monsoon Data Assimilation and Analysis (IMDAA) precipitation data set (source domain) from 1979 to 2014. Transfer learning capabilities were evaluated by applying the pretrained model without targetdomain adaptation to 11 precipitation products (target data set) differing in source and spatial resolution. The target data sets were compared with the source domain to examine how well they reproduce regional climatic patterns. Transfer learning performance was evaluated over two distinct periods: the overall study period (20012019), partially overlapping with training, and the unseen testing period (20172019). Spatial evaluation is performed across six climatic zones delineated based on the KoppenGeiger classification. We utilized the 3month Standardized Precipitation Evapotranspiration Index (SPEI3) to quantify meteorological drought at a seasonal scale. The pretrained model maintained stable performance across data sets and climate zones, with correlations exceeding 0.7 during winter and premonsoon seasons but declined during the monsoon. Transfer learning evaluation revealed consistent performance across most data sets, with RMSE and MAE within 0.05 and NSE within 0.1 compared to the source domain. The composite skill score rankings showed that ERA5 and MSWEP were the closest to the IMDAA benchmark during the unseen period. The variation in performance across data sets underscores the importance of data setspecific finetuning for operational reliability. , Plain Language Summary Developing a drought forecasting model that works across different resolutions and data sources is essential for datascarce regions like India. We evaluated the transfer learning capabilities of a transformer based pretrained model across India. The transfer learning performance was evaluated across two time periods (20012019 and 20172019) and six climate zones in India. The pretrained model was evaluated across 11 different precipitation data sets from various sources and spatial resolutions. Our results showed that the model performed stable across the majority of data sets with small error margins. Performance across the ERA5 and MSWEP data sets aligned best with our source domain performance. These results highlight that transformers can generalize across resolutions and data sources. However, the study highlights that targeted finetuning remains important for ensuring reliable and longterm operational predictions. , Key Points Most data sets showed RMSE and MAE within 0.05 and NSE within 0.1, proving stable transferability The forecasting skill across target data sets peaked during winter and premonsoon seasons (correlations > 0.7) but declined during the highvariability monsoon ERA5 and MSWEP showed strong transferability while matching the forecasting performance across the source domain"
    },
    {
        "DOI": "10.1016/J.ENVRES.2026.124375",
        "Title": "Shifting flow regimes and water quality regulate CH4 dynamics in anthropogenically perturbed tropical rivers and canals",
        "Year": 2026,
        "Abstract": "Rivers transport and process carbon along the landocean continuum and emit CH4 to the atmosphere. Widespread human perturbation of river networks has disrupted the natural flow regime, regulating the supply of allochthonous matter and subsequently affecting the in-stream biogeochemical processes. The spatiotemporal variability of CH4 in human-impacted rivers remains less explored, especially in the highly populated regions of South Asia, resulting in uncertainties in the global flux estimates. This study investigates the evolution of CH4 in tropical rivers and engineered canals along a gradient of increasing anthropogenic disturbances. Sampling was conducted along different sections (selected based on the level of human perturbations) of the Sabarmati River, Mahi River, and Narmada Canal, located in western India, during high and low flow conditions. It was observed that water stagnancy resulted in higher dissolved CH4 in the lined and stagnant Sabarmati riverfront (a discontinuity in the free-flowing river continuum) compared to the lined and flowing Narmada Canal. Furthermore, the release of wastewater resulted in a 70 km low-oxygen environment accompanied by extremely high CH4 concentrations in the downstream section of the Sabarmati. CH4 exhibited positive correlations with temperature, dissolved inorganic nitrogen, and particulate organic carbon. 13CCH4 indicated that acetoclastic methanogenesis was the major pathway of CH4 production, and evidence of CH4 oxidation was observed in the stagnant riverfront of the Sabarmati. This study provides estimates of CH4 fluxes from the region and highlights the climate implications of river development projects, along with the dominant role of wastewater in rivers with reduced natural flow."
    },
    {
        "DOI": "10.1016/J.NEXRES.2026.101707",
        "Title": "Robust Spatio-temporal reconstruction of XCO2 using OCO-2 and OCO-3 satellite observations and deep learning-based uncertainty quantification",
        "Year": 2026,
        "Abstract": " Carbon dioxide (CO₂) remains the principal greenhouse gas driving global warming. Rapid urbanization, intensified fossil fuel consumption, and the conversion of forested land to agriculture continue to accelerate CO₂ emissions, contributing to ocean acidification, extreme climate variability, and an increased frequency of environmental disasters. The column-averaged dry-air mole fraction of atmospheric carbon dioxide (XCO₂) serves as a vital metric for understanding the spatial and temporal dynamics of CO₂ concentrations in the Earth's atmosphere. In this study, we leverage observational data from two satellite missions, Orbiting Carbon Observatory-2 (OCO-2) and Orbiting Carbon Observatory-3 (OCO-3), to generate high-resolution XCO₂ estimates at both regional and global scales. These platforms offer complementary coverage, yet their measurements are often hindered by cloud interference and limited temporal resolution, resulting in substantial data gaps. Traditional approaches for interpolating or reconstructing missing satellite observations frequently suffer from limited accuracy and scalability, especially in complex spatio-temporal contexts. To overcome these limitations, we propose a novel hybrid deep learning architecture that synergistically combines one-dimensional Convolutional Neural Networks (CNN1D) for local feature extraction with Long Short-Term Memory (LSTM) units to capture temporal dependencies across sequences. A key innovation in our framework is the integration of Monte Carlo Dropout (MCD) during inference. This technique introduces stochasticity into the prediction process, enabling the model to perform multiple forward passes and thereby quantify predictive uncertainty. By embedding MCD within the architecture, the model not only reconstructs missing XCO₂ values with high fidelity but also provides robust confidence estimates, an essential feature for downstream climate analysis and decision-making. Model performance was evaluated using quantitative metrics including Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and MSE-based training loss, benchmarked against ground-truth data from the Total Carbon Column Observing Network (TCCON). The CNN1D-LSTM-MCD model achieved superior predictive accuracy with a lowest average RMSE of 1.28 ppm, outperforming other known models in the literature. Uncertainty quantification via MCD revealed consistently low predictive uncertainty (<1.5 ppm) across evaluations. These findings validate the model’s robustness, generalization capability, and reliability under limited data conditions. Visual assessments further confirmed the model’s output consistency, spatial uniformity, and effective gap-filling performance. "
    },
    {
        "DOI": "10.1007/978-3-031-91835-3_6",
        "Title": "Retrieval of Sun-Induced Plant Fluorescence in the O$$_2$$-A Absorption Band from DESIS Imagery",
        "Year": 2025,
        "Abstract": "Abstract We provide the first method allowing to retrieve spaceborne SIF maps at 30 m ground resolution with a strong correlation ( $$r^2=0.6$$ r 2 = 0.6 ) to high-quality airborne estimates of sun-induced fluorescence (SIF)SIF estimates can provide explanatory information for many tasks related to agricultural management and physiological studies. While SIF products from airborne platforms are accurate and spatially well resolved, the data acquisition of such products remains science-oriented and limited to temporally constrained campaigns. Spaceborne SIF products on the other hand are available globally with often sufficient revisit times. However, the spatial resolution of spaceborne SIF products is too small for agricultural applications. In view of ESAs upcoming FLEX mission we develop a method for SIF retrieval in the O $$_2$$ 2 -A band of hyperspectral DESIS imagery to provide first insights for spaceborne SIF retrieval at high spatial resolution. To this end, we train a simulation-based self-supervised network with a novel perturbation based regularizer and test performance improvements under additional supervised regularization of atmospheric variable prediction. In a validation study with corresponding HyPlant derived SIF estimates at 740 nm we find that our model reaches a mean absolute difference of $$0.78 \\, \\, \\mathrm {mW\\, nm^{-1} \\, sr^{-1} \\, m^{-2}}$$ 0.78 mW nm - 1 sr - 1 m - 2 ."
    },
    {
        "DOI": "10.5194/WES-11-961-2026",
        "Title": "How well can the Mann model describe typhoon turbulence?",
        "Year": 2026,
        "Abstract": "Abstract. More and more wind farms are planned and built in regions prone to tropical cyclones. However, the current International Electrotechnical Commission (IEC) standard provides no clear guidelines on how to account for turbulence occurring during tropical cyclones. This study investigates how well the Mann uniform shear model, a model referenced by the IEC, can model turbulence during tropical cyclone conditions. We analyzed sonic anemometer measurements at 60 m from four typhoon cases in the South China Sea. The Mann model was fit to the one-point spectra in different locations in the typhoon structure. We found that the Mann model can fit the observed spectra outside the typhoon eye and the rainbands to a certain extent. However, several deficiencies are identified. (1) In the outer-cyclone region, spectral energy at wavenumbers smaller than 310-3 m1 is generally larger than predicted by the Mann model, likely reflecting the presence of mesoscale wind fluctuations. (2) Consistent with previous studies, excess spectral energy is observed at wavenumbers larger than 101 m1 in the inner-cyclone and eyewall regions of one typhoon; however, it cannot be ruled out that this excess energy may be related to measurement quality. (3) In the inner-cyclone region, the peak wavenumbers of the alongwind and crosswind spectra are often closer together than predicted by the Mann model. In these cases, the crosswind component exhibits larger-than-predicted spectral energy within the energy-containing subrange. This study can serve as a baseline for further research addressing turbulence in tropical cyclones in the context of structural engineering."
    },
    {
        "DOI": "10.1029/2025GL120662",
        "Title": "Climatology and Trends in Spatial Scales of Extreme Precipitation Over Land in the Contiguous US",
        "Year": 2026,
        "Abstract": "Abstract Extreme precipitation (EP) poses a severe risk under a changing climate. Here we use daily gridded precipitation data (19802024) and a connectedcomponent labeling algorithm to characterize the climatology and regional trends of daily EP spatial scale over land in the contiguous United States (US). EP event sizes follow a logarithmic distribution; thus, the largestarea EP events disproportionately drive total yearly EP area. These largestarea events exhibit different seasonality and have higher average intensities than smaller events. We find a widespread decline in smallarea EP events and overall days with EP, concentrating EP into fewer days per year but with larger areas on those days. In the eastern US, we find a rising frequency of largearea events, driving significant growth in yearly EP area. These findings highlight the importance of spatially informed analyses in understanding the changing nature of EP for better climate risk assessments, adaptation planning, and economic loss projections. , Plain Language Summary Extreme precipitation (EP) events are a major threat under climate change. To date, many studies have investigated rainfall intensity, frequency, or duration, but there is relatively less research on the spatial area of daily EP events. We analyze the baseline and trends in the spatial size of daily EP events over land in the contiguous US from 1980 to 2024. We find that, while smallarea EP events are more numerous, the largestarea EP events are responsible for most EP in the US. These events are also more intense on average than smallarea events. We also find that the number of days with smallarea EP events is decreasing and that EP events are becoming concentrated into fewer days per year, meaning that when EP events happen in a certain region, they now tend to be larger. The eastern US is also experiencing an increase in the frequency of largearea EP events, which are also more intense on average. Taken together, these results hint at the risk of outsized future flood impacts from widespread EP events, and advocate for the inclusion of spatial statistics when identifying trends in EP to better capture the changing nature of precipitation extremes in the US. , Key Points The distribution of US daily extreme precipitation spatial size is logarithmic, and largearea events dominate trends and climatology Largearea extreme precipitation events tend to be more intense than smallarea events, and are increasing in frequency in the eastern US Small events are becoming less frequent across the US, driving an increase in extreme precipitation area on days with extreme events"
    },
    {
        "DOI": "10.1029/2025GL121586",
        "Title": "PrecipitationDriven Thickening and WindInduced Erosion of the Ocean Barrier Layer Under Tropical Cyclones",
        "Year": 2026,
        "Abstract": "Abstract Barrier layer (BL) thickness (BLT) modulates tropical cyclone (TC) intensity, yet its evolution mechanisms remain debated. While statistical studies attribute BL thickening to heavy precipitation in strong or slowmoving TCs, numerical experiments suggest that enhanced winddriven mixing tends to erode it. Here, using reanalysisbased statistical and composite analyses, we reconcile previously conflicting findings. Weak TCs predominantly thicken the BLT through precipitationdriven surface freshening. In contrast, strong TCs tend to erode the BL owing to enhanced winddriven mixing and Ekman upwelling, which weaken salinity stratification and shoal the isothermal layer base, respectively. TC translation speed modulates these responses via the duration of forcings: fastmoving TCs induce relatively weak BLT changes, whereas slowmoving TCs generate stronger thickening and erosion. Across all translation speeds, however, precipitation effects dominate over winddriven processes, making thickening the prevailing response. These results clarify the competing mechanisms controlling BLT evolution and provide guidance for improving TC intensity forecasts. , Plain Language Summary Tropical cyclones (TCs) influence the upper ocean by redistributing heat and salt through heavy rainfall and strong winds. One important ocean feature affected by these processes is the barrier layer, a warm surface layer that can help sustain TC intensity. However, how the barrier layer responds to TCs of different strengths and movement speeds has remained unclear. Using reanalysis data for both the ocean and atmosphere, this study shows that weak TCs mainly thicken the barrier layer because rainfall freshens the ocean surface near the storm center. In contrast, strong TCs tend to erode the barrier layer, as intense winds enhance ocean mixing and draw cooler, saltier water upward. Storm motion also plays a key role: slowmoving TCs cause larger changes because their effects last longer, while fastmoving TCs produce much weaker responses. Despite these differences, rainfall generally has a stronger impact than wind, making barrier layer thickening the dominant response overall. These findings improve our understanding of oceanstorm interactions and may help improve future predictions of TC intensity. , Key Points Tropical cyclones thicken the barrier layer near the storm center through precipitation and erode it in the outer region via wind stress Barrier layer thickening weakens while erosion strengthens with increasing storm intensity due to enhanced winddriven mixing and upwelling Both thickening and erosion decrease with increasing storm translation speed as precipitation and wind forcing act for shorter durations"
    },
    {
        "DOI": "10.1029/2026GL122409",
        "Title": "Variable Sensitivity of Lake Surface Temperatures to Short and LongTerm Atmospheric Warming",
        "Year": 2026,
        "Abstract": "Abstract Thermal sensitivity represents the magnitude of water temperature association to changes in air temperature (AT), yet global patterns in the responsiveness of lake surface water temperature (LSWT) to atmospheric warming at different temporal scales remain poorly characterized. Using LSWTs from 35,263 lakes worldwide, we quantify the spatiotemporal variability in LSWT sensitivity to both longterm atmospheric warming and shortterm extremes. On average, LSWTs respond at 76% to the rate of longterm atmospheric warming (0.76C LSWT change per 1C change in AT). The sensitivity of lake heatwave frequency, duration, and cumulative intensity to corresponding atmospheric heatwave metrics is 55%, 84%, and 59%, respectively. These sensitivities are regulated primarily by shortwave radiation, wind speed, and geographic location, particularly latitude and longitude. Our findings reveal nonlinear and scaledependent lakeclimate interactions driven by competing physical processes, advancing mechanistic understanding of lake thermal responses to ongoing climate warming. , Plain Language Summary Lakes respond differently to gradual, longterm atmospheric warming and short, intense heatwaves. Using satellite and modeled data from over 35,000 lakes, we found that lake surface temperatures track about 76% of longterm air temperature (AT) increasesmeaning that for every 1C rise in AT, water temperature increases by roughly 0.76C. During heatwaves, lakes respond most strongly to their duration, with an average sensitivity of 84%, while sensitivity to heatwave frequency or intensity is lower (55%59%). Interestingly, sensitivity to longterm atmospheric warming is negatively correlated with sensitivity to atmospheric heatwaves: lakes that warm rapidly under gradual warming do not necessarily respond as strongly to shortterm extremes. These patterns are shaped by sunlight, wind, evaporation, and lake location. More incoming solar radiation increases sensitivity to heatwaves, while stronger winds reduce sensitivity by mixing the water and dissipating heat. Overall, lake warming is complex and nonlinear. Lakes respond in different ways to gradual atmospheric warming and shortterm heat extremes, which can affect stratification, oxygen levels, and the growth of algae. Understanding these responses is critical for predicting how climate change will influence water quality, aquatic ecosystems, and the availability of freshwater resources in the future. , Key Points Lake surface temperatures show differentiated sensitivity to longterm atmospheric warming and shortterm heatwave events Lake surface temperatures increase by 76% of air temperature change under longterm warming and 66% during heatwaves Shortwave radiation, wind speed, and location jointly shape spatiotemporal patterns of lake thermal sensitivity"
    },
    {
        "DOI": "10.1007/S11150-026-09851-3",
        "Title": "Air Pollution and Intimate Partner Violence in India",
        "Year": 2026,
        "Abstract": "Abstract We combine individual-level data from the Demographic and Health Survey for India with high-resolution spatial data on air pollutants to investigate how exposure to high levels of PM2.5 influences spousal violence. For identification, we use atmospheric wind directions as an instrument for local pollution concentrations. We find that a 10 $$\\:\\mu\\:g/{m}^{3}$$ increase in PM2.5 levels has a statistically significant impact on intimate partner violence, raising the incidence of any violence by 4.7% over the sample mean. Heterogeneity analysis shows the effects are concentrated among rural households and poor households. We also find that air pollution in rural areas is associated with lower probability of both women and men working. This is consistent with the hypothesis that air pollution could affect intimate partner violence indirectly through reduced employment."
    },
    {
        "DOI": "10.1029/2025GL121172",
        "Title": "Observed Increase in Tropical Vegetation Droughts Over the Past Three Decades",
        "Year": 2026,
        "Abstract": "Abstract Tropical terrestrial vegetation is critical to the global carbon cycle but faces escalating drought threats. Traditional assessments using fixed climate thresholds often ignore actual physiological responses and nonmoisture disturbances. To address this, we developed a novel framework that isolates the true physiological impacts of atmospheric and soil moisture (SM) deficits to identify growingseason vegetation droughts (19822019). Results reveal pantropical increases in drought intensity, with tropical forests experiencing significantly sharper intensifications than other biomes. Regionally, African forests exhibit the most severe expansions in drought intensity and area. Interpretable machine learning attributes this intensifying drought predominantly to declining SM (NDVI: 52%; LAI: 53%). Finally, while reliable historical reconstruction is vital for future projections, CMIP6 models fail to reproduce these observed trends. These findings highlight mounting drought pressures on tropical forests and underscore the critical need for improved climate models to inform mitigation strategies. , Plain Language Summary Tropical forests, vital for absorbing carbon dioxide, face growing threats from intensifying droughts. Analyzing data from 1982 to 2019, we developed a new method focusing on dry air and soil water deficits to identify droughts that specifically stress plants. We found that vegetation droughts have intensified pantropically, with African forests being hit the hardest. Declining soil moisture is the primary driver, responsible for over half of this drought stress. Furthermore, current climate models poorly represent these historical trends, struggling to correctly capture moisture changes and plant responses. This highlights an urgent need for improved models to better protect these crucial ecosystems. , Key Points Pantropical forest droughts intensified by 0.0027 0.0013 per year, with Africa facing the most severe spatial expansion Declining soil moisture, driven by warming and higher evaporative demand, was the primary cause of over 50 percent of these droughts Current climate models show large uncertainty in simulating these droughts, highlighting a need for better vegetation drought projections"
    },
    {
        "DOI": "10.1029/2025JG009307",
        "Title": "Informing Robust Functional Relationship Benchmarks: An Evaluation of the Temperature Sensitivity of Ecosystem Respiration Across the ArcticBoreal Region",
        "Year": 2026,
        "Abstract": "Abstract During land model development, simulated carbon dynamics are often benchmarked against observational data sets to evaluate model performance. Functional relationship benchmarks are the relationship between a driving variable (e.g., temperature) and a response variable (e.g., ecosystem respiration) and are a promising tool for assessing model performance by evaluating modeled sensitivities to changing environmental conditions. However, observed functional relationships can be influenced by choices made during data collection and throughout the benchmarking process, impacting the inferred skill of land models. To avoid misrepresenting a model's true performance, it is necessary to systematically evaluate best practices when constructing functional relationship benchmarks. We developed a set of guidelines for constructing functional relationship benchmarks, considering the choice of data set, number of daily observations, temporal extent, and temporal resolution across Alaska and Canada over a 20year period from 2001 to 2020. The temperature sensitivity of ecosystem respiration from observations, evaluated through an apparent Q 10 , is highly variable both spatially and as a result of the data processing approach applied in the benchmark formation. When benchmarking 13 models from the Warming Permafrost Model Intercomparison Project (WrPMIP), the range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks. The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent, followed by choice of benchmark data set and temporal averaging. Results from this analysis can guide the development of consistent and robust functional relationships for future model evaluation studies. , Plain Language Summary Land models are often compared against data sets to evaluate model performance, referred to as model benchmarking. However, data sets themselves can contain errors and vary based on the measurement approach, and choices made during the processing of data can influence benchmarks derived from observational data products. Here, we evaluated how ecosystem respiration responds to temperature across Alaska and Canada, and how this relationship can be influenced by the choice of data set, number of data points throughout the month, number of years, and averaging over time. We found that the relationship between ecosystem respiration and temperature varies widely according to these choices, particularly the number of daily observations and temporal extent. When applying different subsets of the data to test model performance, we found that these choices produced a large range in model scores compared with those when applying the full data set. As a result, we developed a set of best practices that can serve as a guide during model benchmarking and can provide a more accurate sense of model performance. , Key Points The range in inferred model skill is substantially impacted by the choices applied in constructing functional relationship benchmarks The inferred performance of a given model is most sensitive to the number of daily observations and temporal extent of the benchmark data set These results can guide the development of consistent and robust functional relationships for future model evaluation studies"
    },
    {
        "DOI": "10.5194/TC-19-4525-2025",
        "Title": "Brief communication: Reanalyses underperform in cold regions, raising concerns for climate services and research",
        "Year": 2025,
        "Abstract": "Abstract. Many changes in cold regions are amplified by nonlinear processes involving ice and have important consequences locally and globally. We show that the average ensemble spread of the mean annual air temperature (1.5 C) in the reanalyses is 90 % greater in cold regions compared to the other regions and shows pronounced disagreement in the trend. The ensemble spread in the mean annual maximum snow water equivalent is found to be greater than the ensemble mean. The reduced quality of reanalyses in cold regions, coinciding with sparse in situ observations and low population, points to challenges in how we represent cold-region phenomena in simulation systems and limits our ability to support climate research and services."
    },
    {
        "DOI": "10.3389/FPLS.2026.1838206",
        "Title": "Timingdependent responses of Fusarium graminearum suppression and malting quality in barley following PSP1 elicitor application",
        "Year": 2026,
        "Abstract": "Introduction Plant defence elicitors have emerged as promising tools and a sustainable alternative to enhance crop resilience. In barley, the potential benefits of elicitors on agronomic performance remain insufficiently understood. The present study aimed to evaluate the effects of the defence elicitor Plant Stimulator and Protector 1 (PSP1) on the Fusarium graminearum barley pathosystem in the Argentine Pampas, considering application timing. Methods Field experiments were conducted in 2022 and 2023 using two contrasting commercial tworow spring barley genotypes. The PSP1 was applied at three phenological stages: tillering (T1), stem elongation (T2), and heading (T3), with plots artificially inoculated with F. graminearum (DC.55) . Disease parameters, yield components, commercial grain traits and industrial malting quality variables were assessed. Results and discussion The results showed that applications close to heading resulted in low, nonsignificant reductions in FHB incidence (10%) and severity (5%) relative to earlier applications. Grain yield components were largely unaffected by PSP1, whereas malting quality showed a clear change in response to defence activation. Late applications tended to negatively affect malt extract, friability, and the Kolbach index (5%), as well as FAN and filtration time (25%) compared to the control. In contrast, malt protein, grain size, and wort pH increased. Under lowmoderate FHB pressure, PSP1 application timing was a key determinant of barley agronomic and technological outcomes, with earlier applications linked to better malting quality. These results provide novel fieldbased insights into elicitor use in barley, supporting the design of future multienvironment studies to optimize the deployment of elicitorbased strategies."
    },
    {
        "DOI": "10.1029/2025JA034510",
        "Title": "Lower Atmospheric Drivers of Upper Atmospheric DaytoDay Variability Over Alaska in Arctic 20182019 Winter",
        "Year": 2026,
        "Abstract": "Abstract Daytoday variability in the Arctic winter thermosphere is driven, in part, by weather in the middle and lower atmosphere. The research presented here investigates the link between daytoday stratospheric conditions, and local IT variability over Alaska during the 20182019 Arctic winter. This work uses 4.3m brightness temperature perturbation variances from the Atmospheric Infrared Sounder (AIRS) to quantify GW activity in the stratosphere, temperature perturbations from the High Altitude Mechanistic general Circulation Model (HIAMCM) to infer GW activity in the thermosphere, and Incoherent Scatter Radar electron density measurements for Medium Scale Traveling Ionospheric Disturbances (MSTID) activity in the ionosphere. We find that GW activity in the stratosphere and thermosphere, and MSTID activity in the ionosphere were suppressed during the 20182019 sudden stratospheric warming. These MSTID observations are in good agreement with HIAMCM thermospheric GW activity output. MSTID activity over Alaska is linked to stratospheric GW activity over Europe and NE Russia at different times during the season, highlighting the importance of crosspolar GW propagation. MSTID amplitudes over Alaska are also positively correlated with polar vortex strength and stratospheric wind speeds over Alaska at 50 km. These results support the conclusion that the IT region is strongly coupled to the lower atmosphere through GW interactions with the polar vortex and their subsequent crosspolar propagation. While the importance of lower atmospheric drivers has previously been shown at midlatitudes, this research emphasizes the importance of the polar vortex and lower atmospheric GW coupling in driving MSTID and thermospheric GW variability at high latitudes. , Plain Language Summary Earth's upper atmosphere (90500 km) is an important region for lowEarth satellite orbit that can be divided into the neutral part, the thermosphere, and the charged part, the ionosphere. It is important to understand the dynamics of the upper atmosphere as this region affects satellite orbits, GPS navigation, and communication. It is regularly perturbed by waves originating in the lower atmosphere. This study focuses on the impact of lower atmospheric waves originating over Alaska, Europe, and NE Russia and their subsequent influence on the ionosphere and thermosphere. Both ground and satellitebased instrumentation were used along with a general circulation model to understand the link between waves in the middle atmosphere (stratosphere) and variations in the upper (thermosphere/ionosphere) atmosphere over Alaska during the 20182019 Arctic winter. The study confirms that perturbations at most altitude regions are lessened when winds in the stratosphere are weak. , Key Points Gravity waves (GW) over Alaska in the stratosphere/ionosphere/thermosphere were suppressed during the 20182019 Sudden Stratospheric Warming (SSW) Modeled thermospheric GWs and observed MSTID amplitudes over Alaska were suppressed during the 20182019 SSW MSTID amplitudes over Alaska positively correlated with polar vortex strength and stratospheric wind speeds in the 20182019 Arctic winter"
    },
    {
        "DOI": "10.1080/15481603.2026.2651958",
        "Title": "Climate warming reshapes vegetation responses in China from temperature to precipitation",
        "Year": 2026,
        "Abstract": "Climate change is creating temporal mismatches between hydrothermal supply and vegetation demand, thereby altering the dominant climate controls on vegetation, but patterns mechanisms of these changes remain unclear. Through a synergistic analysis multisource multitemporal remote sensing data, this study investigated reshaping process factors China's from 1982 to 2022. It also examined spatial heterogeneity across topographic subregions types, explored driving mechanisms. Results showed that response intensity more than 90% was underestimated without considering time, while rest overestimated. primarily temperature-driven. However, under warming, 52.71% shifted temperature- precipitation-driven regimes. Vegetation responses precipitation accelerated intensified over particularly in cold, dry, high-altitude regions. In contrast, direction regions differed markedly those warm, humid, low-altitude Grasslands, forests, wetlands exhibited increasing sensitivity precipitation, drought further amplified precipitation. Overall, control shifting temperature This transition suggests potential early signal increased global with important implications for predicting ecological dynamics developing adaptation strategies."
    },
    {
        "DOI": "10.1029/2025JD044760",
        "Title": "Assessing the Contribution of Marine Isoprene Emissions to GroundLevel Ozone Formation in East Asia",
        "Year": 2026,
        "Abstract": "Abstract Marine isoprene emissions (MIEs), primarily from biogenic sources influence oceanic/coastal atmospheric chemistry but receive less attention than terrestrial emissions. To date, their contributions to ozone (O 3 ) pollution in East Asia remain poorly quantified. Here, we investigate the contribution of MIEs to nearsurface O 3 in East Asia in 2017 using a sourcetagging method integrated into a regional chemical transport model. The results indicate that MIEs increase O 3 by up to 4.6 ppb over coastal seas and 12 ppb inland. The contribution is highest in summer (up to 10 ppb), with notable influences in spring and autumn in southern coastal regions. For 12 coastal cities, MIEs contributed an average of 0.52.9 ppb to surface O 3 (accounting for 1.2%4.9% of total) during January, April, July, and October, peaking at South Korea's Kanghwa/Cheju (up to 9%, 2.82.9 ppb). Diurnal peaks occur in the afternoon under strong solar radiation. Specifically, MIEs increase O 3 via two pathways: a direct pathway (peak 3.9 ppb near marine sources) driven by local rapid photochemical reactions, and an indirect pathway (1.5 ppb inland) relying on longrange transport of oxidation products. Furthermore, MIEs enhance radicalmediated O 3 production and suppress the net nonloss reaction between O 3 and NO through NO depletion, ultimately leading to a net increase in O 3 concentrations. These findings highlight the nonlocal contribution of MIEs and emphasize the need to incorporate marine isoprene sources in regional O 3 assessments. , Plain Language Summary This study investigates the contribution of marine isoprene emissions (MIEs) to the groundlevel ozone formation over East Asia and its surrounding seas in 2017. The results show that MIEs not only affect ozone levels in marine areas but also contribute to ozone levels on land. The highest contribution, reaching 4.6 ppb, is observed over the Yellow Sea, East China Sea, and Sea of Japan. Marine isoprene emissions also have a broad but relatively low contribution to inland ozone levels ranging from 1 to 2 ppb. In 12 coastal cities, these emissions account for up to 2.9 ppb of the maximum daily average ozone, representing up to 4.9% of the total ozone. The contribution of MIEs is strongest in summer (up to 10 ppb), with notable levels also observed in spring and autumn, particularly in southern coastal areas. Cities near the Yellow Sea, such as Kanghwa and Cheju, experience the highest contributions, especially in summer, with values reaching up to 9%. MIEdriven ozone enhancement mainly occurs in the afternoon when sunlight is strongest. These findings highlight the importance of considering marine sources in ozone pollution assessments in East Asia. , Key Points Marine isoprene emissions (MIEs) contribute up to 4.6 ppb to ozone over the seas and 12 ppb across inland East Asia MIEs contribute more in summer and daytime, with higher contributions under high ozone levels MIEs promote O 3 production via HO 2 /RO 2 + NO and suppress O 3 loss via O 3 + NO"
    },
    {
        "DOI": "10.1088/2515-7620/AE654D",
        "Title": "Combining remote sensing and cell-phone users mobility data to monitor the impact of transportation on NO2 concentrations in India*",
        "Year": 2026,
        "Abstract": "Abstract Estimating the extent to which transportation contributes to air pollution levels has been hampered by the difficulty in separating the relative degree of ambient NO 2 generated by transportation, power generation, and industrial activityall of which play roles. This paper addresses this gap by isolating the impact of ground-level mobility on air pollution in India through a combination of remotely sensed tropospheric NO 2 measures and data from mobile-phone users locations. We construct vectors of ground-level movement of cell phones to estimate the impact of daily changes in mobility within a given district, controlling for both daily thermal electricity generation from upwind power plants and for trends in ambient pollution concentrations over time and space. We find that tropospheric NO 2 concentrations are very responsive to changes in mobility, and that the effect varies with population density. In the most densely-populated regions, our findings show that a 1% increase in mobility increases NO 2 concentrations by more than 2%, suggesting that traffic congestion plays a significant role in air pollution."
    },
    {
        "DOI": "10.1029/2025GL120986",
        "Title": "Capturing Antarctic Precipitation With a 3D Atmospheric River Algorithm",
        "Year": 2026,
        "Abstract": "Abstract Atmospheric rivers (ARs) typically lead to intense precipitation and play an essential role in the Antarctic ice surface mass balance. Their detection in the Antarctic region is challenging, preventing consistent evaluation of their role at a global scale. In this study, we extended a conventional method and developed a new threedimensional AR detection algorithm, which we applied over Antarctica. The results showed that the detected ARs at Dome Fuji during the 44th Japanese Antarctic Research Expedition were associated with more than half of the significant precipitation events and contributed to approximately 40% of the total precipitation. Climatologically, from 1979 to 2023, the ARs occurred less than 10% of the time but contributed to 30%60% of the annual total precipitation. Furthermore, ARrelated precipitation effectively captured the regional variability of longterm trends in Antarctic precipitation, indicating that ARs exert substantial control over Antarctic precipitation variability. , Plain Language Summary Atmospheric rivers (ARs) are long, narrow corridors of substantial moisture transport from lower to higher latitudes. When ARs encounter colder air or mountainous terrain, the moisture they carry condenses, producing heavy precipitation. In Antarctica, such precipitation can contribute to ice mass gain and help mitigate global sealevel rise. Thus, it is important to understand AR activity over the continent. However, detecting ARs over Antarctica is challenging owing to its unique topography and extremely dry conditions. In this study, we developed a new threedimensional (3D) AR detection algorithm by extending a conventional twodimensional approach. The results showed that more than half of the significant precipitation events observed at Dome Fuji on the East Antarctic Plateau during the 44th Japanese Antarctic Research Expedition were associated with 3DARs. The total precipitation from these events accounted for approximately 40% of the total during the observation period. Between 1979 and 2023, ARs occurred less than 10% of the time but their contribution to annual total precipitation was approximately 30%60% in the Antarctic interior. Furthermore, the spatial pattern of ARrelated precipitation trends closely matched that of the total Antarctic precipitation, suggesting that ARs play a dominant role in controlling Antarctic precipitation variability. , Key Points A new algorithm identifies the threedimensional structure of atmospheric rivers (ARs) intruding into the Antarctic interior ARs occurred 5% of the time but contributed 40% of the annual precipitation at Dome Fuji Variations in ARs largely control the longterm trend of Antarctic precipitation"
    },
    {
        "DOI": "10.1029/2025JH001104",
        "Title": "Decoding XCO2 Distributions Over the Indian Subcontinent Using Deep Neural Network Based HighResolution LongTerm Satellite Observations",
        "Year": 2026,
        "Abstract": "Abstract India often faces challenges in monitoring atmospheric carbon dioxide (CO 2 ) through satellite observations due to persistent cloud cover, especially during the monsoon season. This limitation affects the continuous tracking of carbon and hinders accurate assessments of carbonclimate interactions. To address this, we developed a highresolution (0.25) monthly columnaveraged CO 2 (XCO 2 ) data set for 20032020 using a Machine Learning (ML)based Deep Neural Network (DNN) downscaling and integration of three satellite retrievals of XCO 2 (SCanning Imaging Absorption SpectroMeter for Atmospheric ChartographY; SCIAMACHY, Greenhouse gases Observing SATellite; GOSAT and Orbiting Carbon Observatory; OCO2) across India. The MLpredicted XCO 2 shows strong agreement with OCO2 data for 20182020 (correlation coefficient, CC > 0.9; standard deviation: 0.39 ppm), and latitudinal biases range within 2 ppm. Also, seasonal mean biases are lower compared to global models (CAMS, CT) with values of 0.64 ppm (AMJ), 0.54 ppm (JAS), and 0.03 ppm (ON) except during DJFM (1.06 ppm), suggesting better seasonal consistency. Further, estimated XCO 2 growth rate (GR) from ML (3.73 ppm/year) closely matches NOAA surface observations (3.65 ppm/year) during strong El Nino (20152016). Importantly, the interannual variability (IAV) of MLXCO 2 GR aligns well with satellite observations (CC = 0.86) outperforming CAMS (0.11) and CT (0.73), indicating the ability of ML in capturing the IAV. The model also accurately detects regional hotspots of CO 2 , especially over the IndoGangetic Plains, consistent with the ODIAC fossil fuel emission inventory. These results demonstrate the ability of MLbased downscaling for a deeper understanding of regional carbon dynamics and their response to climate variability. , Plain Language Summary Atmospheric carbon dioxide (CO2) is the main greenhouse gas responsible for global climate change. Understanding the sources and sinks of CO2 is critical at both global and regional scales for effective climate mitigation strategies. However, due to the lack of groundbased in situ observations, current estimations report substantial uncertainties. In this study, we employed a machine learning (ML) model to generate a longterm, highresolution data set of the columnaveraged dryair mole fraction of CO2 (XCO2) over the Indian subcontinent. The ML model effectively reproduces seasonal distributions and longterm trends, with growth rates closely matching the observed satellite CO2 data. It also captures finescale spatiotemporal variations that are often missed by coarse resolution global models such as CAMS and CT. Moreover, the seasonal cycle amplitudes of MLXCO2 across different regions show good agreement with those from satellite observations. Importantly, the ML model is also capable of identifying hotspot regions associated with fossil fuel emissions. , Key Points For firsttime, Deep Neural Network is employed to generate longterm monthly XCO 2 at a spatial resolution of 0.25 for 20032020 Latitudinal bias in Machine Learning based XCO 2 falls in a range of 2 ppm, indicating good agreement with OCO2 observations Interannual variability of growth rate shows a strong correlation (0.86) with OCO2, whereas CT (CAMS) shows a lower value of 0.73 (0.11)"
    },
    {
        "DOI": "10.1016/J.JAG.2026.105170",
        "Title": "Salinity-induced global pattern of atmospheric water constraints on mangrove photosynthetic activity revealed by time series Sentinel-2 data",
        "Year": 2026,
        "Abstract": " Global analysis reveals salinity regulates VPD constraints on mangrove photosynthesis. Coupling Sentinel-2 red-edge with climate and data quantifies responses. Dry-climate high-salinity mangroves are most vulnerable to future warming drying. Atmospheric drought stress limits photosynthetic activity, this constraint can be further amplified by high salinity, yet their combined global effects remain poorly understood. Here, we integrated multi-source Earth observation geoinformation datasets, including position (a proxy for canopy activity), vapor pressure deficit from TerraClimate, seawater Copernicus reanalysis, investigate how the sensitivity of photosynthesis atmospheric during 20192023. Datasets were harmonized analyzed through reproducible workflows at 10 m0.5 resolutions, enabling large-scale coupling analyses between remote sensing proxies drivers. We found that constrained activity worldwide, stronger limitations in tropical savannahs than rainforests. Marine exposed persistent more sensitive estuarine influenced freshwater inflow. These results reveal a pattern which amplifies water Mangroves dry climates habitats therefore Our findings confirm integrating satellite observations provides an effective, approach assessing vegetation vulnerability identifying conservation priorities climate-sensitive ecosystems."
    },
    {
        "DOI": "10.1029/2025GL118492",
        "Title": "Impact of InterBasin Interactions on ENSOAssociated Hadley Circulation Adjustments",
        "Year": 2026,
        "Abstract": "Abstract El Nino events are usually accompanied by Hadley circulation (HC) adjustment extending beyond the Pacific to the Atlantic and Indian Oceans. These remote HC adjustments arise through both pure atmospheric and oceanatmosphere coupling mechanisms, yet their relative importance remains unclear. In this study, we integrate observations with climate model experiments to assess the roles of pure atmospheric and coupled oceanatmosphere pathways linking ENSO to the global HC adjustments. Results show that El Nino intensifies deep convection over the Pacific but suppresses convection over the Atlantic and Indian Oceans via the tropospheric temperature mechanism. Meanwhile, El Ninoinduced Atlantic and Indian Oceans warming enhances convection and regional HC. These two pathways exert opposing influences, with pure atmospheric pathway as the dominant driver. These findings establish a novel interbasin dynamical perspective for ENSOrelated tropical circulation adjustments and clarify the relative importance of different pathways, offering important implications for understanding ENSOinduced global climate impacts. , Plain Language Summary The Hadley Circulation (HC), a thermally driven atmospheric overturning system in the tropics, is strongly influenced by the El NinoSouthern Oscillation (ENSO) on interannual timescales. From the regional perspective, ENSO can alter HC patterns not only within the Pacific but also across the Atlantic and Indian Oceans. These remote responses result from both pure atmospheric and coupled oceanatmosphere pathways, with the former directly induced by ENSO and the latter mediated by the ENSOrelated crossbasin sea surface temperature (SST) anomalies. However, the relative importance and underlying processes of these pathways remain poorly understood. In this study, we explore how ENSO influences HC adjustments by disentangling the respective roles of two distinct pathways, using observations and numerical experiments. Our results show that El Nino warming heats the entire tropical troposphere via the tropospheric temperature mechanism, enhancing vertical stability and suppressing convection over the Atlantic and Indian Oceansultimately weakening regional HC. In contrast, El Nino also induces crossbasin SST warming in these regions, which enhances convection and strengthens regional HC. These two pathways have opposing effects, with the pure atmospheric pathway generally dominating. Our findings underscore the complexity of interbasin interaction in shaping tropical circulation, with potentially farreaching implications for ENSO teleconnections. , Key Points ENSO drives Hadley circulation (HC) changes over Atlantic and Indian Oceans through pure atmospheric and coupled oceanatmosphere pathways During El Nino, tropospheric warming suppresses AtlanticIndian Ocean convection, weakening HC through dominant pure atmospheric pathway El Nino also induces AtlanticIndian Ocean warming and intensifies convection, strengthening HC through coupled oceanatmosphere pathway"
    },
    {
        "DOI": "10.1029/2025GL118384",
        "Title": "Tropical Cyclone OutertoInner Brightness Temperature Ratio: A New SizeAdaptive Parameter Reflecting Storm Intensity and Its Change",
        "Year": 2026,
        "Abstract": "Abstract The intensity and size of a tropical cyclone (TC) are profoundly shaped by its internal convective distribution. However, comparing convective structures across TCs is challenging because fixedradius metrics cannot adapt to TC size variations. We introduce the OutertoInner Brightnesstemperature Ratio (TBR), a sizeadaptive metric quantifying the relative convective strength between the inner and outer region. Analysis of Northwest Pacific TCs (20012017) reveals TBR is strongly correlated with intensity ( r 0.6), significantly higher than correlations considering only the inner ( r = 0.34) or outer region convection ( r = 0.24). TBR is also strongly linked to intensification, with the frequency of rapid intensification peaking within a narrow highTBR window (1.151.20). Furthermore, for the intensification stage, sustaining a highTBR statea strong inner convection and quiescent outer regionis crucial for achieving a higher lifetime maximum intensity, a process linked to more efficient inward angular momentum transport. TBR provides a powerful tool linking sizeadaptive convection features to TC intensity and evolution. , Plain Language Summary Why do some tropical cyclones become devastatingly powerful while others of the same size remain significantly weaker? Our research suggests the key is how a TC organizes its convection. Is the storm's power concentrated in its core, or is it scattered across messy, sprawling rainbands? To measure this, we developed a new index called the Tropical cyclone OutertoInner Brightnesstemperature Ratio (TBR). A highTBR TC has cleaner outer regions, allowing energy and moisture to be funneled more efficiently into the core to fuel intensification. By analyzing thousands of TC records over 17 years, we found that the TBR index not only reflects a storm's current strength but, more importantly, also predicts its future changes in intensity and size. Our research reveals that storms able to consistently maintain a high TBR during their development ultimately become far more powerful than those with messy, lowTBR structures, even when they appear to be the same size. This discovery offers forecasters a new tool, demonstrating that monitoring the convective difference between a TC's inner and outer regions provides critical clues to its ultimate destructive potential and its likelihood of rapid intensification. , Key Points A new innerouter convective ratio (TBR) explains intensity differences in samesized tropical cyclones TBR reflects current intensity and is a key precursor for future changes, including rapid intensification Sustaining a highTBR state is crucial for reaching a higher lifetime maximum intensity"
    },
    {
        "DOI": "10.3992/JGB.20.4.57",
        "Title": "CLIMATE CHARACTERIZATION OF GUATEMALA FOR ENERGY-EFFICIENCY CONSIDERATIONS IN BUILDINGS",
        "Year": 2025,
        "Abstract": "ABSTRACT The certification process for buildings in Guatemala promotes using the ASHRAE 90.1 standard. Its proper implementation relies on climate information to establish zones characterized by energy performance. However, the quality of applications has been declining due to deficiencies in the local climate services provided. This study uses a climate variability satellite-based method to determine climate zones according to the ASHRAE 90.1 2010 Standard. The research identified five zones that should be considered: 1A (very hot humid), 2A (hot humid), 3A (warm humid), 3C (warm marine), and 4A (mixed humid). This was achieved by conducting a statistical analysis of climate variability from 1980 to 2022, using temperature satellite data from GLDAS-2.0 and GLDAS-2.1. The study demonstrated that satellite data is a reliable source of meteorological information for implementing the Standard in building design and for the energy modeling process. It also revealed climate variability across Guatemala, with only half of the territory experiencing significant changes that affect energy demand, and that the effects of the ENSO ( El Nino -Southern Oscillation) phenomena do not lead to significant changes."
    },
    {
        "DOI": "10.1029/2025GL117623",
        "Title": "Reversal Trends in ShallowSoil Temperature Over the QinghaiTibet Plateau During 19502014",
        "Year": 2025,
        "Abstract": "Abstract Soil temperature changes over the QinghaiTibet Plateau (QTP) exert considerable influence on regional climate. Here, we identify a distinct reversal from cooling during 19501983 (0.169C decade 1 ) to significant warming during 19842014 (0.388C decade 1 ), exceeding the global average by 29%87%. The early cooling period was mainly driven by interactions between greenhouse gases (GHG) and aerosols (AER), with AER exerting a dominant cooling influence. After 1984, the GHG contribution increased from 11.2%29% to 40%47.9%, leading to accelerated warming. The influence of interactions was strongly modulated by the relative dominance of GHG and AER, acting as a critical factor in the reversal and amplification of shallowsoil temperature trend across the QTP. These findings highlight the sensitivity of highaltitude soil thermal regimes to shifts in anthropogenic forcing. , Plain Language Summary Soil temperature is a key indicator of ecological and environmental change, integrating the effects of various surface and subsurface processes. On the QinghaiTibet Plateau (QTP), soil temperature variations influence permafrost thawing, carbon cycling, hydrological processes, and biodiversity. In this study, we apply a quantitative attribution framework to separate the contributions of greenhouse gases (GHG), aerosols (AER), and their interactions (INT) to shallowsoil temperature changes over the QTP during 19502014. We reveal a marked reversal from cooling during 19501983 to accelerated warming during 19842014, with the latter exceeding the global average warming rate by 29%87%. The post1984 warming was primarily driven by the increasing dominance of GHG, while INT emerged as the critical factor triggering the reversal and amplifying the subsequent warming trend. These findings offer insights into soil temperature dynamics under future climate scenarios. , Key Points From 1950 to 2014, shallow soil on the QinghaiTibet Plateau shifted from cooling to warming around 1984 Since 1984, greenhouse gases (GHG) accounted for the highest contribution to shallowsoil temperature changes at 40%47.9%, dominating the warming Interactions between GHG and aerosols were critical in reversing and amplifying the warming trend"
    },
    {
        "DOI": "10.1029/2025JD044821",
        "Title": "Anthropogenic Forcings Intensify Droughts More Severely in Drylands than in Humid Regions",
        "Year": 2026,
        "Abstract": "Abstract Droughts of different durations affect water resources and ecosystems in distinct ways. Human activities have been confirmed to contribute to the increased occurrence of droughts; however, the dependence of these impacts on the durations of drought, and whether they differ between drylands and humid regions, remains insufficiently understood. This study investigates the human influence on droughts of different durations using the Standardized Precipitation Evapotranspiration Index (SPEI), derived from multisource observations and four sets of Coupled Model Intercomparison Project Phase 6 (CMIP6) multimodel simulations. The results show that human activities cause an intensification of longterm droughts, particularly in drylands. This is primarily attributed to rising greenhouse gas (GHG) emissions, with both GHGs and aerosols exerting stronger impacts on droughts in drylands than in humid regions, though aerosols partly offset the intensifying effect. GHGs contribute to more extreme multiyear droughts over drylands by amplifying temperatureinduced water demand, whereas aerosols reduce drought occurrence in drylands by enhancing precipitation, in contrast to their precipitationsuppressing effects in humid areas in the past decades. , Plain Language Summary Droughts are among the most damaging climaterelated disasters, and their frequency and severity have increased under global warming. This study investigates how human activities, especially greenhouse gas (GHG) and aerosol emissions, have affected droughts of different durations across global drylands and humid regions. Using CMIP6 model simulations and observational data sets, we find that longterm droughts have intensified more significantly in drylands than in humid regions. GHGs raise temperature and water demand, leading to more severe and persistent droughts, particularly in drylands. In contrast, aerosols tend to offset drought in drylands by increasing precipitation, while reducing rainfall in humid regions in the past decades. These findings highlight strong regional contrasts in how anthropogenic forcings influence drought risk, which are especially important for water management and climate adaptation in dryland ecosystems. Understanding these differences improves our ability to project future hydroclimatic extremes and supports targeted climate resilience planning. , Key Points Human activities intensify longterm droughts, especially over global drylands GHGs enhance PET more strongly in drylands than in humid regions, intensifying drought risk The contrasting impact between drylands and humid regions is strongest for extreme multiyear droughts"
    },
    {
        "DOI": "10.1029/2025JD044820",
        "Title": "Spatiotemporal Characteristics of Mesoscale Convective Systems Over East Asian Monsoon Region Simulated by a ConvectionPermitting Model",
        "Year": 2025,
        "Abstract": "Abstract Mesoscale convective system (MCS) is a major contributor to extreme precipitation over East Asia, but their longterm trends remain insufficiently understood. Here, we assess the capability of the weather research and forecasting model, configured as a convectionpermitting model (CPM) to simulate MCS characteristics and 23year trends over East Asian monsoon region from 2001 to 2023 (JuneSeptember) by comparing with highresolution observational data. We employed an MCS tracking method PyFLEXTRKR, which can identify and track MCSs based on precipitation and brightness temperature. The CPM effectively captures key MCS characteristics, including lifetime, lifecycle total precipitation amount, and movement speed. However, it also has systematic biases: the model underestimates MCS size and meso MCS frequency while overestimating meso MCS occurrence and both mean and maximum MCSs precipitation intensities. Despite these biases, the model captures increasing (decreasing) trends in total and MCS precipitation over Manchuria and eastern China (Taiwan). In contrast, it struggles to reproduce observed total and MCS precipitation trends over North China Plain (NCP) and the Korean Peninsula (KP). These biases stem from the model's inability to capture enhanced moisture transport into East Asia, resulting in an underestimation of lowlevel moisture over NCP and KP, as indicated by trends in vertically integrated moisture flux and 850 hPa specific humidity. By characterizing systematic and regional model biases, this study lays the groundwork for more reliable CPMbased assessments of MCS responses to climate variability and change. , Plain Language Summary In East Asia, a significant proportion of heavy precipitation events is associated with mesoscale convective systems (MCSs). Recent highresolution observational studies have documented an increasing trend in MCS activity over East Asia. However, the limited duration of these observational data poses a challenge in identifying the driving factors of this trend, mainly whether it is influenced by natural variability or anthropogenic activities. Our study is the first to evaluate the ability of a convectionpermitting model to simulate MCS characteristics and recent trends over East Asian monsoon region, thereby establishing a foundation for subsequent research to diagnose how natural variability and human activities influence longterm MCS variability. , Key Points A convectionpermitting model overestimates rain intensity and yields smallerscale mesoscale convective systems (MCSs) over East Asia The model fails to simulate increasing trends of total and MCS precipitation over the North China Plain (NCP) and Korea, showing a wettodry bias This bias is linked to the convectionpermitting model's underestimation of lowlevel moisture trends over the NCP and Korea"
    },
    {
        "DOI": "10.1016/J.JASTP.2026.106759",
        "Title": "Governing factors of the unprecedented extreme rainfall over Rameswaram Island",
        "Year": 2026,
        "Abstract": "On 20-21 November 2024, extreme precipitation, the first of its kind to occur in the past 125 years, resulted in severe flooding across the southern coastal region of Tamil Nadu in particular over Rameswaram. The dynamic and thermodynamic conditions in the atmosphere that precede this extreme precipitation event are examined using reanalysis datasets of ERA5, brightness temperature, and Sea Surface Temperature. The analysis reveals deep convective clouds, a large amount of precipitable water, elevated specific humidity, and wind convergence in the lower troposphere at Rameswaram. The positive sea surface temperature (SST) anomaly of 2C in the Bay of Bengal could lead to significant evaporation. Intense moisture is transported from the Bay of Bengal by easterly winds, and the westward-propagating tropical easterly waves facilitate the advection of positive PV anomalies towards Rameswaram. The presence of a cyclonic circulation progressing towards the southwest coast of the Indian Peninsula is evident in the moisture flux divergence. Convection was supported and sustained by enhanced upward vertical velocity between 850 and 200 hPa. Moreover, the positive potential vorticity (PV) tower extending from 850 hPa to 200 hPa, points to the development of dynamic instability. Vertical and horizontal PV dipoles, signatures of deep convection and instability, are also identified over Rameswaram during heavy rain. The time-height evolution of the divergence, vertical velocity (), and PV anomaly, along with the mid-level vortex, suggests that the 'top-down' dynamics can be responsible for the torrential downpour over Rameswaram."
    },
    {
        "DOI": "10.1029/2025EF006261",
        "Title": "Quantifying Historical and Future Surface Soil Moisture Drying Using Deep Learning and Remote Sensing",
        "Year": 2026,
        "Abstract": "Abstract Understanding historical and future surface soil moisture (SSM) drying is pivotal due to its close links with droughts, heatwaves, and wildfires, yet debates regarding its evolution persist. In this study, we leverage advanced deep learning techniques to fill gaps of remote sensingbased SSM data during 19832020 and therefore use these gapfilled observations to constrain SSM estimates from 23 Earth System Models (ESMs) during 19012100. Our enhanced observations reveal that approximately half of Earth's landmass experienced SSM drying over the past four decades. However, in contrast to projections from currentgeneration ESMs, observationconstrained simulations indicate a less pronounced drying trend in drywet transitions and monsoon margins during 20212100 compared to 19011980. Current ESMs may overestimate SSM drying in these regions, likely due to their limited representation of soil moistureatmosphere feedback. These findings highlight the need to integrate remote sensing and artificial intelligence into ESMs to improve projections of future droughts and their socioeconomic consequences. , Plain Language Summary Understanding how surface soil moisture (SSM) changes in the past and future is essential for agriculture production, water resources management, and climate regulation. Using deep learning and satellite data, our study improves soil moisture estimates and refines future climate model predictions. We found that nearly half of the Earth's land has become drier over the past 40 years, but future drying trends may not be as severe as previously thought. Current ESMs may overestimate drying because they do not fully capture how soil moisture interacts with the atmosphere. Our findings also challenge the widely accepted paradigm that dry get drier and wet get wetter. Instead, we see more complex regional changes. By integrating deep learning and remote sensing, this research provides support for a better understanding of drought evolution in a changing climate. , Key Points An advanced deep learning technique is adopted to enhance surface soil moisture (SSM) observations and constrain SSM estimates from Earth System Models (ESMs) Enhanced earth observations reveal that approximately half of Earth's landmass experienced SSM drying over the past four decades Current ESMs may overestimate SSM drying likely due to their limited representation of soil moistureatmosphere feedback"
    },
    {
        "DOI": "10.1029/2025GL118712",
        "Title": "Confronting Historical Precipitation Trends in Models With Observations: Forced Signal and Atmospheric Internal Variability",
        "Year": 2026,
        "Abstract": "Abstract Future precipitation projections rely heavily on climate models, underscoring the need to evaluate their ability to simulate historical precipitation changes. Using multiple atmospheric models and ensemble simulations, we estimate the forced signals driven by sea surface warming and the direct effects of greenhouse gases and aerosols, as well as the atmospheric internal variability in precipitation trends since 1980. We find that forced precipitation trends are generally consistent across models, while atmospheric internal variability significantly influences regional patterns. Additionally, a few model members can reasonably well reproduce the observed pattern of precipitation trends. We highlight some regional wetting and drying are likely driven by forcings rather than the atmospheric internal variability. Zonalmean trends over land reveal a wet gets wetter, dry gets drier paradigm in the Northern Hemisphere, while the Southern Hemisphere shows drying near 45S associated with jet stream shifts. These results help improve our understanding of historical precipitation changes. , Plain Language Summary To improve our confidence in future precipitation changes, we need to ensure that climate models can reproduce observed historical precipitation trends. From an atmospherelandonly perspective, historical trends reflect a combination of forced signals (e.g., greenhouse gases, aerosols, sea surface warming) and atmospheric internal variability (unforced natural and random shifts in wind, air pressure, and temperature patterns). We utilize multiple atmospheric models and their ensemble simulations to separate precipitation trends since 1980 into components driven by forcings and those due to atmospheric internal variability. We find that patterns of forced precipitation trends are similar across models, indicating a robust and strong forced signal. Atmospheric internal variability plays an important role in shaping the patterns of precipitation trends, and a few ensemble members are able to closely capture the observed trends. By comparing observed trends with forced trends across multiple models, we identify regional precipitation changes that are likely driven by forcings rather than internal variability. The zonalmean precipitation trend over land follows a wetgetswetter, drygetsdrier paradigm in the Northern Hemisphere, while in the Southern Hemisphere, the expected drying around 30S shifts poleward to around 45S, consistent with poleward shift of jet stream. These findings help us better understand historical precipitation changes. , Key Points Forced historical annual precipitation trends are broadly consistent across models, highlighting a robust atmospheric response to forcings Atmospheric internal variability shapes regional precipitation trend patterns, with a few ensemble members closely capturing observed trend Zonalmean trends show wet gets wetter, dry gets drier paradigm across Northern Hemisphere land, and drying near 45S linked to jet shift"
    },
    {
        "DOI": "10.1016/J.SOLENER.2026.114516",
        "Title": "A climate-adapted framework for multi-temporal resource assessment of global horizontal irradiance under changing atmospheric conditions",
        "Year": 2026,
        "Abstract": "Enhancing the bankability of gridded global horizontal irradiance (GHI) products is inevitable to expedite integration of photovoltaic (PV) systems particularly in regions characterized by climatic diversity and limited ground-based data. This study proposes a climate-adapted framework for the multi-temporal validation and statistical metric-based risk-scoring methodology for mapping of gridded GHI datasets, using South Asia as a representative case. Seven widely used gridded GHI products encompassing satellite-derived, reanalysis-based, and modelled datasets are evaluated against ground measurements from 24 stations spanning eight Koppen-Geiger climate classes. It integrates performance evaluation across three temporal scales (hourly, daily, monthly), and typical meteorological year dataset, climate-stratified diagnostics of error patterns, and the influence of atmospheric parameters such as aerosol optical depth and cloud cover. MERRA-2 performs best at hourly resolution across all climates, except in tropical wet (Af) zones, where CERES achieves the highest correlation (R: 0.750.97). At monthly scale and TMY-dataset, CERES and ERA-5 lead in terms of minimal bias (5.5% to 2.5%). SARAH-3.0 shows strongest agreement in cold semi-arid (BSk) regions, while ERA-5 performs well in tropical and highland climates. POWER, PSM, and SUNY datasets offer minimal bias at their native temporal resolutions. Temporal aggregation reduces bias in most climates, except BSk, where it increases by 171%. Seasonal analysis reveals reduced GHI reproducibility during monsoon and smog periods. Eventually, the framework produces 23-year climate-adapted GHI maps with annual means of 141256 W/m2. This adaptable framework provides a robust tool for climate-stratified solar resource assessment and supports informed PV deployment in regions with complex atmospheric dynamics."
    },
    {
        "DOI": "10.1016/J.AGRFORMET.2026.111120",
        "Title": "Foliar litter cover enhances surface warming in global forest ecosystems",
        "Year": 2026,
        "Abstract": " Forest litter, which resembles a coating layer covering the ground surface and increases the surface resistance in forest ecosystems, profoundly affected underlying surface temperature by altering the heat and water transfer processes. However, how does forest foliar litter cover influence surface temperature remains to be investigated. In this study, we first proposed a method that integrates a Penman-Monteith based evapotranspiration model with energy balance method (EBPMEL), which accounts for the effects of forest litter on water and energy transfer processes, to quantify the effect of forest litter on surface temperature. This method was subsequently applied to global forest ecosystems to evaluate the influence of forest foliar litter cover on surface temperature and to analyze the underlying mechanism. The results indicate that: (1) the EBPMEL method has been validated to accurately estimate surface temperature compared with flux sites measurements across various forest ecosystems, exhibiting an overall percent bias (Pbias) of approximately 1 % and a Nash-Sutcliffe efficiency (NSE) value exceeding 0.98, indicating the high accuracy and model performance; (2) generally, forest foliar litter cover enhances surface warming due to its role in increasing surface resistance, resulting in an average temperature rise of 0.31±0.19°C. And this warming effect exhibits distinct latitudinal variations, with the most significant impact noticed at mid-latitudes. The effect is most pronounced in deciduous broadleaf forests (DBF), followed by evergreen needleleaf forests (ENF), mixed forests (MF), and evergreen broadleaf forests (EBF); (3) the warming effect is primarily regulated by either litter fraction or available energy, with the dominant controlling factor varying across forest types and transitioning between the June–July–August (JJA) and December–January–February (DJF) periods. The study has advanced our understanding of heat and water transfer processes within forest ecosystems, providing a scientific basis for modeling land surface processes and land-atmosphere interactions in forest ecosystems. It offers critical insights with significant implications for global climate change research. "
    },
    {
        "DOI": "10.1029/2025GL118880",
        "Title": "Advancing Tropical Cyclone Rainfall Simulation and Projection With EddyResolving Climate Models",
        "Year": 2026,
        "Abstract": "Abstract Tropical cyclone (TC) plays a critical role in driving hydrological extremes. However, current generation climate models often fail to capture TC innercore dynamical structures, leading to substantial underestimation (>50%) of TC rainfall (TCR). Here, using a set of eddyresolving highresolution (HR) simulations from the Community Earth System Model (CESM), we show that simulated TCR closely aligns with observations, primarily due to the improved TC upward motion. Under the Representative Concentration Pathway 8.5 warming scenario, projected TCR increases in noneddyresolving models reach only 15%50% of those in HR CESM, likely reflecting their large historical TCR biases. Owing to suppressed TC upward motion, the projected TCR fractional increase by noneddyresolving models remain comparable to or below the ClausiusClapeyron (CC) scaling. In contrast, HR CESM projects a much larger TCR increase rate (12.0% K 1 ), exceeding the CC rate and driven by a strengthened TC upward motion. , Plain Language Summary Tropical cyclones (TCs) often bring intense rainfall that can cause devastating floods in coastal and island regions. However, many current climate models underestimate TC rainfall because they do not have high enough resolution to capture the storm's innercore structure. In this study, we use a new set of highresolution climate simulations that can well resolve mesoscale ocean eddies and their atmospheric interactions, which are important for TC evolution. These simulations produce more realistic TC rainfall patterns and intensities when compared with satellite observations. Looking ahead, the highresolution model projects that rainfall from TCs will increase much more with global warming than what conventional models suggest. This larger increase is mainly caused by stronger upward motion inside TCs, which is not captured in most existing models. Our results highlight the importance of using eddyresolving models in both the ocean and atmosphere to better predict how TC rainfall may intensify in a warmer climate. , Key Points Eddyresolving CESM markedly improves TC rainfall (TCR) simulation through a more realistic representation of innercore ascent Projected TCR increases in eddyresolving CESM are more than twice those in noneddyresolving models under RCP8.5 Projected TCR fractional increase in CESM (12% K 1 ) exceeds the ClausiusClapeyron rate (7% K 1 ) due to enhanced TC upward motion"
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135006",
        "Title": "High predictability potential of highly synchronized widespread floods in monsoon regions",
        "Year": 2026,
        "Abstract": "The spatio-temporal distribution characteristics of widespread flood events and their prediction are topics of global concern. However, there is a noticeable absence of thorough investigations and analyses regarding the spatio-temporal characteristics, predictability of global widespread flood events, and the corresponding impact of climate indices. We bridge this gap by employing recurrence quantification analysis to evaluate the predictability potential of globally widespread flood events. We further examine how this potential correlates with highly synchronized widespread flood events in monsoon regions. Our results show that regions with high flood predictability potential (HFP) account for 20.09% of the world's total land grid points. Specifically, 69.29% of the HFP grid points among them are located in eight monsoon regions. Highly synchronized and widespread flood events (HSEs) exhibit higher predictability potential in monsoon regions. We uncover that HSEs in the AustralianMaritime Continent Monsoon and Equatorial South America Monsoon regions are profoundly and intricately influenced by climate indices. Our findings establish a connection between the predictive capacity and the occurrence of widespread flood events, viewed through the lens of complex systems. This contributes a crucial reference point for understanding and forecasting future globally widespread flood events."
    },
    {
        "DOI": "10.1016/J.RSASE.2025.101857",
        "Title": "Seasonal patterns and atmospheric modulators of erythemal UV radiation in a sensitive region of the Brazilian Amazon: Implications for environmental health risk assessment",
        "Year": 2026,
        "Abstract": "Ultraviolet (UV) radiation is a critical environmental driver influencing ecological and human health, with its variability shaped by atmospheric factors and climate dynamics. This study examined the seasonal patterns and temporal trends of the erythemal UV radiation and key atmospheric variables in the Brazilian Amazon, using satellite remote sensing data from OMI/Aura and climate reanalysis data from CAMS spanning 2005 to 2022. Temporal trends were assessed using robust statistical approaches, while the relative influence of atmospheric drivers on erythemal UV variability was quantified using SHAP (Shapley Additive Explanations). A Susceptibility Index (SI) for UV-related health risks was developed, integrating biological, behavioral, and socioeconomic dimensions. Results revealed a distinct seasonal erythemal UV cycle, with peaks from January to April and lows from June to August, maintaining predominantly \"very high\" to \"extreme\" levels year-round. Statistically significant trends were observed in cloud optical thickness (COT) and total ozone column (TOC), while SHAP analysis indicated that variables such as water vapor (through its association with cloud processes), aerosols, and TOC emerged as primary predictors of surface UV, followed by PM2.5 and PM10, thereby reinforcing the model's potential as a tool for environmental health risk assessment. The SI indicated moderate to high susceptibility among most individuals, strongly modulated by social inequalities and sun exposure habits. The empirical validation of the SI through estimated UV dose and Minimal Erythemal Dose (MED) exceedance supports its potential as a tool for environmental health risk monitoring. These findings underscore the importance of integrated strategies that consider atmospheric and social factors to mitigate UV-related health risks in tropical regions under climate change scenarios."
    },
    {
        "DOI": "10.1016/J.PCE.2026.104362",
        "Title": "Glacier dynamics in the Upper Tons Basin (19932023): A multi-sensor approach using SAR coherence, thermal, and optical remote sensing",
        "Year": 2026,
        "Abstract": "We used hybrid multi-sensor method for debris-covered glacier mapping. Upper Tons Basin glaciers shrank by 16.7% from 1993 to 2023. Clean ice glaciers lost more than debris-covered glaciers. The glaciercount increased from 44 to 59 due to fragmentation. Increasing trends observed in temperature, precipitation and black carbon since 1993."
    },
    {
        "DOI": "10.1111/1365-2664.70251",
        "Title": "Successional and native forests predict the occurrence and infection status of Chagas disease vectors in Panama",
        "Year": 2026,
        "Abstract": "Abstract Changes in land use and land cover (LULC) due to agricultural expansion, commercial land management and other humandriven modifications significantly influence the ecology of pathogens and vectors. This underscores the urgent need to understand how these respond to rapid and dynamic land use changes in these ecosystems and, critically, to identify strategies for mitigating their impacts. In tropical Central and South America, palm trees serve as primary habitats for Rhodnius kissing bugs, vectors of Trypanosoma cruzi , the etiologic agent of Chagas disease. This study investigates how LULC, weather and traits of the palm Attalea butyracea predict the occurrence and infection of Rhodnius pallescens , integrating field data collection, molecular detection and spatial and hierarchical analyses across a rural landscape in Panama. Rhodnius pallescens were collected from 46 palms in 11 communities with different landscape compositions including native forests, grasslands, successional forests and artificial structures. Robust occupancy modelling using land cover data at 10 m 2 resolution revealed that successional forest cover at 300 m spatial scale predicted greater occurrence of R. pallescens , whereas native forest predicted lower occurrence. Quadratic models outperformed linear models, indicating occupancy peaks at intermediate land covers and palm tree traits. Realtime PCR assays detected Trypanosoma infections in 70% of R. pallescens across communities. Spatial autocorrelation analyses showed significant spatial clustering for T. cruzi but not for Trypanosoma rangeli . We used generalized additive mixed models to assess the influence of palmlevel and landscapescale attributes on parasite infection and identified significant nonlinear positive associations between T . cruzi infection and native forest and grassland, with high predictive accuracy (AUC = 0.90). Synthesis and applications . Findings here show that successional forest predicts greater kissing bug infestation risk in palm trees, whereas native forest predicts lower kissing bug occurrence but greater infection with T. cruzi . These insights can guide land use planning towards vegetation management practices that help minimize T. cruzi transmission risks for rural communities. Importantly, vector surveillance should target forestgrassland ecotones and consider forest successional stages near settlements, with intensified monitoring after disturbances; this approach is applicable to other vectorborne pathogen systems shaped by land use change. , Resumen Los cambios en el uso y la cobertura del suelo debido a la expansion agricola, la explotacion comercial y otras modificaciones impulsadas por actividades humanas influyen significativamente en la ecologia de patogenos y vectores. Esto resalta la necesidad de comprender como estos organismos responden a cambios rapidos y dinamicos del uso del suelo en estos ecosistemas y, prioritariamente, identificar estrategias para mitigar sus impactos. En Centro y Suramerica, las palmas sirven como habitats claves de las especies del genero Rhodnius , vectores de Trypanosoma cruzi , el agente etiologico de la enfermedad de Chagas. Este estudio investiga como el uso y la cobertura del suelo, el clima y las caracteristicas de la palma Attalea butyracea predicen la presencia e infeccion de Rhodnius pallescens , combinando datos de campo, deteccion molecular y analisis espaciales y jerarquicos en un paisaje rural de Panama. Recolectamos R. pallescens en 46 palmas de 11 comunidades con diferentes composiciones del paisaje, incluyendo bosques nativos, pastizales, bosques en sucesion y superficies artificiales. Modelos de ocupacion usando datos de cobertura del suelo a 10 m 2 revelaron que el porcentaje de bosque en sucesion a escala espacial de 300 m predice mayor presencia de R. pallescens , mientras que el porcentaje de bosque nativo predice menor presencia. Modelos cuadraticos tuvieron mejor desempeno que los lineales, indicando maximos de ocupacion en valores de cobertura intermedias. PCRs en tiempo real detectaron infecciones con Trypanosoma en el 70% de R. pallescens . Analisis de autocorrelacion espacial indicaron una agregacion significativa para T. cruzi , pero no para Trypanosoma rangeli . Usamos modelos aditivos generalizados mixtos para evaluar la influencia de caracteristicas a nivel de palma y del paisaje sobre la infeccion parasitaria e identificamos asociaciones positivas no lineales entre la infeccion con T. cruzi y el bosque nativo y los pastizales, con alto valor predictivo (AUC = 0.90). Sintesis y aplicaciones . Los hallazgos indican que los bosques en sucesion predicen mayor probabilidad de ocurrencia de Rhodnius en las palmas, mientras que el bosque nativo predice menor ocurrencia, pero mayor probabilidad de infeccion con T. cruzi . Estos resultados pueden orientar la planificacion del uso del suelo hacia practicas de manejo que reduzcan los riesgos de transmision en comunidades rurales. La vigilancia vectorial debe fortalecerse en ecotonos bosquepastizal y considerar etapas de sucesion forestal cercanas a asentamientos, ademas de monitoreos intensificados postperturbaciones; este enfoque es transferible a otros sistemas de patogenos influenciados por cambios en el uso del suelo."
    },
    {
        "DOI": "10.1029/2025JD044492",
        "Title": "Comparison of Daily Ozonesonde Measurements and Chemical Reanalyses Over South Korea Based on 2021 PreACCLIP Data: An Ozone Intrusion Case",
        "Year": 2025,
        "Abstract": "Abstract This study investigates an ozone intrusion event observed during the PreAsian Summer Monsoon Chemical and Climate Impact Project in August 2021, using 26 consecutive daily ozonesonde measurements over South Korea. A pronounced enhancement in total column ozone was observed between 17 and 19 August, which can be largely attributed to an ozone intrusion in the upper tropospherelower stratosphere (UTLS), accounting for approximately 60% of the increase. The upper tropospheric circulation patterns demonstrate a clear signature of anticyclonic Rossby wave breaking (AWB) on the northeastern edge of the Asian summer monsoon anticyclone, aligned with the summertime jet stream. This AWB, accompanied by a cutoff low and tropopause folding, facilitated downward transport of stratospheric ozone into the upper troposphere. In addition, the ozone variability is investigated in two chemical reanalysis data sets: ModernEra Retrospective Analysis for Research and Applications, Version 2 (MERRA2) and European Centre for MediumRange Weather Forecasts (ECMWF) Atmospheric Composition Reanalysis 4 (EAC4). MERRA2 and EAC4 capture the ozone intrusion event with relevant synopticscale circulation patterns and ozone variability. However, discrepancies of ozone data in the chemical reanalyses were found in vertical ozone structures and persistence in the troposphere. MERRA2 better represented the secondary ozone peak in the UTLS but underestimated lowertropospheric ozone. In contrast, EAC4 showed a systematic positive bias particularly in the stratosphere and near the surface. Continued integration of temporally highresolution ozone measurements is beneficial for understanding synopticscale ozone variability and evaluating emerging chemical reanalyses. , Plain Language Summary This study used 26 daily balloonborne ozone sensors launched in South Korea during August 2021. These observations captured a rapid ozone increase caused by stratospheric air intrusion into the upper troposphere. Analysis reveals that the increase in ozone was directly induced by wave breaking and mixing processes between the troposphere and the stratosphere near the Asia summer monsoon anticyclone. We used these measurements to evaluate two widely used chemical reanalyses (ModernEra Retrospective Analysis for Research and Applications, Version 2 (MERRA2) and European Centre for MediumRange Weather Forecasts (ECMWF) Atmospheric Composition Reanalysis 4 (EAC4)) ozone. The reanalyses captured the ozone enhancement event qualitatively, however they showed notable quantitative differences vertically. The EAC4 tended to overestimate ozone in the stratosphere and near the surface. The MERRA2 also overestimated ozone in the stratosphere, but underestimated ozone closer to the surface. These differences are likely related to how satellite data is used and how the atmospheric chemistry is handled in the reanalyses. Our results emphasize that daily ozonesonde measurements are useful for evaluation of the synoptic variabilities in the chemical reanalyses. , Key Points Daily ozonesonde measurements captured an intense synopticscale ozone enhancement event in the upper tropospherelower stratosphere (UTLS) over Korea in August 2021 The enhanced ozone event was directly linked to anticyclonic wave breaking on the eastern periphery of the Asia summer monsoon anticyclone ModernEra Retrospective Analysis for Research and Applications, Version 2 and European Centre for MediumRange Weather Forecasts (ECMWF) Atmospheric Composition Reanalysis 4 well reproduced synoptic ozone patterns but exhibited substantial differences in the UTLS and near the surface"
    },
    {
        "DOI": "10.1038/S41597-026-06839-7",
        "Title": "WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks",
        "Year": 2026,
        "Abstract": "High-quality openly-accessible machine learning (ML)-ready datasets play a foundational role in developing new artificial intelligence (AI) models or fine-tuning existing for scientific applications such as weather and climate analysis. However, despite the growing development of deep climate, there is scarcity curated, pre-processed ML-ready datasets. Curating high-quality challenging particularly because modality input data varies significantly different downstream tasks addressing atmospheric scales (spatial temporal). Here we introduce WxC-Bench (Weather Climate Bench), multi-modal dataset designed to support generalizable AI various use-cases research. supports examining several processes from meso- (20 - 200 km) scale synoptic (2500 km), aviation turbulence, hurricane intensity track monitoring, analog search, gravity wave parameterization, natural language report generation. We provide comprehensive description also present technical validation baseline The code prepare have been made publicly available on Hugging Face, can be accessed using Python package."
    },
    {
        "DOI": "10.1029/2025GL119782",
        "Title": "Remote Forcing of Super Typhoon Mawar on the 2023 Quebec Wildfires",
        "Year": 2026,
        "Abstract": "Abstract Typhoons and wildfires are major global climate and environmental hazards, yet their potential interactions remain poorly understood, particularly through remote atmospheric forcing. Taking the 2023 Quebec wildfires as an example, we investigate how a tropical cyclone can influence wildfirefavorable conditions. Observational analyses and Linear Baroclinic Model simulations reveal that western North Pacific Super Typhoon Mawar remotely intensified a North American blocking high by triggering a Rossby wave train, thereby driving dry lightning and anomalous dry air conditions that favored wildfire ignition and spread. Based on Weather Research and Forecasting model sensitivity experiments, we show that Mawar contributed 34 6%, 41 3%, and 55 5% to the blocking's total amplification during the first three days of its rapid development, respectively. This findings highlight that western North Pacific typhoons can remotely modulate blocking highs to drive North American wildfire weather, advancing our understanding of remote typhoonwildfire teleconnections. , Plain Language Summary Typhoons and wildfires are devastating disasters that threaten the environment and human society. While it well acknowledged that weather patterns influence these events, the connections between typhoons and wildfires across long distances remain poorly understood. This study investigates how Super Typhoon Mawar in the western North Pacific contributed to severe wildfires in Quebec, Canada, in early June 2023. We found that the typhoon triggered largescale atmospheric waves that strengthened a longlasting highpressure system over Canada. The stronger high pressure created favorable conditions for wildfire ignition and spreading by increasing lightning activity and drying out the air. These findings reveal that typhoons can influence wildfire across continents, improving our understanding of how distant extreme weather events are linked globally. This finding could help improve wildfire prediction and risk assessment. , Key Points Super Typhoon Mawar remotely intensified North America blocking high by triggering a Rossby wave train Mawar contributed approximately 34%55% to blocking amplification during the first three days of rapid development Intensified blocking high enhanced lightning and anomalous dry air, favoring Quebec's extreme wildfire initiation and maintenance"
    },
    {
        "DOI": "10.1029/2025JD045361",
        "Title": "Hybrid Model Resolved Impacts of COVID19 Lockdowns on PM2.5 Sources in Bhopal, India: Role of Meteorology, Secondary Inorganic Aerosols, and Crop Residue Burning",
        "Year": 2026,
        "Abstract": "Abstract Air pollution in India is complex due to the multitude of sources and varying topography, rendering the interplay between meteorology and emission sources significant. To address this challenge, this work presents an integrated methodology for PM 2.5 source apportionment in Bhopal, central India, combining dispersionnormalized positive matrix factorization (DNPMF) with a machinelearning interpretability approach using Random Forest and SHAP (RFSHAP). DNPMF improves conventional source identification by incorporating air dilution effects, yielding refined source contributions for nine factors in Bhopal. Seasonal factor contributions peaked during periods with a lower boundary layer height, such as secondary sulfate during the winter season (21.3 g m 3 , 31.7%). The COVID19 lockdowns, a quasinatural emissions reduction experiment, led to a decrease in aerosol contributions from industrial, residential and trafficrelated sources. However, during this period, crop residue burning was exposed as a major anthropogenic contributor, which together with unfavorable meteorology resulted in increased mean PM 2.5 (50.6 24.3 g m 3 ) during the lockdowns compared to the reference period (36.7 9.7 g m 3 ). Using RFSHAP the influence of meteorology and emission sources in driving secondary inorganic aerosol formation was examined. Secondary nitrate and residential fuel were identified as key contributors to exceedances of the Indian National Ambient Air Quality Standards (60 g m 3 , 24hr average) at the study site. Integrating DNPMF with RFSHAP (driver analysis) enhanced source attribution by linking source contributions with their driving factors, establishing a framework for assessing pollution dynamics. This framework can help strengthen improved air quality initiatives in India, including the national Smart Cities Mission. , Plain Language Summary This study uses receptor modeling techniques, including Dispersion NormalizedPMF (DNPMF) and machine learning using RFSHAP, to determine the sources of PM 2.5 pollution in Bhopal, central India. DNPMF refined source apportionment by considering how weather conditions, like wind speed and mixing height, affect the concentration of aerosols. Nine different sources of PM 2.5 were identified, with significant contributors including secondary sulfate and residential fuel, peaking during the winter season due to reduced mixing heights. While the COVID19 lockdowns acted as a quasinatural experiment in reducing emissions from large anthropogenic sources, crop burning emissions during this period stayed high. Consequently, together with stagnant atmospheric conditions, average PM 2.5 was higher during the lockdown phase compared to the reference period. Machine learning (RFSHAP) analysis indicated that meteorological variables such as temperature and humidity along with primary sources substantially affect the formation of secondary pollutants, including nitrates and sulfates. High PM 2.5 concentrations, often higher than India's national air quality standards, were largely due to secondary nitrate and residential fuel combustion. This study methodology and results provide inputs and guidance for air quality management in Bhopal and other cities, particularly within the framework of India's Smart Cities Mission. , Key Points DNPMF resolved nine PM 2.5 factors, with secondary inorganic aerosols and combustionrelated emissions as dominant factors Crop residue burning coupled with stagnant atmospheric conditions increased PM 2.5 concentrations during the COVID19 lockdowns in central India RFSHAP analysis identified secondary nitrate and residential fuel factors as key contributors to the 24hr PM 2.5 NAAQS exceedances"
    },
    {
        "DOI": "10.1029/2025JD044599",
        "Title": "Interbasin Analysis of the Poleward Expansion of Tropical Cyclone Potential Intensity",
        "Year": 2026,
        "Abstract": "Abstract Favorable thermodynamic environments for tropical cyclone (TCs), as measured by potential intensity (PI), expand to higher latitudes in a warming climate, in both observations and future simulations. However, this expansion rate has yet to be compared systematically between hemispheres and across basins. This work quantifies the poleward expansion of PI globally and compares it across basins using ERA5 reanalysis and an ensemble of CMIP6 historical and future climate scenarios. We quantify trends in the latitude of PI thresholds associated with TC intensity and directly decompose PI trends into thermodynamic drivers using a novel PI budget equation that distinguishes ClausiusClapeyron response to warming from other effects. Our results show that PI consistently expands strongly poleward near Western Boundary Currents (WBCs) in the Western North Pacific, North Atlantic, South Indian, and South Pacific. It expands slowly near EBCs in the Eastern North Pacific and South Indian basins. Basins with WBC outflow exhibit enhanced coastal PI trends linked to SST warming, outflow cooling, and poleward ocean heat transport. Southern Hemisphere basins display weaker expansion, except near strong WBCs. CMIP6 models capture this eastwest asymmetry in PI trends across basins, but underestimate the magnitude of poleward expansion rates due to misrepresenting changes in outflow temperature and nearsurface air temperature and humidity. Our analysis suggests that ocean circulation plays a critical role in shaping regional PI trends and future increases in TC risk at higher latitudes will be unevenly distributed and may depend locally on the orientation of the coastline relative to oceanic boundary currents. , Plain Language Summary Tropical cyclones (TCs) are expanding toward higher latitudes in a warming climate. This study examines where and why the environments that support strong TCs, measured by potential intensity (PI), are shifting poleward. Using global climate data and model simulations, we compare trends across different ocean basins in both the Northern and Southern Hemispheres. We find that expansion of environments that produce strong storms occurs faster in the Northern Hemisphere, especially in regions like the North Atlantic and Western North Pacific, where warm ocean currents carry heat poleward. Though slower than Northern Hemisphere expansion, the Western South Indian and Western South Pacific Ocean also have enhanced trends, showing an eastwest asymmetry in ocean basins. These regions show the strongest increases in PI at higher latitudes. In contrast, Eastern ocean basins show weaker and more uniform trends. Climate models capture some of this expansion, mainly that driven by rising sea surface temperatures, but they miss important atmospheric responses to ocean circulation changes that also affect PI. As a result, models underestimate how much TC environments are shifting poleward, especially in key hotspot regions. These findings help us understand which regions may face increased TC risk in the future and highlight where climate models need improvement to better predict these changes. , Key Points Tropical cyclone potential intensity expands to high latitudes fastest in NH basins, with hotspots along western boundary currents CMIP6 models capture the PI response to SST trend, but not changes to lowlevel circulation and outflow temperature trends Future increases in tropical cyclone risk at higher latitudes will be unevenly distributed and may depend locally oceanic boundary currents"
    },
    {
        "DOI": "10.1016/J.ASR.2026.03.013",
        "Title": "An assessment of regional variations in cloud characteristics using machine learning models: a case study over north-western and north-eastern regions of India",
        "Year": 2026,
        "Abstract": "The present study investigated two diverse geographical areas such as north-western (NW) and north-eastern (NE) regions of India, by studying cloud characteristics using 23 years of observations (20022024). Satellite retrievals such as cloud droplet effective radius (CER), cloud liquid water path (CLW), cloud optical thickness (COT), and cloud fraction (CF) were investigated during the Indian Summer Monsoon (ISM). Low mean values were found for CER (14.26 m), CLW (118.5 g/m2), COT (12.72), and CF (0.66) over the NW region, whereas high mean values were found over the NE region with a difference of 3.83, 88.76, 5.21 and 0.26 respectively indicating highly significant differences between both regions with statistical analyses (p-values below 0.05) of all assessed indices, including CLW, COT, CF, CER, and AOD. These differences inferred that a high fraction of thicker clouds over the NE region consists of more liquid water and bigger cloud droplets. Various machine learning (ML) techniques were suggested to forecast rainfall, utilizing statistical analysis embedded with ML. This study elucidates the perspective of spatial heterogeneity using ML and further concludes that CER was predicted better with random forest (RF) and XGBoost. Statistical analysis revealed that RF and XGB predicted comparatively better with prominent root mean square error (RMSE) values (<1.31) and R2 (>0.81) over the NW and NE regions, respectively, with XGB being best. Using K-means to analyze clustering with silhouette scores varying from 0.33 (NE) to 0.43 (NW), suggesting better cluster cohesiveness. Temporal cluster analysis from 2002 to 2024 shows that dominating centroids have migrated towards higher CER and CLW values, particularly in the NW. Thus, ML techniques are important in providing valuable insights about cloud dynamics on a global scale."
    },
    {
        "DOI": "10.1088/2515-7620/AE219B",
        "Title": "Observed sharpening of summer rainstorms over the Tibetan Plateau during 20012020",
        "Year": 2025,
        "Abstract": "Abstract Summer rainstorms contribute substantially to the annual total precipitation over the Tibetan Plateau (TP), but the observed spatial structural changes of these rainstorms remain poorly understood. Based on multi-source data, this study investigates changes in the spatial structure of summer rainstorms over the TP during 20012020 and explores the underlying physical mechanisms. Results indicate that compared with the period of 20012010, summer rainstorms in both the transition zone and westerly-dominated zone of the TP became more spatially concentrated during 20112020. Precipitation enhancement at rainstorm centers exceeded the average increase across the entire rainstorm area. The sharpening of spatial structure was more notable for moderate to extreme rainstorms than for non-extreme rainstorms. For extreme rainstorms (>90th percentile), although the rainstorm-averaged precipitation increase in both zones was only 10%, precipitation at rainstorm centers increased by approximately 40% (transition zone) and 30% (westerly-dominated zone). The sharpening of rainstorms contributes negatively to the increase of rainstorm-induced total precipitation, by about 4 to 5% for extreme events in both zones. Further analysis suggest that the sharpening of rainstorms is primarily resulted from the dynamical changes in rainstorms environments, with the increase of upward air motion at the rainstorm center being larger than that of the average across the rainstorm area. The dynamical changes in rainstorms environments are closely associated with the large-scale circulation changes. Strengthened westerlies and a deepened trough over the northern TP, along with enhanced southerlies over the southeastern TP, intensify horizontal wind convergence."
    },
    {
        "DOI": "10.1007/S00382-026-08094-3",
        "Title": "Enhanced atmospheric water cycle and intensified future change over the Tibetan Plateau in convection-permitting regional climate simulations",
        "Year": 2026,
        "Abstract": "The summer atmospheric water cycle over the Tibetan Plateau (TP) is crucial to regional climate and water resources. However, simulations and projections of the TP's atmospheric water cycle using convection-permitting models have not been well addressed. In this study, based on a set of decade-long convection-permitting (at a resolution of 3.3 km) and convection-parameterized (at a resolution of 13.2 km) regional simulations with Icosahedral Nonhydrostatic Weather and Climate Model (ICON) over the TP, we demonstrate that the convection-permitting ICON simulation shows evident added value in capturing the summer atmospheric water cycle across the TP, with reduced wet biases in precipitation, evapotranspiration, and moisture convergence. The reduction in the simulated precipitation in ICON_3.3 km is primarily balanced by the reduction of total moisture convergence, which is associated with the weaker water vapor inflow across the southern boundary of the TP. Further sensitivity experiments suggest that the explicit convection in ICON_3.3 km, rather than the increased horizontal resolution, is the main factor for this improvement. The explicit convection weakens the Indian summer monsoon circulation and reduces the conversion of advected and evaporated moisture to precipitation over the TP. Future convection-permitting projections with the pseudo-global-warming experiment suggest a significant intensification of the TP's summer atmospheric water cycle, followed by the enhanced water vapor inflow, particularly along the southwestern boundary of the TP. Despite the increased precipitation and evapotranspiration, the precipitation recycling ratio decreases, indicating a greater dependence on external moisture sources. These findings enhance our understanding of the TP's water cycle and its future changes."
    },
    {
        "DOI": "10.1029/2025GL118946",
        "Title": "Biases in Southern Ocean Precipitation From Shallow Convection: The Role of Cloud Morphology",
        "Year": 2026,
        "Abstract": "Abstract Precipitation from marine boundary layer clouds is a critical yet highly uncertain feature of postfrontal conditions over the Southern Ocean (SO), where shallow convection dominates. This study evaluates whether satellite retrievals (GPMIMERG and CloudSat) and reanalysis (ERA5) represent the contrasting precipitation between open and closed mesoscale cellular convection (MCC) observed over the SO, from limited in situ records. Substantial discrepancies are found across data sets. While ERA5 captures the expected contrast, GPMIMERG underestimates precipitation, especially from open MCCs, and fails to reflect morphological distinction. Both CloudSat products detect higher mean precipitation for open MCCs, though with differences in intensity distribution. Using observed precipitation rates and occurrence frequencies, open MCCs are estimated to contribute 9%13% and closed MCCs 1%11% of the annual precipitation between 40S and 50S. These results highlight the importance of improving the representation of marine atmospheric boundary layer cloud morphologies in models and observational data sets to better constrain the SO water cycle. , Plain Language Summary Light precipitation from shallow convection covers vast portions of the Southern Ocean (SO), commonly occurring after the passage of cold fronts. During these times, quantitative precipitation estimates from various products can suffer from large differences, underscoring the uncertainty in these products. The precipitation intensities from GPMIMERG and ERA5 are evaluated during periods of open and closed mesoscale cellular convection (MCC) against CloudSat estimates and surface observations made at the kennaook/Cape Grim Observatory. Consistent with other locations, precipitation is more intense during periods of open MCC. GPMIMERG underestimates the intensity during periods of open MCC, being insensitive to the structure of the convection. ERA5 is sensitive to the structure, but overestimates light precipitation from closed MCC, potentially reflecting the drizzle problem. Combined, precipitation from open and closed MCC contributes 10%24% of the total precipitation across the SO, being a vital component of the regional water budget. , Key Points Quantitative precipitation estimates are evaluated during periods of shallow convection over the lower latitudes of the Southern Ocean Precipitation intensity varies strongly with convection structure, which most products capture but GPMIMERG represents poorly Precipitation from open and closed mesoscale cellular convection contributes 9%13% and 1%11% of the total precipitation from 40S to 50S"
    },
    {
        "DOI": "10.1007/S11069-026-08032-W",
        "Title": "Improvements in tropical cyclone forecasting using CCAM compared with the operational Unified Model: A case study of tropical cyclone Idai",
        "Year": 2026,
        "Abstract": "Abstract Tropical cyclones are among the most destructive weather systems, yet prediction skill in the South-west Indian Ocean (SWIO) lags due to limited observations and modelling constraints. This study evaluates the Conformal-Cubic Atmospheric Model (CCAM) in simulating track and intensity of Tropical Cyclone Idai, relative to the operational Unified Model (UM) at the South African Weather Service (SAWS). Simulations were performed at multiple resolutions (CCAM: 25 km, 6 km; UMGA: 10 km; UM: 4.4 km) and lead times (72 h, 48 h, 24 h), validated against independent observational and reanalysis datasets. Both models reproduced Idais track more accurately at finer resolution and shorter lead times. CCAM exhibited a southward track bias, while UM deviated northward. Landfall position and timing were generally captured, with position errors reduced to < 30 km at 24 h, although CCAM 6 km showed a late landfall bias of ~3 h at 72 h. Intensity was underestimated by all simulations, however, CCAM 6 km better matched best-track winds and central pressures, while UM 4.4 km aligned more closely with ERA5. Rainfall patterns differed, with CCAM overestimating rainfall extent and intensity, whereas UM 4.4 km more realistically captured IMERG and CHIRPS patterns, though with weaker magnitudes. Results show that high horizontal resolution is crucial for representing TC characteristics, with microphysical processes becoming increasingly influential at convection-permitting scales. Differences in model initialisation also contributed to track and intensity biases. These findings underscore the value of high-resolution modelling, improved initialisation and observations to advance TC forecasting and early warning in the SWIO."
    },
    {
        "DOI": "10.34133/REMOTESENSING.1045",
        "Title": "Mapping Paddy Rice Cropping Intensity and Planting Dates in Monsoon Asia at 20 m Resolution during 20182021 from Multi-source Satellite Data",
        "Year": 2026,
        "Abstract": "Accurate monitoring of paddy rice cropping intensity and planting dates across Monsoon Asia (MA) is crucial for food security and sustainable agriculture. While high-resolution (<30 m) maps are essential due to small field sizes in most parts of MA, there is currently no single high-resolution satellite-based paddy rice distribution, cropping intensity, or planting date map that covers the entire MA. This is due to primary challenges such as frequent cloud disturbance in high-resolution optical satellite images across subtropical and tropical MA and the interference from many factors other than rice transplanting in synthetic-aperture radar (SAR) backscatter signals, which has constrained the large-scale applicability of existing SAR-based mapping methods. To alleviate this constraint, we performed grid-specific checks on the location time, value, width, prominence, and sharpness of all troughs in SAR backscatter time series to remove those unrelated to rice transplanting. Thresholds for these checks are determined automatically when referring to optical satellite images, infrared remote-sensing-based land surface temperature, and passive microwave-based surface soil moisture. Based on this more universal algorithm, we created a satellite-based 20-m-resolution rice cropping intensity and planting date maps for the entire MA during 2018 to 2021. The rice distribution map achieved a high accuracy of 82% to 84% against 2,305 self-collected reference samples and 1,484 public samples, outperforming most existing regional maps. Our rice cropping intensity map generally agreed with existing regional maps, while the regional averages of our rice planting date estimates also correlated well with the statistics-based RiceAtlas dataset ( R 2 : 0.92; root mean squared error: 28 d)."
    },
    {
        "DOI": "10.3390/CLI14040082",
        "Title": "Accuracy Assessment of CMORPH and GPCP Satellite Precipitation Products Across Iran",
        "Year": 2026,
        "Abstract": "Reliable precipitation data are fundamental for climate and hydrological research, especially in regions with sparse ground-based observations. This study evaluates and compares the accuracy of two satellite-based precipitation productsCMORPH and GPCPacross daily, monthly, and annual scales over Iran. Daily, monthly, and annual precipitation estimates from CMORPH and GPCP were validated against observations from 128 meteorological stations distributed throughout the country. The assessment employed two statistical indicescorrelation coefficient (CC) and root mean square error (RMSE)alongside three categorical indices: probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI). At the daily scale, CMORPH outperformed GPCP in terms of CC, RMSE, POD, and CSI, while GPCP exhibited a lower FAR. At the monthly scale, correlations between satellite-derived and station-based precipitation were stronger than those at the daily scale; CMORPH achieved the highest correlation (CC = 0.84), whereas GPCP yielded a lower RMSE, with a mean value of 26.2 mm. At the annual scale, GPCP demonstrated better performance in CC, while CMORPH showed superior accuracy in RMSE. CMORPH consistently underestimated precipitation, whereas GPCP tended to overestimate rainfall across Iran. Although both datasets provided reliable precipitation estimates at the national scale, CMORPH demonstrated higher overall accuracy and efficiency. Its superior performance across most indices makes CMORPH the more suitable dataset for precipitation monitoring in Iran, despite its tendency to underestimate rainfall relative to ground observations."
    },
    {
        "DOI": "10.1093/QJE/QJAG005",
        "Title": "Insuring Peace: Index-Based Livestock Insurance, Droughts, and Conflict",
        "Year": 2026,
        "Abstract": "Abstract We provide quasi-experimental evidence of how an innovative market-based solution using remote-sensing technology can mitigate drought-induced conflict. Droughts are a major driver of conflict in Africa, particularly between transhumant pastoralists and sedentary farmers. Index-Based Livestock Insurance (IBLI), a program piloted in Kenya, provides automated, preemptive payouts to pastoralists affected by droughts. Combining variation in rainfall and the staggered rollout of IBLI in Kenya over 20002020, we find that IBLI strongly reduces drought-induced conflict. Key mechanisms include a reduction in herd sizes, as well as income smoothing and asset price stabilization, contributing to an overall reduced migratory pressure for pastoralists. Our study suggests that market-based solutions are a scalable, cost-effective pathway to mitigate conflict, complementing political solutions such as power-sharing agreements and institutional reforms."
    },
    {
        "DOI": "10.1029/2025EF007502",
        "Title": "Future Changes in Power Grid Exposure to Urban Flooding Over Eastern Coastal China",
        "Year": 2026,
        "Abstract": "Abstract As one of the serious disasters, urban flooding has substantial impacts on power grid infrastructures. This study develops a twostage random forest model, comprising a classifier and a regressor, designed to assess urban flooding frequency (UFF) in eastern coastal China. The model utilizes Digital elevation model, slope, imperviousness ratio (IR) and extreme rainfall events frequency (EREF) as key predictors to simulate urban flooding risks. The analysis reveals that expanding IR and increasing EREF are the primary drivers of urban flooding risk changes under the SSP12.6, SSP24.5, and SSP58.5 scenarios throughout the 20402050 and 20902100 periods. Spatially, projections indicate highUFF centers are concentrated in the Yangtze River Delta (YRD) and ZhejiangFujian coastline during 20402050, subsequently expanding to northern Jiangsu, central Zhejiang, and Fujian's mountainous regions by 20902100. Consequently, by overlaying the power grid distribution with UFF projections, the number of highrisk clusters for power grid exposure to urban flooding increases from two (YRD and ZhejiangFujian coastline) to four (adding northern Jiangsu and central Zhejiang) over the same period. It is noted that both UFF and power grid exposure are generally greater under the highemission scenario (SSP58.5) compared to the loweremission scenarios. These findings highlight escalating exposure risks for power grids as urban flooding intensifies, emphasizing the need for adaptive strategies addressing both urbanization and climate impacts. , Plain Language Summary Urban flooding has become an increasingly serious threat to power grid infrastructures, leading to cascading failures in power systems. However, due to the insufficient data set in both power grids and urban flooding, it is still a challenge to estimate the power grid exposure to urban flooding. By using a twostage random forest model including a classifier and a regressor, we assess urban flooding risks in eastern coastal China under future scenario projections and further estimate the power grid exposure to urban flooding. Our results demonstrate that combined effects of increasing extreme rainfall events frequency and expanding imperviousness ratio will significantly increase urban flooding risks under SSP12.6, SSP24.5, and SSP58.5 scenarios throughout the 20402050 and 20902100 periods. Key hotspots include the Yangtze River Delta, the ZhejiangFujian coastline, northern Jiangsu, and central Zhejiang. As a result, power grid exposure to urban flooding is projected to intensify and expand geographically over the same period. This escalation is more pronounced under highemission scenario (SSP58.5) than the loweremission scenarios, highlighting the urgency of climateresilient power grids. , Key Points A twostage random forest model is established to assess urban flooding risks under future scenarios over eastern coastal China The changes of urban flooding are predominantly driven by expanding impervious surfaces and increasing extreme rainfall events frequency Power grid exposure increases in the Yangtze River Delta and ZhejiangFujian coastline, extending to northern Jiangsu and central Zhejiang"
    },
    {
        "DOI": "10.1146/ANNUREV-STATISTICS-042424-052920",
        "Title": "Model-Based Spatial Data Fusion",
        "Year": 2026,
        "Abstract": "With increased data collection, the need to fuse data sources has emerged as an important and rapidly growing research activity in the statistical community. In considering spatial and spatio-temporal datasets to examine complex environmental and ecological processes of interest, we often have multiple sources that are jointly informative about features of interest of the processes. Model-based data fusion aims to leverage information from these sources to improve inference and prediction. In the spatial statistics setting, these data could be geostatistical; areal; or point patterns with varying spatial resolutions, supports, and domains. Given two or more sources, we explore stochastic modeling to implement a suitable fusion with full inference and uncertainty quantification. We illustrate these ideas using three environmental and ecological examples: precipitation, marine mammal abundance, and joint species distributions."
    },
    {
        "DOI": "10.3390/W18070818",
        "Title": "Impacts of Climatic Phenomena and Terrain on December 2021 Extreme Rainfall over Peninsular Malaysia",
        "Year": 2026,
        "Abstract": "An extreme rainfall event that occurred from 16 to 18 December 2021 along the coastal regions of Peninsular Malaysia (PM) caused widespread flooding and substantial socioeconomic impacts. This study investigates the mechanisms leading to this event, focusing on the roles of climatic phenomena and local terrains. Two atmospheric interactions play key roles in triggering the event. Firstly, a strong cold surge (CS) associated with the East Asian winter monsoon (EAWM) interacted with the easterly surge over the southern South China Sea, leading to the formation of Borneo vortex. Secondly, a strong northeasterly and CS largely contributed to enhancing and transporting the vortex towards the PM and across the Titiwangsa mountain ranges. The phase change of the Indian Ocean Dipole (IOD) facilitated the eastward propagation of the vortex. Sumatra and PM terrains significantly modulated vortex evolution and moisture convergence over the Strait of Malacca. These findings are analyzed to shed light on interactions between large-scale climate drivers and localized terrain in generating extreme rainfall, emphasizing the necessity of multi-scale analysis for model accuracy."
    },
    {
        "DOI": "10.1007/S41748-026-01153-Z",
        "Title": "Assessing Uncertainty in Multi-Source Precipitation for a Semi-Arid Mediterranean Catchment Using SWAT",
        "Year": 2026,
        "Abstract": "Assessing the performance of global precipitation products in semi-arid Mediterranean catchments is crucial for improving hydrological modeling and water resource management, particularly in regions where in-situ observations are scarce or nonexistent. This study evaluates five precipitation datasets: observed rainfall, CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data), PERSIANN (Remotely Sensed Information using Artificial Neural Networks), GPM-IMERG (Global Precipitation Measurement Integrated Multi-Satellite Retrievals), and ERA5 over a 17-year period from 2001 to 2017, including one year of model warm-up for a semi-arid catchment in central Tunisia. The Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model, was calibrated using observed precipitation data and then applied with alternative datasets to quantify the influence of precipitation sources on model performance. Calibration with observed data yielded satisfactory results, with a Nash-Sutcliffe Efficiency (NSE) of 0.63 and a Kling-Gupta Efficiency (KGE) of 0.72. When alternative datasets were introduced, CHIRPS consistently provided acceptable results even without recalibration, achieving a KGE value of 0.32, which indicates moderate model performance although it remains below the commonly accepted satisfactory threshold. ERA5 and PERSIANN showed slight improvement after calibration but often yielded low KGE values. During calibration, CHIRPS and ERA5 also achieved satisfactory performance, with NSE values of 0.51 and 0.50 and KGE values of 0.58 and 0.61, respectively. In contrast, GPM-IMERG results remained largely unchanged, showing minimal sensitivity to calibration parameters. These findings confirm that precipitation is the principal source of uncertainty in hydrological modeling and that each dataset imposes specific ranges on calibrated parameters. For the Haffouz catchment, CHIRPS demonstrated the best spatial distribution and overall hydrological performance, highlighting the value of satellite-based precipitation products as reliable alternatives in data-scarce regions. This study provides critical insights, emphasizing the importance of carefully selecting precipitation datasets to reduce uncertainty and improve the reliability of hydrological simulations in semi-arid Mediterranean context."
    },
    {
        "DOI": "10.1029/2025EF007833",
        "Title": "Future Changes to Rainfall Extremes Over Puerto Rico in a ConvectionPermitting Model",
        "Year": 2026,
        "Abstract": "Abstract Islands in the Caribbean are vulnerable to anthropogenic warming due to sea level rise and their reliance on rainfall for agriculture. These islands are particularly prone to rainfall extremes, such as the 1,029 mm of daily maximum rain in Puerto Rico due to Hurricane Maria in 2017. Rainfall extremes mostly occur in the early rainy season (ERS) from AprilJune and late rainy season (LRS) from AugustNovember. While global climate models project reduced rainfall in the Caribbean by the end of the century, they are too coarse to properly resolve the complex coastline and terrain of Puerto Rico and associated convection that is often induced by seabreeze convergence and orographic uplift. Here, we resolve this issue by running the Model for Prediction Across ScalesAtmosphere (MPASA) using a 603 km global variable mesh centered over the Caribbean to downscale extreme rainfall days from coarser transient simulations during 20012021 and 20412061. This model configuration allows for the evaluation of dynamical and thermodynamic future changes at convectionpermitting scales over Puerto Rico using MESACLIP as forcing data, although these simulations underestimate extreme rainfall amounts. Results show that by midcentury, rainfall extremes increase in the ERS but decrease in the LRS, mainly associated with changes in isolated convection. Stronger upward motion and sea breeze convergence support future increased rainfall in the ERS, while stronger subsidence likely reduces LRS rainfall extremes. These results suggest that more attention needs to be given to the increasing risk of ERS rainfall extremes over Puerto Rico. , Plain Language Summary Puerto Rico is an island in the Caribbean that is vulnerable to the impacts of global warming due to sea level rise and its dependence on rainfall for agriculture. Most of the rainfall over the island falls from AprilJune and AugustNovember due to thunderstorms initiated by the terrain and winds hitting the coastline, which cannot be resolved in coarser atmospheric models. We run an atmospheric model globally that resolves finer scales over Puerto Rico and the Caribbean to address this issue in order to understand changes to the most extreme rainfall by midcentury. We find that rainfall extremes increase in AprilJune and decrease in AugustNovember, mainly due to changes in upward and downward motions as well as the winds converging along the coastline. , Key Points Future changes to rainfall extremes over Puerto Rico are simulated in a global model at convectionpermitting scale over the Caribbean Extreme rainfall accumulations increase in the early rainy season but decrease in the late rainy season by midcentury These seasonal changes are related to differences in subsidence, island convergence, and updraft velocity"
    },
    {
        "DOI": "10.1080/19475705.2026.2659164",
        "Title": "Integrating machine learning and physically based hydrodynamic modeling for flood hazard mapping: a case study of the Takkalasi watershed, Indonesia",
        "Year": 2026,
        "Abstract": "Floods are among the most frequent and damaging natural disasters in Indonesia, with increasing intensity frequency driven by climate land cover changes. Physically based hydrodynamic models such as HEC-RAS 2D Rain-on-Grid can simulate flood processes accurately but require extensive data computational resources, limiting their application data-scarce regions. This study develops an integrated framework that combines simulations Random Forest (RF) algorithm for hazard mapping Takkalasi watershed, South Sulawesi, Indonesia. Flood depth inundation maps from 21 December 2024 event simulated were used training RF classification regression models. The achieved high predictive accuracy low flood-depth error, producing probability, susceptibility, maps. results show upstreamdownstream rainfall synchronization, flat downstream topography, limited drainage capacity, changes strongly influence extent depth. approach enables rapid using readily available spatial be applied to other watersheds. Its implementation supports adaptive planning disaster risk reduction, particularly regions hydrological observations."
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135261",
        "Title": "SM2RAINdual: a global rainfall fusion product derived from multi-source satellite soil moisture observations",
        "Year": 2026,
        "Abstract": "As a core input parameter for hydrological modelling and ecological assessment, long-term stability and high quality of rainfall data are critically important. The SM2RAIN (Soil Moisture to Rain) algorithm, which follows a \"bottom-up\" theoretical framework, offers unique advantages in estimating cumulative surface rainfall. However, the region-dependent uncertainties among different global remote sensing soil moisture products lead to substantial regional disparities in SM2RAIN-derived rainfall estimates, thereby limiting its global applicability. To address this issue, we selected five widely used soil moisture productsthe Soil Moisture Active and Passive (SMAP), the Advanced SCATterometer (ASCAT), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture and Ocean Salinity (SMOS), and the European Space Agency Climate Change Initiative (ESA CCI)as inputs to the SM2RAIN algorithm. Using in situ observations from 1009 uniformly distributed points across Italy, the United States, Australia, and India, we evaluated the regional performance of the SM2RAIN algorithm driven by different satellite soil moisture products, and developed a rainfall data fusion scheme to generate a global rainfall product. Our results show that: (1) For individual satellite products, SMAP-based rainfall estimates yielded the highest Pearson's correlation coefficient (R) in the United States, while ASCAT-based estimates achieved the best R performance in Italy. In India, ASCAT also demonstrated notably superior NashSutcliffe efficiency (NS) performance; (2) Fusion at the rainfall level outperformed fusion at the soil moisture level. In particular, the combination of SMAP and ASCAT yielded the best results at the rainfall level (median R = 0.62; median NS = 0.38), surpassing the performance of ESA CCI-derived rainfall (median R = 0.55; median NS = 0.34); (3) Using MSWEP as a reference, we generated a fused remote sensing rainfall productSM2RAINDualfrom 2015 to 2022 based on SMAP and ASCAT, featuring a spatiotemporal resolution of 10 km and daily intervals. Triple-collocation (TC) analysis with GPCC and GPM-LR showed that SM2RAINDual achieved lower error variance in 88% of global regions and higher correlation in 34% of regions with respect to the other two products. This study introduces a rainfall-level fusion strategy that overcomes the regional limitations of single-source satellite products, offering a more reliable precipitation input for hydrological modelling in areas with sparse ground-based observations."
    },
    {
        "DOI": "10.3390/RS18081127",
        "Title": "Remote Sensing of Water: The Observation-to-Inference Arc Across Six Decades and Toward an AI-Native Future",
        "Year": 2026,
        "Abstract": "Over six decades, satellite remote sensing of water resources has evolved from manual interpretation of weather photographs to AI systems that learn hydrologic predictions directly from satellite imagery. This review traces that evolution through the observation-to-inference arca framework for the progressively tightening coupling between what satellites observe and what hydrologists infer. Using illustrative applications in precipitation, evapotranspiration, soil moisture, snow, surface water, and groundwater, we show how early observations (19601985) remained disconnected from operational hydrology; how calibrated retrieval algorithms (19852000) established a one-way pipeline from satellites to models; how operational infrastructure (20002015), anchored by MODIS, GRACE, GPM, and Sentinel, achieved assimilative coupling through computational feedback between models and observations; and how deep learning (2015present) is beginning to collapse this pipeline. Multi-source data fusion has been a recurring enabler at each stage. We articulate a four-level AI vision and research trajectory, from AI-assisted interpretation through AI-native retrieval and AI-driven inference to autonomous Earth observation intelligence. Persistent challenges in mission continuity, calibration, equity of access, and translating satellite-derived information into operational water management decisions provide essential context for evaluating both the promise and limits of this trajectory."
    },
    {
        "DOI": "10.1029/2025JD045027",
        "Title": "Roles of Surface Latent Heat Flux and Gravity Waves in Offshore MCS Development in the Coastal Eastern Tropical Pacific",
        "Year": 2026,
        "Abstract": "Abstract The eastern tropical Pacific (ETP) coastal region, one of the rainiest places on Earth, is characterized by distinct diurnal offshore rainfall propagation primarily driven by mesoscale convective systems (MCSs). Previous studies have shown that MCS initiation in this region peaks in the early morning, with diurnally generated gravity waves from the Andes proposed as a key triggering mechanism. Additionally, enhanced lowlevel moisture has been hypothesized to be a crucial contributing factor in MCS development. However, the relative roles of these processes in triggering MCSs over the region remain insufficiently quantified. This study explores these mechanisms using an ensemblebased satellite data assimilation experiment focused on a representative nocturnal MCS event. Our results reveal a clear preMCS cooling trend in the lower troposphere, linked to gravity waves generated by afternoon inland convection. Concurrently, substantial lowlevel moistening occurs, driven by increased surface latent heat flux and horizontal moisture advection from the Panama lowlevel jet. These processes together destabilize the lower troposphere, creating favorable thermodynamic conditions for convection. Additionally, an offshore anabatic surface front enhances lowlevel convergence, promoting vertical lifting of unstable air and providing dynamic support for MCS initiation. Sensitivity experiments further demonstrate that MCS development is highly sensitive to lowlevel moisture availability, which is strongly influenced by surface windinduced evaporation. , Plain Language Summary The eastern tropical Pacific coastal region is the rainiest place on Earth. Much of this rainfall comes from large clusters of thunderstorms that form offshore during the night. Previous studies have suggested that these nighttime storms may be triggered by atmospheric gravity waves generated by the Andes. Another possible contributor is the increase in lowlevel moisture at night. However, the exact importance of each of these processes has not been clearly determined. In this study, we analyzed a typical nighttime storm event using a weather model that was finetuned with real satellite data. We found that gravity waves from earlier inland storms helped cool the lower atmosphere. At the same time, strong winds from the Panama region enhanced ocean evaporation and transported extra moisture into the area where the storms formed. We also discovered a local surface front offshore that helped lift the warm, moist air upward. The cooler air near the surface, more moisture, and lifting together made the atmosphere more unstable and ready for storm development. We also found that small changes in lowlevel moisture can have a big impact on when and how storms form and grow. , Key Points Gravity waves induced by afternoon inland convection precondition the offshore environment for nocturnal convection initiation (CI) CI and cloud development are highly sensitive to local lowlevel moisture content Moisture supplied by the Panama lowlevel jet via surface evaporation plays a crucial role in offshore convection development"
    },
    {
        "DOI": "10.3103/S1068373926030052",
        "Title": "Developing a Database of Hydrological, Meteorological, and Physiographic Characteristics for River Catchments of the Russian Federation",
        "Year": 2026,
        "Abstract": "The paper presents the results of developing a database of hydrological, meteorological, and physiographic characteristics for 1886 river gauges on the territory of the Russian Federation. The database was created using self-developed algorithms for spatial data processing. Average daily water discharges and levels were obtained from the Automated Information System for State Monitoring of Water Bodies. Watershed boundaries were delineated for each gauging station. Meteorological data were obtained from various gridded datasets: ERA5, ERA5-Land, GPCP, and MSWEP. Average daily precipitation, maximum and minimum temperatures were compared with data of meteorological observations from the resource of All-Russian Research Institute of Hydrometeorological InformationWorld Data Center. Physiographic characteristics were obtained for each catchment from the HydroATLAS database."
    },
    {
        "DOI": "10.1088/2515-7620/AE5B4B",
        "Title": "Low level jet controlled dynamical and thermodynamical regimes of the diurnal cycle of rainfall over the western ghats",
        "Year": 2026,
        "Abstract": "Abstract Summer monsoon rainfall over the Western Ghats (WG) of India exhibits pronounced variability across multiple timescales, including the diurnal scale, which plays a key role in shaping regional precipitation. Although recent observational studies have improved understanding of the WG diurnal cycle, the physical mechanisms governing variability in storm structures and convective processes remain unclear. Using high-resolution satellite and reanalysis data, this study shows that diurnal rainfall variability over the WG is primarily governed by two physical regimes: a dynamical regime (DR) and a thermodynamical regime (TR). We demonstrate that contrasts in low-level jet strength and landsea breeze intensity regulate the diurnal amplitude, phase, and storm depth. The DR (TR) is associated with a strong (weak) Somali jet and weak (strong) landsea breeze within the boundary layer, with landsea breeze intensity nearly four times stronger during TR. Consequently, diurnal rainfall is more vigorous and spatially extensive during TR. Shallow storms dominate during DR, indicating warm-rain processes, whereas TR features deeper convection involving both warm and cold microphysics, with storm-top heights reaching 5 km in DR and 8 km in TR. Favourable vertical profiles of equivalent potential temperature, geopotential height, and vertical velocity during TR, together with stronger afternoon CAPE, lower midday CINE, and increasing total column water vapour, support deep convection. In contrast, DR exhibits localized intense rainfall mainly over the northern WG with a latitudinal shift in diurnal peak timing. Although daily mean rainfall is higher during DR, the diurnal component contributes about 10% and 50% of the daily mean rainfall during DR and TR, respectively. These results provide a framework for regime-dependent diurnal rainfall variability over the WG and have implications for improving weather and climate models."
    },
    {
        "DOI": "10.1016/J.RSASE.2026.101927",
        "Title": "How armed conflict shapes environmental trends in Yemen?",
        "Year": 2026,
        "Abstract": "The ongoing conflict in Yemen has resulted in widespread socio-economic disruption and environmental degradation, with more than half of the population requiring humanitarian aid. In such conflict-affected contexts, direct field assessments are often unfeasible, making remote sensing an essential tool for environmental monitoring. This study integrates multi-temporal satellite observations, including the Moderate Resolution Imaging Spectroradiometer (MODIS), the Global Precipitation Measurement Version 6 (GPM v6), and the Tropical Rainfall Measuring Mission (TRMM). It also uses station-based and gauge-corrected climate products, such as the Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) and TerraClimate, along with atmospheric reanalysis data from the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5). These datasets were integrated to evaluate the impacts of prolonged armed conflict on vegetation dynamics, water stress, and climate variability across Yemen from 2001 to 2024. We examined national-scale spatiotemporal patterns using annual trends, breakpoints, and spatial comparisons between pre-conflict and conflict periods. The escalation of the civil war during 2011-2015 coincides with a marked decline in the aridity index and the Normalized Difference Vegetation Index (NDVI), accompanied by an increase in potential evapotranspiration (PET). These changes suggest conflict-driven impacts on vegetation and water balance, likely resulting from agricultural disruption. From 2016 onward, a partial recovery in NDVI and the aridity index, alongside reduced PET, indicates the onset of environmental stabilization following the initial conflict shock. Although precipitation shows no persistent long-term trend, it displays pronounced interannual variability. Surprisingly, agricultural areas in Yemen have increased between 2010 and 2020 over some regions despite the ongoing conflict, which may have forced populations to settle in new areas, converting previously unused or marginal land into farmland. Overall, the findings reveal the complex interactions between conflict, climate variability, and ecosystem resilience, underscoring the value of satellite monitoring in politically inaccessible regions."
    },
    {
        "DOI": "10.3390/METEOROLOGY4020013",
        "Title": "Variability of the Diurnal Cycle of Precipitation in South America",
        "Year": 2025,
        "Abstract": "A seasonal climatology of the diurnal cycle of precipitation (DCP) and the assessment of its observed trend since the beginning of the 21st century using the IMERG product are performed for South America (SA). Its high spatialtemporal resolution (x=0.1, t=0.5 h) enables the examination of the fine-scale features of the DCP associated with the complex physical characteristics of SA. Using 20 years of precipitation rate data, diurnal and semi-diurnal scale processes are analyzed through harmonic analysis. Diurnal metricsincluding the hourly mean precipitation rate, normalized amplitude, and phaseare employed to quantify the DCP. The results indicate that large-scale mechanisms, such as the South American Monsoon System (SAMS), seasonally modulate the DCP. These mechanisms in combination with local factors (e.g., land use, topography, and water bodies) influence the timing of peak and intensity of precipitation rates. Cluster analysis identifies regions with homogeneous DCP; however, some distant regions are classified as homogeneous, suggesting that local-scale physical processes triggering precipitation onset operate similarly across these regions (e.g., thermally induced local circulations). The trend analysis of the DCP reveals that, over the past 20 years, the tropical region of SA has undergone changes in the intensity and hourly distribution of this fine-scale climate variability mode. This trend is heterogeneous in space and time and is possibly associated with land-use changes."
    },
    {
        "DOI": "10.1007/S12040-025-02706-Y",
        "Title": "Paleoglacial coverage and paleoclimatic reconstruction of MIS 3b and the gLGM in the TurgenAsgat Catchment, Altai Mountains",
        "Year": 2026,
        "Abstract": "This study aims to enhance the understanding of climatic evolution by quantitatively reconstructing the paleoglacial coverage and paleo-equilibrium line altitude (ELA) in the TurgenAsgat Catchment during the marine isotope stage (MIS) 3b and the global last glacial maximum (gLGM), based on glacial geomorphology and 10Be exposure dating. The coupled 2D ice flow model and precipitationtemperature relationship model (PT model) were employed in this reconstruction. The results indicate that during marine isotope stage 3b (MIS 3b), the total paleoglacial area in this basin reached 189.9 km2 with an ice volume of 43.2 km3, and the ELA stood at 2846.5 m (a.s.l.). By the gLGM, the paleoglacial area had decreased by 6.4%, ice volume by 3.9%, while the ELA rose by 36.6 m. Furthermore, after combining proxy data such as pollen records and treeline altitudes (in m a.s.l.) from the surrounding areas, it was found that precipitation in the study area during MIS 3b was 90110% of modern levels, with mean summer temperatures 2.92.6C lower than today's; during the gLGM, precipitation was ~4050% of modern levels, with summer temperatures 4.42.8C lower than the present day. By comparing these reconstructed results with modern climatic conditions in neighbouring regions, it was inferred that factors such as topography and the monsoonal environment may have influenced the degree of climatic variation. This study reconstructs the scale of palaeoglacier in the TurgenAsgat Catchment of the Altai Mountains in Mongolia, enriching the evolutionary history of high-latitude glaciers in the high-altitude Asia region. It also enriches the climate indices of the Altai region based on glacier evolution. This study reconstructs the scale of palaeoglacier in the TurgenAsgat Catchment of the Altai Mountains in Mongolia, enriching the evolutionary history of high-latitude glaciers in the high-altitude Asia region. It also enriches the climate indices of the Altai region based on glacier evolution."
    },
    {
        "DOI": "10.1109/JSTARS.2026.3655802",
        "Title": "Response of Terrestrial Water Storage to Climate: Global Spatial Patterns and Driving Mechanism",
        "Year": 2026,
        "Abstract": "As a key component of global water cycle, understanding the response terrestrial storage (TWS) to climate variables is essential for managing resources under change. Traditional statistical methods have been applied across various regions, often with limited consideration time lagged responses or by simplistically shifting series few months. Consequently, spatial patterns TWS and its driving mechanisms, particularly those nonlinear relationships, require further investigation. Utilizing Anomalies (TWSA) from Gravity Recovery Climate Experiment (GRACE) follow-on mission, this study employed an zones based explainable artificial intelligence framework investigate relative contribution globally. A Long Short-Term Memory (LSTM) network integrated SHapley Additive exPlanations (SHAP) was reconstruct TWSA quantify impact precipitation (PRE) temperature (TEMP) zones, leveraging both satellite- model-based data. The results demonstrate significant PRE TEMP distinct latitudinal variation. well-reconstructed data acceptable accuracy in vast majority regions. SHAP-based importance analysis revealed pronounced heterogeneity dominant drivers TWSA. serves as factor low-latitude while prevails medium high latitudes. Additionally, primary vary at shorter lead times making greatest contribution."
    },
    {
        "DOI": "10.1109/IGARSS52108.2023.10283130",
        "Title": "Unsupervised Burned Area Mapping in Greece: Investigating the Impact of Precipitation, Pre- and Post-Processing of Sentinel-1 Data in Google Earth Engine",
        "Year": 2023,
        "Abstract": "Wildfires are one of the most significant threats to ecosystems and increasing in frequency globally. The aim this study is monitor evolution selected wildfires Greece that occurred during August 2021 using Sentinel-1 SAR data unsupervised k-means clustering Google Earth Engine. First, changes time series after start fire influence precipitation were investigated. In study, different speckle filters post-classification on results was tested. difference Normalized Burn Ratio Index (dNBR) derived from Sentinel-2 used as a validation dataset assess accuracy F1-score, overall accuracy, omission commission error. best achieved F1-scores higher than 0.70 with error lower 35% all areas, where Lee filter an 11x11 kernel window size 2 ha performed best."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108827",
        "Title": "Intercomparison and sensitivity analysis of WRF parameterization schemes for convection-permitting modeling of precipitation distribution along the Yarlung Zangbo River",
        "Year": 2026,
        "Abstract": "Convection-permitting simulations offer a promising approach for improving precipitation estimation in complex terrain, yet their added value and sensitivity to parameterizations remain poorly understood in the Yarlung Zangbo River basin. This study systematically intercompares fifteen 3-km WRF simulations to examine how parameterization choices (radiation, cloud microphysics, planetary boundary layer, shallow convection, and orographic drag) influence precipitation characteristics critical for hydrological applications: intensity, duration, frequency (IDF), maxima, and diurnal cycles. Results indicate that convection-permitting simulations add clear value by mitigating the \"drizzle bias\" typical of coarse-resolution models and by producing higher-intensity, shorter-duration events that better reflect the region's convective nature. While higher resolution improves event structure, the accuracy of mean precipitation depends strongly on parameterization. Sensitivity analysis reveals that cloud microphysics schemes primarily govern intensity, duration, and the timing of the diurnal peak, with the Thompson scheme best reproducing the nighttime peak observed in near-normal years. The planetary boundary layer scheme dominates precipitation frequency, with the MYNN2 scheme demonstrating robust performance across interannual variability. Comparisons with a convolutional neural network-based product and coarse-resolution reanalyses indicate that dynamical downscaling is more robust across interannual variability (dry versus wet years). Despite substantial uncertainty among satellite observational products, these findings show that optimized convection-permitting models capture realistic valley-scale gradients and diurnal propagation, providing essential guidance for hydrological modeling in complex terrain."
    },
    {
        "DOI": "10.3390/HYDROLOGY13010002",
        "Title": "Performance Evaluation of a Distributed Hydrological Model Using Satellite Data over the Lake Kastoria Catchment, Greece",
        "Year": 2025,
        "Abstract": "It might be difficult in many countries to find extended time series of measurements related to parameters of lakes hydrology and their interactions with catchments. Nowadays, the combined use of satellite imagery and spatially distributed hydrological models may contribute substantially to this direction. In this study, in order to assess for a long period of years a lakes surface elevation (LSE) and its water balance components, Lake Kastoria and its catchment, under Greeces dry-thermal conditions, were selected as the case study. This research employed the MIKE SHE coupled with the MIKE HYDRO River (MHR) hydrological modeling system, fed with precipitation and leaf area index (LAI) data coming from a ground weather station, typical values of LAI for the specific area, and satellite products from NASA for the precipitation and from Copernicus Global Land Service for the LAI. In all cases where satellite data were used, the simulation of the long-term LSE was very satisfactory, with minor to medium changes to the inflow and outflow components of the water balance in both the catchment (from 0.32 to 7.36%) and the lake (from 1.47 to 11.3%). The above changes were also reflected in the runoff coefficients. In conclusion, the above satellite products can adequately be used for the prediction of the LSE. Furthermore, a plethora of quantified information in relation to the catchments water balance can be extracted and used in decision-making processes."
    },
    {
        "DOI": "10.1029/2025AV001907",
        "Title": "Leaf Shedding During Drought Reduces Hydraulic Stress in Trees",
        "Year": 2026,
        "Abstract": "Abstract Leaf area has long been a proxy for ecosystem function. However, it can be highly variable even in the same forest types across space and time due to variations in local ecohydrology and climatic extremes such as droughts and heatwaves. Leaf shedding in response to drought has been documented at sitescales, theoretically to avoid hydraulic damage. Yet it is a major unknown if such leaf area declines are adaptive, in that they minimize the impacts of water limitation, or are simply diagnostic of declining ecosystem function. Here we use a trait based, hydraulicallyenabled, tree model that adaptively adjusts leaf area annually to maximize tree fitness in response to changes in water availability. We generate predictions of annual leaf area with a focus on the worst drought that occurred for points across the continental United States during the 20year analysis period. We compared model predictions to interannual variations in remotely sensed leaf area index (LAI). We found that a majority of ecosystems reduced LAI during drought and that the model predicted the LAI anomaly as well or better than Standardized PrecipitationEvapotranspiration Index, a commonly used drought index. Leveraging the mechanistic insights of the model, we found that reduced leaf area during drought in order to maximize carbon gain led to an overall reduction in hydraulic stress, but with a wide range of amplitudes across climates and forest ecosystem types. These results illustrate that crown area reductions during droughts are widespread in water limited regions, and likely adaptive in nature. , Plain Language Summary The area of leaves on trees is often used to determine the response of a forest or ecosystem to the environment, particularly during a drought. However, it is not always clear whether fewer leaves means a less healthy tree or forest. In some cases, trees may strategically reduce their leaves to reduce the amount of water they need to use in dry conditions and avoid water stress. We found that most ecosystems have fewer leaves during drought and were able to predict this leaf shedding with a model of a tree that accounts for the tree's characteristics and how water stress affects trees. We found that during drought, reduced leaf area both avoided water stress and increased tree carbon gainsuggesting that this could be a positive strategic behavior. , Key Points A traitbased physiological plant hydraulics model predicts drought response, and annual variation, in leaf area index (LAI) across dry forests This prediction of LAI matches or outperforms drought indices and most more complex terrestrial biosphere models Leaf shedding during drought consistently avoids additional water stress"
    },
    {
        "DOI": "10.1029/2025EF007037",
        "Title": "Assessing Flash Drought Development and Propagation Across the Contiguous United States Using Remote Sensing",
        "Year": 2026,
        "Abstract": "Abstract Flash droughts are characterized by rapid onset and intensification, with severe impacts on agriculture and ecosystems. They often begin as meteorological droughts and, if conditions worsen, evolve into agricultural droughts. While precipitation deficit is often the primary driver, atmospheric and hydrological anomalies can exacerbate flash drought development. This study characterizes flash droughts across the Contiguous United States using remote sensing data from 2003 to 2020. A combination of satellitederived meteorological, agricultural, and ecological variables are used to investigate largescale flash drought development. Events are defined using rootzone soil moisture. We used the Aridity Index to assess how background aridity influences agricultural and ecological impacts. Crosscorrelation and Cross Wavelet analyses are applied to examine the propagation of flash droughts from meteorological to agricultural and ecological stages. Results show that flash drought characteristicsincluding frequency, duration, and onset/recovery ratesare significantly influenced by landscape aridity characteristics. Precipitation is identified as the main driver across all climate regimes while Relative Humidity (RH) and Vapor Pressure Deficit (VPD) also indicate early signals. Time lags between meteorological variables and Soil Moisture (SM), as well as between soil moisture and ecological variables, vary across climates. Generally, results show that ecosystems respond to flash drought after soil moisture. Solar Induced Fluorescence (SIF), a measure of ecological stress, detects flash drought onset earlier than SM, highlighting its potential for early detection and monitoring. , Plain Language Summary Flash droughts develop quickly and can cause sudden stress to crops and ecosystems. They usually start with a drop in rainfall but are also influenced by other factors like high temperatures and dry air. This study uses satellite data across the continental United States from 2003 to 2020 to track the propagation of flash drought from meteorological drivers to soil moisture and subsequent effects on vegetation. Our results show that the onset of flash drought in soil moisture is characterized by dry spells in combination with low humidity. Vegetation shows a delayed negative response the rapid onset of soil moisture drought. However, satellite observations of Solar Induced Fluorescence (SIF), which track plant activity, responds earlier than soil moisture highlighting SIFs potential for flash drought detection before rapid declines in soil moisture. These findings can help improve early warning and management of flash droughts. , Key Points Flash drought frequency, duration, and onset vary with regional aridity, with humid regions being notably vulnerable Precipitation triggers flash droughts, while higher temperatures and vapor pressure deficits accelerate their intensification across the US Solarinduced fluorescence provides early flash drought warnings in humid climates, while leaf area index shows a delayed response"
    },
    {
        "DOI": "10.1029/2025GL121487",
        "Title": "Deriving AllHour Aerosol Optical Depth Over China From Automated Visibility Observations",
        "Year": 2026,
        "Abstract": "Abstract Allhour aerosol monitoring remains challenging due to limited spatiotemporal coverage of current observational systems. Here we developed a machinelearning based framework that derives 24hr aerosol optical depth (AOD) from automated visibility measurements. Trained on Himawari8 daytime AOD and enhanced with spatialcontext feature engineering, our model effectively extends robust AOD estimation into the nighttime, yielding an hourly AOD data set at 2,280 stations across China for 20162021. Independent validation demonstrates good agreement with groundbased and satellite AOD products, outperforming established reanalysis data. Furthermore, the data set reveals policydriven variations in diurnal AOD pattern, and hourlyresolution tracking of dust storm and haze events. This work substantially advances aerosol monitoring capability and provides valuable support for air quality diagnostics and climate modeling. , Plain Language Summary Aerosol Optical Depth (AOD), a key indicator of air pollution and climate assessment, is primarily measured by satellites and groundbased instruments. However, these conventional methods suffer from significant spatiotemporal gaps especially during nighttime, which prevents a full characterization of the aerosol diurnal cycle. This study presents a machine learning framework designed to generate a timecomplete, hourly AOD data set from automated visibility observations. The model was trained on Himawari8 daytime AOD and employs a spatial feature engineering strategy to learn from the environmental context of neighboring sites. The resulting data set demonstrates good consistency with other remotesensing AOD products, and achieves higher accuracy than commonly used reanalysis data, particularly in resolving nighttime aerosol variations. This robust record enables detailed analysis of urban pollution cycles and the hourbyhour tracking of extreme events like dust storms, providing a critical resource for understanding aerosol variability and supporting climate model evaluations. , Key Points Allhour aerosol optical depth (AOD) is robustly retrieved from automated visibility observations Results agree well with AERONET and MODIS AOD, outperforming MERRA2 reanalysis Visibilityderived AOD captures complete diurnal pattern and pollution evolution"
    },
    {
        "DOI": "10.1029/2025GL118648",
        "Title": "UpperLevel Turbulence in the North American and Asian Summer Monsoon Regions Sampled in Recent Aircraft Campaigns",
        "Year": 2026,
        "Abstract": "Abstract Smallscale turbulence above 10 km is investigated using observations from two recent airborne field campaigns: the Asian Summer Monsoon Chemical & Climate Impact Project (ACCLIP) and the Dynamics and Chemistry of the Summer Stratosphere (DCOTSS). Turbulence is enhanced by factors of 224 inside clouds, within 100 km of active deep convection, above atmospheric jets, and above mountains coincident with strong lowlevel winds. In DCOTSS, which sampled outflow from overshooting convection over the continental United States, turbulence occurred most frequently near the tropopause, indicating enhanced tropospherestratosphere exchange. In ACCLIP, conducted over the northwestern Pacific, turbulence was most common in the upper troposphere. We evaluate the performance of several turbulence diagnostics computed from the European Centre for MediumRange Weather Forecasts' fifth reanalysis (ERA5) and operational forecasts, along with the clearair turbulence flag recently introduced in the forecasts. Among the diagnostics tested, TI3 computed from operational forecast data is most skillful. , Plain Language Summary Airborne science campaigns involve equipping airplanes with scientific instruments and flying them through areas of Earth's atmosphere that we want to know more about. Just like passenger jets, these research airplanes encounter turbulence. We investigate turbulence in two recent airborne campaigns that sampled the atmosphere over the continental United States, and over South Korea, Japan, and their surrounding ocean. We combine aircraft measurements with satellite observations, global weather model output, and Earth topography data, to build a more complete picture of the atmosphere that the plane flew through. We find that turbulence is most common when the airplane is incloud, in cloudfree air near to a thunderstorm, flying above strong winds, or flying over tall mountains. We also evaluate several turbulence diagnostics, which are quantities calculated from weather model data that indicate where turbulence is likely, and identify which one gives the most accurate results. Atmospheric scientists can use this knowledge to forecast turbulence more accurately and to include it in their computer models of the atmosphere. , Key Points Asian Summer Monsoon Chemical & Climate Impact Project and Dynamics and Chemistry of the Summer Stratosphere sampled upper tropospheric/lower stratospheric turbulence over the northwestern Pacific and continental US, respectively Turbulence is enhanced in cloudy air, and in cloudfree air near deep convection, above atmospheric jets, and above orography Commonly used turbulence diagnostics computed from reanalysis and operational forecasts from the European Centre for MediumRange Weather Forecasts are evaluated against observations"
    },
    {
        "DOI": "10.1016/J.EJRH.2026.103372",
        "Title": "Sinkhole risk forecasting in the LithuaniaLatvia Karst region using artificial intelligence",
        "Year": 2026,
        "Abstract": "The LithuaniaLatvia transboundary gypsum karst region is highly prone to sinkhole formation, posing a significant geohazard infrastructure, agriculture, and groundwater resources. Risk assessment challenged by sparse monitoring networks strongly heterogeneous hydrogeology. This study develops an end-to-end, remote-sensinginformed data-driven workflow reconstruct missing daily groundwater-level (GWL) records forecast monthly formation risk. Daily GWL gaps were reconstructed using supervised machine-learning models driven satellite-derived climate water-storage variables. Reconstructed signals aggregated resolution translated into risk classes Random Forest classifier. A defensible operational target was applied at each well empirical 90th-percentile threshold (4 newly formed sinkholes per month). Model training employed fold-scoped preprocessing class-imbalance controls ensure robust evaluation. Across seven wells (20032024), combining level, seasonal encoding hydroclimatic features outperformed single-domain baselines, achieving accuracy of 0.96, high-risk precision 0.98, recall 0.85. Explainable analyses highlight multi-week preconditioning as the dominant driver, with clusters occurring within 30 days peaks. By integrating forecasted remote-sensing inputs, framework can be implemented decision-support tool or dashboard deliver up-to-date alerts, supporting coordinated cross-border infrastructure protection management. Remote sensing AI used fill level in terrains. Groundwaterseasonality yield best sinkhole-risk forecasts. High-risk months detected high strong recall. Workflow supports early-warning data-scarce region."
    },
    {
        "DOI": "10.1029/2025GL119875",
        "Title": "Photosynthetic Recovery Dynamics Reveal Declining Vegetation Functional Resilience in Tropical Ecosystems",
        "Year": 2026,
        "Abstract": "Abstract Ecosystem resilience, the ability to recover from disturbances, is crucial for sustaining ecosystem health and functionality. Traditional greennessbased resilience measures often overlook early physiological stress. Here, we use solarinduced chlorophyll fluorescence (SIF), an indicator for photosynthesis, to assess global vegetation functional resilience from 2000 to 2019. Using indicators of critical slowing down, we derive recovery rates as a measure of resilience from variance and autocorrelation in SIF time series across natural vegetation. Our results reveal marked latitudinal contrasts, with faster recovery in boreal regions and persistent vulnerability in tropical and lowlatitude ecosystems. Longterm trends show resilience loss in the Eurasian high latitudes, while shortterm trends indicate accelerating resilience decline in 60.7% of the global tropics, driven by heat, vapor pressure deficit, and soil moisture stress. These findings highlights the need to monitor ecosystem functional resilience through physiological indicators to anticipate ecological tipping points and inform conservation and climate adaptation strategies. , Plain Language Summary Ecosystem resilience governs the stability of carbon and water cycles, yet conventional assessments based on vegetation greenness often overlook early physiological stress. By leveraging solarinduced chlorophyll fluorescence (SIF), an indicator of photosynthesis, we quantify global patterns of functional resilience over two decades. Our analyses show strong latitudinal contrasts with rapid recovery in boreal systems and widespread resilience declines across Eurasian high latitudes. In recent years, tropical ecosystems show accelerating resilience loss driven by intensifying heat, vapor pressure deficit, and soil moisture stress. This emerging global mosaic of resilience gains and losses highlights the urgency of monitoring functional resilience as a critical basis for ecosystem conservation, adaptive land management, and climate mitigation strategies. , Key Points SIFbased recovery shows clear latitudinal gradients with fast recovery in boreal zones and persistent vulnerability in the tropics Longterm trends show resilience loss in Eurasian high latitudes Shortterm trends show accelerating tropical resilience loss driven by heat, vapor pressure deficit, and soil moisture stress"
    },
    {
        "DOI": "10.1029/2025JA034183",
        "Title": "Coupling Between Sudden Stratospheric Warming and MidLatitude Ionosphere of European Sector  A Case Study",
        "Year": 2026,
        "Abstract": "Abstract The 10 hPa level is conventionally used for determining sudden stratospheric warming in studies. Our study examines the winter of 20052006 from 1 to 10 hPa pressure levels, identifying three significant warming episodes in January 2006 instead of only one based on the 10 hPa level definition. The first two episodes with temperature peaks occurring around 3 and 10 January, respectively, are identified at the upper stratosphere, where they are stronger than the official warming peak on 22 January defined at 10 hPa. Increased wave activity at these upper levels, derived from the EliassenPalm flux divergence, highlights the intensity of warming and circulation changes at the upper stratospheric layers. Ionospheric analysis of January 2006 revealed substantial impacts on the F2 layer ionosphere, particularly in Juliusruh. These effects are linked to planetary wave periodicities detected in wavelet spectrum analysis. These periodicities are noted in the temperature and wind, predominantly in the upper stratosphere. Further analysis employs a wavelet coherence between the upper stratosphere and the critical frequency of the F2 layer that affirms that the planetaryscale periodicities observed in the upper stratosphere are mirrored in the F2 layer midlatitude ionosphere over station Juliusruh. These findings suggest a relationship between the upper stratosphere and the ionosphere, particularly at stations near the climatological vortex edge around 60N. The novelty of this paper is that for studying the impact of sudden stratospheric warmings on the midlatitude ionosphere, it is better to use the major SSW definition at upper stratospheric levels rather than the traditional 10 hPa level. , Plain Language Summary Sudden Stratospheric Warming is the strongest perturbation in the wintertime stratosphere. SSW related studies analyze the temperature and zonal wind at the 10 hPa to study its impacts on both the upper and lower atmospheres. This study examines three warming episodes, including the official 10 hPa based warming episode during the 20052006 winter. The other two episodes were found to be stronger warmings in the upper stratosphere than the one initiated at 10 hPa. Wave activity was also found to be enhanced in the upper stratosphere, substantiating the changes in temperature and zonal wind in the upper stratosphere. Significant effects are shown in the ionospheric F2 layer over the European sector, particularly in Juliusruh. The observed oscillations in the ionosphere are due to the planetary wave periodicities, which are more prominent in the upper stratosphere relative to the middle stratosphere. The observations indicate that there is a significant relationship between the upper stratosphere and the ionosphere during SSW events. The main novelty of this paper is that, for studying the impact of sudden stratospheric warmings on the midlatitude ionosphere, it is better to consider the major SSW definition using upper stratospheric levels, not the traditional 10 hPa level. , Key Points Upper stratosphere SSW definition better captures the sudden stratospheric warmingionosphere impact than the traditional 10 hPabased definition Major warming episodes show significant impacts on the European midlatitude F2 region ionosphere, with clear latitude dependence Zonal wind shows a strong link with foF2 at planetary wave periods over Juliusruh, suggesting wavedriven effects on the F2 layer in January 2006"
    },
    {
        "DOI": "10.1029/2025JC023452",
        "Title": "Ocean Mesoscale Processes Heat Atmosphere Over the Western Boundary Currents and Their Extensions",
        "Year": 2026,
        "Abstract": "Abstract Ocean mesoscale processes are ubiquitous across the global ocean, generating prominent sea surface temperature anomaly (SSTA) that in turn drive turbulent heat flux anomaly (THFA) at the airsea interface. Although the response of THFA to mesoscale SSTA has been extensively studied, it remains poorly assessed whether the THFA induced by mesoscale SSTA could cause a mean heat exchange between the ocean and atmosphere. Here, we address this issue based on an eddyresolving coupled global climate simulation. The results show that the response of THFA to mesoscale SSTA is nonlinear primarily due to the response of surface wind speed anomaly to mesoscale SSTA and secondarily to the nonlinearity in the ClausiusClapeyron relation. Such a nonlinear response of THFA to mesoscale SSTA causes a significant mean heat release of the order of 10 W m 2 from the ocean to the atmosphere over the western boundary currents and their extensions. Our findings suggest an important role of ocean mesoscale processes in heating the atmosphere, providing new insights into the closure of the ocean and atmosphere heat content budgets. , Plain Language Summary Ocean dynamics with horizontal scales from a few tens to several hundred kilometers, termed ocean mesoscale processes, are the most striking feature in the global upper ocean, especially over the strong currents at the western boundary of ocean basins. They generally have significantly warmer or colder temperatures at the sea surface compared with those of a broader background, with the former minus the latter called sea surface temperature anomaly (SSTA). A warm (cold) SSTA tends to drive more (less) turbulent heat release from the ocean to the atmosphere, corresponding to a positive turbulent heat flux anomaly (THFA). We found that the THFA are not proportional to the magnitude of SSTA of mesoscale processes, but in a nonlinear way. That is, the larger the SSTA, the more sensitive THFA is to SSTA. As the warm and cold SSTA are nearly mirrored, this nonlinear relationship can contribute a mean heat release of the order of 10 W m 2 from the ocean to the atmosphere over these western boundary currents, due to the stronger capability of warm SSTA to drive heat flux than cold ones. Our findings suggest an important role of ocean mesoscale processes in heating the atmosphere. , Key Points The response of the surface turbulent heat flux to the mesoscale sea surface temperature anomaly (SSTA) is nonlinear The nonlinearity contributes a mean heat release to the atmosphere of O (10 W m 2 ) over the western boundary currents and their extensions The nonlinearity arises from the response of surface wind speed to SSTA and the nonlinearity in the ClausiusClapeyron relation"
    },
    {
        "DOI": "10.1029/2026AV002350",
        "Title": "Large Overestimation of Projected Western U.S. Wildfire Burned Forest Area With Warming",
        "Year": 2026,
        "Abstract": "Abstract Wildfires are projected to increase with warming in the western United States. Since vapor pressure deficit (VPD) is highly correlated with wildfire burned area historically, many studies have argued that large projected increases in VPD with warming imply large increases in burned area. Here, we argue that those projections are overestimated by as much as an orderofmagnitude. First, we show that both soil moisture and VPD are well correlated with historical burned forest area. Second, we demonstrate that projected changes in VPD with warming are much larger than those in soil moisture, leading to wildly divergent projections of burned forest area: with 3 K (4 K) of warming relative to preindustrial, the VPDbased projection is about 16 (66) times the historical burned area, whereas the soil moisturebased projection is only 2 (3) times the historical burned area. A similar divergence arises in more complex models that include VPD as only one of many explanatory variables. Third, we argue that the VPDbased projections are incorrect. VPD is used as a measure of atmospheric evaporative demand, but recent advances have demonstrated that VPD and related quantities are actually poor measures of atmospheric evaporative demand and overstate projected drying with warming. We conclude that the rate at which wildfire burned forest area will increase with warming has been greatly overestimated by some studies. , Plain Language Summary Wildfires burn hotter and spread farther when fuel is drier. Many studies have argued that climate change will make fuel much drier in the western U.S., but these studies used the vapor pressure deficita measure of atmospheric drynessas an indirect measure of fuel dryness. Recent advances in terrestrial hydrology show that this is a poor approximation. Instead, we use soil moisture, a measure of land surface dryness that is more relevant to wildfire fuel, to show that wildfire burned forest area increases much less than expected with warming. , Key Points Wildfires in western U.S. forests burn hotter and spread farther when fuel is drier Studies using vapor pressure deficit as a proxy for land dryness claim that burned area will increase dramatically with climate warming Using a more appropriate proxy (soil moisture), we find that burned area will increase by much less than expected"
    },
    {
        "DOI": "10.1016/J.EJRH.2026.103503",
        "Title": "Reconstructing reservoir water levels using basin-scale GRACE TWS and machine learning: A case study in the Upper Parana River Basin",
        "Year": 2026,
        "Abstract": "We examine 14 large reservoirs (1151156 km2) in the Upper Parana River Basin, Brazil, where reservoir water levels are shaped by hydrological variability and operational management decisions. Effective monitoring this basin is challenged limited in-situ observations, creating a need for alternative approaches data-scarce environments. This study evaluates whether satellite-derived Terrestrial Water Storage (TWS) anomalies from GRACE, combined with precipitation, temperature, reservoir-specific attributes, can accurately reconstruct height across diverse system. Linear Regression, Polynomial Random Forest, Long Short-Term Memory networks were trained evaluated using multi-satellite radar altimetry observations as target. In addition, feature importance analysis was conducted to identify dominant inputs driving variability. Results reveal trade-off between system-wide stability accuracy. Regression achieved most consistent (MAE = 1.50 m), while Forest excelled at individual sites < 1.0 m 9 out of reservoirs), revealing fundamental site-specific optimization. TWS emerged influential input, underscoring integrating basin-scale storage dynamics site-level characteristics. work demonstrates scalable framework reconstructing levels, improving understanding historical behavior, supporting flood mitigation drought preparedness data-limited regions. Reservoir level reconstruction machine learning algorithms. Remote sensing GRACE data. Improvement meteorological datasets."
    },
    {
        "DOI": "10.5194/ACP-26-6035-2026",
        "Title": "Variability and trends of upper-tropospheric aerosols over the Asian summer monsoon region: an AeroCom multi-model study",
        "Year": 2026,
        "Abstract": "Abstract. Aerosols in the upper troposphere play an important role in Earth's radiative balance and atmospheric composition. Satellite observations show recurring enhancements of aerosol extinction coefficient (AEC) in the upper troposphere and near the tropopause over the Asian summer monsoon (ASM) anticyclone (ASMA) region during JulyAugust. However, substantial uncertainties remain regarding the roles of ASM dynamics, climate variability, and surface emissions in shaping upper tropospheric aerosols, as well as global model performance in this region. We present results from an AeroCom-coordinated multi-model study addressing these issues with nine global models covering the period 20002018. Large inter-model spread is found in non-volcanic AEC over the ASMA region, with coefficients of variation of 64 %86 %. Diagnostics using standardized tracers show that approximately half of this spread arises from differences in transport and wet removal processes, with discrepancies in wet scavenging contributing roughly eight times more to the inter-model variance than transport. The multi-model ensemble simulates a significant increase in non-volcanic AEC in ASMA over the two-decade period at 1.2 % yr1, primarily driven by rising anthropogenic emissions in Asia. In contrast, interannual fluctuations are modulated by climate variability, represented by Multivariate ENSO Index. Comparison with satellite-retrieved AEC also reveals persistent model deficiencies, especially in representing volcanic aerosols. These findings highlight the importance of improving the aerosol wet scavenging schemes and provide a benchmark for future coordinated aerosol modeling and evaluation."
    },
    {
        "DOI": "10.1029/2025WR042802",
        "Title": "Impacts of Fire on Flow Magnitude and Variability in the Southeastern Amazon Basin",
        "Year": 2026,
        "Abstract": "Abstract Increasing fire within the Amazon rainforest is a significant disturbance that has the potential to alter flow regimes and subsequently impact local ecology and ecosystem services. While previous studies have examined the impact of land cover change on streamflow, the specific effects of fire on the hydrology of Amazonian forested catchments remain uncertain, especially in previously undisturbed forests. This study investigates the impacts of fire on hydrology in the Amazon using a beforeafter controlimpact (BACI) pairedwatershed approach. Our analysis tested for postfire changes in the magnitude and variability of the streamflow regimes of five watersheds in the southeastern Brazilian Amazon. Flow regime magnitude and variability were quantified using monthly basin yield and coefficient of variation, number of reversals, and average rise rate. Differences in metrics before and after fire disturbance were assessed relative to nearby reference watersheds. Following fire disturbance, three of five watersheds showed increases in magnitude and four watersheds showed increases in variability. Fire effect size ranged from 6.01% to 7.98% for streamflow magnitude and from 10.9% to 70.3% for variability. Our findings confirm that fire affects streamflow in the Amazon, with the primary impacts being increases in streamflow variability. Overall, this study provides insights into the impact of fire on Amazonian catchment hydrology and highlights the importance of considering fire as a factor in managing and protecting tropical forest ecosystems. , Plain Language Summary Changes in land cover, including fire, can affect how much water flows in rivers and how variable those flows are over time. This study compares river flow before and after fire in five watersheds in the southeastern Brazilian Amazon, paired with nearby unburned catchments, to assess the impacts of moderateextent fires (6%20% of the catchment burned) on streamflow magnitude and variability. We found that fire altered river flow in all five watersheds, generally increasing both the amount and variability of flow. Following fire, streamflow increased by 6%8%, while streamflow variability increased much more, up to 70%. This study shows that river hydrology is sensitive to even moderate wildfires, with the biggest impacts on streamflow variability. Given that many aquatic species (like fish) rely on specific patterns of river flows, this research suggests that fires in the uplands have the potential to affect aquatic ecosystems in the years following fire disturbance. , Key Points Five watersheds were studied using a beforeafter controlimpact approach to test for differences in streamflow after fire disturbance Postfire, all watersheds experienced increases in streamflow magnitude, variability, or both compared to reference catchments Fire had a greater effect on streamflow variability than magnitude"
    },
    {
        "DOI": "10.5194/ACP-26-6869-2026",
        "Title": "Top-down estimate of regional carbon sinks over East Asia for 20102019 using satellite observations",
        "Year": 2026,
        "Abstract": "Abstract. East Asia is a major source of fossil fuel emissions and strongly influences regional and global CO2 concentrations. Quantifying natural carbon sinks in this region is therefore essential for improving climate projections and informing mitigation strategies. We estimated the Net Ecosystem Exchange (NEE) and ocean carbon fluxes over East Asia (18.554 N, 73146 E) during 20102019 using a Bayesian inversion framework. The GEOS-Chem chemical transport model was combined with GOSAT ACOS v9 XCO2 retrievals, and region-specific prior uncertainties were assigned using standard deviations from land and ocean models. Posterior estimates show enhanced carbon uptake relative to the prior, with NEE increasing from 0.17 0.08 to 0.31 0.06 PgC yr1 and ocean uptake changing slightly from 0.20 0.03 to 0.21 0.03 PgC yr1. Simulated CO2 concentrations based on posterior fluxes agreed better with independent observations than those from prior fluxes. East Asia's terrestrial ecosystems exhibited net carbon uptake during 20102019, consistent with increasing Enhanced Vegetation Index (EVI) trends. However, several regions showed temporary positive NEE during 20152016, likely linked to the strong 2015/2016 El Nino. When fossil fuel and biomass burning are included, East Asia released a net flux of +3.45 PgC yr1 to the atmosphere during 20102019. Natural sinks offset only 13.6 % of fossil fuel emissions, leaving a substantial residual source. Despite increased posterior sinks, they remain insufficient to counter regional emissions, sustaining elevated CO2 levels and continued outflow from East Asia."
    },
    {
        "DOI": "10.1109/TGRS.2024.3523484",
        "Title": "Spatial Representativeness of Soil Moisture Stations and Its Influential Factors at a Global Scale",
        "Year": 2025,
        "Abstract": "The spatial representativeness error of in situ soil moisture (SM) is recognized as a major source uncertainty when validating satellite SM products with resolution tens kilometers. Site underrepresentation primarily caused by environmental heterogeneity, but their relationship remains poorly understood. Here, we assessed the from 322 strictly screened stations worldwide relative to coarse-resolution (~0.25) footprint based on extended triple collocation (ETC) method. We then evaluated influence heterogeneity four factors (soil texture, land cover types, elevation, and vegetation coverage) site representativeness. Moreover, calculated variability within 1-km data explore its heterogeneity. Results indicate that about 63% sites have relatively good (ETC-derived correlation coefficient $\\ge 0.7$ ). Soil texture exhibit greater across mid high latitudes Northern Hemisphere. larger elevation coverage found regions significant ridges dense vegetation, respectively. Land factor influencing sites, increase enhances variability, which negatively impacts can be more representative proportion type where located higher or there are fewer types footprint. it newly proposed metric similar area ratio measure effectively reflect variability. This also serve supplementary criterion for selecting particularly situations sparse ETC method inapplicable. These findings provide useful references robust evaluation measurements (e.g., upscaling deployment)."
    },
    {
        "DOI": "10.1016/J.RSE.2024.114579",
        "Title": "A Sentinel-1 SAR-based global 1-km resolution soil moisture data product: Algorithm and preliminary assessment",
        "Year": 2025,
        "Abstract": "High spatial resolution of satellite-based soil moisture (SM) data are essential for hydrological, meteorological, ecological, and agricultural studies. Especially, watershed hydrological simulation crop water stress analysis, 1-km SM have attracted considerable attention. In this study, a dual-polarization algorithm (DPA) estimation is proposed to produce global-scale, dataset (S1-DPA) using the Sentinel-1 synthetic aperture radar (SAR) data. Specifically, forward model was constructed simulate backscatter observed by SAR, retrieval achieved minimizing error different vegetation states. The produced S1-DPA products cover global land surface period 20162022 include both ascending descending with an observation frequency 36 days Europe 612 other regions. validation results show that reproduces spatio-temporal variation characteristics ground-observed SM, unbiased root mean squared difference (ubRMSD) 0.077 m 3 /m . generated product will facilitate application high-resolution in field hydrology, meteorology ecology. A novel method only single-pass observations. Dual-polarization simultaneously roughness. First at 1 km based on Sentinel-1. C-band can be globally controlled within 0.08"
    },
    {
        "DOI": "10.1186/S40645-025-00720-8",
        "Title": "Machine-learning-based spatial analysis of the spring states in the southernmost Eurasian permafrost, Hangai Mountains, central Mongolia",
        "Year": 2025,
        "Abstract": "Abstract Springs are the vital ecosystem resource for pastoral livelihood in the southernmost regions of Eurasian permafrost but are currently experiencing depletion. However, their spatial distribution and factors influencing this depletion remain unassessed. This study evaluates the recent status of springs that were discharging several decades ago in the Hangai Mountains, Mongolia. A total of 1,620 spring sites were identified, and states (either still discharging or already depleted) of 228 springs were determined through field observations conducted in July and August 2019, along with visible satellite imagery taken between 2008 and 2020. To predict the states of remaining 1392 springs, machine learning approaches, including logistic regression (LR), random forest (RF), and support vector machine (SVM), were applied, incorporating vegetation indices and topographically derived hydrological parameters as explanatory variables. Given the imbalanced datasets (depleted: 44, discharging: 192) and the small sample size of training and test datasets, cross-validation and minmax normalization were employed to minimize overfitting problems. The models demonstrated high predictive performance, with area under curves of receiver operating characteristics (AUC-ROC) are 0.84 (LR), 0.86 (RF), and 0.84 (SVM). Additionally, the AUC of precision and recall (AUC-PR), criteria indicating the model performance for imbalanced datasets, increased by 0.320.52 compared to the case in random classification. The most important explanatory variables for determining spring states were the Modified Normalized Difference Water Index (MNDWI) and the Normalized Different Vegetation Index (NDVI); springs in the relatively wet and vegetated environments tended to remain discharging, whereas those in arid environments were more likely to be depleted. An ensemble of the three models predicted that 22.5% of springs in the Hangai Mountains have already been depleted. In extensive permafrost regions, spring depletion is largely driven by decline in modern precipitation. In transitional zones where permafrost and non-permafrost areas coexist, the thickening of active layer and increases in unfrozen water contents help to sustain spring discharge despite ongoing aridification."
    },
    {
        "DOI": "10.1007/S10236-025-01706-2",
        "Title": "Spatiotemporal variability of coastal upwelling in response to monsoonal forcing and semi-enclosed basins in the indonesian and adjacent seas",
        "Year": 2025,
        "Abstract": "The Indonesian seas, characterized by complex geometry and semi-enclosed basins, play a pivotal role in both regional and global ocean circulation. Coastal upwelling in this region is primarily driven by the interplay between wind-induced circulation and the physical constraints imposed by the semi-enclosed basins. This study investigates the mechanisms governing upwelling variability, focusing on the modulation of key oceanographic parameters, including sea surface temperature (SST), sea surface chlorophyll-a concentration (SSC), sea surface wind (SSW), sea surface density (SSD), sea level anomaly (SLA), and precipitation. These parameters are vital for understanding marine productivity, particularly in the context of climate variability associated with the El Nino-Southern Oscillation (ENSO). The results demonstrate that seasonal monsoonal winds, large-scale oceanic pressure gradients, and complex bathymetry collectively generate a spatially and temporally dynamic upwelling system. Steep coastal regions, such as southern Java, Bali, and the Nusa Tenggara islands, experience pronounced upwelling during boreal summer (June-August), while shallower areas such as the Java Sea and Arafura Sea exhibit weaker and less persistent upwelling due to limited vertical nutrient flux. Interannual variability further highlights the interaction between ENSO and local oceanographic processes. Strong ENSO coupling is evident in parameters such as SST and SLA, which exhibit high variance in the leading mode of Empirical Orthogonal Function (EOF-1) analysis and are directly linked to changes in thermal structure and circulation patterns. Moderately correlated variables, such as Ekman pumping velocity (EPV) and precipitation, are influenced by both ENSO phases and regional wind stress anomalies. In contrast, weakly ENSO-correlated variables, including SSC and SSD, are primarily controlled by local freshwater fluxes, nutrient dynamics, and regional current systems. Enhanced high-resolution monitoring of ENSO indicators within the Indonesian seas is recommended to improve early detection and mitigation of ENSO-related impacts on upwelling processes and marine biodiversity."
    },
    {
        "DOI": "10.1109/TGRS.2026.3685876",
        "Title": "Improve OMI Observations on Ground-Level NO2 Using Multiple Observations, Simulations, and Machine Learning",
        "Year": 2026,
        "Abstract": "Nitrogen dioxide (NO2) is a criteria air pollutant with adverse impacts on human health and the environment. An accurate ground-level NO2 dataset high spatial resolution beneficial for pollution management public studies. In this study, we leverage long-term Ozone Monitoring Instrument (OMI) observations by converting OMI vertical column densities into concentrations improving its to 1 km using Light Gradient Boosting Machine (LightGBM) learning model in three metro areas United States: Metro New York, York (NY), Baltimore, Maryland (MD), Houston, Texas (TX). Our improved products achieved robust performance across all regions, correlation coefficient of 0.897 0.876 0.914 Houston. The corresponding root mean square error (RMSE) was 3.278 ppb 2.385 2.084 respectively. Furthermore, effectively captured temporal variations observed cities. We also found that emissions, population density, boundary layer height (BLH), winds are consistently important contributors cities, while each city has own significant factors. This attribution analysis will be helpful state local agencies their efforts."
    },
    {
        "DOI": "10.1175/JTECH-D-24-0110.1",
        "Title": "Event-Based Verification of IMERG Precipitation Estimates over Complex Terrain in the Southern Appalachian Mountains",
        "Year": 2026,
        "Abstract": "Abstract Partitioning precipitation by event type provides a robust analysis and physically based error diagnostic study when comparing in situ observations with gridded remotely sensed precipitation estimates. We outline a novel event-based method for comparing in situ and satellite-based observations. In this sense, we are able to provide a robust, independent verification of satellite-based precipitation estimates. Specifically, more than a decade of precipitation observations from the Duke Great Smoky Mountain Rain Gauge Network (GSMRGN) are compared with half-hourly and daily satellite-based estimates from the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (IMERG) mission. The ability of IMERG to detect and estimate precipitation in a complex terrain region is analyzed, and the errors are documented with respect to the season and direction of the events. IMERG demonstrated a positive bias relative to the GSMRGN throughout the study domain with variations in magnitude based on the direction and season. The bias was reduced for events propagating from the northwest consistent with underobserving variability from the orographic precipitation enhancement and rain shadow effects. Accordingly, the GSMRGN demonstrated spatial nonlinearities across gauges northwest precipitation profiles that do not meet the assumptions of the Kalman filter used in IMERGs data assimilation."
    },
    {
        "DOI": "10.5194/AMT-19-1801-2026",
        "Title": "Enhanced characterization of SO2 plume height and column density using the second UV spectral band of TROPOMI",
        "Year": 2026,
        "Abstract": "Abstract. Volcanic emissions of sulfur dioxide (SO2) affect the environment, climate, and society. Their detection and quantification rely extensively on remote sensing techniques, which are used to track SO2 and monitor volcanic activity worldwide. In particular, nadir-viewing satellites measuring total SO2 vertical column densities (VCDs) have provided valuable insights into volcanic emissions for decades. However, the determination of the SO2 layer height (LH) is more challenging. In this study, we present an improved SO2 LH (and VCD) retrieval algorithm, applicable to the second UV spectral band (BD2) of the TROPOspheric Monitoring Instrument (TROPOMI). This band exhibits a stronger SO2 absorption than the third band (BD3) that is traditionally used for SO2 retrievals. To assess the impact of various spectral, atmospheric, and observation conditions, we conducted sensitivity analyses from a set of synthetic spectra representative of TROPOMI measurements using the Look-Up Table COvariance-Based Retrieval Algorithm (LUT-COBRA). Our results demonstrate that BD2 retrievals result in more accurate estimates of the SO2 heights and columns, particularly in the upper troposphere and lower stratosphere (UTLS), with LH errors reduced by at least a factor of 2. The algorithm was applied to real TROPOMI observations of volcanic eruptions and degassing episodes, and compared to BD3 retrievals. BD2 shows an improved sensitivity, with less noise, and a detection limit as low as 2 DU, surpassing the current operational TROPOMI SO2 product by an order of magnitude. Furthermore, our plume height estimates align closely with independent measurements from the Infrared Atmospheric Sounding Interferometer (IASI) and Microwave Limb Sounder (MLS), confirming the reliability of the approach."
    },
    {
        "DOI": "10.1029/2025GL120206",
        "Title": "Influence of Tibetan Plateau Snow Cover on ENSO Variability via the DustIron Fertilization",
        "Year": 2026,
        "Abstract": "Abstract The El NinoSouthern Oscillation (ENSO) and Tibetan Plateau, as key drivers of Earth's climate system, exert bidirectional controls that complicate causal attribution. Here, we integrate satellitederived Tibetan Plateau snow cover (TPSC) with causal inference to establish TPSCENSO conversion factor. Using this factor, we estimate that TPSC anomalies contribute 24.8% (6.4%44.1%) of September ENSO variability. Notably, TPSCmodulated biogeochemical processes are as influential as equatorial zonal wind mechanisms, constituting an additional ENSO driver. Reduced TPSC intensifies the Tibetan Plateau heat source, driving ascendant easterly anomalies. This accelerates the tropical easterly jet, transporting more Saharan dust to the tropical Pacific. Dustiron fertilization stimulates phytoplankton accumulation across ironlimited centraleastern Equatorial Pacific, reducing solar irradiance penetration depth, lowering upper ocean heat content by 21% (7%29%), and promoting La Nina development. Conversely, high TPSC favors El Nino development. These findings quantify TPSC's impact on ENSO variability, unveiling a biogeochemical pathway linking dustiron fertilization to ocean energetics. , Plain Language Summary The El NinoSouthern Oscillation (ENSO) is a known modulator of global climate anomalies, including variations in Tibetan Plateau snow cover (TPSC). However, whether TPSC, acting as a hemisphericscale climate factor, influences ENSO occurrence has remained unclear. This study reveals how Tibetan Plateau snow cover (TPSC) affects ENSO. We show that reduced snow on the Tibetan Plateau acts as a heat source, triggering a cascade of effects: it alters wind patterns, leading to increased transport of Saharan dust to the iron limited Pacific Ocean. Iron from the dust fertilizes phytoplankton growth that shades the ocean surface, reducing solar heat absorption and ultimately promoting the development of La Nina conditions. This identified biogeochemical pathway provides a fresh perspective on ENSO dynamics. , Key Points Tibetan Plateau snow cover (TPSC) contributes onequarter of El NinoSouthern Oscillation (ENSO) variability Documenting the biogeochemical mechanism of ENSO formation A dustiron fertilization pathway links reduced TPSC to La Nina development"
    },
    {
        "DOI": "10.1029/2025GL120379",
        "Title": "PhysicsConstrained Network for Enhanced ExtendedRange Precipitation Forecasting in East Asia",
        "Year": 2026,
        "Abstract": "Abstract The complex summer weather in East Asia poses significant challenges of precipitation forecasting. Despite the promising capabilities of deep learning models in conventional forecasting tasks, their blackbox nature limits physical interpretability. In this study, we developed PS2FNet, a physicsconstrained hierarchical spatiotemporal feature fusion network. The model integrates multiscale physical features through a multihead attention mixing block and embeds the vertically integrated water vapor flux term as a physical constraint in the loss function. This physicsinformed design enhances precipitation classification and maintains forecast stability over extended lead times. Compared with the ECMWF IFS, PS2FNet substantially improves precipitation forecast accuracy and can predict heavy rainfall events up to 2 weeks in advance, including both typical monsoonal and typhooninduced extremes. Feature attribution analyses highlight the importance of nonlinear relationships in improving forecast skill. These results provide new insights and methodological support for physicsguided deep learning in East Asian precipitation prediction. , Plain Language Summary The complex summer weather in East Asia poses significant challenges of precipitation forecasting, and while deep learning models show promising results for conventional forecasts, their blackbox nature limits physical interpretability. Here, we developed PS2FNet, a physicsconstrained spatiotemporal network that integrates multiscale physical features and the moisture budget equation as phyloss to enhance precipitation prediction. The results demonstrate that PS2FNet substantially enhances precipitation forecasting accuracy and the early detection of heavy rainfall events. In particular, PS2FNet achieves an RMSE reduction from 35.0% on Day 1%2.4% on Day 15, a PCC improvement from 70.9% on Day 1%4.8% on Day 4, and an MAE decrease from 30.6% on Day 1% to 1.0% on Day 4, compared with the ECMWF IFS. The model structure that integrates physical principles is essential for learning nonlinear relationships, thereby enhancing forecasting skills and providing improvements over other deep learning architectures in precipitation processes. Applying physicsguided deep learning methods for complex weather processes is promising in future meteorological research. , Key Points A physicsconstrained deep learning model, PS2FNet, is developed to improve the 115 days precipitation prediction across East Asia PS2FNet demonstrates superior performance in precipitation forecasts accuracy across 115 days compared to ECMWF IFS The model enhances forecasting skill by capturing multiscale features and incorporating physical constraints on moisture transport"
    },
    {
        "DOI": "10.1029/2025JD044872",
        "Title": "Transport of Peroxyacyl Nitrates (PANs) Across Northern Hemisphere Ocean Basins From Satellite Observations",
        "Year": 2026,
        "Abstract": "Abstract We leverage global satellite observations of Peroxyacyl Nitrates (PANs) from the Crosstrack Infrared Sounder (CrIS) on the Suomi National Polarorbiting Partnership satellite to evaluate seasonal and interannual variations of intercontinental transport in the Northern Hemisphere between 2016 and 2022. April and July are dominant months for transpacific transport of PANs, and summer months (June, July, and August) are dominant for transatlantic transport. Significant interannual variability occurs during months with peak PANs transport. We combine CrIS PANs and Ozone Monitoring Instrument (OMI) NO 2 to explore changes in intercontinental PANs transport associated with COVID19related emissions reductions. CrIS indicates statistically meaningful decreases in PANs over Pacific and Atlantic oceans compared to PreCOVID years (20162019), with changes in PANs smaller than those in NO 2 . May 2020 CrIS observations indicate that PANs (OMI NO 2 ) declined over the NW Pacific by 11% (33%), NE Pacific by 8% (15%), USAAtlantic outflow by 4% (4%), and Atlantic by 11% (11%). The largest change in PANs occurred over the NW Pacific in February 2020, where PANs (OMI NO 2 ) decreased 16% (42%). A chemical transport model is used to simulate PAN changes in response to the pandemic emissions changes, and the model is consistent with the observations. Our observations suggest that PANs over the ocean basins have not fully rebounded to PreCOVID values, consistent with the trend in tropospheric column NO 2 . , Plain Language Summary We used satellite observations from 2016 to 2022 to evaluate the seasonal and interannual variability of Peroxyacyl Nitrates (PANs) transported across Northern Hemisphere Ocean basins. April and July are dominant months of transpacific transport and JuneAugust are important months for transatlantic transport. There is some meaningful yeartoyear variability of transported PANs, especially during peak months. We used satellite NO 2 data to assess the impact of COVID19induced NO 2 reductions in transported PANs. We find significant declines in PANs over both the Pacific and Atlantic Oceans in response to these reductions, although changes in PANs were smaller than those in NO 2 . The largest decline in PANs was observed over the Northwest Pacific. Model simulations are used to explore changes in PANs due to pandemicrelated emission reductions, and the results are consistent with satellite observations. Our observations also suggest that the values of PANs over the oceans have not fully rebounded to preCOVID values, consistent with trends in tropospheric NO 2 . , Key Points Spring and summer are peak times for intercontinental transport of PANs COVIDrelated NO x emission reductions in spring 2020 led to lower PANs over the Pacific and Atlantic COVIDrelated changes in PANs were smaller than those of NO x over the Pacific and Atlantic outflow regions"
    },
    {
        "DOI": "10.1029/2025JD044636",
        "Title": "Characterizing PointSource Carbon Emissions by Combining TROPOMI CO and OCO CO2 Data",
        "Year": 2026,
        "Abstract": "Abstract Understanding and independently validating carbon emissions from concentrated point sources is vital to support climate policy. Satellitebased quantifications of point source emissions have been limited by the spatial coverage of current satellite instruments. We combine three different satellite instruments to determine carbon monoxide (CO) and carbon dioxide () emissions of seven large cities and six industrial complexes. We first estimate CO emission rates using TROPOMI CO observations with the CrossSectional Flux method. Subsequently, emission rates are calculated by multiplying with the ratio of TROPOMIobserved CO enhancements and enhancements from OCO2 and OCO3, also representing the combustion efficiency. We use synthetic observations to validate our approach and show that the inclusion of TROPOMI CO observations increases the number of possible emission quantifications. Using 20182023 observations, we find lower CO emission rates for Delhi and Lahore than the EDGAR emission inventory version 8. In contrast, our CO emission estimates exceed bottomup inventory estimates for most industrial sources. This is caused by observed combustion efficiencies that are generally lower than those reported in emission inventories. Our emission estimates show better agreement with EDGAR than the CO emissions, especially for industrial sources. We find higher emission rates than EDGAR for Delhi, Lahore, and Cairo that better agree with the ODIAC inventory. Our work shows the importance of CO as a coemitted species, and paves the way for a similar approach to be applied to the combination of TROPOMI, its successor Sentinel5, and the future CO2M satellites. , Plain Language Summary Because of the changing climate it is important to know how much we are emitting into the atmosphere. We have tried to answer this question for a few big cities and factories by looking with a satellite. We have focused on two gases, carbon monoxide (which pollutes the air) and carbon dioxide (the most important greenhouse gas). Not only does carbon monoxide reduce air quality, it is also very important for the three most important greenhouse gases (carbon monoxide, methane, and ozone). Another reason to look at carbon monoxide is that it can help us understand carbon dioxide emissions. Because there is a lot of carbon dioxide in the air already, it is difficult to find the signal of an individual city or factory (similar to how it is difficult to find a single car in a big parking lot). There is much less carbon monoxide in the atmosphere, and almost all processes that emit carbon dioxide also emit carbon monoxide. In this way carbon monoxide can help us to find and investigate carbon dioxide better (similar to attaching a balloon to your car in a parking lot). , Key Points We combine CO observations from the TROPOMI satellite with data from OCO to characterize carbon emissions from concentrated sources We determine combustion efficiencies and emission rates of CO and , revealing significant differences with bottomup inventories Our method is extendable to the upcoming Sentinel5 and CO2M satellite missions"
    },
    {
        "DOI": "10.1029/2025JC023726",
        "Title": "The RecordBreaking Godzilla Dust Event: Triple Pathways and Divergent Chlorophylla Concentration Responses",
        "Year": 2026,
        "Abstract": "Abstract Dust aerosols play a vital role in marine ecosystems and the airsea carbon exchange. In June 2020, a recordbreaking Saharan Godzilla dust event occurred. Here, we analyzed its threedimensional transport pathways and effects on chlorophylla (Chla) concentration in different sea areas. Results show that under the combined effects of the North Atlantic Subtropical High and the Mediterranean Low, the dust was transported along three distinct pathways: westward to the Atlantic Ocean, southeastward to the Indian Ocean, and northeastward to the Pacific Ocean. In these three oceanic study regions, the maximum daily dust deposition amounts were 141 mg m 2 , 90 mg m 2 , and 17 mg m 2 , respectively. However, the marine responses differed: compared to predeposition levels, Chla concentration increased by 30% in the Atlantic and 15% in the Indian, while in the Pacific, a brief increase was followed by an overall decline of 18%. This study reveals that a single dust event can be associated with opposing marine ecosystem responses, with phytoplankton growth enhanced in some regions and suppressed in others, which is attributed to variations in dust transport pathways and environmental factors. These findings highlight the complexity of dust impacts on marine ecosystems and the importance of improving the accuracy of future projections of ocean carbon sequestration potential. , Plain Language Summary In June 2020, a massive dust storm named Godzilla carried Saharan dust across thousands of kilometers. We investigated where this dust went and how it affected marine ecosystems by supplying nutrients to the ocean surface. Our analysis showed that specific wind patterns pushed the dust along three main routes: westward over the Atlantic Ocean, southeast toward the Indian Ocean, and northeast toward the Pacific Ocean. The amount of dust reaching each ocean region differed substantially. The Atlantic received the largest deposits, followed by the Indian Ocean, whereas the Pacific received comparatively little. Interestingly, the response of marine phytoplankton (measured by changes in chlorophylla concentration) varied by region. In the Atlantic and Indian Ocean study areas, chlorophylla increased noticeably after the dust arrived. However, in the Pacific region, we observed a temporary rise followed by an overall decline. This suggests that dust can have complex and sometimes opposite effects on marine life, depending on local ocean conditions and other factors. These findings illustrate that even a single dust event can influence ocean ecosystems in multiple ways. Understanding these interactions helps scientists better predict how the ocean's capacity to absorb carbon dioxide may change in the future. , Key Points Godzilla dust spread via three pathways: west to the Atlantic, southeast to the Indian, and northeast to the Pacific Three dust transport pathways were mainly driven by the North Atlantic Subtropical High and Mediterranean Low systems This dust event triggered notably distinct changes in chlorophylla concentrations across different oceanic regions"
    },
    {
        "DOI": "10.1029/2025GH001437",
        "Title": "Evaluation of a Novel ClimateDriven SIR Model for Cholera Prediction",
        "Year": 2026,
        "Abstract": "Abstract The causative agent of cholera, Vibrio cholerae , is a bacterium native to the aquatic environment and commensal to zooplankton, namely copepods. V . cholerae thrives in warm, moderately saline water and its incidence is strongly influenced by environmental factors, which have proven critical for predictive awareness of cholera by identifying outbreak locations and timing. SusceptibleInfectedRecovered (SIR) models provide useful information for understanding transmission dynamics and epidemic curves of disease outbreaks. Previous such models lacked predictive ability due to limited data in regions where cholera persists. Here, we include climate variability parameters derived from currently available remote sensing data as primary input, allowing greater utility, compared to traditional SIR models. We present models for two African countries where cholera is endemic, Democratic Republic of Congo (DRC) ( R 2 = 0.769) and Nigeria ( R 2 = 0.756), that incorporate data for temperature, precipitation, and drought index and have been calibrated using weekly cholera case data from 2017 to 2019. Results suggest these models can be used for reasonably accurate retrospective analyses at both countrywide scale for which they were calibrated and modified for smaller spatial extent, including cholera outbreaks in Borno State, Nigeria and North Kivu, DRC. However, results also suggest predicting future epidemic transmission will be challenging due to data limitations in case reporting and intervention strategies. Thus, climate factors should be considered for future SIR modeling efforts, but further advances in data collection are required for these SIR models to become viable predictive tools. , Plain Language Summary The causative agent of cholera, Vibrio cholerae , is strongly influenced by environmental factors, which proved critical for predictive awareness of cholera by identifying outbreak locations and timing. SusceptibleInfectedRecovered (SIR) models provide useful information for understanding transmission dynamics of disease outbreaks. Previous such models lacked predictive ability due to limited data in regions where cholera persists. Here, we include climate variability data derived from currently available remote sensing data as primary input, allowing greater utility, compared to traditional SIR models. We present models for two African countries where cholera is endemic, Democratic Republic of Congo (DRC) ( R 2 = 0.769) and Nigeria ( R 2 = 0.756), that incorporate climate data and are calibrated using weekly cholera case data from 2017 to 2019. Results suggest these models are useful for examining previous outbreaks including those at smaller spatial extent, though with recalibration and intervention parameterization required, including cholera outbreaks in Borno State, Nigeria and North Kivu, DRC. However, results also suggest predicting future epidemic curves will be challenging due to data limitations in case reporting and intervention strategies. Thus, climate factors should be considered for future SIR modeling efforts, but further advances in data collection are required for these SIR models to become viable predictive tools. , Key Points Prior susceptibleinfectedrecovered models for cholera have lacked predictive ability because of limited data in choleraafflicted regions Here climate data are included as primary input for SIR models and greater utility is achieved using currently available remote sensing data Results indicate these models are useful for retrospective analyses but further work is needed for accurate predictive scenarios"
    },
    {
        "DOI": "10.1029/2025JD045791",
        "Title": "CloudTop Relative Humidity Modulates Aerosol Effects Across Marine Cloud Regimes",
        "Year": 2026,
        "Abstract": "Abstract Aerosolcloud interactions are estimated to offset 1/4 of greenhouse gasinduced warming. However, significant uncertainty remains, largely due to the counteracting influence of fine and coarse aerosols as well as the contrasting responses of various cloud regimes. Using a decadelong data set of geostationary satelliteretrieved cloud properties, reanalysis aerosol and meteorological data, along with strict meteorological binning and bivariate regression, we quantify the regimedependent susceptibilities of cloud properties and radiative effects (CRE) to fine aerosols (FA) and coarse seasalt aerosols (CSA). FA consistently decreases droplet size, while enhancing albedo, cloud fraction, and net cooling across stratocumulus (MSC), tradewind cumulus (Cu), and shallow tropical convection (STC). A 50% increase in FA mass concentrations induces average Net CRE changes of 14.7 W m 2 (MSC), 5.1 W m 2 (Cu), and 4.1 W m 2 (STC), far exceeding CSA effects, which results in a cooling enhancement of less than 3 W m 2 and even slight warming in STC. The magnitude and pathway of these regimedependent responses are strongly modulated by cloudtop relative humidity (RH). Under dry cloudtop conditions (RH < 35%), liquid water path (LWP) decreases with increasing FA, as enhanced evaporation outweighs precipitation suppression. Under humid cloudtop conditions (RH > 70%), LWP consistently increases with FA across regimes, as suppressed evaporation allows precipitation suppression to accumulate cloud water. At intermediate humidity (35%70%), MSC shows a nonmonotonic LWP response, marking a transition between the two mechanisms. CSA effects show the opposite RHdependent behavior. Thus, cloudtop RH plays a crucial role in modulating aerosol forcing and the efficacy of marine cloud brightening. , Plain Language Summary Aerosols, tiny particles in the atmosphere, interact with clouds and influence Earth's climate. These interactions can either cool or warm the planet depending on the type of aerosol and cloud. This study focuses on two types of aerosols: fine aerosols (FA) with radii between 0.05 and 1 m, and coarse seasalt aerosols (CSA) with radii larger than 1 m. By analyzing satellite observations from various warm cloud regimes over the oceans, including marine stratocumulus, tradewind cumulus, and shallow tropical convection, we found that FA generally cools the Earth by making clouds brighter, particularly in humid conditions. In contrast, CSA has a weaker cooling effect and may even cause slight warming in humid shallow tropical convection regions. The way clouds respond to these aerosols is largely modulated by the cloudtop relative humidity. The cloud physics behind these aerosolcloud interactions involves the balance between cloud droplet formation, cloud cover, albedo (reflectivity), precipitation, and evaporation processes, all of which are shaped by the aerosol size and the moisture content above the cloud. These findings enhance our understanding of how aerosols affect clouds and help reduce uncertainties in predicting their role in climate change. , Key Points Variations in cloudtop relative humidity modulate how aerosols alter cloud water content and radiative effect in marine clouds Dry tops: evaporation dominates and cloud water decreases with fine aerosol; humid tops: precipitation suppression increases cloud water A 50% increase in fineaerosol cools 4 to 15 W m 2 from humid to dry cloudtop regimes; 50% more coarse sea salt gives 2.8 to +1.6 W m 2"
    },
    {
        "DOI": "10.1029/2025GL119841",
        "Title": "Understanding the Evolution of Global Atmospheric Rivers With a Vapor Kinetic Energy Framework",
        "Year": 2026,
        "Abstract": "Abstract Atmospheric rivers (ARs) often cause damaging winds, rainfall, and floods. However, the physical mechanisms governing their evolution remain poorly understood. To close this gap, we perform a global Vapor Kinetic Energy (VKE) budget analysis. Using two formulations of VKE, we show that ARs are governed by similar mechanisms regardless of ocean basins. ARs intensify primarily through the conversion of potential energy to kinetic energy (PEtoKE), with horizontal convergence of vapor kinetic energy providing a secondary contribution in some regions. ARs decay mainly through condensation and turbulent dissipation, while their propagation is governed by the downstream convergence and upstream divergence of vapor kinetic energy. We also find PEtoKE conversion varies spatially and strengthens in regions of greater baroclinic instability or enhanced topographic lifting, for example, along North America's west coast. Collectively, these findings demonstrate that the VKE framework provides a powerful diagnostic for how physical processes shape AR evolution and regional variability. , Plain Language Summary Atmospheric rivers (ARs) are narrow bands of fastmoving, concentrated water vapor in the midlatitudes. They often bring strong winds, heavy rainfall, and flooding. In this study, we use an energybased budget analysis to show that ARs follow a common pattern of evolution around the world. An AR grows when atmospheric instabilities convert potential energy into kinetic energy. It weakens when water vapor condenses into droplets and when turbulence dissipates its energy. Its movement is associated with the differences in energy flux between its upstream and downstream directions. We also find that the conversion of potential energy to kinetic energy depends on vertical air motion and its density anomaly, which is influenced by how unstable the atmosphere is and the shape of the land surface below. , Key Points We develop a vapor kinetic energy framework to examine atmospheric rivers' development across different ocean basins Potential energy (PE) to kinetic energy (KE) conversion is the primary driver sustaining atmospheric river intensity across all regions Eastward propagation is driven by vapor kinetic energy convergence, aided by PEtoKE conversion near the North American west coast"
    },
    {
        "DOI": "10.1016/J.ISPRSJPRS.2026.04.012",
        "Title": "First mapping of subsurface soil temperature profiles by integrating diurnal cycles of microwave and thermal infrared observations",
        "Year": 2026,
        "Abstract": "Soil temperature (ST) is a fundamental meteorological variable that governs energy, water, and carbon exchanges across the landatmosphere interface, with profound implications for hydrological forecasting, ecological modeling, and climate change projections. Despite its critical importance, global subsurface soil temperature profiles remain poorly characterized by existing remote sensing capabilities, which are limited to instantaneous, single-depth retrievals with inadequate temporal sampling. This study proposes a Diurnal Soil Thermal (DST) model founded on the one-dimensional heat conduction equation, representing daytime temperature propagation as a harmonic wave and nighttime cooling as an exponential decay. Building upon this physical model, we propose the Diurnal Soil ThermalMapping Algorithm for Profiles (DST-MAP), which integrates land surface temperature observations from Fengyun-3 multi-frequency microwave radiometer and MODIS thermal infrared sensors to retrieve diurnal temperature cycle parameters. The damping depth characterizing vertical attenuation is constrained by soil texture and satellite-derived soil moisture using a physically based parameterization. DST-MAP generates continuous hourly soil temperature estimates at 5, 10, 20, and 30 cm depths. Validation against in situ measurements from 450 + stations demonstrate robust performance, with correlation coefficients exceeding 0.87 and root-mean-square errors (RMSE) of 3.54.5 K across all depths. Accuracy is highest in shallow layers, progressively attenuating with depth yet remaining reliable even at 30 cm. Triple Collocation Analysis (TCA) among DST-MAP, MERRA-2, and GLDAS indicates theoretical uncertainties below 4 K for most land areas, with DST-MAP exhibiting superior consistency in arid regions compared to MERRA-2 and comparable performance to GLDAS. This work establishes an observation-driven, physically grounded framework for hourly-resolved subsurface temperature profiling that captures the complete diurnal thermal evolution, providing essential support for the development of next-generation land surface hydrothermal coupling models and satellite payloads."
    },
    {
        "DOI": "10.1029/2025GL117102",
        "Title": "Assessing Aerosol Wet Removal Efficiency in Conventional and Multiscale Modeling Framework Configurations of the Community Earth System Model",
        "Year": 2026,
        "Abstract": "Abstract Aerosol particles play a crucial role in the global climate by absorbing and scattering radiation and influencing cloud properties. This study explores the role of resolved convection on precipitation and subsequent removal by wet deposition of aerosol in the Community Earth System Model (CESM2.1.0) by comparing two configurations with distinct representations of precipitation characteristics. We contrast the conventionally parameterized configuration (CAM5ZM) with the multiscale modeling framework (CAM5MMF), which uses embedded 4 km cloudresolving models to explicitly simulate convection. We compare the results against observations from Integrated Multisatellite Retrievals for Global Precipitation Measurement, MODIS, MERRA2 Reanalysis, and simulations from GEOSChem and Aerocom. The CAM5MMF configuration better captures the frequency and intensity of rainfall by reducing the overestimation of light precipitation frequency in CAM5ZM. Improved precipitation frequency is associated with aerosol lifetimes and removal rates that better match observations, leading to higher black carbon and primary organic matter burdens, with implications for future climate forcing and air quality changes. , Plain Language Summary In this study, we conducted a climate model simulation with improved accuracy of rain events, called the multiscale modeling framework (MMF). This framework addresses the common issue present in conventional simulations that produce too much light rain. When the issue of light rain frequency is addressed, the behavior of aerosols (small particles) is also improved, lasting longer in the atmosphere due to weaker removal by the excess rainfall. The redistribution of precipitation also alters aerosols' landocean contrasts, increasing particle transport over to the oceans by allowing them to live in the atmosphere for longer time periods. Specifically, the MMF doubles black carbon and primary organic matter lifetimes in the atmosphere relative to conventional models. As a result, the multiscale modeling framework results align with observational estimates of aerosols, reducing the low biases of aerosol optical depth, a measure of aerosol reflectivity, particularly over subtropical land regions like India, the Arabian Peninsula, and West Africa. The increase in aerosol burden leads to a 51% reduction in the globalmean bias when compared to reanalysis data. This study highlights how precipitation biases impact aerosol particles and provides insight into further improvement of aerosolclimate interaction in Earth system modeling approaches. , Key Points Multiscale modeling frameworks (MMF) reduce light rain biases in the Community Earth System Model (CESM), adjusting aerosol wet deposition The resulting change in wet deposition leads to longer aerosol lifetimes and higher global aerosol burdens and optical depth in the MMF Aerosol lifetimes and burden in the MMF better match estimates from reanalysis, satellite, and AeroCom results compared to standard CESM"
    },
    {
        "DOI": "10.1016/J.JARIDL.2026.04.001",
        "Title": "Modeling decadal snow and ice dynamics and their hydrological impacts in the Balkhash Lake Basin, Central Asia",
        "Year": 2026,
        "Abstract": "The Balkhash Lake Basin (BLB), a vital Central Asian watershed, faces hydrological uncertainty under climate warming. This study integrated multi-source remote sensing data (Sentinel-1 snow depth, Randolph Glacier Inventory (RGI) v.7.0 glacier inventory, and Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) mass balance) with degree-day model to reconstruct decadal ice dynamics across 13 sub-basins analyzed their impacts from 1950 2014. results showed that: (1) while flows the downstream river of BLB decreased 1982 due land surface changes, runoff increased significantly after in Ili River (18.0%) moderately most rivers east (1.3%8.3%), driven by precipitation melt. Runoff Ayaguz catchment (no glaciers highest warming) declined (10.5%); (2) warming reduced falling as caused melt water decline (0.030.22 mm/a) BLB, leading downward shifts coefficient, especially east. However, during AprilJune positively correlated contributing an upward shift Basin; (3) meltwater glacierized areas (<5.0% basin area) contributed 14.3% total ablation water. Net provided substantial excess (11.6 m 3 /s <1.0 east), generally counterbalancing negative effect rising potential evaporation at scales correlating coefficient. Therefore, stress may be more severe future accelerating abrupt increase air temperature 2000, continuing melt, significant inter-annual variations precipitation."
    },
    {
        "DOI": "10.1029/2025EF006593",
        "Title": "Interpretable Machine Learning for Revealing Main and Interactive Effects of Ozone and Meteorological Factors on Crop Photosynthesis",
        "Year": 2026,
        "Abstract": "Abstract Elevated ozone (O 3 ) and severe meteorological conditions could significantly influence crop photosynthesis in winter wheat and summer maize in the North China Plain. However, the complex climatepollution interactions make it difficult to use traditional methods to separate each factor's direct effects from their combined impact, thereby hindering our ability to understand and manage crop productivity under varying environmental conditions. Here, we quantified the main and interactive effects of O 3 and meteorological conditions on the photosynthesis of winter wheat and summer maize by integrating ground/satellite measurements with a machine learning model and an interpretability algorithm. Results showed that O 3 and meteorological factors each had nonlinear main effects on crop photosynthesis in both winter wheat and summer maize. Specifically, O 3 had negative effects on crop photosynthesis above thresholds of 111.7 1.7 g/m 3 for winter wheat and 130.8 0.5 g/m 3 for summer maize. Meanwhile, interactions between O 3 and meteorological factors accounted for 21.92% and 20.79% of the variation in photosynthesis in winter wheat and summer maize, respectively. These interactive contributions were substantially higher than the isolated main effects of O 3 alone (3.3% and 3.19%, respectively), with dominant interactions involving temperature in winter wheat, and CO 2 concentration in summer maize. Typically, interactions between O 3 and various meteorological elements exceed the main effect of at least one of them. These findings highlighted the importance of complex nonlinear interactions between air pollution and meteorological factors in regulating crop photosynthesis. They helped identify highrisk situations in agricultural production arising from the combined effects of atmospheric pollution and meteorological conditions. , Plain Language Summary High ozone (O 3 ) levels and climate change pose an increasing threat to crop production in the North China Plain, particularly affecting winter wheat and summer maize. However, the combined impact of ozone pollution and climate conditions on crop health remains unclear. We developed an interpretable machine learning framework to quantify the main and interactive effects of O 3 and meteorological conditions on photosynthesis. We found that both O 3 and meteorological factors have nonlinear main effects on crop photosynthesis. Furthermore, ozone begins to damage crops at threshold levels, and the damage is more severe when combined with certain meteorological factors, such as high temperatures or intense sunlight. This study shows that reducing crop losses will require managing both pollution and extreme weather, rather than tackling each issue in isolation. These findings can help farmers and policymakers develop better strategies to protect crops in the face of climate change. , Key Points Crop photosynthesis responds nonlinearly to ozone and meteorological conditions Photosynthesis declines when ozone levels exceed the specific thresholds for each crop type Interactions between ozone and meteorological conditions account for more variation in crop photosynthesis than ozone alone"
    },
    {
        "DOI": "10.1029/2025MS005318",
        "Title": "PhySCATNet: A PhysicsInformed Deep Learning Framework for Optimizing Hydrometeor Bulk Scattering Properties Using Satellite Observations",
        "Year": 2026,
        "Abstract": "Abstract Accurate modeling of hydrometeor bulk scattering properties (BSPs) is essential for the effective assimilation of satellite microwave observations in cloudy and precipitating conditions. Nevertheless, current radiative transfer models use oversimplified hydrometeor BSP parameterizations, leading to significant simulation errors and biases that limit the full potential of allsky assimilation. To address this challenge, this study developed PhySCATNet, a physicsinformed deep learning (DL) framework that integrates forward and Jacobian operators of a physical radiative transfer model into the neural network training, enabling efficient optimization of the BSP models against satellite observations. Applied to vertically and horizontally polarized radiances from the Global Precipitation Measurement Microwave Imager 166.5 GHz channels, the framework selectively finetunes the snow BSP model while temporarily fixing other hydrometeor types. Results demonstrate that the optimized DL model substantially improves agreement between simulated and observed brightness temperatures. Across most regions globally, mean observationminusbackground (OB) biases are reduced to within 1K, and the JensenShannon divergence decreases by orders of magnitude. The error distributions, which were previously highly skewed and therefore problematic for data assimilation, are now roughly symmetrical. Furthermore, PhySCATNet enables the DL model to extract polarimetric information of nonspherical ice particles directly from observed radiances, demonstrating superior performance compared to existing empirical schemes. It successfully reproduces the distributions of polarization differences and their nonmonotonic relationship with brightness temperature. , Plain Language Summary Simulating satellite microwave observations in allsky conditions requires precise knowledge of how hydrometeors scatter and emit radiation. Current radiative transfer models (RTMs) use simplified assumptions about cloud particle shapes, sizes, and other microphysical properties, leading to significant simulation errors and biases, particularly for frozen hydrometeors with diverse morphologies. To address these limitations, this study explored the application of deep learning (DL) techniques to parameterize and optimize hydrometeor bulk scattering properties (BSPs). The key innovation lies in embedding the forward and Jacobian operators of a physical RTM into the DL training process, creating a differentiable pathway between satellite radiances and BSPs. This approach enables the DL model to automatically learn optimal scattering properties by minimizing differences between simulated and observed satellite radiances. The framework achieves substantial improvements in radiative transfer simulations, with simulated brightness temperature distributions showing much closer agreement with satellite observations. Moreover, the optimized DL model successfully captures complex polarization signals by learning directly from observed radiances. The model accurately reproduces the observed distributions of polarization differences and their characteristic curved relationship with brightness temperature. This demonstrates that the developed DL framework provides a promising pathway to improve the assimilation of satellite observations in cloudy and precipitating conditions. , Key Points We developed PhySCATNet, a physicsinformed deep learning framework to optimize hydrometeor bulk scattering properties using satellite observations PhySCATNet significantly improved brightness temperature simulations and made skewed error distributions nearly symmetrical PhySCATNet learned complex polarization signals directly from observed radiances, outperforming existing empirical methods"
    },
    {
        "DOI": "10.1029/2025JD045687",
        "Title": "Analysis of CCCma Radiative Transfer Calculations for SingleLayer Overcast Ice Clouds",
        "Year": 2026,
        "Abstract": "Abstract Understanding interactions between incoming shortwave (SW) solar radiation and clouds is essential for quantifying and modeling Earth's Radiation Budget (ERB). Ice clouds are particularly problematic due to their wide variability in crystal habits, sizes, and shapes. In this study, data from NASA's Cloud and Earth Radiative Energy System (CERES) are used to identify singlelayer overcast ice clouds and calculate surface and topofatmosphere (TOA) SW fluxes using the Canadian Centre for Climate Modeling and Analysis (CCCma) Radiative Transfer Model (RTM). A total of 361 SW flux observations from 11 surface sites spanning different climatic regions, together with CERES SYN1deg satellite observations at the TOA, are used to evaluate the CCCma RTM's performance. The CCCma RTM exhibits mean bias errors (MBEs) of +3.7 W m 2 at the surface and +4.1 W m 2 at the TOA, with root mean square errors (RMSEs) of 72.7 and 33.2 W m 2 , respectively. Correspondingly, the CERES SYN1deg FuLiou RTM shows MBEs of 12.1 and +18.5 W m 2 and RMSEs of 75.0 and 34.5 W m 2 for surface and TOA, respectively. MBE differences between the two RTMs are due to differing treatments of model physics, while their larger RMSEs at the surface result from both imprecise inputs and spatial variabilities of both inputs and surface observed flux. , Plain Language Summary Understanding how clouds interact with incoming sunlight is crucial for studying how energy enters and leaves the climate system. Ice clouds, in particular, are challenging to study because their ice crystals vary widely in shape and size. In this work, ice clouds are identified using NASA's Clouds and the Earth's Radiant Energy System (CERES) data over 11 ground sites that measure sunlight intensity. Atmospheric radiative transfer models are used to calculate sunlight reaching the surface and how much is reflected back to space (topofatmosphere, TOA). To test the models' performances, their estimates are compared with ground site observations as well as satellite measurements. Given myriad uncertainties in both model input data and point measurements made at the surface, both models predict surface and TOA radiant fluxes satisfactorily well. , Key Points 361 singlelayer overcast ice clouds were selected over 11 surface sites using NASA CERES SYN1deg cloud product Compared to the observations, the CCCma has mean biases of +3.7 W m 2 at the surface and +4.1 W m 2 at the TOA The large RTMs' RMSEs at the surface are due to input inaccuracies and spatial variability rather than model physics"
    },
    {
        "DOI": "10.1016/J.AGRFORMET.2026.111132",
        "Title": "Agricultural drought stress forecasting across different biomes in Brazil using machine learning",
        "Year": 2026,
        "Abstract": "Drought events across Brazil have become more common in recent years. The Northeast, marked by the dry semi-arid Caatinga biome, experiences most extreme drought events, which can a large impact on economic productivity including disruptions to energy and transport infrastructure as well agricultural losses. However, droughts are an issue significant effects vegetation six biomes make up country. stress, defined reduction health index (VHI) below 40% threshold is indicative of crop yield reductions. Here we evaluate monthly forecasted reductions VHI using machine learning. We then use results understand how climatological features influence stress each biome. This achieved through combination Empirical Orthogonal Function (EOF) analysis, Shapley plots, correlation analysis predicted spatial trends. observe distinct behaviour biome rooted its unique regional climate patterns physical geography. For other biomes, Vegetation (VHI), Root zone soil moisture (RZSM) precipitation less strongly linked; however high inertia allows for forecast performance. findings imply that forecasting models should treat Northeast system, training specifically this region improve prediction accuracy integrated indices."
    },
    {
        "DOI": "10.1029/2026GL121860",
        "Title": "Future Changes in the Atmospheric Water Cycle Over the Tibetan Plateau",
        "Year": 2026,
        "Abstract": "Abstract The Tibetan Plateau (TP) atmospheric water cycle (TPAWC), involving moisture sources, transport, and sinks with the TP precipitation and evaporation, faces unclear changes under global warming. This study projects TPAWC changes under two Shared Socioeconomic Pathways scenarios (SSP245 and SSP585) using Lagrangian moisture tracking. Results reveal significant TPAWC enhancement, particularly under SSP585. Strengthened moisture transport and evaporation from external moisture sources contribute precipitation trends of 14 to the TP's southern exorheic basins, accounting for over 60% of total precipitation increases. Enhanced TP evaporation contributes to precipitation increases in the TP's northeastern endorheic basins and drives 0.30.4 trends for external Yellow and Huaihe basins, representing approximately 40% of external Yellow River precipitation increases. These findings demonstrate that TPAWC intensification strengthens hydrological connections between the TP and surrounding basins, highlighting the importance of an atmosphereland perspective for projecting future water availability. , Plain Language Summary The Tibetan Plateau (TP) plays a crucial role in Asia's water resources. This study examines how the TP's atmospheric water cycle (TPAWC) will respond to future warming under two scenarios (SSP245 and SSP585). The TPAWC includes where moisture comes from, how it's transported, where it falls as precipitation on the TP, and where the TP's evaporated moisture travels. The TPAWC is projected to intensify under both scenarios, with stronger effects under SSP585. Under SSP585, increased moisture from external regions could lead to more rainfall (1040 mm per decade) in southern TP river basins flowing to oceans, accounting for of total rainfall increase in these basins. More evaporation from the TP accounts for of the rainfall increases in its northeastern inland basins. This increased evaporation could also boost rainfall by 34 mm per decade in basins outside the TP, such as the external portions of the Yellow and Huaihe River basins, contributing approximately 40% of the rainfall increase in the external Yellow River basin. These findings demonstrate that intensified TPAWC strengthens hydrological connections between the TP and surrounding basins, highlighting the importance of understanding atmospheric processes for projecting water availability across this region. , Key Points External moisture sources and Tibetan Plateau (TP) recycling intensify under future scenarios with stronger enhancement under SSP585 Under SSP585 external sources with enhanced transport ability contribute >60% of southern exorheic basin precipitation increases Under SSP585 enhanced TP evaporation drives 0.30.4 a 2 export trends to external Yellow (40% of precipitation increase) and Huaihe"
    },
    {
        "DOI": "10.1029/2025JC023816",
        "Title": "LandOcean Pathways Linking IndoPacific Sea Surface Temperature Variability to Tropical Atlantic Coastal Salinity",
        "Year": 2026,
        "Abstract": "Abstract This study aims to identify and understand the drivers of interannual variability in coastal salinity in the tropical Atlantic Ocean. Using a combination of satellite, in situ and reanalysis data, we show how oceanbasinscale sea surface temperature gradients in the tropical Pacific and Indian Oceans associated with major climate modes such as the El Nino Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) modify large scale atmospheric circulation and moisture transport and thus the hydrological cycle and coastal salinity in the tropical Atlantic ocean. We find that interannual variability in coastal salinity in western tropical Atlantic (WTA) is significantly correlated with changes in the Amazon basin precipitation and river discharge, suggesting a significant causal relationship with ENSO. Additionally, interannual variability in coastal salinity in eastern tropical Atlantic (ETA) is correlated with changes in the hydrological cycle over the Congo basin significantly influenced by IOD. ENSO and IOD account for 44% and 43% of the interannual variance in coastal SSS over the WTA and ETA, respectively, with no significant contribution from the tropical Atlantic climate indices. The sequence of linkages between land precipitation, river discharges and coastal salinity variability in WTA and ETA is associated with a measurable lag of a few months. This study reveals a multiphase, multilocation super teleconnection linking WTA with the tropical Pacific and ETA with the tropical Indian Ocean through coupled atmospherehydrologylandsea interactions. The identified lag relationships between climate modes, rainfall, river discharge, and coastal salinity offer improved predictability of nearsurface coastal hydrography in the tropical Atlantic Ocean. , Plain Language Summary Changes in the tropical sea surface temperature patterns in the Pacific and Indian Oceans result in the occurrence of major climate modes such as El Nino Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) respectively. ENSO and IOD are known to have wideranging effects on regional and global climates, including extreme droughts, floods, and wildfires. Using a combination of satellite, in situ and reanalysis data, we analyze the varying impact of these climate modes on the hydrological cycle over the Amazon and Congo river basins and the coastal sea surface salinity in the tropical Atlantic ocean. We find that ENSO is a key factor contributing to interannual changes in the Amazon basin hydrological cycle and coastal salinity in the western tropical Atlantic Ocean. On the other hand, IOD is one of the significant drivers of the interannual changes in the Congo basin hydrological cycle and coastal salinity in the eastern tropical Atlantic Ocean. Our study has implications for a better understanding of interbasin interactions between the three major tropical oceans. The lag relationship between the occurrence of the climate modes, rainfall, river discharge and coastal salinity is crucial for improving the predictability of floods/droughts over the Amazon and Congo basins and hydrographic conditions in the coastal tropical Atlantic Ocean. , Key Points A mechanistic link across distant tropical oceans is uncovered through a cascade of atmospheric and land water cycle processes ENSO impacts Amazon hydrology and western tropical Atlantic salinity, while IOD influences Congo hydrology and eastern Atlantic salinity No interannual seesaw oscillation between western and eastern Atlantic coastal salinity, as they are driven by independent climate modes"
    },
    {
        "DOI": "10.1029/2025JD045499",
        "Title": "Toward Closing the Hydroxyl Radical (OH) Budget: Assessing the Feasibility and Uncertainties in Constraining primary OH Production From Space",
        "Year": 2026,
        "Abstract": "Abstract Recent progress in constraining the atmosphere's primary oxidant, the hydroxyl radical (OH), with machine learning (ML) and satellite data raises the intriguing possibility of also constraining individual OH chemical production and loss terms. Here, we present a methodology to constrain primary OH production (i.e., OH production from the reaction of water vapor with O 1 D) from 60S60N at 500m above ground level (magl) (P OH _500) using a combination of ML, satellite observations, and meteorological data. The aim of this work is to establish methodological feasibility and to assess and quantify the uncertainties of that methodology. This methodology produces geophysically credible distributions of P OH _500 across all seasons, with seasonal variability being driven primarily by changes in water vapor and ozone photolysis rates. Regions with quantifiable 1 uncertainties of 25% or less comprise approximately 68%73% of global P OH _500, suggesting the product is of sufficient quality to inform the relationship between P OH and trends and variability in OH. The incorporation of additional satellite retrievals into the machine learning model as well as increased spatial and temporal averaging could reduce errors in regions with higher uncertainties, such as those areas with frequent clouds or biomass burning. Ultimately, the results presented here can provide a blueprint to observationally constrain other production and loss terms within the OH budget. , Plain Language Summary The hydroxyl radical (OH) removes many important chemicals from the atmosphere, but despite its importance, there are few observational constraints on the reactions that lead to its formation. Here, we present a methodology that combines satellite observations and machine learning to assess the feasibility of constraining OH production from reactions involving ozone and water vapor. We demonstrate that this methodology can constrain approximately 70% of global nearsurface OH production from these reactions. We also show that, for 2005, most seasonal and spatial variability in OH production results from changes in incoming ultraviolet light and water vapor. While the uncertainty in OH production in most areas is 25% or less, regions with frequent clouds and/or biomass burning can have significantly larger uncertainties. Satellite retrievals of additional chemicals and increased sampling are needed to improve the constraints on OH production in these regions. Ultimately, the results presented here can provide a blueprint to observationally constrain other reactions that control OH production and loss. , Key Points Satellite data & machine learning can constrain 70% of global, nearsurface primary hydroxyl radical production with limited uncertainty Increased observational frequency in cloudy and biomass burning regions is needed to reduce uncertainties further The methodology presented here provides a blueprint to constrain other terms in the hydroxyl radical budget"
    },
    {
        "DOI": "10.1029/2025GL121408",
        "Title": "PrecipitationBuoyancy Relationships in the Life Cycle of Tropical Mesoscale Convective Systems",
        "Year": 2026,
        "Abstract": "Abstract This study aims to establish processlevel benchmarks linking Mesoscale Convective Systems (MCSs) at various stages of their life cycle to their thermodynamic environment. The relationship between MCS precipitation and an empirical buoyancy measure () is examined using collocated satelliteobserved MCS tracks and reanalysis data. A positive relationship is identified between the frequency of tropical MCSs and that of high conditions. The buoyancy measure, integrating instability and entrainment, helps elucidate thermodynamic characteristics throughout the MCS life cycle. Environments with high instability and moderate subsaturation are frequently linked to the initial stage, while environments with low instability and near saturation are frequently linked to the mature stage. Stable and highly subsaturated environments are more likely associated with the termination of the life cycle. These associations are qualitatively similar for oceanic and land MCSs. Overall, the MCSenvironment relationships can serve as observational benchmarks with which to diagnose MCSresolving models. , Plain Language Summary Mesoscale convective systems (MCSs) contribute nearly half of tropical precipitation, yet their initiation and thermodynamic states remain poorly understood. This study examines relationships between precipitation and a measure that estimates convective plume buoyancy from the environmental temperature and moisture, including both nonentraining convective instability and modifications by mixing of subsaturated air into the plume. MCS frequency increases with high buoyancy occurrences in the tropics. Distinct buoyancy traits emerge across lifecycle stages: high instability with moderate saturation during initiation, low instability and near saturation at maturity, and stable, highly subsaturated conditions at termination. These stagedependent characteristics link buoyancy evolution to convective intensity and provide observational benchmarks for evaluating tropical MCSs in models capable of resolving them. , Key Points Spatial distribution of satelliteobserved mesoscale convective systems (MCSs) over the tropics is constrained by a lowertropospheric buoyancy measure The growing and mature stages of MCSs feature high buoyancy and strong precipitation, whereas the decay stage typically has lower buoyancy Sensitivity of MCS buoyancy/precipitation to instability/moisture across the MCS life cycle offers valuable benchmarks for model evaluation"
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135291",
        "Title": "Spatiotemporal dynamics and drivers of water and carbon use efficiency in the Yellow River basin with harmonized MODIS and GLASS data",
        "Year": 2026,
        "Abstract": "The interplay of global environmental change and human activities has increasingly disrupted ecosystem stability, posing significant challenges for effective water and carbon resource management. Water use efficiency (WUE) and carbon use efficiency (CUE) are critical indicators for assessing ecosystem responses to climate change and resource utilization. However, inconsistencies in remote sensing datasets and limited observational records pose significant challenges to the synchronous and long-term assessment of WUE and CUE, underscoring the need for more robust and harmonized datasets. This study aimed to harmonize long-term MODIS and GLASS datasets and explore the spatiotemporal evolution of water and carbon use efficiency in the Yellow River Basin (YRB) using machine learning models. This study employs Cubist, Random Forest (RF), XGBoost, and Min-Max Stretch Transformation models to harmonize MODIS and GLASS vegetation datasets, generating a robust Gross Primary Productivity (GPP) and Net Primary Productivity (NPP) dataset for the YRB from 1982 to 2023. Using this harmonized dataset, we systematically investigated the spatiotemporal patterns of WUE and CUE, as well as their underlying drivers. Results show that WUE demonstrated a statistically significant increasing trend (p < 0.05) over the 42-year period, whereas CUE showed a notable decreasing trend (p < 0.05). Approximately 54.51% of the YRB exhibited a coupled pattern of increasing WUE and decreasing CUE, with 69.47% of croplands, 49.71% of grasslands, and 22.07% of forest ecosystems exhibiting this trend. Geodetector analysis revealed that among climatic factors, minimum temperature (Tmin), mean temperature (Tmean), and maximum temperature (Tmax) were key determinants of CUE, whereas WUE was primarily influenced by Tmin, Climate Water Deficit (CWD), and Tmean. Interactions between precipitation and other factors exhibited particularly strong impacts. The RF model analysis further highlighted the varying influence of environmental factors on WUE and CUE across diverse ecosystems before and after 2000. This study provides a robust, globally transferable framework for harmonizing long-term datasets and analyzing water-carbon coupling dynamics, offering critical insights for advancing ecosystem management strategies and promoting sustainable development in regions facing similar environmental challenges."
    },
    {
        "DOI": "10.1016/J.JASTP.2026.106787",
        "Title": "Changing patterns of evapotranspiration and its feedbacks on precipitation under a changing climate across India's homogeneous regions using GLDAS data",
        "Year": 2026,
        "Abstract": "This study investigates the spatio-temporal variability and trends of evapotranspiration (ET) across India's rainfall homogeneous regions. The results reveal significant spatial and temporal variability across different regions, with a pronounced intensification in the positive trend (i.e., steeper increase in ET) observed after 2001. This increase may indicate a shift in climatic conditions, potentially driven by changes in atmospheric parameters, such as winds, latent heat flux (LHF), sensible heat flux (SHF), partitioning of surface energy, soil moisture (SM), and soil temperature (ST) as well as land use change and urbanization. These changes show strong monthly variability during the monsoon season, with June exhibiting the most significant positive trends, especially in northern and northwestern India. In contrast, a strong negative trend is noted during September over the same region. This suggests that monthly analyses are crucial for accurately understanding the impact of ET's on precipitation, rather than relying solely on seasonal averages. The study also identifies strong correlations between ET and climate factors such as air temperature, soil moisture, and wind speed, which play essential roles in shaping the hydrological cycle and regional moisture dynamics. The observed rise in ET after 2003 aligns with increasing temperatures and wind speeds in most regions, emphasizing the importance of these factors in driving ET rates. Overall, this research underscores the need for a region-specific approach to understanding ET trends and their implications for water resource management, agricultural planning, and climate adaptation strategies. The findings offer valuable insights into India's complex climate interactions, with significant implications for future research and policy-making aimed at mitigating the impacts of climate change."
    },
    {
        "DOI": "10.1002/MET.70185",
        "Title": "TURAAB : A 34Year Dust and Atmospheric Regional Reanalysis for the Middle East, North Africa and MediterraneanFirst Insights",
        "Year": 2026,
        "Abstract": "ABSTRACT The Mediterranean, Middle East and North Africa (MENA) region exhibits distinctive climate behavior, shaped by complex topography and largescale circulation, while warming and aridification intensify extremes. Desert dust, present yearround at high concentrations, perturbs the radiative budget, interacts with clouds, and modulates the evolution of highimpact eventsdirectly affecting health, transport, aviation, and regional economies. Existing regional and global aerosol reanalyses offer broad coverage, but in most cases do not provide longterm, dustinclusive fields at high spatial resolution for the MediterraneanMENA region. To address this gap, we introduce TURAAB, a 34year (19902023), 6 km horizontal resolution, hourly regional reanalysis for the MENAMediterranean domain. The climatology delivers a consistent suite of atmospheric and dustrelated fields across surface, column, and pressure levels. We evaluate core meteorological variables against groundbased observation networks and place particular emphasis on dust, benchmarking against established observational and reanalysis products. Overall, the system performs robustly across regions and regimes. TURAAB supports both climatological analyses and eventscale diagnostics, enabling detection of trends, anomalies, and potential climate shifts in meteorological and dust fields. By coupling high spatialtemporal detail with multidecadal consistency, it provides a policyrelevant foundation for research, climaterisk assessment, mitigation planning, and other applications in air quality and health, solarenergy performance, and transport/aviation operations. The dataset also offers operational relevance for dustprone regions, where highfrequency dust events demand finescale, highfrequency monitoring."
    },
    {
        "DOI": "10.3390/RS18091442",
        "Title": "Assessing Debris-Flow Susceptibility at Local and Global Scales: A Deep-Learning-Based Comparative Study ofSichuan, China, and Worldwide",
        "Year": 2026,
        "Abstract": "Debris flows pose a significant global geohazard, causing a large number of deaths and infrastructure damage every year. Effective protection and land-use planning in the affected regions requires understanding susceptibility to these events. Although a global phenomenon, previous studies have focused extensively on local areas with specialized models and accordingly complex feature selections. In this study, we investigate whether a unified debris-flow susceptibility prediction paradigm can be achieved regardless of regional scale, using only very few global public remote sensing data sources. To this end, this work contributes in the following ways: (1) A novel two-step negative sample generation scheme is proposed, and two open debris-flow datasets are constructed based on global debris-flow locations and locations in Sichuan, China. (2) An open-source end-to-end machine learning platform using remote sensing features directly is proposed, which achieves state-of-the-art results with 0.947 and 0.957 AUC in both scales compared to 0.88 for previous methods on the same location data, while using far fewer features. (3) A comparative feature importance analysis shows that, given the significant feature distribution difference on global vs local datasets, alleviating the scale-level gap is possible by leveraging the advanced deep learning technologies. This allows our unified framework to be easily applied to any regional study of debris-flow susceptibility prediction."
    },
    {
        "DOI": "10.1029/2026JD046570",
        "Title": "PseudoRadar Reflectivity by Applying UNet Models to Global Passive Microwave Brightness Temperatures",
        "Year": 2026,
        "Abstract": "Abstract Spaceborne radar systems such as the Global Precipitation Measurement Mission (GPM)'s core satellite Dualfrequency Precipitation Radar (DPR) provide global insight into precipitation structure, storm morphology, and hydrological cycles. However, their limited spatial and temporal sampling and high cost constrain their ability to continuously monitor precipitation globally. This study explores the use of Convolutional Neural Networks (CNNs) to generate pseudoradar reflectivity profiles from Passive Microwave (PMW) Brightness Temperatures (Tbs) alone, aiming to fill gaps in spaceborne radar observations. Collocated GPM Microwave Imager (GMI) and DPR data sets from 2020 to 2021 are used to train and evaluate models with a UNet architecture. Two strategies are investigated: a Full Frequencies (FF) ensemble using all 13 GMI channels (10183 GHz), and a High Frequencies (HF) ensemble restricted to 6 channels 89 GHz, reflecting configurations of radiometers on CubeSats. Results show that CNN models successfully reproduce largescale precipitation occurrence and echo top distributions, with over 50% of echo tops estimated within 1 km of DPR observations. The FF ensemble outperforms HF for warm rain and shallow systems due to the sensitivity to liquid water added by lowfrequency channels, whereas HF shows comparable skill at higher altitudes dominated by icephase hydrometeors. Both models systematically underestimate reflectivity, particularly at higher values (e.g., >40 dBZ) and altitudes, highlighting challenges in capturing extreme events. The findings demonstrate the potential of AIdriven pseudoradar products from abundant PMW observations to complement spaceborne radar measurements, with implications for future precipitation monitoring and nowcasting capabilities from PMW sensors on CubeSats. , Plain Language Summary Radars on satellites can measure the height and strength of rain and snow in storms, but they are expensive and only observe small portions of the Earth at any given time. In contrast, passive microwave radiometers are cheaper, fly on many satellites, and can consequently observe the whole planet frequentlybut they do not directly show a storm's vertical structure. In this study, we trained Artificial Intelligence (AI) models to fill in this missing information by learning the relationship between passive microwave measurements and radar observations from the Global Precipitation Measurement (GPM) mission. Using 2 years of GPM data, we tested two approaches: one using all available microwave channels, and one using only higherfrequency channels like those used in small, lowcost satellites. The AI successfully reproduced largescale patterns of precipitation and estimated the heights of storm tops within 12 km of radar measurements in most cases. Although the models underestimated the most extreme events, they showed strong potential for providing radarlike information from widely available radiometer data. This approach could expand storm monitoring to areas and times where radar is unavailable, improving weather tracking and climate studies. , Key Points Pseudoradar 3d information can be generated through AI by using passive microwave brightness temperatures alone Generating pseudoradar profiles without frequencies below 89 GHz does not significantly reduce the quality of the results at high altitudes AI estimated pseudoradar profiles provide a promising solution to fill gaps in monitoring the global precipitation's vertical structure"
    },
    {
        "DOI": "10.5194/GMD-19-3801-2026",
        "Title": "Advancing the BRAMS wildfireatmosphere modelling system: application to an extreme wildfire event",
        "Year": 2026,
        "Abstract": "Abstract. Wildfire smoke significantly perturbs atmospheric composition and radiative balance, with implications for air quality, weather, and climate. Accurately simulating smokeradiationconvection interactions remains a scientific challenge, particularly at meso- to local scales. This study presents developments in the BRAMS v6.0 modelling system, including the integration of crown fire spread into SFIRE and dynamic coupling of fire-emitted smoke fluxes. These enhancements enable physically consistent simulations of wildfire behaviour, smoke emissions, and their radiative impacts. Fire spread and heat release are used to compute Fire Radiative Power, which drives smoke emissions in real time. These emissions are fully integrated with aerosolradiation interactions and atmospheric dynamics. The system was applied to the 15 October 2017 wildfire in central Portugal using high-resolution simulations. Model performance was evaluated by comparing a diagnostic Smoke Optical Depth (SOD), computed offline from BRAMS-simulated PM2.5 using a Mie-based framework, with MERRA-2 Aerosol Optical Depth (AOD). Statistical comparison shows that SOD and MERRA-2 AOD share a coherent spatiotemporal structure, with correspondence maximised during the active fire phase. Peak extinction reached 67 m1 at 400 nm and absorption approached 5 m1 at 550 nm in the near-source plume core, consistent with an OC-dominated scattering regime and localized BC-driven shortwave absorption. The resulting radiative heating contributed to the upward displacement of the CIN layer ( 100200 m) and to the partial erosion of low-level inversions, producing transient stability modifications. Although the model occasionally produces very high near-source PM2.5 and optical-depth values confined to a small number of grid cells, additional diagnostics show that plume-integrated mass and optical properties remain physically consistent and are not dominated by boundary effects. These results demonstrate that the enhanced BRAMS system captures the coupled fireatmosphereradiation feedbacks of intense wildfires, improving the interpretation and prediction of smoke-induced thermodynamic and radiative perturbations."
    },
    {
        "DOI": "10.1029/2025JD045904",
        "Title": "Changes in Wind Extremes Shaped the Summertime Weakening of the Eurasian Subtropical Westerly Jet",
        "Year": 2026,
        "Abstract": "Abstract The Eurasian subtropical westerly jet (ESWJ), a key feature of the uppertropospheric circulation, plays a crucial role in regulating regional weather and climate. Observations reveal a significant summertime weakening of the ESWJ in recent decades. However, whether this weakening reflects a uniform shift in the windspeed distribution remains unclear. Here, we quantify the contributions of winds with different intensities from an occurrencebased perspective. Our analysis shows that while both an increased frequency of lowertail winds and a reduced frequency of uppertail winds contribute to the areaaveraged weakening trend, their spatial manifestations are markedly different, indicating that although the areamean trend appears as a uniform shift, the spatial patterns exhibit a heterogeneous structural reorganization. Crucially, the reduction of strong winds (above the 80th percentile) specifically shapes the structural weakening of the jet core, whereas changes in the lower tail exhibit a distinct geographical footprint that diverges from the overall trend. Further insights from moist thermal wind decomposition indicate that anthropogenic aerosols drive the weakening primarily by suppressing the dry thermodynamic component, accounting for 76% of the total weakening. In contrast, greenhouse gas forcing triggers a tugofwar between dry and moist processes, resulting in comparable contributions from both to the net change. These results highlight that changes in wind extremes exert a controlling influence on the mean jet behavior. Given the substantial impacts of jetrelated regional climate phenomena such as aviation turbulence and weather extremes, our findings underscore the importance of explicitly considering circulation extremes, particularly the upper tail, in future climate risk assessments. , Plain Language Summary The Eurasian subtropical westerly jet is a highaltitude, fastmoving band of wind that influences regional weather and climate. Changes in the jet can affect aviation safety, extreme weather, and climate patterns. Using reanalysis and model simulations, we examine how winds ranging from weak to strong contribute to the jet's summertime weakening over the past few decades. We find that, while the jet's average weakening across the region appears nearly uniform, the changes in different areas are uneven. Weak winds have become more frequent, but mostly in regions away from the jet core. In contrast, changes in strong winds (the strongest 20% of daily winds) dominate the longterm weakening pattern of the jet. Humanmade aerosols drive much of the weakening by suppressing the dry thermodynamic process linked to a weaker northsouth temperature contrast, while greenhouse gases trigger a tugofwar between dry and moist processes, leading to a smaller net effect on the jet's strength. Our results highlight the key role of wind extremes in shaping mean circulation patterns. Understanding how these extremes evolve, particularly the strong wind events, is essential for assessing future climate risks and preparing for impacts such as storms, turbulence, and extreme weather events. , Key Points Changes in the uppertail winds dominate the spatial pattern of the Eurasian subtropical westerly jet weakening Strong wind days (top 20%) broadly drop under AER forcing but rise regionally under greenhouse gas (GHG) forcing, reflecting opposing thermodynamic effects AER forcing drives weakening mainly by dry thermodynamic effect, while GHG forcing triggers a tugofwar between dry versus moist processes"
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.109074",
        "Title": "Diagnosing cloudbursts over Indian megacities near land-sea boundary during the 2025 Monsoon: Multi-platform observations, atmospheric drivers and model predictability",
        "Year": 2026,
        "Abstract": "This study presents a unified, multi-platform diagnosis of two catastrophic, short-duration extreme rainfall/cloudburst episodes that struck the megacities of Chennai (3031 August 2025) and Kolkata (2223 September 2025) near the land-sea boundary of the Bay of Bengal. Utilizing high-resolution observations from INSAT-3DS, Global Precipitation Measurement (GPM) Microwave Imager, and S-band Doppler Weather Radar, together with ERA5 reanalysis and the Python FLEXible object TRacKeR (PyFLEXTRKR) algorithm, the microphysical and dynamical pathways governing these intense convective events (>100 mm h1) are comprehensively analyzed. Observational evidence reveals that both systems were driven by deep convection, characterized by rapid cloud-top cooling (cloud-top brightness temperatures, CTBT 200K), deep convective cores, and enhanced hydrometeor loading extending above the freezing level. A novel diagnostic framework incorporating vertical eddy moisture flux analyses demonstrates contrasting dynamical regimes: while the Kolkata event exhibited a robust deep-tropospheric \"moisture-pump\" mechanism and, the Chennai event was characterized by shallower, episodic eddy transport and comparatively weaker vertical coupling. Large-scale precursor analysis relative to the 19802024 climatology further reveals that both events were embedded within anomalously moist atmospheric columns associated with persistent positive anomalies in mid-tropospheric relative humidity, total column water vapour, and moisture flux convergence. These anomalies indicate that the parent mesoscale convective systems (MCSs) developed within environments significantly preconditioned for deep convection, highlighting the critical role of long-term climatological departures in facilitating extreme rainfall occurrence. The focus of the present investigation is placed on these two recent, uniquely documented cases, which were well captured by multiple independent observational platforms, thereby providing rare and valuable insight into the structure and dynamics of cloudburst-producing MCSs near the land-sea boundary regions. Furthermore, a systematic evaluation of four operational global forecasting systems (ECMWF-IFS, NCEP-GFS, UKMO, and GFS-T1534) reveals persistent deficiencies in resolving the intensity, structure, and localization of these mesoscale extremes at 24- and 48-h lead times. These findings highlight critical gaps in the predictability of coastal urban extremes and suggest that improved representation of sub-grid-scale eddy moisture transport processes may be a critical pathway for reducing forecast error in such events; however, dedicated model sensitivity experiments are required to robustly establish this linkage."
    },
    {
        "DOI": "10.1029/2025JD046050",
        "Title": "Can ERA5 Be Used to Study Mesoscale Convective System Climatological Characteristics?",
        "Year": 2026,
        "Abstract": "Abstract Mesoscale convective systems (MCSs) produce more than half of tropical rainfall and are central to the global hydrologic cycle. As the climate warms, environments favorable for MCSs may become more common; however, limited observational records hamper understanding of how MCSs respond to variations and changes in their environments. Here, we evaluate how well MCSs are represented in ERA5, a widely used global highresolution reanalysis product. Using PyFLEXTRKR, which jointly tracks topofatmosphere infrared brightness temperature and surface precipitation, we identified MCSs in ERA5 and compared them with those identified in satellite observations using the same detection algorithm. This comparison analysis spans 20072020 using hourly data at 0.25 horizontal resolution focusing over the tropics. ERA5 reproduces observed brightnesstemperature statistics and captures the geographic distribution and seasonal and diurnal cycles of MCS cold cloud shields. However, ERA5 precipitation exhibits an intensity biastoo much light rain and too little heavy rainwhich shifts the rainrate distribution and reduces the frequency of MCSs relative to observations. Within MCSs, ERA5 precipitation exhibits the same pattern of bias, yielding a systematic underestimation of MCS precipitation intensity. Consistent with these biases, ERA5 underestimates the contribution of MCS to tropical rainfall by 25%34% in key regions. Overall, ERA5 is suitable for studying MCS cold cloudshield climatology and evolution, but precipitationbased MCS characteristics (including eventlevel precipitation features and the geospatial distribution of MCS precipitation) should be interpreted with caution. These findings clarify which aspects of MCS behavior are robustly represented in ERA5 for climatological applications. , Plain Language Summary Large, organized thunderstorm systems, known as mesoscale convective systems (MCSs), produce over half of tropical rainfall. Storm formation and rain production may change under global warming, as the moisture content of the tropical atmosphere is expected to increase, but we lack consistent observations. We ask whether one popular data set, the ECMWF Reanalysis v5 (ERA5), which blends models with observations, can reliably represent these storms. Using a program that tracks storms over time, we compared storms identified in ERA5 with those identified from satellites over 20072020 across tropical regions. We find that ERA5 captures the timing, location, and cloudtop temperatures of large areas of cold, high clouds. However, its rainfall is biased: too much light rain and too little heavy rain. As a result, it underestimates rain totals from these storms and their share of total tropical rainfall by up to onethird. The ERA5 data set is a useful tool for studying when and where these storm clouds occur and their evolution. It should be used with caution for rainfall during individual storms and is not reliable for mapping geographical distributions of rain from these systems. These insights can help researchers choose the right data set for understanding the longterm average behavior of MCSs. , Key Points ECMWF Reanalysis v5 (ERA5) captures some aspects of the geographical, diurnal, and seasonal mesoscale convective system (MCS) climatologies derived from satellite observations Tropical MCS biases in ERA5 arise from an overabundance of light rain and a deficit of heavy rain ERA5 is unsuitable for geospatial mapping of MCS precipitation and should be used carefully for eventlevel precipitation characteristics"
    },
    {
        "DOI": "10.1029/2025JD044949",
        "Title": "Representation of Stratocumulus and Shallow Cumulus Cloud Fraction Near the Southeast Pacific Ocean ITCZ in ERA5 and MERRA2 Reanalyses",
        "Year": 2026,
        "Abstract": "Abstract Model precipitation biases in the intertropical convergence zone (ITCZ) are often tied to the underestimation of stratocumulus (Sc) and shallow cumulus (Cu) clouds. We design a method to distinguish between Sc and Cu cloud regimes under subsidence on daily timescales based on cloud top pressure and vertical velocity to investigate the spatial distributions and seasonal evolution of their cloud fractions and associated largescale atmospheric conditions near the southeast Pacific ITCZ. We compare the Cumulus and Stratocumulus CloudsatCALIPSO Data set (CASCCAD) with the European Center for Mediumrange Weather Forecasts ERA5 reanalysis and the ModernEra Retrospective analysis for Research and Applications, Version 2 (MERRA2). In both reanalyses, the spatial patterns, vertical structure, and seasonal cycle of Sc cloud fraction are faithful to CASCCAD observations, but the amplitudes are underestimated. Cu cloud fraction vertical structure and spatial patterns are well simulated in ERA5, but both reanalyses vastly underestimate Cu cloud fraction magnitude. MERRA2 cannot capture the Cu vertical structure and has no Cu clouds near the southern ITCZ in March. An investigation of Sc and Cu days suggests that both reanalyses have realistic conditions for Sc clouds, however, MERRA2 has significantly weaker vertical motions and larger cloud liquid water content during Cu days. Lastly, there is a significant increase in Sc cloud fraction and a decrease in Cu cloud fraction in the southern hemisphere when the northern ITCZs are prevalent. The opposite happens during southern ITCZs. Overall, despite similar mean southern ITCZ precipitation, ERA5 provides more realistic Sc and Cu cloud populations than MERRA2. , Plain Language Summary The Intertropical Convergence Zone (ITCZ) is an area in the tropics near the equator where the trade winds converge and rainfall is most intense. The ITCZ stays primarily north of the equator, but has a brief season when rainfall forms south of the equator during northern hemisphere springtime. General circulation models simulation this southern ITCZ too often in the Eastern Pacific. We investigate the role that low clouds may play prior to southern ITCZ development in the Eastern Pacific. By comparing with satellite observational products, we determine that stratocumulus clouds (Sc) and shallow cumulus (Cu) clouds differ significantly in reanalysis products, which are data sets that combine model output with observational data. Two different reanalyses, ERA5 and MERRA2, show similar skill in representing Sc, but MERRA2 severely underestimates the horizontal extent of Cu, seemingly due to the weak ascent above the cloud top and a cooler and moister environment at the cloud top. This study provides a framework for understanding and evaluating Sc and Cu feedback in reanalyses and other model data sets. , Key Points We develop a method to classify stratocumulus (Sc) and shallow cumulus (Cu) low cloud regimes and apply it to ERA5 and MERRA2 reanalyses ERA5 has more realistic Sc and Cu clouds; MERRA2 vastly underestimates Cu, with much weaker vertical motions and larger cloud liquid water Southern hemisphere Sc (Cu) cloud fraction decreases (increases) prior to the southern intertropical convergence zone (ITCZ) and vice versa prior to the northern ITCZ"
    },
    {
        "DOI": "10.1016/J.ECOINF.2026.103698",
        "Title": "Assessing the role of gridded evapotranspiration products in improving streamflow simulation and reducing hydrological modeling uncertainty",
        "Year": 2026,
        "Abstract": "Streamflow simulation is crucial for ecological conservation and water resource planning. Hydrological models are the main tools streamflow simulation, typically calibrated using measurements. However, this approach cannot guarantee accurate estimates of other hydrological variables, leading to large uncertainty in simulation. The inclusion such as evapotranspiration (ET), offers a potential overcoming limitation. This study systematically evaluated incorporation gridded ET products constrain reduce prediction uncertainty. To end, three calibration experiments, established based on observations (benchmark), products, their combination, were applied process-based model Baihe River basin. Results indicated that single-variable with provided acceptable monthly estimates, average NashSutcliffe Efficiency (NSE) values 0.75 0.67 during validation periods, respectively. Multi-variable additional data achieved results comparable benchmark, NSE 0.84 0.87, Model single variable could lead parameters simulated results. In contrast, multi-variable improved reliability outputs reduced overall parameter uncertainty, providing better constraints representing hydrologic processes. Compared original data, bias-corrected performed reducing estimation median low flows. These findings provide valuable insights applying contribute scientific support sustainable resources management protection decision-making. Direct only (ET) can ungauged regions. reduces enhances reliability. bias correction improves flows"
    },
    {
        "DOI": "10.1016/J.EJRH.2026.103376",
        "Title": "Comparative machine learning and deep learning approaches for agricultural drought monitoring: Dual-index modeling in Iran",
        "Year": 2026,
        "Abstract": "This study considers Iran, encompassing hyper-arid to humid hydroclimates and major agricultural plains. Using 70 synoptic stations (20012022), we collocated station observations with satellite/reanalysis predictors from the Global Precipitation Measurement (GPM) mission, Moderate Resolution Imaging Spectroradiometer (MODIS), Famine Early Warning Systems Network Land Data Assimilation System (FLDAS), Copernicus Climate Change Service (C3S). Agricultural drought monitoring benefits combining indicators of meteorological forcing land-surface response, yet many studies rely on a single index or combine indices without an operational integration logic. We propose dual-index framework for Iran integrating Soil Moisture Deficit Index (SMDI) 3-month Standardized PrecipitationEvapotranspiration (SPEI-3). stability selection leakage-safe forward expanding cross-validation held-out most-recent test window compare Light Gradient Boosting Machine (LightGBM), Random Forest, Elastic Net, feature-tokenizer Transformer. SMDI is estimated more reliably (best RMSE = 0.80, R2 0.82) than SPEI-3 0.96, 0.55). Uncertainty quantified absolute errors via empirical quantiles (50% 90%); SMDI, 50% predictions fall within 0.5 units 90% 11.5 units. These quantile error bands are attached as confidence qualifiers monthly classes in framework, where anchors severity supports early-warning escalation. Dual-index unites soil moisture climate signals. Monthly satellite data integrated across Irans farm regions. Forward-in-time evaluation ensures realistic, leak-free model testing. learning deep best capture soil-moisture patterns. LightGBM achieved 0.80 (R2 0.96"
    },
    {
        "DOI": "10.1016/J.KNOSYS.2026.115923",
        "Title": "A human-in-the-loop active learning framework for scalable wind energy potential suitability assessment",
        "Year": 2026,
        "Abstract": "High-resolution wind energy potential (WEP) suitability assessment typically requires expert judgements on a large number of candidate sites. Such labelling is subjective, costly, time-consuming and difficult to scale, especially when siting criteria policy constraints evolve over time. To address this challenge, study proposes human-in-the-loop (HITL) active learning framework that aims (i) minimise annotation effort, (ii) enable scalable, high-resolution WEP mapping, (iii) provide interpretable, expert-consistent model outputs. Within the framework, limited set expert-style labels first generated for subset locations train an intelligent classifier. An strategy then iteratively selects most informative additional annotation, classifier updated until desired performance reached. Shapley Additive exPlanations (SHAP) are integrated as interpretability tool quantify contribution each criterion explain behaviour at both global local scales. The proposed demonstrated BeijingTianjinHebei region using GIS-based spatial datasets. Among suite machine models, Transformer coupled with entropy-based achieves best trade-off between accuracy requiring only about one quarter many location random sampling reach comparable level performance. resulting maps cross-validated against existing farms further explained SHAP reveal criterion-level location-specific driving mechanisms. case demonstrates HITL can label-efficient, adaptive transparent decision support planning in complex metropolitan settings."
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135567",
        "Title": "Characterization of uncertainty in ground-based validation of soil moisture products: A case study of QLB-NET in the Tibetan Plateau",
        "Year": 2026,
        "Abstract": "Direct validation through in-situ measurements is a primary approach for assessing the accuracy of soil moisture (SM) products. However, validation results carry significant uncertainties due to errors inherent in ground measurements, representativeness errors, and geolocation mismatches between in-situ sites and product pixels. The mechanisms through which these factors contribute to the overall uncertainty of pixel-scale reference \"truth\" remain unclear, complicating uncertainty control. This study is the first to investigate the uncertainty of validation results obtained from in-situ measurements. It reveals the magnitude and influencing factors of representativeness errors, proposes a method for identifying pixel geolocation shifts in sub-pixel-scale satellite SM products, and quantifies the resulting errors. The results indicate that representativeness errors are significantly larger than errors caused by geolocation mismatches, dominating the overall uncertainty of pixel-scale reference \"truth\". Representativeness errors result in an underestimation of SM product consistency and an overestimation of uncertainty. More than half of the sites exhibit representativeness errors exceeding 40%, with a maximum reaching 154%. Representativeness errors are primarily determined by spatial heterogeneity within the validation pixel and the number and spatial location of in-situ sites. The impact of spatial heterogeneity can be mitigated by optimizing site locations, and increasing the number of sites to 3 can reduce representativeness errors to below 5%. Errors due to geolocation mismatches are related to the degree of pixel shift and the spatial heterogeneity of the surrounding area. This type of error exceeds 15% approximately half of the time, with a maximum reaching 51.8%."
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135448",
        "Title": "A multi-source precipitation blending method combining hydrological model-guided precipitation adjustment and double transfer learning-based data merging",
        "Year": 2026,
        "Abstract": "Precipitation blending has been established as an effective approach for enhancing quantitative precipitation estimation accuracy. However, conventional precipitation blending methods exhibit two inherent limitations: (1) poor applicability in regions with sparse rain gauge networks; and (2) degraded estimation performance due to insufficient accuracy of input data sources. To address these limitations, this study proposes a multi-source precipitation blending (MSPB) method to integrate three datasetsground, gridded precipitation, and auxiliary meteorological datasetscharacterized by high spatiotemporal resolution and real-time capability. The methodology involves: (1) preprocessing all datasets into standardized inputoutput structures; (2) implementing an HBV-guided precipitation adjustment (HBV_PA) model to enhance data quality; (3) developing a double transfer learning (DTL) model using the corrected datasets to generate the blended precipitation product; and (4) conducting comprehensive performance evaluation against comparative objects, with demonstration through a case study in the source area of the Yellow River (YRSA). The evaluation metrics include probability of detection (POD), false alarm ratio (FAR), and critical success index (CSI) for event detection, as well as the Nash-Sutcliffe efficiency (NSE), Kling-Gupta efficiency (KGE), and root mean square error (RMSE) for quantitative assessment. Results demonstrate that HBV_PA-adjusted precipitation products significantly outperform the three original sources (IMERG-Late, GSMaP-Gauge, and ERA5-Land) across most statistical metrics in both temporal and spatial dimensions. Following the application of DTL to fuse HBV_PA-adjusted multi-source precipitation inputs, the MSPB method exhibits enhanced rainy-event detection performance (POD = 0.87 0.01, FAR = 0.23 0.01, CSI = 0.69 0.01) alongside optimal precipitation quantification capabilities (NSE = 0.46 0.02, KGE = 0.62 0.01, RMSE = 2.28 0.04), and outperforms other multi-source precipitation products and benchmark methods. Benchmark experiments conducted in the TangnaihaiLanzhou reach (TLR) further confirm that the proposed DTL model outperforms all benchmark methods by effectively leveraging extra-basin information, offering a superior solution for reliable estimation in data-sparse basins. Contribution analysis further reveals distinctive functional roles of input data sources in merging performance, confirming the substantial contribution of multi-source precipitation data and auxiliary meteorological information to precipitation estimation accuracy. This study establishes the MSPB method as a scalable solution for developing high-precision precipitation products. By integrating physically meaningful adjustments with a powerful transfer learning capability, the framework achieves robust predictive performance while effectively overcoming the critical challenge of in-situ data scarcity in sparsely gauged basins."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.109028",
        "Title": "Synergistic mechanisms of gravity waves and windward slopes in intensifying regional rainstorms: A Sichuan Basin case study",
        "Year": 2026,
        "Abstract": "Slope topography is conducive to triggering gravity waves and can also act as the windward slope, enhancing updrafts. Gravity waves and windward slopes may synergistically enhance precipitation. In this study, based on potential vorticity thinking, the synergistic enhancement effect of the windward slopes of the Daba Mountain and Wuling Mountain, and two gravity waves triggered by the Western Sichuan Plateau and Yunnan-Guizhou Plateau on precipitation is analyzed. Results show that flow-over and flow-around motions caused by the peak-valley terrain on the plateau, together with gravity wave drag, jointly weaken the horizontal wind speed near the ground, and enhance the vertical wind shear between the upper and middle troposphere, which is conducive to the excitation and maintenance of large-amplitude gravity waves formed in the upper troposphere by the shear instability mechanism. Compared to the smoothed plateau terrain, the presence of small-scale, high-frequency topographic features can excite gravity waves with larger amplitudes and longer durations, forming stronger, chain-like distributed heating centers, which results in the rapid strengthening of positive potential vorticity anomalies in the upper troposphere. After these anomalies merge with the positive potential vorticity area caused by the heating center near the windward slope of the lower troposphere, more vigorous deep convection is formed. As the windward slope, the Daba Mountains enhance updrafts through topographical lifting and boundary-layer convergence, thereby strengthening latent heat release in the lower troposphere and enhancing positive potential vorticity near the windward slope. In contrast, the potential vorticity enhancement effect of the Wuling Mountains as the windward slope is relatively small. The intensification of diabatic heating reduces atmospheric pressure, facilitating the formation of the Southwest Vortex circulation."
    },
    {
        "DOI": "10.1029/2025EF007607",
        "Title": "Latitude Dictates Global Marine Diatom Dynamics: Insights From 25 years of Satellite Data",
        "Year": 2026,
        "Abstract": "Abstract Marine diatoms are key players in global biogeochemical cycles, yet their spatiotemporal dynamics and the underlying drivers remain poorly understood. Here, we developed a novel remote sensing model and utilized 25 years of global satellite data to investigate marine diatom biomass. Our observations reveal distinct latitudedependent distributions, with higher biomass in highlatitude and coastal waters. Trend analysis reveals a widespread increase in diatom biomass across most ocean regions, with significant growth rates ranging from 0.1 to 1.6 10 5 mg m 3 per month. This trend stands in sharp contrast to the declines observed in equatorial waters and is accompanied by pronounced seasonal oscillations. Causality analysis identifies sea surface temperature, mixed layer depth, and photosynthetically active radiation as dominant drivers of diatom dynamics in midlatitudes. Nutrients, particularly nitrate in subtropical waters and silicate, also exert a significant influence. While broadscale climate indices showed limited explanatory power for global diatom anomalies, their impact was most evident in equatorial oceans. These findings provide the first comprehensive view of the multifaceted drivers of global marine diatom dynamics, offering crucial insights into their role in ocean biogeochemistry and responses to environmental change. , Plain Language Summary The global distribution and trends of marine diatoms, critical primary producers, have been poorly quantified. Using a 25year satellite record, we show that diatom abundances are highest in highlatitude and coastal oceans and have increased globally over the past decades, except in the equatorial region. Our analysis establishes environmental drivers (temperature, light, mixedlayer depth) as the principal controls on these patterns, with nutrients as secondary factors. The influence of largescale climate indices is largely confined to the equatorial oceans, with relatively minor effects globally. These results provide a foundational perspective on what governs diatom populations worldwide, which is crucial for forecasting their fate under future climate scenarios. , Key Points First highresolution (0.25) global data set of marine diatom biomass, surpassing NOBM/PhytoDOAS in coverage and precision Novel analysis of intrinsic drivers, revealing how environmental/climate factors regulate diatom dynamics globally Documented longterm spatiotemporal changes, highlighting global trends and regional anomalies in diatom biomass"
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108974",
        "Title": "XCO2 over East Asia: Assessing consistency, growth, and seasonality from multiple satellites and models",
        "Year": 2026,
        "Abstract": "Satellites and models can both provide global CO2 mole fraction data. Satellite measurements are derived from the observed spectra, but they are often hampered by incomplete spatiotemporal coverage mainly due to cloud coverage. Model data is spatially and temporally continuous, but its uncertainty still remains relatively large. Therefore, assessing the spatial coverage, temporal trend, accuracy, and precision of multiple satellite and model products is critical for multi-source XCO2 fusion and joint applications in carbon cycle studies. In this study, we conduct a comprehensive evaluation of the consistencies among the column-averaged mole fraction of CO2 (XCO2) products from four satellites (GOSAT, GOSAT-2, OCO-2, and OCO-3) and three carbon models (CAMS, CT2025, and GEOS) over East Asia between August 2019 and November 2023. OCO-2 and OCO-3 XCO2 measurements show a good agreement, and their differences are generally within 1.5 ppm. Model XCO2 products tend to be larger than the satellite XCO2 measurements across most of East Asia, except the Tibetan Plateau. The satellite and model data are also validated with the ground-based TCCON measurements. GOSAT-2, whose XCO2 data is not bias-corrected, has the largest systematic biases of 3.315.76 ppm, with random uncertainties of 3.485.99 ppm at six TCCON sites. The annual growth rates of XCO2 derived from the four satellite and model products are 1.48 0.402.74 0.36 and 2.23 0.072.64 0.11 ppm/year, respectively, while TCCON-derived values are 2.19 0.152.63 0.12 ppm/year. The satellite and model datasets show systematic biases in the seasonal cycle, characterized by overestimated amplitudes and a consistent 510 day phase delay relative to TCCON. Overall, discrepancies exist among these four satellites and three model XCO2 datasets over East Asia, and users must exercise caution when using them together."
    },
    {
        "DOI": "10.1109/IGARSS55030.2025.11243806",
        "Title": "Seasonal Bias in OCO-2 XCO2 Satellite Observations",
        "Year": 2025,
        "Abstract": "Accurate monitoring of atmospheric carbon dioxide (CO2) is essential for understanding fluxes and guiding climate mitigation strategies. This study investigates the seasonal variability bias between satellite-based OCO-2 XCO2 observations ground-based TCCON measurements over nine years (20142023) at Caltech station. Grouping data by observation month mode (nadir or glint) enabled us to analyze distributions deviations from bias.Our results reveal distinct patterns in variability. The nadir observations' mean exhibited relatively stable medians, ranging -0.6 0.4 ppm, indicating minimal deviation bias. In contrast, glint showed significant variability, with absolute median values exceeding 1 ppm during January, March, September. Skewness analysis highlighted asymmetry presence outliers, displaying a notable positive skewness September demonstrating high negative periods elevated vegetation cover. Furthermore, dynamics, represented monthly NDVI values, were strongly correlated (R2 = 0.76), underscoring its sensitivity bright conditions surface reflectance Nadir mode, correlation, reflecting relative stability. These findings emphasize importance accounting operational differences environmental factors, such as cycles, when interpreting data.This provides novel insights into temporal dynamics satellite-ground measurement discrepancies addressing role biases. Such are improving retrieval algorithms, enhancing accuracy CO"
    },
    {
        "DOI": "10.1016/J.JAG.2025.104711",
        "Title": "Closing Africas water balance budget: Assessing the suitability of eight latest precipitation products",
        "Year": 2025,
        "Abstract": "While numerous studies have evaluated precipitation products by comparing them with in-situ observations, the suitability of these for closing water balance budget in Africa remains largely unexplored. This study assesses performance eight latest products, namely MSWEP v2.8, CRU v4.07, CHIRPS v2.0, ERA5-Land, CPC, NOAH, IMERG v7, and MSWX v1, over Africa. We begin assessing general consistency among using rain-gauge measurements multiple linear regression analysis, followed generalized three-corner-hat (GTCH) method applied to estimate relative uncertainties signal-to-noise (SNR) ratios which help identify potential causes non-closure To evaluate budget, we compare Gravity Recovery And Climate Experiment (GRACE) its follow-on (GRACE-FO, hereafter GRACE) satellite missions derived total storage changes (TWSC) those equation each product. The influence climate zones terrain on evaluations is also examined. results reveal significant discrepancies trends products. GTCH analysis indicates that achieves lowest highest SNR values most cases, CHIPRS. Regarding NOAH exhibits GRACE-derived TWSC than other IMERG, whereas CPC achieved suitability. appears be more influenced climatic regimes terrain. All indicate approximately 20% Africa, likely due underestimation annual signals, as revealed principal component (PCA). underscores utilizing from same data source runoff evapotranspiration demonstrates high data. These findings suggest estimates should merge multi-source datasets consider can reduce provide a reliable foundation hydrological assessed. Approximately observed Water cycle components single demonstrate closure"
    },
    {
        "DOI": "10.1016/J.JHYDROL.2025.134379",
        "Title": "Water tracer model-assessed contributions of source waters to changing circumpolar Arctic terrestrial evapotranspiration and river discharge",
        "Year": 2026,
        "Abstract": " Tracer model quantitatively separated contributions of source waters to evapotranspiration and discharge in the Arctic river basins. Rain snowmelt water are major sources discharge, respectively, shows seasonally varying sources. plays a key role driving interannual, seasonal, regional variability discharge. The individual roles rain groundwater increasing during 1979 2016. Climate change has resulted alteration snow cover, permafrost degradation, vegetation infilling, precipitation apportionment across terrestrial region, which turn affected balance hydrological processes including seasonality interaction snowmelt, rain, soil storage. Effects on have been widely observed, although relatively few studies provided detailed assessments underlying causes change, adjustments timing relative (i.e., rainwater, water, thaw) effects altered regimes. Principal challenges included limitations observational networks, often frustrated efforts reliably complex changes that underway. This study explores tracer-aided ecohydrological was used assess from circumpolar basin, based three meteorological forcing datasets period model, provides additional constraints partitioning using isotopic evidence, revealed accounted for 67% 39% annual averaged over period, respectively. Rainwater were found trends whereas showed fairly stable or insignificant negative trends. determined be dominant growing season, while sustained by seasonally-varying Earlier events also appear increased proportion peak with rainwater being linked largely autumn storage previous year. We attribute higher summer reduction resulting interannual trend. Both thaw likely contributed increases cold season mostly low. Our analysis renders new perspective partitioning, especially enhanced contributions, as driver region."
    },
    {
        "DOI": "10.1029/2024AV001529",
        "Title": "The Arctic Ocean Double Estuary: Quantification and Forcing Mechanisms",
        "Year": 2025,
        "Abstract": "Abstract The Arctic Ocean double estuary is a threelegged overturning system in which inflowing waters are converted into both lighter and denser waters before being exported equatorwards. As the northern terminus of the Atlantic Meridional Overturning Circulation (MOC), it thus both affects, and is affected by, the Atlantic MOC. Here we quantify the magnitudes of the two overturning cells in density space, and then decompose the water mass transformation rates into net panArctic contributions from surface forcing and diapycnal mixing. We use a highresolution, quasisynoptic ice and ocean hydrographic data set spanning the four main Arctic Ocean gatewaysFram, Davis and Bering Straits, and the Barents Sea Opening. Two surface flux reanalyses and a hydrographic climatology are used to generate estimates of surface water mass transformation rates by density class. A box model then determines the profiles of turbulent mixing transformation rates, and associated turbulent diffusivities. We show that turbulent mixing and surface forcing drive transformations of similar magnitudes, while mixing dominates in the upper cell and surface fluxes in the lower cell. Consideration of uncertainties and timescales leads to the tentative suggestion that our results might be representative of recent decades. We discuss the possible significance of tides and sea ice brine rejection as energy sources driving turbulent mixing. Finally, we speculate as to whether water mass transformation rates may change in future as ocean heat transport into the Arctic increases. As sea ice declines and the efficiency of atmospheretoocean momentum transfer increases, the Arctic Ocean is expected to spin up, causing more intense turbulent mixing, with uncertain consequences. , Plain Language Summary The Arctic Ocean has been covered by sea ice yearround for much of the past, inhibiting the impact of wind on the ocean, with the consequence that Arctic Ocean currents are generally slow and turbulent mixing weak. However, recent decades have seen accelerated warming of the Arctic atmosphere and a reduction in sea ice cover, and more recently, an expansion of warm Atlantic waters in the region is beginning to be observed. Motivated by these changes, here we use observations from the Arctic Ocean's boundaries to investigate its vertical circulation, in which inflowing Atlantic waters are transformed into both lighter (upwelling) and denser (downwelling) waters. We find that the Arctic Ocean's upwelling limb is mainly driven by turbulent mixing, while the downwelling limb is primarily forced by loss of oceanic heat to the atmosphere in the icefree Barents Sea. We build on these new insights to discuss how the vertical circulation of the Arctic Ocean may change in future as sea ice retreats and the wind's influence on the ocean increases, leading to speeding up of Arctic currents and more intense turbulent mixing. , Key Points The Arctic Ocean double estuary circulation is assessed from measurements at the four oceanic gateways to the region Atlantic Water lighter than 0 = 27.75 kg m 3 upwells into less dense waters at a rate of 1.8 Sv (1 Sv = 10 6 m 3 s 1 ), driven by tidallyinduced turbulent mixing Denser Atlantic Water downwells into denser classes at a rate of 1.5 Sv, driven by surface heat loss in the Barents Sea"
    },
    {
        "DOI": "10.1029/2025GL119493",
        "Title": "Observed and Modeled Trends in Downward Surface Shortwave Radiation Over Land: Drivers and Discrepancies",
        "Year": 2025,
        "Abstract": "Abstract Incoming surface shortwave radiation () has exhibited regionallyvariable multidecadal trends and variability over land that have variously been linked to aerosols and clouds. However, limitations in the spatiotemporal coverage of the observations, combined with apparent disagreements between climate models and observations, have precluded global analyses. Here, we first demonstrate that both the variability and trends in ERA5 agree favorably with highquality estimates from satellite and in situ sources, and then show evidence of substantial continental brightening from 1980 to 2024, including in places like the central United States, Brazil, and central Asia that do not show large trends in aerosol concentrations over the same period. The brightening in these regions is colocated with reductions in total cloud cover, and trends in both are at the edge or entirely outside an ensemble of 237 CMIP6era climate model simulations, whose spatial pattern of trends more directly reflect the pattern of aerosol concentrations. , Plain Language Summary Sunlight that reaches the Earth's surface, known as downward surface shortwave radiation, affects weather, solar power generation, and ecosystems. Observations show that the amount of this radiation has changed over the past several decades, but it has been difficult to assess these changes in a consistent manner worldwide because direct measurements are not available in many locations. Based on a global product that compares favorably with direct and satellitebased measurements, we find that many land areas have brightened, or received more sunlight, between 1980 and 2024. While this was expected for regions like Europe and the eastern US that have had large decreases in air pollution, we also identified brightening in the central United States, Brazil, and central Asia even though air pollution in these areas has not changed much. Instead, these increases align with reductions in cloud cover. The observed patterns of brightening and cloud changes are larger and differently distributed than those simulated by a large set of climate models, which tend to link surface sunlight changes more directly to pollution. , Key Points Variability and trends in ERA5 downward surface shortwave radiation agree well with highquality in situ and satellitebased estimates Recent (19802024) trends in shortwave over land largely align with trends in cloud cover, including in regions with small aerosols trends CMIP6era simulations show a more direct shortwave response to aerosols, and miss observed brightening in multiple regions"
    },
    {
        "DOI": "10.1016/J.RSE.2025.115207",
        "Title": "Assessment and intercomparison of 23 global satellite and model-based soil moisture products using cosmic ray neutron sensing observations over Europe",
        "Year": 2026,
        "Abstract": "Comprehensive evaluation of satellite and model-based soil moisture (SM) products is essential for their further development application. With the advent Cosmic Ray Neutron Sensing (CRNS), which has an observation radius 130240 m, spatial representativeness mismatch between these grid-based SM ground single-point observations during process can be feasibly relieved. In this study, we systematically evaluated 23 gridded products, including single-sensor satellite, multi-sensor merged, using 68 CRNS measurement sites across Europe. Our revealed that SMAP-INRAE-BORDEAUX (SMAP-IB) retrievals showed superior consistency with measurements among all analyzed demonstrating both high correlation ( R = 0.80) low unbiased root mean square error (ubRMSE 0.050 m 3 /m ). The CCI/C3S combined active-passive ranked second in performance > 0.75, ubRMSE <0.060 bias analysis, 17 had negative (0.003 to 0.190 ) against measurements, while AMSR2-LPRM at C1 C2 bands active passive positive (0.011 0.161 It was also found capabilities degraded terms increasing vegetation density, topographic complexity wetness. Most lowest highest values cropland compared other land cover types. study emphasizes substantial potential cosmic field-scale validation satellite- our findings have advance algorithm refinement, product improvement, hydrometeorological applications."
    },
    {
        "DOI": "10.1016/J.AGRFORMET.2026.111217",
        "Title": "Global solar-induced chlorophyll fluorescence reconstruction crossing three platforms",
        "Year": 2026,
        "Abstract": " Solar-induced chlorophyll fluorescence (SIF), an emission of light that occurs simultaneously with plant photosynthesis, serves as an effective probe for photosynthetic activity. In recent years, satellite-retrieved SIF data have gained extensive attentions across ecological, hydrological, and climate change studies. However, these applications are largely limited by inconsistencies in retrieval methods, instrumental characteristics, overpass times and viewing-illumination geometries of a single satellite platform. Moreover, the spatiotemporal discontinuity and low spatiotemporal resolution of SIF retrievals also restrict the application of SIF for monitoring global ecological processes. To address such issue, this study develops a framework for harmonizing SIF retrievals from SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY), Global Ozone Monitoring Experiment 2 (GOME-2), and Orbiting Carbon Observatory-2 (OCO-2) satellites with cumulative distribution function matching and machine learning algorithm to generate the harmonized SIF (HSIF) - a global daily SIF product spanning the period of 2003–2023 with a 0.05° spatial resolution. Across the study period, the global mean SIF was 0.18 mW m⁻² nm⁻¹ sr⁻¹ with a significant increasing trend of 5.7 × 10−4 mW m⁻² nm⁻¹ sr⁻¹ yr−1, resulting in a cumulative increase of 7%. Validation against 10 tower-measured SIF showed robust performance in reproducing GPP-SIF relationship, and inter-comparisons with 4 reconstructed SIF products showed HSIF shares consistent spatiotemporal patterns. The HSIF product provides a valuable data source for advancing the understanding of global photosynthetic activity, carbon fluxes, and ecosystem responses to transient vegetation dynamics. "
    },
    {
        "DOI": "10.1029/2025EF006678",
        "Title": "ClimateDriven Hydraulic Traits Shift in Natural and Planted Forests: Patterns, Drivers, and Future Acclimation",
        "Year": 2026,
        "Abstract": "Abstract Plants modify their functional traits in response to changing environmental conditions under climate change. However, it remains unclear whether tree planting alters patterns and acclimation of hydraulic traits across spatial scales. Here, we compiled a sitelevel data set of hydraulic traits in natural (NF) and planted forests (PF) to examine trait patterns and relationships, quantified environmental and ecological drivers on ecosystemscale hydraulic traits of PF and NF across China, and computationally projected future trait acclimation using the spacefortime approach. We identified distinct differences in hydraulic traits between NF and PF, with PF exhibiting higher hydraulic safety but lower hydraulic efficiency than NF at the species level. NF demonstrated a tradeoff between hydraulic efficiency and safety, whereas PF exhibited a contrasting positive correlation between these traits. We confirmed that both environmental and ecological factors influence ecosystemscale hydraulic traits in NF and PF, although dominant drivers vary among specific traits. Projections under future climate scenarios suggest that, despite persistent differences in trait acclimation between NF and PF, both forest types tend to exhibit increased wateruse efficiency and enhanced drought resistance in response to rising precipitation and air dryness. These findings provide a valuable benchmark for estimating potential changes in hydraulic traits under climate change, supporting improved simulations of carbon and water fluxes in response to climate and anthropogenic influences. , Plain Language Summary As the climate changes, plants adjust their characteristics to cope with new environmental conditions. However, it's not well understood whether tree planting affects how these adjustments happen across different areas. In this study, we compared the hydraulic traits (how trees move and store water) of trees in natural forests and planted forests across China. We found that planted forests have trees with better water safety but less efficient water use than trees in natural forests. Interestingly, in natural forests, there is a tradeoff between water efficiency and safety, while in planted forests, these two traits seem to improve together. Both environmental factors (like weather) and ecological factors (such as tree age and height) affect these water traits, but the main influences vary. Looking ahead, we predict that both types of forests will become better at using water and resisting drought as a response to increasing rainfall and drier air. These findings are important for understanding how forests might change with climate change and for improving models that predict how forests impact the carbon and water cycles. , Key Points Planted forests show higher hydraulic safety but lower efficiency than natural forests, with opposite trait tradeoffs Environmental and ecological factors influence ecosystem hydraulic traits, but key drivers differ between natural and planted forests Both forest types tend to exhibit greater wateruse efficiency and drought resistance despite differing acclimation patterns"
    },
    {
        "DOI": "10.1016/J.ATMOSENV.2025.121754",
        "Title": "An assessment of a CO2 transport model simulations using surface, aircraft and satellite data (20152021)",
        "Year": 2026,
        "Abstract": "Estimation of accurate CO2 fluxes remains challenging because of limited high-quality data and inaccuracies in atmospheric chemistry-transport models (ACTMs). While satellite observations of total-columns (XCO2) have improved global data coverage, integration of co-located CO2 observations from multiple platforms and consistent methodologies are yet to be fully developed for altitude-wise model evaluations. In our study, we used MIROC4-ACTM simulations, surface and aircraft observations (ATom, Amazon, and CONTRAIL projects - considered as ground truth), and Orbiting Carbon Observatory-2 (OCO-2) XCO2 covering 20152021. MIROC4-ACTM and ATom profiles show mean differences of 0.1 0.48 and 0.01 0.3 ppm over land and ocean, respectively (p < 0.05), and those are 0.34 1.07 and 0.2 0.51 ppm for OCO-2 XCO2 sampled at ATom profile locations. Height-wise analysis shows that CO2 differences are concentrated in the lower troposphere (02 km), where model simulation are strongly influenced by surface fluxes. In Amazon, MIROC4-ACTM inversion does not have CO2 observation sites and limited vertical coverage of aircraft profiles right above the forest canopy (0.15 km) to 4.4 km, leading to poor ACTMOCO-2 (0.88 1.02 ppm) and ACTM-aircraft (0.105 2.58 ppm) agreements mainly due to lower troposphere. Over the airports in Asian megacities (i.e., emission hotspots), the model shows a higher difference with CONTRAIL (1.06 0.58 ppm) than OCO-2 (0.15 0.53 ppm). The larger ACTMCONTRAIL difference reflects ACTM's coarse resolution (approx. 2.8 2.8), which limits its ability to resolve smaller scale urban fossil fuel emissions, while the smaller ACTMOCO-2 difference likely also results from OCO-2's limited sensitivity below the boundary layer."
    },
    {
        "DOI": "10.1029/2025GL120545",
        "Title": "The Influence of Tropopause Temperature Biases on Climate Model Simulations of Tropical Cyclones",
        "Year": 2026,
        "Abstract": "Abstract Potential intensity (PI) is a key indicator for tropical cyclone (TC) activity, yet it exhibits considerable variability across global climate model (GCM) simulations, even with identical sea surface temperatures (SSTs). We show that the spread in PI across GCMs is primarily driven by differences in outflow temperature, a consequence of different upper atmospheric temperatures. To explore the impacts of these biases on TC activity, we conduct several idealized experiments with altered temperature profiles. In these experiments, global TC frequency and accumulated cyclone energy change by 35% and hurricane frequency by 80%. There are smaller but still significant impacts on lifetime maximum intensity. These findings highlight an underappreciated role of upper atmospheric model biases in modulating TC activity in GCMs, how future changes in TC activity may be influenced by responses of upper atmospheric temperature to anthropogenic emissions, and that TCs are more directly influenced by PI than SST alone. , Plain Language Summary The maximum potential intensity (PI) of tropical cyclones (TCs) differs across GCMs, despite these GCMs using identical SSTs. We show that these differences in PI are driven by different temperature conditions in the upper atmosphere, specifically at the tropopause and within the lower stratosphere. Cooler tropopause temperatures produce cooler outflow temperatures, which results in a higher PI and viceversa. Differences in PI can affect longterm average TC activity within these GCMs, especially TC intensity. Using a single highresolution GCM capable of producing realistic global TCs we modify the upper atmospheric temperatures to investigate their impacts on TC activity. We find a significant relationship between upper atmospheric temperatures, PI, and resolved TC activity. The simulations with cooler upper atmospheric temperatures and higher PI produce stronger and more frequent TCs. This suggests that upper atmospheric temperatures can influence future changes in TC activity due to climate change, in addition to the already welldocumented dependence on surface temperature. , Key Points Climate models exhibit large intermodel differences in temperature at and above the tropopause level The resulting intermodel spread in outflow temperature produces substantially different tropical cyclone potential intensities These differences in potential intensity induce biases in climate model simulations of global tropical cyclone intensity and frequency"
    },
    {
        "DOI": "10.1016/J.BIOCON.2026.111738",
        "Title": "Wildfire smoke reduces the vocal activity of imperiled grassland birds in New York State",
        "Year": 2026,
        "Abstract": "Smoke from new fire regimes driven by climate change may affect biodiversity in new regions of the world. Wildfires that occurred in eastern Canada in 2023 burned nearly 7.8 million hectares of forest, sending smoke throughout the northeastern United States. We leveraged passive acoustic monitoring to investigate real-time effects of wildfire smoke on vocalization behavior of globally imperiled grassland birds during the breeding season in open land covers across New York State. We determined an overall negative effect of elevated smoke levels on breeding grassland bird vocal activity. We observed the strongest vocalization responses in Bobolink (Dolichonyx oryzivorus) a colonial breeding, grassland-obligate species; Bobolink vocal activity sharply dropped during intense smoke early in the breeding season, yet increased during a milder smoke event later in the breeding season. Our results indicate that wildfire smoke can present an additive stressor to already imperiled grassland bird species via potential fitness reductions from decreased communication. While some aspects of smoke exposure may be uncontrollable, our results suggest that increased attention to conservation practices that promote grassland birds in the Northeast could be prioritized to offset negative effects of increased smoke associated with global change."
    },
    {
        "DOI": "10.1029/2025SW004779",
        "Title": "Quasi4Day Waves During the 2018/2019 SSW and Their Coupling to the Ionosphere Based on Whole Atmosphere Data Assimilation",
        "Year": 2026,
        "Abstract": "Abstract A strong westward zonal wavenumber2 quasi4day wave (Q4DW) during the 2018/2019 Northern Hemisphere sudden stratospheric warming (SSW) is both captured by Aura Microwave Limb Sounder (MLS) observations and our new whole neutral atmosphere data assimilation system. The Q4DW during this SSW is characterized by a doublepeak altitudinal structure in temperature, geopotential height, and neutral winds ranging from 40 km up to the lower thermosphere. The EliassenPalm flux diagnostics show that the wave source at 55 km over 45N75N, the excitation, propagation, and amplification of which are controlled by the critical layer and atmospheric barotropic/baroclinic instability in the polar stratosphere related to SSW. The first Houghmode decomposition analysis of Q4DW indicates that the enhancement of the Rossby (2, 3) normal mode is mainly responsible for the amplification of Q4DW, the latitudinal structure of which is distorted by the anomalous background winds during this SSW. In the ionosphere, a simultaneous quasi4day oscillation (Q4DO) is found in the Wuhan University total electron content (TEC) product near 10:0012:00 LT at magnetic latitudes of +15 and 25 with maximum amplitudes of 1.1 TECU and 1.2 TECU, respectively. Besides, the Q4DO also displays significant interhemispheric asymmetry and longitudinal variations. Interestingly, the secondary wave components ( s = 4, T = 10.7 hr and s = 0, T = 13.7 hr) in neutral winds from the nonlinear interactions between the Q4DW and the migrating semidiurnal tide are detected in the dynamo region, which may play a dominant role in generating Q4DO in the Fregion ionosphere. , Plain Language Summary Scientists have long sought to understand how slow, globespanning waves in the middle atmosphere influence the ionosphere that supports radio wave propagation. Despite extensive research, the quasi4day wave (Q4DW, westward zonal wavenumber 2) remains poorly characterized, and its ionospheric response has not been clearly demonstrated. To address this gap, we combine Aura/MLS satellite observations with a new wholeatmosphere data assimilation system to trace a strong Q4DW from the middle to the upper atmosphere during the 2018/2019 SSW. Results show that unusual winter winds influence the wave growth, upward propagation, and structural modification. Furthermore, we provide the first quantitative confirmation that this Q4DW conforms to a classic Rossby wave pattern from atmospheric theory. A simultaneous 4day oscillation is also detected in the ionosphere using the Wuhan University TEC data, which fills a longstanding observational gap. Interactions between the Q4DW and the semidiurnal tide are found to create an effective pathway that transmits the 4day signal upward, producing the observed ionospheric response. These findings highlight the essential role of the Q4DW in mediating atmosphereionosphere coupling. , Key Points Strong quasi4day wave (Q4DW) activity during the 2018/2019 sudden stratospheric warming (SSW) is well reproduced by NEDAS + WACCM Hough mode decomposition shows that the Q4DW results from the enhancement of Rossby (2, 3) normal mode by atmospheric instabilities A distinct quasi4day oscillation is also observed in the ionosphere during this SSW, mainly due to Q4DWSW2 nonlinear interaction"
    },
    {
        "DOI": "10.1029/2025JD044363",
        "Title": "The Predictive Power of Combining Chemical and Dynamical Variables for Explaining the OH Distribution and Spatiotemporal Variability",
        "Year": 2026,
        "Abstract": "Abstract The hydroxyl radical (OH) is chemically coupled to other atmospheric constituents including water vapor, NO x , ozone, CO, and methane that provide the sources and sinks of OH. These species have longer lifetimes than OH itself and consequently undergo atmospheric transport, allowing dynamics to indirectly affect OH. We investigated whether a combination of meteorological variables and idealized tracers can predict the OH distributions for 40S40N simulated by multiple models. We find that they can explain 70% or more of the variance in July spatial anomalies in OH with the zonal mean removed at 400 hPa, and 59% or more for tropospheric column OH (tcolOH). We find two constituents observed from space, water vapor and NO 2 , can together serve as proxies for much of the 40S40N spatial variability in OH at 400 hPa, especially over the ocean. Multiple linear regression (MLR) on water vapor and NO 2 columns versus tcolOH results in r 2 > 0.5 for the interannual variability in January tcolOH over more than half of the 40S40N domain in most models. These results highlight the value of satellite observations of water vapor and NO 2 for constraining simulated OH variability. However, the relative sensitivity of OH to each of these two variables differs between models. Consequently, understanding individual models' relative sensitivities can help maximize the value of these observational constraints. The results of our proofofconcept study are encouraging and justify additional research to fully explore the potential of other satelliteobservable variables for the development of processbased diagnostics and constraining the spatiotemporal variations of tropospheric OH. , Plain Language Summary The hydroxyl radical (OH) plays a key role in tropospheric chemistry. Atmospheric chemistry models are an important tool for understanding the global OH distribution. We use output from multiple models to investigate whether a combination of meteorological variables and atmospheric transport tracers can predict the variability of simulated OH. We focus on the region between 40S and 40N, where the bulk of tropospheric OH is located. We find that these variables have considerable skill in reproducing the variability in OH between 40S and 40N at the 400 hPa level of the troposphere but less skill in the lower troposphere. Water vapor and NO 2 , which are both observed from satellites, can together explain many features of the distribution of OH over the ocean at the 400 hPa level. This finding highlights the value of satellite observations of water vapor and NO 2 for constraining OH variability. However, the relative sensitivity of OH to water vapor versus NO 2 differs between models, so understanding how an individual model's OH relates to these two factors can help maximize the value of observations for constraining OH. This work provides a proof of concept for the development of observationbased diagnostics that could be expanded in the future. , Key Points Dynamical variability can influence OH through its impact on longerlived constituents that are chemically coupled to OH Dynamical variables and idealized tracers have higher predictive skill for the OH distribution at 400 hPa than in the lower troposphere Specific humidity and the NO 2 column together explain much of the 400 hPa OH variability, with their relative importance varying by model"
    },
    {
        "DOI": "10.1016/J.FECS.2026.100423",
        "Title": "Comparison of multiple satellite-derived products for assessing vegetation productivity and evapotranspiration in Nepal: Toward understanding carbon and water coupling in a mountainous region",
        "Year": 2026,
        "Abstract": "Mountain ecosystems are highly sensitive to climate change, as they regulate carbonwater dynamics that underpin critical ecosystem services. Satellite remote sensing serves a powerful tool for large-scale monitoring in mountainous regions where ground-based measurements scarce. However, it remains unclear how satellite-derived gross primary productivity (GPP) and evapotranspiration (ET) vary with elevation the magnitude of discrepancies across different datasets. This case study focuses on Nepal systematically investigate spatiotemporal consistency six GPP products (EC-LUE, GOSIF, MODIS, MuSyQ, PML_v2, VPM) three ET (ETMonitor, PML_v2) during 20012016, validation against eddy covariance flux measurements. Our results indicate no single dataset outperforms others all elevational gradients. Based relatively superior datasets (VPM PML_v2 ET), we reveal strong dependence GPP, ET, water use efficiency (WUE = GPP/ET): The highest multi-year mean values observed lowland (< 200 m), greatest interannual variability occurs midland zones (1,0003,000 m). Across most datasets, exhibit consistent upward trends, accompanied by concurrent decline WUE. Notably, at pixel scale, only 11.2%, 33.3%, 0.5% terrestrial areas show long-term trends WUE, respectively. Such inconsistencies significantly hinder efforts elucidate coupling processes ecosystems. findings sustained increases vegetation may exacerbate hydrological loss Nepal, while also underscoring urgent need targeted improvements products."
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135281",
        "Title": "Characteristics and influencing factors of soil moisture memory across mainland China",
        "Year": 2026,
        "Abstract": "Soil moisture memory (SMM), which is defined as the time required to \"forget\" a perturbation and reflects the strength of landatmosphere coupling, plays a crucial role in understanding hydrological and eco-meteorological processes. However, large-scale estimates of SMM based on in situ observations remain scarce, and recently proposed SMM metrics have not yet been comprehensively evaluated using extensive ground measurements. In this study, a total of 2,218 available daily in situ soil moisture observations were collected across mainland China. An exponential drydown model was constructed based on these data to simulate the soil moisture drydown process and to estimate three key parameters including the e-folding time scale (), the estimated lower bound of soil moisture (^w) and magnitude of the drydown event (). Independent site-specific wilting point observations are employed to evaluate the model performance, showing good agreement with fitted ^w values (adjusted R2 = 0.58), which indicates that the model reliably captures variations in soil moisture status. Then we investigate the spatial characteristics of SMM, and explored the effects of soil depth, texture, and climatic conditions. Results show that all fitted parameters exhibit pronounced spatial heterogeneity across mainland China, especially between North and South China and between coastal and inland areas. As soil depth increases, decreases, ^w increases, and shows a non-monotonic pattern, first increases and then decreases. Among the controlling factors, ^w primarily governed by soil texture, especially clay content, whereas is more sensitive to meteorological factors, particularly evapotranspiration. Furthermore, we compared derived from in situ observations with that from multiple satellite and reanalysis soil moisture products. Most products systematically underestimate and show spatially inconsistent patterns, reflecting limitations in current land surface models in capturing soil drying processes. This study provides the first comprehensive assessment of SMM over mainland China based on a large amount of in situ soil moisture observations, revealing its spatial and depth-dependent characteristics and key controlling factors. The results offer new insights into the mechanisms governing SMM and have implications for improving drought diagnosis and landatmosphere interaction modelling."
    },
    {
        "DOI": "10.3390/ATMOS17010005",
        "Title": "How Do Different Precipitation Products Perform in a Dry-Climate Region?",
        "Year": 2025,
        "Abstract": "Dry climate regions face heightened risks of flooding and infrastructure damage even with minimal rainfall. Climate change is intensifying this vulnerability by increasing the duration, frequency, and intensity of precipitation events in areas that have historically experienced arid conditions. As a result, accurate precipitation estimation in these regions is critical for effective planning, risk mitigation, and infrastructure resilience. This study evaluates the performance of five satellite- and model-based precipitation products by comparing them against in situ rain gauge observations in a dry-climate region: The fifth generation European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) (analyzing maximum and minimum precipitation rates separately), the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), the Western Land Data Assimilation System (WLDAS), and the Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG). The analysis focuses on both average daily rainfall and extreme precipitation events, with particular attention to precipitation magnitude and the accuracy of event detection, using a combination of statistical metricsincluding bias ratio, mean error, and correlation coefficientas well as contingency statistics such as probability of detection, false alarm rate, missed precipitation fraction, and false precipitation fraction. The study area is Palm Desert, a mountainous, arid, and urban region in Southern California, which exemplifies the challenges faced by dry regions under changing climate conditions. Among the products assessed, WLDAS ranked highest in measuring total precipitation and extreme rainfall amounts but performed the worst in detecting the occurrence of both average and extreme rainfall events. In contrast, IMERG and ERA5-MIN demonstrated the strongest ability to detect the timing of precipitation, though they were less accurate in estimating the magnitude of rainfall per event. Overall, this study provides valuable insights into the reliability and limitations of different precipitation estimation products in dry regions, where even small amounts of rainfall can have disproportionately large impacts on infrastructure and public safety."
    },
    {
        "DOI": "10.3390/RS18010048",
        "Title": "Effect of Desert Dust Intrusion on the Detection of Marine Heatwaves",
        "Year": 2025,
        "Abstract": "The effect of desert dust intrusion on the detection of marine heatwaves (MHWs) has not been discussed in previous publications. In this study we investigated this effect in the Eastern Mediterranean (EM) by separate use of microwave (MW) and infrared (IR) satellite radiometry of nighttime sea surface temperature (SST); they are represented by the SST-MW and SST-IR datasets. For the first time, our analysis provides observational evidence that there was no effect of dust intrusion on the detection of MHWs by SST-MW, when aerosol optical depth (AOD) ranged within an extremely wide interval of 0.3 to 5. In contrast to SST-MW, in the presence of strong dust intrusion (AOD of up to 5), SST-IR was incapable of detecting MHWs. We found an inverse correspondence between daily variations in both SST-IR and AOD. The inverse correspondence indicates that SST-IR was profoundly influenced by desert dust, causing erroneous daily variations in SST-IR. This prevented the detection of MHWs. An essential point of our study is that even in the presence of weak dust intrusion (AOD ranged from 0.3 to 0.4) SST-IR was incapable of detecting MHWs due to the occurrence of erroneous short-term sharp drops in SST-IR. This was because of dust appearance at high altitudes. Our findings highlight that the SST-IRs incapability to detect MHWs (in the presence of dust intrusion) led to an underestimation of the presence of MHWs by the SST datasets which integrate MW and IR radiometry, i.e. the Multiscale Ultrahigh Resolution (MUR) Global Foundation SST analysis."
    },
    {
        "DOI": "10.1016/J.JHYDROL.2025.134803",
        "Title": "Prior weight dilution in Bayesian model averaging for groundwater modeling",
        "Year": 2026,
        "Abstract": "It is unrealistic to build independent alternative models to constitute the model space of Bayesian model averaging (BMA) in groundwater/surface water modeling. Using uniform prior weights can lead to overweighting models with similar structures, as well as biased posterior model weights and BMA predictions. This study applied a correlation matrix R to measure the correlations among alternative models. And two weighting schemes based on R, namely the cos-square (CS) and capped eigenvalue (CE), were used to dilute models' prior weights. Additionally, the effective model number (Neff) metric derived from R was proposed to measure the effectiveness of BMA model set. Based on two real-world cases (snowmelt runoff modeling and groundwater modeling), and a synthetical groundwater case, we validated the importance of prior weight dilution and the important value of the R-based methods in improving BMA prediction. The results demonstrated that the prior weight dilution schemes redistribute models' prior weights by penalizing highly correlated models while rewarding those with relatively independent structures. The BMA predictive performance is improved using the weight dilution schemes, with the CS scheme outperforming the CE scheme. In addition, the correlation matrix provides insight into the rationality of the model structures in the BMA model set. The metric of Neff can serve as an effective tool for quantifying the effectiveness of the model set, which provides an important reference for updating the model set and improving BMA predictions with prior weight dilution schemes."
    },
    {
        "DOI": "10.3390/RS18010127",
        "Title": "Assessing the Impact of T-Mart Adjacency Effect Correction on Turbidity Retrieval from Landsat 8/9 and Sentinel-2 Imagery (Case Study: St. Lawrence River, Canada)",
        "Year": 2025,
        "Abstract": "In inland waters, Atmospheric Correction (AC), including Adjacency Effect (AE) correction, is a major challenge for water quality retrieval using optical satellite data. This study evaluated three image pre-processing options for turbidity retrieval in the St. Lawrence River using Sentinel-2 (S2) and Landsat 8/9 (L8/9) imagery with the Light Gradient Boosting Machine (LightGBM) model: (1) No pre-processing, i.e., use of Top-of-Atmosphere (TOA) reflectance, (2) AC pre-processing, obtaining water-leaving reflectance (Rw) from AC for the Operational Land Imager lite (ACOLITE)s Dark Spectrum Fitting (DSF) technique, and (3) AE pre-processing, correcting for the AE using T-Mart before obtaining Rw from DSF. Results demonstrated that AE pre-processing outperformed the other two options. For L8/9, AE pre-processing reduced the Root Mean Square Error (RMSE) and improved the median symmetric accuracy () by 48.8% and 19.0%, respectively, compared with AC pre-processing, and by 48.5% and 50.7%, respectively, compared with No pre-processing. For S2, AE pre-processing performed better than AC pre-processing and also outperformed No pre-processing, reducing RMSE by 28.4% and by 50.8%. However, No pre-processing yielded the lowest absolute symmetric signed percentage bias (||) among all pre-processing options. Analysis indicated that AE pre-processing yielded superior performance within 0300 m from shore than other options, where the AE influence is strongest. Turbidity maps generated using AE pre-processing were smoother and less noisy compared to the other pre-processing options, particularly in cloud-adjacent regions. Overall, our findings suggest that incorporating AE correction through T-Mart improves the performance of the LightGBM model for turbidity retrieval from both L8/9 and S2 imagery in the St. Lawrence River, compared to the alternative pre-processing options."
    },
    {
        "DOI": "10.1109/TGRS.2026.3658572",
        "Title": "Comprehensive Validation of Temperature and Humidity Profile Retrievals From FengYun-3E",
        "Year": 2026,
        "Abstract": "The FengYun (FY) meteorological satellites, equipped with combined microwave and infrared sensors, provide temperature humidity profiles under both clear cloudy conditions, supporting global atmospheric monitoring weather forecasting. This study evaluates the FY-3E Vertical Atmospheric Sounding System (VASS) using collocated radiosonde observations, reanalysis data, satellite measurements including Cross-track Infrared Microwave Sounder Suite (CrIMSS) onboard JPSS-1. Under all-sky VASS exhibit a correlation of 0.99 reach 0.80. biases (RMSEs) are 0.63K (3.07 K) clear-sky, 0.82K (3.56 cloudy-sky, 0.67K (3.26 conditions. corresponding 4.63% (18.36%), 6.21% (21.38%), 4.76% (19.36%). As compared CrIMSS products coincident retrievals show larger errors in humidity, yet consistent reliable diverse These results confirm current utility operational climate services, while highlighting need for continued algorithm development to further reduce retrieval uncertainties."
    },
    {
        "DOI": "10.1016/J.JVOLGEORES.2026.108528",
        "Title": "Revealing mechanism of phreatic eruptions derived by satellite- and field-based water interactions at Tangkuban Parahu Volcano, West Java, Indonesia",
        "Year": 2026,
        "Abstract": "The Tangkuban Parahu Volcano, located in West Java, Indonesia, is an active stratovolcano known for its frequent phreatic eruptions. These eruptions result from complex interactions between meteoric water and subsurface heat sources, typically occurring without magma extrusion. This study aimed to develop a conceptual model of phreatic eruption mechanisms under tropical conditions by integrating satellite-based structural analysis and field-based hydrological measurements. Using Sentinel-1 and Sentinel-2 imagery, we identified fracture networks and surface permeability through lineament density analysis. Field experiments were conducted for measuring water infiltration rates using minidisk, double-ring, and water level data logger (HOBO) infiltrometers at depths of 02 m. Soil and rock samples were analyzed to determine their mineral composition and degree of alteration. Results revealed a dominance of clay minerals, particularly illite, which reduce pore permeability and promote fracture-controlled infiltration. A moderate positive correlation (R2 0.72) was found between fracture density and water infiltration rates, indicating that subsurface water movement is primarily governed by geological structures. The conceptual model suggests a three-phase eruption mechanism: (1) heat source activation via a dike-like magmatic intrusion at a depth of approximately 1 km, thermally connected to a deeper reservoir (at approximately 4 km); (2) delayed infiltration of meteoric water over six months, with significant rainfall peaks in November, March, and May preceding the July 2019 eruption; and (3) pressure buildup and release beneath impermeable cap rocks. These insights provide a valuable framework for forecasting phreatic eruptions and improving hazard-mitigation strategies in tropical volcanic regions."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108748",
        "Title": "Case study of aircraft icing in-cloud measurements and explicit supercooled water prediction in Eastern China",
        "Year": 2026,
        "Abstract": "In the morning of 12 November 2022, severe aircraft icing occurred over Anhui Province, Eastern China during weather modification operations. In-situ airborne measurements revealed the presence of a substantial concentration of supercooled droplets with effective diameter exceeding 45 m, and liquid water content (LWC) above 1.2 g m3 during the icing event. Given the importance of microphysical parameterization (MP) scheme for icing conditions, simulations using different multi-moment MP schemes, i.e., WDM6, NSSL, Milbrandt-Yau (MY) schemes, are conducted at 1-km grid spacing for the case. Comparisons against satellite observations indicate that the general evolution of the frontal system and surface precipitation are well reproduced by the simulations. However, all simulations underpredict upper-level clouds with cloud-top temperature below 240 K over the northeastern part of the front. Besides, WDM6 scheme produces ice cloud-top area (CTA) closest to satellite observations but only produces approximately half of the observed CTA for supercooled cloud tops. The Milbrandt-Yau scheme shows superior performance in simulating the cloud top features during the icing event. Examinations of explicit supercooled cloud water (SCW) prediction skills indicate that WDM6 generates excessive total number concentration (Nt) of small SCW, with Nt reaching up to 1011 m3 and effective diameter (ED) below 20 m. In contrast, the NSSL scheme produces significantly larger SCW particles but substantially lower Nt at approximately 107 m3 and ED of above 200 m. Notably, the particle size distribution of SCW predicted by MY scheme is more realistic compared with in-situ aircraft measurements."
    },
    {
        "DOI": "10.1029/2025JC022918",
        "Title": "In Situ Observation of a Strong Diurnal Warming Event in the Labrador Sea Undetected by Satellites",
        "Year": 2026,
        "Abstract": "Abstract Diurnal warming (DW) at the ocean surface occurs when there is a combination of solar heating in the absence of vertical mixing typically derived from wind stress. DW has been well described, mostly from satellite data, but also with some in situ observations. Evidence of DW has mostly been restricted to the subtropics, and there are very few reports of DW at northerly latitudes. We present here observations of a DW event of 1.5C confined to the upper 2 m in the Labrador Sea at N. These measurements were conducted with the AirSea Interaction Profiler (ASIP), an upwardly rising, ocean microstructure instrument. Cloud cover obscured the ocean surface to passive remotesensing instruments and as a result no evidence of this particular DW event was available from the nine independent satellite products that were analyzed. Therefore, the event would have gone undetected without the deployment of ASIP at precisely this time and location. The ASIP observations were used to derive a heuristic set of criteria for potential occurrences of DW in the Labrador Sea region: (a) shortwave radiation above 600 W m 2 and (b) 10m wind speed below 4 m s 1 . These criteria were subsequently applied to 40 years of the ERA5 reanalysis product indicating that DW events in the Labrador Sea have the potential to occur more frequently than satellites observe. Attaching microstructure temperature sensors on Argo floats would provide a more accurate assessment of the occurrence of DW events globally as well as their effect on surface mixing rates. , Plain Language Summary This study presents observations of a strong increase in the temperature of the upper 2 m of the ocean in the Labrador Sea. This is known as diurnal warming, as it occurs during the day when adequate solar heating is available. Diurnal warming also requires sufficiently low wind speeds to prevent mixing of the deeper colder waters, which eradicates the temperature gradients observed here. Diurnal warming can be important for the exchange of heat and carbon between the ocean and the atmosphere, which is a major contributor to climate regulation. At lower latitudes, diurnal warming has been observed extensively mostly from satellite observations, but there are very few reports closer to the Arctic. Because the measurement region was largely obscured by clouds, satellites were unable to detect the diurnal warming event observed here, which was performed with an autonomous ascending profiler. Combining these observations with a climatological reanalysis data set, we derived conditions for the potential for diurnal warming to occur and conclude that diurnal warming could occur more frequently than satellites can observe in the Labrador Sea region. We also suggest that microstructure temperature sensors be attached to Argo floats, so that these diurnal warming events can be more readily detected. , Key Points A diurnal warming event of 1.5C in the top 2 m of the ocean was detected in the Labrador Sea with the ASIP microstructure instrument Coincident data from nine different satellites, the most prevalent detection method, showed no evidence of the event due to cloud cover Diurnal warming has the potential to occur more frequently than satellites observe based on heuristic criteria from this in situ data set"
    },
    {
        "DOI": "10.1016/J.JHYDROL.2025.134879",
        "Title": "The trend and interannual variability in the global terrestrial evapotranspiration are respectively dominated by humid regions and drylands",
        "Year": 2026,
        "Abstract": "Despite characterized by large interannual variability (IAV), global terrestrial evapotranspiration (ET) has existed a consistent increasing trend since the 1980s. However, the regions and processes governing the present ET trend and IAV still remain unclear. Using an ensemble of process-based hydrological models, remote sensing-based, machine learning, and land surface products, we find that the increasing trend and substantial IAV in global land ET are driven by divergent regions. Result from models ensemble shows that the humid regions, especially in the Northern Hemisphere, contribute the 72.47 5.77 % of the increase in global land ET over 19822020. In this domain, climate warming and vegetation greening (increased leaf area index (LAI)) have caused the increase in ET of 0.43 0.22 and 0.30 0.13 mm yr2, respectively, although the increased LAI is the largest contributions (63.69 25.13 %) to the global ET increases. Especially, climate warming in the humid regions at the high latitudes has prolonged the growing season, and provided sufficient water through freezethaw process for the enhanced plants photosynthesis in spring and even summer. The IAV of the global land ET, however, is dominated by drylands (with contribution fractions being 59.66 16.89 %), and dominant role in this region is mainly due to the fact that the precipitation, which serves as a primary source of moisture supply, has the large interannual oscillation with the El Nino/Southern Oscillation (ENSO) events and is largely allocated to evaporation. With future anthropogenic warming, global land ET is expected to continue rising with the trait of a significant interannual variations, and the dominant roles of humid regions and drylands still remains and are stronger than that in present. This study will merit more attention about regional roles for understanding and projecting dynamics of the global water cycle."
    },
    {
        "DOI": "10.1038/S41597-026-06569-W",
        "Title": "A high-resolution daily CO2 dataset for China (20162020)",
        "Year": 2026,
        "Abstract": "High-resolution column-averaged dry-air CO2 mole fraction (XCO2) data are essential for characterizing carbon sources and sinks, advancing cycle research, supporting climate policy goals such as peaking neutrality. However, current satellite retrievals often spatially fragmented temporally discontinuous due to cloud cover aerosol interference. To address these limitations, this study utilizes an XGBoost model optimized via Bayesian optimization (XGBoost-BO) construct a robust mapping relationship between atmospheric XCO2 concentrations multi-source auxiliary parameters. Crucially, the incorporation of SHAP (SHapley Additive exPlanations) methodology enhances interpretability, ensuring that reconstruction captures physically meaningful spatiotemporal dynamics across China. The reconstructed dataset exhibits high consistency with OCO-2 observations, achieving coefficient determination (R2) 0.98, Root Mean Square Error (RMSE) 0.58 ppm, Absolute Percentage (MAPE) 0.07%. model's reliability is further validated against ground-based TCCON measurements in China, R2 0.92 (RMSE = 1.16 MAPE 0.2%) at Hefei site 0.70 2.00 0.4%) Xianghe site."
    },
    {
        "DOI": "10.1029/2025GB008499",
        "Title": "Surface Water Iron Deposition Histories and the Initiation of Phytoplankton Blooms in the North Pacific Subtropical Gyre",
        "Year": 2026,
        "Abstract": "Abstract Highly productive summer phytoplankton blooms in the central North Pacific Subtropical Gyre (NPSG) are an annual occurrence that leads to the export of considerable amounts of surface particulate carbon to depth. The mechanisms that control the formation of these blooms remain unresolved, but iron (Fe) availability may be an important factor. From July to October 2022, a large, persistent phytoplankton bloom was detected near 23.3N, 154.6W in satellite imagery and in situ measurements. Elevated Fe concentrations and nitrogen (N 2 ) fixation activity measured within the bloom suggest that high Fe may have supported enhanced diazotrophic activity. To evaluate whether aerosol deposition created favorable conditions for bloom formation, we reconstructed the Fe deposition history of the bloom's source waters by integrating surface water back trajectory analyses with aerosol Fe flux simulations. Our results show that waters that hosted the diatomdiazotroph assemblage bloom received up to 20% more soluble Fe through aerosol deposition than its surrounding waters, primarily from a strong wet deposition event that occurred approximately 1 month before the bloom. The observed lag between deposition and bloom suggests a delayed biological response to atmospheric Fe inputs. Although this moderate increase does not represent incontrovertible evidence that the bloom was stimulated by aerosol Fe deposition, our findings establish the potential for episodic delivery of atmospheric Fe to stimulate diazotrophic activity and phytoplankton growth over monthlong timescales in the NPSG. , Plain Language Summary In July 2022, a large phytoplankton bloom formed in the surface ocean northeast of the Hawaiian Islands. Within the bloom, we measured high iron concentrations in waters where high levels of biomass were present. The bloom was enriched in organisms that fix atmospheric nitrogen, an iron intensive process. We observed that a large pulse of atmospheric iron was deposited 4 weeks prior to the formation of the bloom and may have played a role in providing critical nutrients to the waters where nitrogen fixing organisms can rapidly grow. We leveraged models to determine when and how much atmospheric iron was deposited in the surface water where the bloom formed. , Key Points The concentration of iron measured within a phytoplankton bloom was higher than that measured in the absence of a bloom Modeling suggests that a wet deposition event delivered a considerable amount of aerosol Fe to the bloom roughly 1 month before its formation We developed methods that explore the role of aerosol Fe in stimulating marine phytoplankton productivity"
    },
    {
        "DOI": "10.1016/J.RSE.2026.115261",
        "Title": "Characterizing diurnal variability in power plant carbon emissions in Asia: A top-down estimation approach constrained by geostationary NO2 and OCO-3 CO2 observations",
        "Year": 2026,
        "Abstract": "Accurate quantification of carbon dioxide emissions is crucial for addressing climate change. However, traditional top-down CO2 estimates are limited by sparse satellite observations and coarse temporal resolution. Although NO2 data from polar-orbiting satellites can help constrain CO2 emissions, temporal mismatch with OCO-3 measurements introduce additional uncertainty. To address this, we estimate hourly CO2 emissions by combining OCO-3 XCO2 observations with high temporal resolution NO2 data from the GEMS instrument onboard the GEO-KOMPSAT-2B satellite. We developed an algorithm based on a Gaussian plume model and wind rotation techniques to estimate CO2 emissions and NOx/CO2 emission ratios from near-synchronous NO2/CO2 observations. Hourly CO2 emissions were further derived using GEMS-based NOx emissions estimated via the flux divergence method. A total of 59 power plant cases across six Asian countries were identified. For these cases, the estimated CO2 emissions exhibit distinct diurnal, seasonal, and interannual emission variability, primarily driven by heating demand, decarbonization measures, and pandemic-related industrial slowdowns. These top-down estimates, constrained by GEMS NO2 data, show strong consistency with bottom-up inventories (R = 0.89), supporting the validity of our optimization approach. Furthermore, comparisons with daytime mean estimates suggest that CO2 emission estimates constrained by polar-orbiting satellite observations can exhibit biases of approximately 60% relative to GEO-based approaches, underscoring the importance for high-temporal-resolution measurements. This study highlights the value of integrating geostationary NO2 and CO2 observations to capture the diurnal dynamics of power plant emissions and improve the accuracy of top-down CO2 emission monitoring."
    },
    {
        "DOI": "10.1029/2025GH001365",
        "Title": "Characterizing Particulate Matter Impacts of Smoke From 2022 to 2023 Agricultural Burning in South Florida",
        "Year": 2026,
        "Abstract": "Abstract Smoke from agricultural fires is a potentially important source of fine particulate matter (PM 2.5 ) in the US. Sugarcane is burned in Florida to facilitate the harvesting process, with the majority of these fires occurring in the Everglades Agricultural Area (EAA), where there is only one regulatory air quality monitor. During the 20222023 sugarcane burning season (OctoberMay), we used public lowcost PurpleAir sensors, regulatory monitors, and 29 PurpleAir sensors deployed for this study to quantify PM 2.5 from agricultural fires. We found satellite imagery is of limited use for detecting smoke from agricultural fires in Florida due to the cloud cover, overnight smoke, and the fires being small and shortlived. For these reasons, surface measurements are critical for capturing increases in PM 2.5 from smoke, and we used multiple smokeidentification criteria. During the study period, median 24hour PM 2.5 concentrations increased by 2.36.9 g m 3 on smokeimpacted days compared to unimpacted days, with smoke observed on 4%28% of the campaign days (ranges from the different smokeidentification criteria). Further, shortterm PM 2.5 increases were observed over 40 g m 3 during smoke events. We contrast the region near the EAA with large populations of lowincome and minoritized groups to the more affluent coastal region. The inland region experienced more smokeimpacted monitor days than the Florida east coast region, and there was a higher studyaverage smoke PM 2.5 concentration in the inland area. These findings highlight the need to increase air quality monitoring near the EAA. , Plain Language Summary Agricultural fires are used in Florida to burn the leaves and tops of sugarcane plants for harvesting, mostly in the Everglades Agricultural Area (EAA). These fires produce smoke containing fine particulate matter (PM 2.5 ), an air pollutant. Monitoring smoke from sugarcane fires is challenging in the EAA, as there is only one regulatory air quality monitor in the region. To study PM 2.5 from agriculturalfire smoke, we set up 29 lowcost PM 2.5 sensors (PurpleAir) in Florida from October 2022 to May 2023. Satellite observations were not helpful for detecting smoke in Florida because frequent cloud cover blocked satellites from observing the ground; satellites cannot capture nighttime smoke, and sugarcane fires are too shortlived and small to detect. Median PM 2.5 concentrations were higher on days with smoke, and smoke was present on 4%28% of the days. Areas closer to the EAA had smoke more often and a higher studyaverage smoke PM 2.5 concentration compared to the coast. Higher percentages of lowincome and minoritized groups live close to the sugarcane region, making it important to monitor air quality in these communities. This study shows the importance of groundbased monitors in places where satellites are limited and highlights the need for further research on agricultural fire smoke. , Key Points Satellite observations of smoke and fires are of limited utility in southern Florida for detecting small, shortlived agricultural burns During the 20222023 burning season, particulate matter (PM 2.5 ) concentrations increased on days impacted by smoke in southern Florida The region closest to the agricultural area had more days impacted by smoke than the east coast of Florida, with higher total smoke exposure"
    },
    {
        "DOI": "10.1029/2025GL119842",
        "Title": "Resolving Convection Doubles Sahel's Contribution to Global Dust Emission During the Monsoon Season",
        "Year": 2026,
        "Abstract": "Abstract Current climate models struggle to capture the mean state and variability of dust emissions. This is partially due to their inability to resolve convection, a consequence of their relatively coarse spatial resolution. Via analysis of output from an offline dust emission scheme forced with output from a global stormresolving model, we show that resolving convection doubles the contribution of the Sahel, an important dust source region sensitive to environmental change, to global dust emissions from 6.1% to 12.3% during its late monsoon season. Resolving convection induces an increase in friction velocity and a decrease in soil moisture, and thus enhances Sahelian dust emissions. These changes are due to improved representation of mesoscale convective systems associated with African Easterly Waves. These results demonstrate that highresolution modeling with resolved convection can improve understanding of the global dust cycle and how dust emissions are affected by a changing climate. , Plain Language Summary Typical climate models struggle to represent dust emission processes in monsoonal regions, mainly because their relatively coarse horizonal resolution precludes representation of the convective processes that generate high dustgenerating winds there. Our study uses a novel set of highresolution simulations that explicitly represent convective processes to demonstrate that, when such meteorology is represented, there is a 2fold increase in the contribution of dust from the Sahel region of Africa to global dust emission during the monsoon season. Our findings suggest that dust from monsoonal regions is underestimated in current climate models, and that global highresolution modeling is needed to more accurately simulate the global dust cycle. , Key Points Resolving convection doubles Sahel's fraction of global dust emission in the monsoon season The increase in Sahelian dust emission is mainly due to an increase in friction velocity and a decrease in soil moisture These changes are closely linked to high surface winds from Mesoscale Convection Systems associated with African Easterly Waves"
    },
    {
        "DOI": "10.1029/2025GL119667",
        "Title": "Warming of the MidTroposphere Driven by Evapotranspiration During Compound Heatwave and Drought Events",
        "Year": 2026,
        "Abstract": "Abstract During compound heatwave and drought events (CHDE), a significant amount of soil moisture is released into the atmosphere through evapotranspiration. In this study, we used multisource atmospheric water vapor isotope data sets, including satellite and groundbased observations, to investigate the role of evapotranspired water vapor (EWV) during CHDE. We identified a vertical dipole pattern in water vapor isotopes, with depletion in the lower troposphere and enrichment in the middle troposphere. This pattern is primarily driven by vertically transported EWV, which can reach altitudes up to 500 hPa. More importantly, the upward transport of EWV plays a key role in the warming of the middle troposphere, creating a saddleshaped temperature structure of the troposphere. This suggests that enhancing the forecasting capability of numerical weather prediction models for extreme CHDE should involve an adequate consideration of the warming effect of vertically transported EWV within the model framework. , Plain Language Summary Compound heatwave and drought events (CHDE) pose serious challenges to global socioeconomic development and ecological stability. During CHDE, substantial soil moisture is transferred to the atmosphere through evapotranspiration. However, the vertical transport height of evapotranspired water vapor (EWV) and its warming effect remain poorly understood. Water vapor isotopes can trace soil moisture signals in the atmosphere, offering an effective approach to address these issues. Here, we examined these processes by analyzing CHDE that occurred globally in summer 2022. Using integrated multisource atmospheric water vapor isotope data, we identified that EWV can reach altitudes up to 500 hPa. Crucially, EWV contributes to warming in the middle troposphere. These results provide important insights into the mechanisms underlying CHDE. , Key Points Water vapor isotopes exhibit a vertical dipole pattern, with depletion in the lower troposphere and enrichment in the middle troposphere Evapotranspired water vapor can reach altitudes up to 500 hPa during compound heatwave and drought events The upward evapotranspired water vapor contributes to warming in the middle troposphere, creating a saddleshaped temperature structure"
    },
    {
        "DOI": "10.1186/S40537-025-01361-W",
        "Title": "Enhancing satellite-based rainfall estimation in arid regions through AI-driven data fusion",
        "Year": 2026,
        "Abstract": "Accurate precipitation estimation is crucial for hydrological modeling and water resource management, especially in arid data-scarce regions like the UAE. This study presents an AI-driven data fusion framework to enhance satellite-based rainfall estimates by integrating three widely used satellite products, including CMORPH, IMERG, GSMaP, with ground-based observations. Using from 38 gauge stations across UAE, four machine learning (ML) models, Neural Network, Single Layer Perceptron, Support Vector Machine, Logistic Regression, were employed merge datasets. A stacked ensemble approach was further implemented consolidate strengths of individual models produce a refined product. Model evaluation involved statistical measures (e.g., RMSE, MAE, Correlation Coefficient), categorical indices (POD, FAR, CSI), extreme climate (Rx1day R20mm). The model demonstrated superior performance, improving CC 0.89, reducing RMSE MAE 33% 40%, respectively. also captured events more reliably, improvements 25% Rx1day 20% R20mm. Feature importance analysis identified surface shortwave irradiance minimum temperature as key predictors variability. research highlights potential AI large-scale climatic datasets refine estimates. proposed scalable transferable other regions, offering valuable applications flood forecasting, drought monitoring, resilience planning."
    },
    {
        "DOI": "10.1007/S00190-026-02032-1",
        "Title": "DTRF2020: The ITRS 2020 realization of DGFI-TUM",
        "Year": 2026,
        "Abstract": "Abstract DTRF2020 is the latest realization of the International Terrestrial Reference System (ITRS) by DGFI-TUM and is based on the same input data as ITRF2020. It is generated using the DGFI-TUM two-step combination approach, combining cumulative normal equations from the individual techniques GNSS, SLR, VLBI and DORIS. DTRF2020 introduces three key innovations: (1) it is the first secular ITRS realization with scale determined jointly from VLBI and GNSS; (2) it applies non-tidal loading corrections from atmospheric, oceanic, and hydrological models; and (3) it models post-seismic deformation using logarithmic and exponential functions. In addition to SINEX and EOP files, DTRF2020 provides all information required to compute instantaneous station positions: non-tidal loading reductions, post-seismic deformation models, residual and translations time series. Non-tidal loading corrections reduce GNSS height RMS for 99% of stations and significantly decrease annual signals in translation and scale. DTRF2020 agrees well with DTRF2014. Compared to ITRF2020, transformation differences reach up to 3.1 mm in position and 0.13 mm/yr in velocity for GNSS, VLBI, and SLR, and below 4.6 mm and 0.27 mm/yr for DORIS. Height velocities are consistent with GIA and CMR-based models, with regional differences within 3 mm/yr."
    },
    {
        "DOI": "10.1029/2025GL120396",
        "Title": "Enhanced Adiabatic Heating Drives Faster Warming of Early Summer Hot Extremes in North China",
        "Year": 2026,
        "Abstract": "Abstract North China has recently seen frequent hot days above 35C or even 40C before or at the very beginning of June. This raises a concern about the changing seasonality of exceptional hot extremes in the region which might leave population there underprepared. Based on station observations, we found that since 1990 early summer hot extremes have warmed 23 times faster than those in peak and late summer stages. A Lagrangian decomposition points to the main driver for the intraseasonally disproportionate warming rates as the enhanced adiabatic heating, contributed by more frequent arrival and intensified descent of upperlevel air particles from west and south. We further illustrated that the pastdecade prevalence of a ridge pattern well atop North China was essential to the enhanced adiabatic heating. Our results highlight the critical yet overlooked role of changing atmospheric dynamics in altering regional extremes. , Plain Language Summary Compared to the welldocumented increases in frequency and intensity, changes in the occurrence timing of hot extremes have received far less attention but could also be impactrelevant. North China seems to be a hotspot for the seasonality change in hot extremes, as punctuated by frequent occurrences of 40C+ air temperatures as early as in late May in recent years. Using observations from meteorological stations, we prove that early summer hot extremes were indeed warming faster in North China, two to three times the rate for peak or latesummer events. Understanding the faster warming from an air particle level, we found during the past three decades, more highaltitude air particles traveled from west and south during early summer and descended more strongly along their pathways before reaching North China. During the process, the air particles got compressed and heated up (the process called adiabatic heating), elevating nearsurface air temperature thereby. Explaining from a largescale circulation perspective, the prevalence of an early summer anticyclonic pattern over North China in the past decade set the stage for the substantial strengthening of adiabatic processes. Thus, we provided a new explanation for the faster warming of early hot extremes in North China. , Key Points Early summer hot extremes in North China have warmed two to three times faster than those in peak and late summer Enhanced adiabatic heating is the main driver for the faster warming of early summer hot extremes in North China The prevalence of a ridge pattern and the shift of air trajectories to stronger descending routes jointly amplified adiabatic heating"
    },
    {
        "DOI": "10.1016/J.AGRFORMET.2026.111060",
        "Title": "Warm and wet spring compensated for the reduction in carbon sinks due to an extreme summer heatwave-drought event in 2022 in southern China",
        "Year": 2026,
        "Abstract": " During the July-September (JAS) of 2022, a record-breaking heatwave-drought (DH2022) hit southern China, especially in the middle and lower reaches of the Yangtze River basin (MLYR). It caused an unprecedented decline in vegetation photosynthesis, however, its impact on the regional carbon budget remains unclear. Here, we assessed the response of regional terrestrial carbon fluxes to DH2022 using the Global Carbon Assimilation System (GCAS v2) by assimilating OCO-2 XCO2 retrievals. Our results indicate that, relative to 2015-2021, the MLYR region experienced a 45.8 TgC reduction in land sink during JAS, consistent with the TRENDYv13 simulations. Combining our inverse results with satellite proxies for GPP, we find that an unusually wet spring in 2022 boosted vegetation growth in the MLYR, increasing gross primary productivity (GPP) by 46.1 TgC and strengthening the land sink by 24.0 TgC, thereby substantially offsetting the carbon sink reductions observed during JAS. Outside the MLYR region in southern China, annual land sink increased by 49.9 TgC in remaining areas (RAS), also greatly mitigating the impact of the DH2022 on the regional carbon balance. Overall, the annual land sink in MLYR decreased by only 7.1 TgC, whereas in southern China, it increased by 42.8 TgC. During JAS, the decreased land sink in MLYR was primarily driven by a decline in GPP in forests and grass/shrub, coupled with an increase in total ecosystem respiration in croplands. Our study provides a comprehensive assessment of land carbon dynamics in southern China under the influence of DH2022, enhancing our understanding of the impacts of climate extremes on the regional carbon cycle. "
    },
    {
        "DOI": "10.1029/2025WR041565",
        "Title": "An Effective Monitoring of Evolving Groundwater Drought via Multivariate Data Assimilation and Machine Learning",
        "Year": 2026,
        "Abstract": "Abstract Groundwater drought represents one of the most pervasive and difficulttomonitor forms of water scarcity, threatening the reliability of freshwater supply for over 2 billion people worldwide, agricultural productivity, and ecosystem health. Despite its critical importance, monitoring groundwater drought with high spatial and temporal resolution remains challenging due to limited in situ observations, coarseresolution satellite data, and uncertainties in models. In this study, we introduce an observationinformed approach for producing daily groundwater drought maps at 1/8 resolution across the contiguous United States (CONUS). Leveraging highperformance computing, we jointly assimilate Soil Moisture Active Passive soil moisture and GRACEFO terrestrial water storage data into the NoahMP land surface model to enhance the representation of groundwatersurface water interactions while accounting for uncertainties, enabling a more accurate representation of groundwater drought dynamics. Considering the spatial and temporal complexities of drought patterns, we employ the Growing Neural Gas, a machine learningbased pattern recognition algorithm, to identify emergent, evolving, and regionspecific behaviors of groundwater drought. The results reveal the onset of distinct and persistent dry clusters in recent years across the contiguous United States (CONUS), identifying the severe groundwater drought conditions that notably impacted large regions of both the Western and Northeastern CONUS. Our findings highlight the need to reassess groundwater resilience strategies, especially as droughts intensify and persist over large domains. , Plain Language Summary Groundwater drought is a major type of water shortage that is hard to detect but affects over 2 billion people, as well as farming and natural ecosystems. It's difficult to track because there is not enough ground measurements, satellite data is not detailed enough, and computer models have limitations. In this study, we developed a novel approach to create daily maps of groundwater drought at a high resolution across the continental U.S. By combining satellite data from SMAP (soil moisture) and GRACEFO (total water storage) with a detailed land model (NoahMP), we improved how groundwater conditions are represented and accounted for uncertainty. To better understand how droughts behave in different regions and over time, we used a machine learning tool called Growing Neural Gas. This allowed us to identify new and ongoing dry areas, especially in the Western and Northeastern U.S. in recent years. Our findings show that groundwater droughts are becoming more severe and longlasting, and they point to an urgent need to rethink how we manage and protect groundwater resources. , Key Points Observations from SMAP soil moisture and GRACE terrestrial water storage are jointly assimilated into the NoahMP land surface model Machine learning is used to detect evolving groundwater drought hotspots over contiguous United States Results reveal the emergence of distinct and persistent dry clusters in recent years"
    },
    {
        "DOI": "10.1029/2025JD044985",
        "Title": "Water Isotope Model Intercomparison Project (WisoMIP): PresentDay Climate",
        "Year": 2026,
        "Abstract": "Abstract We present the first results of the Water Isotope Model Intercomparison Project (WisoMIP), with Phase 1 focused on modern simulations (19792023) from a suite of isotopeenabled atmospheric general circulation models nudged to ERA5 reanalyses. Water sources, mixing, and rainout history influence the isotopic composition of vapor and precipitation, making these simulations powerful tools for tracing the global water cycle. By prescribing identical winds, sea surface temperatures, and sea ice conditions, we isolate differences in water isotope behavior across models, controlling for variability in atmospheric dynamics and mean climate. Our analyses show that the ensemble mean best matches observations, as individual model errors cancel out to yield a more accurate representation of Earth's isotope distributions. We also evaluate trends and responses to major climate modes during the recent warming period, highlighting regional and temporal sensitivities in the isotope signals. These diagnostics extend beyond traditional model evaluation metrics (e.g., temperature, precipitation) to reveal uncertainties in physical processes and guide improvements in model parameterizations. The resulting modern nudged ensemble data set serves as a benchmark for isotopeenabled model development, satellite product comparison, and understanding of water cycle changes in a warming climate. Given its standardized design and broad participation, WisoMIP provides a valuable isotope reanalysis product for applications ranging from paleoclimate reconstruction to model tuning. Our work demonstrates the importance of coordinated isotope model evaluation in advancing the use of water isotopes as a diagnostic tool in climate science. , Plain Language Summary Water molecules can have slightly different weights depending on the types of hydrogen and oxygen atoms they contain. These are called water isotopologues, or more simply, water isotopes. This study focuses on stable water isotopes, which serve as natural fingerprints that help track how water moves through the atmosphere, oceans, and land. They are especially useful for studying cloud processes, rainfall, temperature, and humidity, and can even reveal past climate conditions using ice cores or cave deposits. Many climate models can now simulate water isotopes, but they often produce different results, making it difficult to assess accuracy. To address this, the international Water Isotope Model Intercomparison Project (WisoMIP) tested several leading climate models by using the same conditions for winds, sea surface temperatures, and sea ice. This study compares WisoMIP model simulations over the period 19792023. By analyzing the models side by side and evaluating them against observations, the project identifies where the models agree, where they diverge, and why. These insights improve our understanding of water movement in the climate system and help refine climate models. The WisoMIP data set offers a valuable global resource for studying Earth's water cycle now and under future climate change. , Key Points Eight isotopeenabled climate models were nudged with the same winds and sea surface temperatures to compare water isotope simulations The ensemble mean best captures oxygen and hydrogen isotope patterns observed in global precipitation, vapor, snow, and satellite data Isotope changes from warming and climate modes reflect shifts in moisture transport, convergence, and largescale atmospheric circulation"
    },
    {
        "DOI": "10.1029/2025JC023192",
        "Title": "Biogeochemistry of Dissolved Manganese in the Western North Pacific Subtropical Gyre: Sources, Fluxes, and Cycling Drivers",
        "Year": 2026,
        "Abstract": "Abstract In the oligotrophic North Pacific Subtropical Gyre (NPSG), manganese (Mn) cycling, despite its ecological importance and role as a tracer, has received less attention than macronutrient dynamics. Here, we present highresolution dissolved Mn (dMn) data across the western NPSG during the Chinese GEOTRACES GP09 cruise. Surface dMn showed regional characteristics, with an average concentration of 1.41 0.50 nmol kg 1 ( n = 142). Atmospheric deposition could explain approximately 35% of mixedlayer dMn, with highly variable wet deposition fluxes. The discrepancy between the observed dMn and atmospheric inputs suggests that additional sources, such as lateral transport or island inputs, are significant. In the euphotic zone, a dMn flux budget revealed that horizontal transport (189 160 mol m 2 year 1 ) exceeded atmospheric deposition by more than an order of magnitude, while oxidative scavenging (140 90 mol m 2 year 1 ) dominated removal processes. This balance yields a dMn residence time of 1.1 0.9 years in the euphotic zone. Intermediate water masses ( 0 = 26.627.4 kg m 3 ) displayed a robust inverse dMnsalinity relationship ( r = 0.89, p < 0.01), with dMn concentrations ranging from 0.25 to 0.36 nmol kg 1 , reflecting isopycnal mixing. Deep water (>1,500 m) dMn distributions showed basinscale depletion (<0.2 nmol kg 1 ) interrupted by two distinct enrichment processes: (a) Hydrothermal inputs from the Mariana Trough vent field generated extreme dMn enrichment (up to 3.05 nmol kg 1 ), with the signal transported conservatively over hundreds of kilometers. (b) Abyssal boundary interactions at the Luzon Strait and YapMariana Junction generated localized peaks (0.921.55 nmol kg 1 ) through resuspension by strong deep currents. , Plain Language Summary Manganese is a vital nutrient for marine life, but its cycling and transport in the open oceanparticularly in nutrientdepleted regions such as the North Pacificremain poorly understood. In this study, we measured dissolved manganese (dMn) across the western North Pacific to map its distribution and identify key sources and sinks. Our findings revealed that surface dMn levels varied regionally but were minimally correlated with salinity or dust deposition, suggesting that nearby islands may be significant sources of dMn. Middepth Mn variations reflected mixing between different water masses from the subarctic and southern Pacific. In deep waters, pronounced Mn hotspots occurred near submarine volcanoes and narrow straits, likely driven by hydrothermal vents and sediment resuspension. These inputs can disperse over long distances before gradual depletion. By demonstrating how ocean circulation and localized sources (islands and vents) govern Mn distributions, our study provides a clearer understanding of the Mn cycle in oligotrophic oceans, with implications for areas where Mn may limit marine productivity. , Key Points Lateral transport dominates the supply of dissolved Mn to upper waters over atmospheric inputs in the oligotrophic North Pacific Subtropical Gyre Basinscale mixing between Pacific and Antarctic waters is revealed by an inverse Mnsalinity relationship in intermediate waters Hydrothermal and sedimentary sources create distinct deepwater Mn enrichments"
    },
    {
        "DOI": "10.3390/RS18040598",
        "Title": "Rainforest Monitoring Using Deep Learning and Short Time Series of Sentinel-1 IW Data",
        "Year": 2026,
        "Abstract": "The latest advances in remote sensing play a central role in providing Earth observation (EO) data for numerous applications in the scope of reaching environmentally sustainable goals. However, over tropical rainforests, optical imaging is often hindered by extensive cloud coverage, which means that analysis-ready images are mostly restricted to the dry season. In this study, we propose combining radar features extracted from short time series of Sentinel-1 Interferometric Wide Swath (IW) data with a deep learning-based classification scheme to continuously monitor the state of forests. The proposed methodology is based on the joint use of SAR backscatter and interferometric coherences at different temporal baselines to perform pixel-wise classification of land cover classes of interest. However, we show that for a sequence of Sentinel-1 time series, different land cover classes exhibit particular seasonal-dependent variations. Another challenge in performing short-term predictions stems from the fact that ground truths are usually available only on a yearly basis. To address these challenges, we propose a seasonal sampling of the training data, masked by potential deforestation, along with a classification based on a modified U-Net model. The classification results show that overall accuracies above 90% can be achieved throughout the whole year with the proposed method, emerging as a potential tool for mapping rainforests with unprecedented temporal resolution."
    },
    {
        "DOI": "10.1016/J.JHER.2026.100699",
        "Title": "Enhancing extreme temperature projections using a hybrid MGWR  Copula approach",
        "Year": 2026,
        "Abstract": "Extreme temperature events in Asia are intensifying, necessitating advanced predictive models that account for spatial heterogeneity and complex dependencies among climate variables. This study integrates Multiscale Geographically Weighted Regression (MGWR) and Copula Regression to enhance the accuracy of future extreme temperature projections. By incorporating General Circulation Models (GCMs), this study assesses the evolution of maximum temperatures under changing dependency structures among key climate variables, particularly geopotential height, precipitation, humidity, and wind speed. Our results demonstrate that the hybrid MGWR-Copula model significantly outperforms conventional machine learning approaches, such as Random Forest and Support Vector Machines, in capturing non-linear dependencies and spatial variations. Compared to global regression models, our approach provides higher predictive accuracy, particularly in regions with complex terrain like South Korea and Japan. Furthermore, projections under different Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) indicate notable increases in extreme temperatures, with high-emission scenarios leading to greater variability and forecast uncertainty. This study presents a robust framework for climate modeling, improving our ability to predict extreme temperatures and informing climate adaptation strategies. By integrating spatial regression, dependence modeling, and machine learning, this offers critical insights for climate risk assessment and policy development in vulnerable regions."
    },
    {
        "DOI": "10.1029/2025JD045878",
        "Title": "ThreeDecade Dust Climatology and Trend (19882022) From Ground Monitoring Over the Western United States",
        "Year": 2026,
        "Abstract": "Abstract Airborne dust exerts myriad effects on climate, weather, human health and safety. In this study, 35year records (19882022) of ground aerosol observations were analyzed to identify windblown dust events from 18 Interagency Monitoring of Protected Visual Environments (IMPROVE) stations over the western United States (US). Over 45% of dust events were recorded at two sites in the Chihuahuan Desert. The intensity of dust events, indicated by regional average concentration of PM10 (particulate matter with diameter 10 m) during these events, decreased by 0.24 g/m3 per year compared to 0.10/m3 per year on nondust days. However, the number and frequency of both severe dust events (24hr PM10 > 40 g/m3) and moderate dust events (30 g/m3 < PM10 40 g/m3) have increased with moderate events increasing faster than severe events. The variability of dust event frequency was strongly associated with that of Pacific Decadal Oscillation (PDO) and El Nino Southern Oscillation (ENSO). The increasing wind speed has a stronger effect on the dust activity in the Northwest, whereas in the Southwest, the decreasing soil moisture has a larger impact on the increase in dust event frequency. This work provides the latest dust climatology, along with the underlying climate drivers and synoptic indicators that control the longterm variations of dust events over the western US."
    },
    {
        "DOI": "10.1029/2025JD044997",
        "Title": "A SynopticScale Anticyclone Bridging Typhoon InFa (2021) and the 217 Predecessor Rain Event in Henan",
        "Year": 2026,
        "Abstract": "Abstract An exceptionally heavy rainstorm (the 217 Predecessor Rain Event, 217PRE) affected Henan Province, China, from 17 to 22 July 2021, coinciding with Typhoon InFa (2021) over the western North Pacific. This study investigates how InFa remotely influenced the 217PRE by decomposing atmospheric fields using the multiscale window transform into basicscale (>64 days), intraseasonalscale (1664 days), and synopticscale (<16 days) motions. A prominent midtolowertropospheric anticyclone, located approximately 880 km west of InFa, acted as intermediary circulation linking the typhoon and the rainfall event. The anticyclone channeled moisture into Henan, producing pronounced meridional moisture convergence and supplying 64% of the total moisture during the event. Meanwhile, intraseasonalscale easterlies between InFa and the western Pacific subtropical high facilitated westward waveenergy propagation, which intensified an upperlevel divergence zone over Henan Province. This upperlevel divergence, together with local mesoscale cyclone activity, played a critical role in sustaining the extreme rainfall. These results indicate that Typhoon InFa exterted a substantial remote influence on the 217PRE through a synopticscale, coldcore anticyclonic anomaly that mediated waveenergy propagation and organized moisture transport. The findings highlight a dynamical pathway by which tropical cyclones can affect distant predecessor rain events beyond classical moistureconveyor and subtropicalhigh paradigms. , Plain Language Summary In July 2021, an exceptionally heavy rainfall event struck Henan Province, China, causing severe flooding and extensive damage. This event, termed the 217PRE, coincided with Typhoon InFa, which was located approximately 1,000 km away over the western North Pacific Ocean. This study examined how Typhoon InFa remotely contributed to the heavy rainfall over Henan Province, despite being approximately 1,000 km away. Using advanced analytical methods, we discovered that a largescale highpressure system located west of the typhoon acted as a bridge, funneling moisture toward Henan and causing intense rainfall. This midtolowertropospheric anticyclone provided about 64% of the moisture feeding the rainfall event. Additionally, upperlevel atmospheric waves generated by Typhoon InFa traveled westward along a jet stream, enhancing the atmospheric conditions that produced heavy rainfall. Our findings show that distant tropical storms can significantly affect weather far from their centers by forming remote atmospheric connections. , Key Points A synopticscale anticyclone west of Typhoon InFa was the crucial intermediary system bridging system linking the typhoon with the 217PRE The persistence of the anticyclone relied on wave energy propagation and upperlevel subsidence of cold air associated with InFa The anticyclone generated significant southtonorth moisture convergence, accounting for 64% of the total moisture supply"
    },
    {
        "DOI": "10.1029/2024WR038564",
        "Title": "Evaluation of Atmospheric Models Over Mountainous Regions Using a Parsimonious Network Routing Model and Streamflow Observations: A Case Study of the Yarlung Zangbo River on the Tibetan Plateau",
        "Year": 2026,
        "Abstract": "Abstract Evaluating kilometerscale atmospheric models in datasparse mountains is challenging because in situ meteorological observations are scarce and remotesensing products are uncertain. Using hydrological models to link atmospheric modelsimulated precipitation to streamflow is equally problematic, because those models carry substantial structural and parameter uncertainty in mountain terrain. We therefore propose a simple, lowuncertainty alternative: route atmospheric model runoff through a network routing model calibrated against observed streamflow. We apply this approach to assess thirteen 3km Weather Research and Forecasting (WRF) experiments over the Yarlung Zangbo River basin on the Tibetan Plateau, with systematically varied parameterizations for radiation, microphysics, planetary boundary layer, and orographic drag. In mountainous basins, routing can be simplified using the Muskingum model. This parsimonious approach proves effective and robust: hourly streamflow simulations achieve a median Pearson correlation of 0.75 across all six examined gauging stations, and calibrated wave celerity shows little sensitivity to the driving WRF experiment. Statistical analysis confirms that routing model uncertainty is small enough to distinguish performance differences among the WRF configurations. Compared with precipitationbased evaluation, the routingbased approach provides complementary and more hydrologically relevant measures of atmospheric model performance, especially when basinmean precipitation is already well captured. The method offers a discriminative tool that leverages the superior spatial representativeness of streamflow observations to evaluate atmospheric models in datasparse mountainous regions. , Key Points Flow network routingbased evaluation leverages the superior spatial representativeness of streamflow observations Muskingum method is effective and robust in mountainous basins; celerity calibration is insensitive to the runoff source Routing model uncertainty in mountainous regions is small enough to discriminate among atmospheric model configurations"
    },
    {
        "DOI": "10.1029/2025MS005299",
        "Title": "Incorporating GasPhase Chemistry Into the Unified Forecast System (UFS) for Global Air Quality Applications",
        "Year": 2026,
        "Abstract": "Abstract The Unified Forecast System (UFS) is a communitybased Earth modeling system designed to support operational forecasts at the National Oceanic and Atmospheric Administration (NOAA), while also facilitating the integration of research advances from the broader scientific community. The Configurable ATmospheric Chemistry (CATChem) library and modeling component is being developed to include comprehensive chemical and aerosol processes for representing atmospheric composition through a flexible, easytomodify, and welldocumented infrastructure. Here CATChem version 1.0 (v1.0) is linked to the UFS High Resolution 3 configuration to create the Unified Forecast System with Chemistry (UFSChem) v1.0. The configurability of UFSChem enables its use for both research and operational applications, reducing time and effort for transitions to operations and enhancing collaboration with the research community. As a first step toward this goal, the gasphase chemistry from the Atmosphere Model version 4.1 (AM4.1), developed at NOAA Geophysical Fluid Dynamics Laboratory (GFDL), is incorporated into CATChem and linked to the UFS as the first UFSChem configuration for global air quality applications. The simulated atmospheric compositions are generally consistent with those in GFDLAM4.1 and agree well with surface observations, aircraft measurements, and satellite retrievals (with biases mostly within 30%), demonstrating atmospheric chemistry is reasonably well represented in the model. This work documents model uncertainties and biases in UFSChem v1.0 to help prioritize further improvements in emissions and processlevel representations. The new global configuration is shown to be robust in representing atmospheric composition and chemical processes and serves as a foundation for future development. , Plain Language Summary The Unified Forecasting System (UFS) is a suite of numerical models developed by the science community to support weather, smoke, and air quality prediction at the National Oceanic and Atmospheric Administration (NOAA). To better represent atmospheric composition and chemical processes in the model, we incorporate full tropospheric and stratospheric gasphase chemistry and aerosol processes into the UFS. The model is evaluated against a variety of observations and is demonstrated to be applicable for global air quality studies. , Key Points Comprehensive gasphase chemistry is incorporated into the Unified Forecast System to simulate atmospheric composition at global scales The model with the gasphase chemistry is able to reproduce observed chemistry parameters and atmospheric composition The new model configuration is demonstrated to be applicable for global air quality research and forecasting applications"
    },
    {
        "DOI": "10.1029/2025GL117732",
        "Title": "Hemispheric Synoptic Patterns Control Rainfall and LongRange Aerosol Transport in the Amazon",
        "Year": 2026,
        "Abstract": "Abstract The transatlantic transport of dust and smoke aerosols from Africa to South America is a largescale, yearround process that affects atmospheric and nutrient cycling in the Amazon rainforest. We analyze daily variations in black carbon at the Amazon Tall Tower Observatory (ATTO) to investigate how Atlantic synopticscale meteorology influences its longrange transport. Black carbon fluctuations during the Amazon wet season were not fully explained by air mass trajectory length or direction. Instead, regionalscale rainfall emerged as the key driver of shifts between clean and polluted days at ATTO. Rainfall maxima along trajectories preceded clean days, indicating effective wet scavenging. Composite analysis linked these rain events to synoptic systems like U.S. cold air outbreaks and South Atlantic highpressure anomalies, which enhance moisture convergence and rainfall, promoting aerosol removal. Climatedriven shifts in tropical Atlantic circulation could alter aerosol and nutrient transport to the Amazon, with unknown impacts on rainforest productivity and resilience. , Plain Language Summary Largescale synoptic circulation patterns influence the transport of black carbon from Africa to the Amazon by modulating tropical rainfall. Climate change could alter these atmospheric dynamics, such as atmospheric rivers, posing a risk to the health of the Amazon ecosystem. , Key Points Daily variations in Amazon black carbon reflected periods of strong African influence alternating with exceptionally clean conditions Synoptic circulation patterns influence tropical rainfall, thus controlling the transport of dust and black carbon from Africa to the Amazon Clean air in the Amazon coincides with higher South Atlantic pressure and U.S. cold outbreaks, strengthening pressure over the eastern U.S."
    },
    {
        "DOI": "10.3390/RS18050814",
        "Title": "Surface Soil Moisture Drydown over the Tibetan Plateau from SMAP: Consistency with In Situ Observations, Spatial Patterns and Controls",
        "Year": 2026,
        "Abstract": "Soil moisture (SM) mediates landatmosphere water and energy exchanges and is therefore central to evapotranspiration, drought evolution, and hydroclimate extremes. The SM drydown timescale (), typically derived from exponential decay fits following rainfall or snowmelt rewetting, provides a compact measure of near-surface memory and drying rate. Despite the availability of microwave satellite SM products, their reliability for drydown characterization over the Tibetan Plateau remains uncertain, and systematic evaluations of drydown events and against in situ networks are still limited. Here, we integrate five Tibetan Plateau (TP) soil moisture sensor networks with SMAP to (i) assess consistency in drydown event detection and estimation across observation systems and (ii) map TP-wide patterns and identify dominant controls using SMAP (20162025). SMAP-derived is generally smaller than in situ , indicating a faster drying signal in the satellite product; this may be attributed to differences in effective sensing depth and spatial representativeness between satellite footprints and point measurements. TP SMAP exhibits a pronounced southeast-to-northwest decreasing gradient, with the shortest over the arid interior. Partial least squares regression identifies elevation, sand fraction, and vegetation conditions as primary drivers of spatial variability. This research provides observational constraints for understanding land-surface hydrological processes and landatmosphere coupling in alpine regions."
    },
    {
        "DOI": "10.1029/2025GL120069",
        "Title": "The Predictability Barrier Phenomenon of Winter Extreme Cold Events in Central and Eastern China and Mechanisms of Error Amplification",
        "Year": 2026,
        "Abstract": "Abstract Previous studies have primarily focused on evaluating the forecast skill of extreme cold events in central and eastern China as a whole, with limited attention to their different stages. This study identifies a distinct predictability barrier phenomenon in the ensemble forecasts, characterized by rapid growth of ensemble mean forecast error in 2mtemperature during the intensification stage of the events. In contrast, the forecast error tends to decrease during the decay stage. Consequently, the decay stage is more accurately forecasted than the intensification stage at the same lead time. Mechanism analyses indicate that error amplification is primarily driven by the interaction between the horizontal wind forecast error and the background horizontal temperature gradient of the event, which is dominantly governed by event intensification. Error reduction during the decay stage is primarily dominated by the conversion of available potential energy error into kinetic energy error. , Plain Language Summary Previous research indicates that extreme cold events can generally be forecast accurately with a lead time of about 7 days. This study finds that the main challenge of forecasting at longer lead times arises from the rapid growth of forecast errors during the intensification stage of events, referred to as the predictability barrier. Unlike the rapid error growth during the intensification stage, the forecast error tends to decrease during the decay stage. This means that the forecast skill during the decay stage is better than during the intensification stage for the same lead time. Mechanism analyses demonstrate that the physical processes governing event intensification play a crucial role in the occurrence of the predictability barrier. Meanwhile, the conversion between available potential energy error and kinetic energy error inhibits the occurrence of the predictability barrier. , Key Points A distinct predictability barrier phenomenon is identified during the intensification stage of cold events The forecast error tends to grow rapidly during the intensification stage, while decreasing during the decay stage Physical processes governing event intensification are responsible for error amplification"
    },
    {
        "DOI": "10.1016/J.RSE.2026.115260",
        "Title": "Leveraging wide snapshot XCO2 pre-training to estimate urban fossil fuel CO2 emissions from space",
        "Year": 2026,
        "Abstract": "Recent and upcoming carbon satellites, such as the Orbiting Carbon Observatory-3 (OCO-3) and the Copernicus Anthropogenic Carbon Dioxide Monitoring Mission (CO2M), offer unprecedented opportunities for top-down estimation of urban CO2 emissions. Their observations, i.e., 8080km2 Snapshot Area Map (SAM) for OCO-3 and 250 km wide swath for CO2M, enable the detection of urban emissions in a single pass. However, accurately identifying urban plumes remains challenging due to their broad spatial extent, low signal-to-noise ratio, and substantial data gaps in quality-filtered XCO2 snapshots. To address these challenges, we propose a Transformer-based deep learning (DL) model for XCO2 interpolation and plume detection. Our approach uses masked pre-training on synthetic CO2M data to learn spatial dependencies and emission-related structures of XCO2 values before fine-tuning for plume detection tasks. Experimental results on synthetic datasets show that the model reconstructs XCO2 with mean absolute errors below the instrumental noise and achieves stable plume detection performance across noise levels. It improves XCO2 gap-filling accuracy especially under regional and swath-missing conditions and significantly outperforms test- and wind-based methods in plume region segmentation accuracy. We further validated the model using 110 SAMs from 39 cities observed by OCO-3, integrating it into a lightweight inversion workflow. The resulting top-down emission estimates show improved consistency with bottom-up inventories compared to baselines (R2 = 0.61, total relative deviation = 0.10), and the city-level aggregation reproduces the bottom-up emission rankings with a Pearson's r of 0.90. These results confirm the transferability and practical utility of our approach across global cities. This study presents a promising approach for reconstructing and detecting urban emission signals from XCO2 snapshots, demonstrating clear potential to support the next-generation carbon monitoring satellites."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108855",
        "Title": "GeoGMI: A generative adversarial framework for virtual 89 GHz microwave brightness temperature retrieval from geo-kompsat-2A infrared observations for tropical cyclone monitoring",
        "Year": 2026,
        "Abstract": "This study proposes a novel framework, GeoGMI, to generate virtual 89 GHz horizontally polarized brightness temperatures (TBs) of the GPM Microwave Imager (GMI) on the geostationary Geo-Kompsat-2 A (GK-2 A) platform. The study aims to overcome the temporal sparsity of low-Earth orbit microwave (MW) observations during tropical cyclone (TC) events by leveraging the high-temporal resolution of GK-2 A. In contrast to conventional statistical techniques, GeoGMI employs a conditional generative adversarial network (cGAN) that integrates optimized input feature selection with a composite loss function to learn storm-scale spatial structures effectively. Quantitative comparisons demonstrate the clear advantage of the proposed deep learning framework: the multiple linear regression (MLR) baseline shows limited predictive skill, with a correlation coefficient (CC) of 0.531 and a root-mean-square error (RMSE) of 15.599 K, whereas GeoGMI achieves a higher CC of 0.647 and a lower RMSE of 14.429 K. These improvements indicate that GeoGMI more faithfully reconstructs physically meaningful deep convective cores, capturing nonlinear relationships that linear models do not represent. Nonetheless, despite the enhanced performance, the reconstruction of fine-scale TC structural features remains less accurate than that from direct instrument observations, highlighting the need for further refinement to resolve sub-storm-scale variability. However, the generated 10-min interval virtual GMI-like TB fields effectively bridge the temporal gaps between GMI overpasses and enable continuous MW-like monitoring of TCs. GeoGMI offers a promising approach to improve near-real-time monitoring of a TC's internal structures using only geostationary satellite observations, thereby enhancing nowcasting capability and reducing disaster-related risks."
    },
    {
        "DOI": "10.1029/2025GL119637",
        "Title": "Southern Ocean ClearSky Brightening From Sea Spray Aerosol Increase Drives Departure From Hemispheric Albedo Symmetry",
        "Year": 2026,
        "Abstract": "Abstract Observations reveal significant negative trends in reflected shortwave radiation over the 21st century. The globalmean darkening is primarily driven by clouds, while the globalmean atmospheric clearsky signal is near zero due to offsetting trends in the two hemispheres. Northern Hemisphere clearsky darkening is dominated by reduced anthropogenic aerosol emissions over population centers. The Southern Hemisphere (SH), in contrast, exhibits an unexpected widespread atmospheric clearsky brightening, particularly over the remote Southern Ocean (SO). We ascribe this brightening to an increase in aerosol optical depth (AOD). Based on observed correlations between monthlymean wind speed and AOD over the SO, we conclude the trends in AOD come from enhanced winddriven sea spray aerosol emissions driven by increasing nearsurface winds. We discuss the implications of SO brightening for deviations away from the observed hemispheric albedo symmetry and more generally as a potential negative Earth system feedback. , Plain Language Summary Satellite observations show that over the 21st century the Earth has been reflecting less sunlight back to space due to a reduction in clouds. But the atmosphere, and particles in the atmosphere, also reflect sunlight in the absence of clouds (the clearsky). In the Northern Hemisphere, the clearsky has been dimming as humans have found cleaner ways to produce energy and emit fewer particles. But surprisingly, the Southern Hemisphere atmosphere has actually been brightening, due to an increase in particles in the atmosphere. In this paper, we show that particles over the remote Southern Ocean are increasing as a result of faster wind speeds driving greater emission of sea spray into the atmosphere. We connect this asymmetry of human versus natural emissions in the Northern and Southern Hemispheres to a growing hemispheric asymmetry in total reflected sunlight. , Key Points Clouds and the Earth's Radiant Energy System observations show brightening over the Southern Ocean (SO) from increased atmospheric clearsky reflection SO brightening is consistent with observed faster winds driving increased emissions of sea spray aerosols SO brightening is driving the trend away from the observed state of hemispheric albedo symmetry"
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135015",
        "Title": "A dynamic soft-constrained deep learning paradigm for spatial downscaling of satellite gravimetry terrestrial water storage",
        "Year": 2026,
        "Abstract": " Efficiency of deep learning aided dynamic soft constrained paradigm for spatial downscaling. Validation higher resolution TWSA to multi-satellite derived products. Proposed localizes high signal both in and spectral domains. Downscaled preserved adhering the balance water cycle. The Gravity Recovery Climate Experiment (GRACE) GRACE Follow-On (GRACE-FO) satellite gravimetry missions have contributed significantly our knowledge variations Earths Terrestrial Water Storage anomalies (TWSA) throughout last two decades. However, ability quantifying hydrometeorological other climate/weather episodes is hindered by limitations current spatiotemporal resolutions at monthly sampling approximately coarser than 300 km. In this study, we used Deep Learning (DL) approach that specifically developed accurate effective downscaling time series from NASAs Jet Propulsion Laboratory (JPLM). Each maps JPLM are downscaled km 50 spanning April 2002 through December 2022 using inherent correlations WaterGAP Hydrology Model (WGHM) TWSA. For purpose, a novel soft-constrained loss function introduced applied adaptively balances while optimizing with low-resolution observations against high-resolution patterns WGHM hydrological model ERA5 inputs. Internal validation shows preserves basin-averaged temporal dynamics (trends, seasonality) JPLM, analyses show it successfully incorporates TWSAs variability. External products also demonstrates their capture El Nino Southern Oscillation (ENSO)-driven interannual variability, glacial mass trends, consistency Soil Moisture Active Passive (SMAP) satellite-derived surface soil moisture band similar predictive skill previous studies. Furthermore, groundwater well indicates effectively represents long-term depletion heavily stressed aquifers enhancing localization or recharging signals relative coarse-resolution"
    },
    {
        "DOI": "10.1029/2025WR041114",
        "Title": "The Effects of Planting Structure on Groundwater Depletion and Optimization Strategies in the North China Plain",
        "Year": 2026,
        "Abstract": "Abstract Planting structure drive agricultural water use and is critical to groundwater depletion in the North China Plain (NCP). However, the effects of planting structure changes on groundwater depletion are rarely quantified, and severely depleted areas are often overlooked in previous planting structure optimization studies. This study developed a groundwater stress index (GWSI) to assess current groundwater drought and future risks and identify high groundwater stress zones (HGSZ). Groundwater depletion was estimated by integrating land surface model and AquaCrop outputs. A structural equation model was developed to assess the effects of planting structure to groundwater depletion, and a GWSIbased optimization model was proposed to alleviate groundwater depletion, particularly in HGSZ. Results identified an HGSZ near the HenanHebei border, where the groundwater decline rate (21.90 mm/year) was more than twice the NCP average (8.73 mm/year). Under present planting structures, groundwater use remained unsustainable, with annual consumption exceeding recharge by 46.53 mm/year across the NCP and 97.09 mm/year in the HGSZ. Depletion was primarily affected by the planting area and spatial dispersion of winter wheat. Planting area expansion mitigated the effect of spatial redistribution on groundwater depletion, and it varied by crop. The optimization model reduced net groundwater depletion by 30.61 mm/year in the NCP and 63.23 mm/year in the HGSZ. The results highlighted the need to adjust planting structures, and revealed the effects to groundwater depletion, and demonstrated that partially converting rotation areas to singleseason cropping and shifting the rest southeastward effectively alleviated groundwater depletion. These findings provided an evidence base for designing regionspecific groundwaterresource management strategies in the NCP. , Key Points A GRACEbased model was developed to identified groundwater highstress zones and guided planting structure adjustments in the NCP Groundwater depletion in the NCP was mainly driven by the area and spatial distribution of winter wheat, with irrigation exceeding recharge Crop planting structure optimization mitigated groundwater depletion by up to 63.23 mm/year in critical zones like the HenanHebei border"
    },
    {
        "DOI": "10.1029/2025RG000885",
        "Title": "Monitoring Flood Inundation Dynamics From Space",
        "Year": 2026,
        "Abstract": "Abstract With the increasing intensity and frequency of flood events worldwide, the need for accurate and timely inundation mapping has never been more critical. Largescale flood extent estimations are vital for coordinating effective disaster response, facilitating recovery, and building future resilience. Traditional groundbased and aerial monitoring methods are often impractical during major floods, limited by cost, safety, and their inability to capture the full scope of an event. Satellitebased remote sensing provides the necessary largescale perspective with a unique vantage point to monitor extreme inundation events. This review assesses the potential of public satellite sensors to capture flood events using a novel analysis of the Dartmouth Flood Observatory (DFO) global flood database. Our analysis quantifies the major performance gaps between these sensors, demonstrating that no single instrument is sufficient for complete and continuous flood monitoring. Passive microwave radiometers are capable of capturing >95% of flood events, albeit at a coarse spatial resolution that may be unsuitable for detailed mapping or local risk assessment. In contrast, popular multispectral sensors such as Landsat and Sentinel2 capture no more than 30% of flood events. The number of sensors capable of capturing flood events doubled between 2015 and 2020, signaling immense potential for multisensor integration. We examine how combining observations from multiple sensors can improve temporal coverage of flood events, however noting that temporal sampling along does not guarantee successful flood detection and how the rapid, dynamic nature of floods compounds the challenges inherent to satellitebased monitoring. , Plain Language Summary Globally, flooding is a marked issue that is growing in importance with the impacts of climate change. Flooding can occur at large scales, making it advantageous to use spacebased remote sensing technology to monitor these hazards. Numerous public satellites are available, but their capabilities vary widely. We provide a datadriven analysis of thousands of global flood events to objectively measure and compare the performance of the different sensors available. Our analysis shows that combining data from different satellites increases the likelihood of capturing flood events. While no single sensor can reliably observe all floods, using multiple sensors together improves temporal coverage, though challenges remain in translating observations into accurate flood maps. The review also highlights the difficulties of using satellite data effectively due to the rapid and dynamic nature of floods, making a multisensor approach logical. , Key Points Sensors capable of observing floods doubled from 2015 to 2020; collectively they capture every major event despite individual gaps Synergistic fusion of frequent operational data and precise scientific imagery is critical to achieve continuous flood monitoring"
    },
    {
        "DOI": "10.1029/2025JD045516",
        "Title": "Convective Organization in African Easterly Waves Observed During the NAMMA and CPEXCV Field Campaigns",
        "Year": 2026,
        "Abstract": "Abstract Fundamental questions remain about where and when convection will occur within African easterly waves. In this study, we aim to better understand the dynamical processes that govern moist convective organization at the mesoalpha scale in tropical easterly waves using NASA airborne field campaigns and satellite observations. We employ the SAMURAI 3D variational analysis technique in a vortexcentric approach, integrating the fifth generation ECMWF (ERA5) reanalysis and research aircraft observations from 20 African easterly wave (AEW) cases sampled during the NASA African Monsoon Multidisciplinary Analyses (NAMMA) in 2006 and Convective Processes ExperimentCabo Verde (CPEXCV) in 2022. Infrared satellite imagery is used to obtain the frequency of occurrence within the wave of clear air, shallow/moderate convection, and deep convection relative to a potential vorticity (PV) centroid location. We identified four clusters of organized deep convection denoted as minimal deep, southern, southwestern, and widespread. Composites of the dynamical fields from the variational analyses show that high PV and relative humidity (RH) at midlevels were approximately colocated with regions of lowlevel convergence and more frequent deep convection, particularly for the southwestern and widespread clusters, which had the highest frequencies of deep convection. Waves with a higher frequency of deep convection are characterized by stronger PV and higher RH at midlevels compared to waves with a lower frequency of deep convection. The results suggest that improved understanding of the causal relationships between PV and RH and deep convection in easterly waves can lead to future forecast improvements of convective organization and tropical cyclogenesis. , Plain Language Summary We do not fully understand how strong thunderstorms come together into organized complexes that can serve as seeds for Atlantic hurricanes. Our goal is to better understand the processes that govern the location and how strong these storm complexes are, using measurements collected during aircraftbased NASA projects. We use an analysis technique called SAMURAI to incorporate an estimate of the state of the atmosphere (ERA5 Reanalysis) and direct aircraft measurements of 20 cases of storm complexes from the NAMMA and CPEXCV projects. We classify the clouds observed from satellite in each of the storm complexes into clear (no clouds), shallow/moderate clouds, and tall/deep clouds, and calculate how often each of these classes occurs in each case. We find four groups of how the deep clouds are organized, that we call minimal deep, southern, southwestern, and widespread, with southern and widespread showing a greater occurrence of deep clouds. The results show that areas with strong rotation and high humidity in the atmosphere are often lined up with regions where air converges near the surface and where deep storms tend to occur more often. Further research is needed to test cause and effect relationships between these different factors and improve weather forecasts. , Key Points NASA aircraft observations of 20 African Easterly Wave (AEW) cases are analyzed in a potential vorticity framework on the mesoalpha scale Four clusters of minimal, southern, southwestern, and widespread deep convection are found relative to the AEW potential vorticity centroids The deep convective organization is associated with observed midlevel potential vorticity and relative humidity and lowlevel convergence"
    },
    {
        "DOI": "10.1029/2025GL119838",
        "Title": "The Impact of OCO2 Seasonally Dependent Sampling on Carbon Flux Estimation in the Northern Tropical Africa",
        "Year": 2026,
        "Abstract": "Abstract The large annual carbon source over northern tropical Africa (NTA), inferred from satellite CO 2 , remains highly debated. Using observing system simulation experiments with Orbiting Carbon Observatory2 (OCO2) sampling, we show that seasonally dependent sampling can lead to overestimated annual fluxes. These biases arise when prior flux seasonal cycle differs from the assumed truth. Since OCO2 provides more observations during the nongrowing season, posterior fluxes are more constrained in that period. When prior fluxes underestimate the seasonal amplitude, the posterior carbon sink during the growing season is underestimated, leading to a net positive bias. This effect is supported by real OCO2 data, where we hypothesize that underestimating fire emissions during nongrowing season and weaker seasonality of prior fluxes may contribute to overestimated annual fluxes. Our results highlight the need to improve prior flux estimates and expand observational coverage during the growing season to reduce biases in regional carbon budget assessments over NTA. , Plain Language Summary Tropical Africa is home to about a quarter of the world's tropical forests, making it one of the most important regions for storing carbon. Yet it is also one of the least measured by groundbased CO 2 stations. Satellites such as Japan's GOSAT (2009) and NASA's OCO2 (2014) increase observation coverage over the region. Surprisingly, these satellites suggest that northern tropical Africa releases a large amount of carbon into the atmosphere each yearmuch higher than the nearneutral estimates from ecosystem models. Our study introduces a new hypothesis: the way OCO2 samples the region through each year may itself affect the results. OCO2 can only measure CO 2 when sunlight is reflected, so it collects more data during the less cloudy dry season, and fewer data during the cloudier wet season, when vegetation is most productive. If the initial guess used in flux inversions underestimates this seasonal cycle, the resulting annual carbon balance will appear too high. Using simulation experiments and results from a multimodel intercomparison, we show that this seasonally uneven sampling can bias flux estimates. These findings highlight the need for better information on the first guess and increasing observation coverage during growing season to improve carbon budgets for tropical Africa. , Key Points Seasonally dependent sampling can lead to overestimated annual fluxes in tropical North Africa The biases arise from the interaction between prior flux seasonality and seasonally dependent sampling Underestimating fire emissions and weaker seasonality of prior fluxes may contribute to overestimated annual fluxes"
    },
    {
        "DOI": "10.1126/SCIADV.ABN2465",
        "Title": "Global seaweed productivity",
        "Year": 2022,
        "Abstract": "The magnitude and distribution of net primary production (NPP) in the coastal ocean remains poorly constrained, particularly for shallow marine vegetation. Here, using a compilation of in situ annual NPP measurements across >400 sites in 72 geographic ecoregions, we provide global predictions of the productivity of seaweed habitats, which form the largest vegetated coastal biome on the planet. We find that seaweed NPP is strongly coupled to climatic variables, peaks at temperate latitudes, and is dominated by forests of large brown seaweeds. Seaweed forests exhibit exceptionally high per-area production rates (a global average of 656 and 1711 gC m2 year1 in the subtidal and intertidal, respectively), being up to 10 times higher than coastal phytoplankton in temperate and polar seas. Our results show that seaweed NPP is a strong driver of production in the coastal ocean and call for its integration in the oceanic carbon cycle, where it has traditionally been overlooked."
    },
    {
        "DOI": "10.1088/1748-9326/AE7135",
        "Title": "Urban rainfall trends in IMERG datasets",
        "Year": 2026,
        "Abstract": "Abstract Satellite precipitation records are increasingly used to assess whether urbanisation modifies local rainfall. However, it remains unclear whether reported urban signals reflect physical processes or artefacts from evolving satellite observing systems. The Integrated Multi-satellitE Retrievals for GPM (IMERG) merges observations from a growing constellation of microwave and infrared sensors, meaning that long-term trends derived from this product may partly reflect changes in the observing system rather than true precipitation changes. Here we analyse rainfall frequency and intensity over 15 major global cities spanning diverse climate regimes using IMERG Version 07B and show that urban areas exhibit a consistent increase in rainfall event frequency with a weaker enhancement in intensity relative to surrounding rural areas. Both signals are dominated by microwave-based retrievals, while infrared-dominated periods largely suppress the urban hotspot pattern, highlighting the sensitivity of detected urban signals to the underlying retrieval type. To isolate physical rainfall changes from observational artefacts, we develop a synthetic time series approach that quantifies the contribution of systematically increasing microwave sampling frequency to apparent long-term trends. We find that sampling artefacts explain up to 20% of observed long-term trends, with locally higher contributions in some cities. After accounting for these effects, the urban rainfall enhancement persists across cities, demonstrating that IMERG captures a robust and physically consistent urban signal in both rainfall frequency and intensity. These findings have direct implications for the reliability of satellite-based urban climate assessments, the interpretation of long-term precipitation trends, and the design of future observing systems."
    },
    {
        "DOI": "10.1007/S00382-026-08149-5",
        "Title": "Subseasonal predictability of Asian summer monsoon precipitation using a seamless multimodel ensemble",
        "Year": 2026,
        "Abstract": "Accurate subseasonal precipitation prediction is crucial for risk alleviation in agriculture, water resources, and disaster management within the densely-populated Asian summer monsoon region. In this study, we conduct a multimodel ensemble (MME) prediction of Asian summer monsoon precipitation (ASMP) by utilizing subseasonal reforecasts from six subseasonal-to-seasonal (S2S) models. A comprehensive evaluation within a seamless framework demonstrates that the MME outperforms any individual model in deterministic prediction across all lead times. A distinct regional heterogeneity becomes evident: the Maritime Continent (MC) displays the highest prediction skill, whereas the East Asia summer monsoon (EASM) presents the lowest. Probabilistic assessment suggests that the prediction skill is higher for strong anomalous precipitation events compared to near-normal events, primarily due to enhanced resolution rather than reliability. A generally positive correlation exists between deterministic and probabilistic skills, although its intensity varies regionally. The potential of the precipitation prediction is greater over the EASM than over the MC. Further analysis reveals that more accurate forecasting of lower- and upper-level zonal winds and sea surface temperature is pivotal for enhancing EASM precipitation prediction. Moreover, the MME effectively mitigates under-dispersion and improves reliability, approaching the ideal ensemble calibration. These findings underscore the significance of the MME approach and offer a scientific foundation for formulating customized forecasting strategies across different regions and event types."
    },
    {
        "DOI": "10.5194/ACP-26-5293-2026",
        "Title": "A spectral perspective of the clear-sky OLR variability driven by ENSO",
        "Year": 2026,
        "Abstract": "Abstract. The study of short-term unforced variability of the Earth radiative budget can provide much information for the understanding of the long-term effect of external radiative forcing, related to the present climate change. In this regard, inter-annual variability of the Outgoing Longwave Radiation (OLR) is strongly shaped by El-Nino Southern Oscillation (ENSO). So far, the relationship between the OLR and ENSO has been investigated using broadband satellite-based observations, such as those of the Clouds and Earth Radiant Energy System, finding that the peak of the OLR response lags the peak of ENSO activity. However, such observations cannot directly inform on the individual processes that drive the radiative response to ENSO. Here, we exploit the spectrally-resolved clear-sky OLR fluxes measured by the Infrared Atmospheric Sounding Interferometer and the Atmospheric Infrared Sounder instruments to expand the observational analysis of ENSO's radiative response, showing that its intensity and lag vary along the spectral dimension. The spectral fingerprint of water vapor, surface and air temperature, and ozone feedback is then calculated using a set of spectral kernels to evaluate the role of individual processes in building the overall response. Results show a strong contribution coming from the ozone absorption band, along with a contribution of opposite sign coming from the core of the carbon dioxide band, which is mainly affected by stratospheric temperature. This analysis confirms the important role of the spectral dimension to study climate processes. In this regard, it sets the basis for a spectral diagnostic to evaluate how ENSO driven variability is reproduced by climate models."
    },
    {
        "DOI": "10.46481/JNSPS.2026.3353",
        "Title": "A hybrid process-based and neural network post-processing model for cowpea yield prediction under climate variability in North Central Nigeria",
        "Year": 2026,
        "Abstract": "Agriculture has sustained human civilisation for centuries, yet it remains a sector in critical need of technological advancement. Existing crop-growth and yield-prediction methods lack a simple and generic framework that relies on climate data with minimal parameters, particularly for leguminous crops. Addressing this gap, this study develops a Crop Growth Rate Computation Model (CGRCM) to simulate crop growth with a focus on soil nitrogen utilisation. The CGRCM integrates climate variables and nine parameters to predict cowpea growth in terms of above-ground biomass and final yield, derived from biomass at maturity and harvest index. Climatic input data and soil parameters were obtained through remote sensing for Makurdi and Mokwa in North Central Nigeria, covering 32 growing seasons (1990-2021). The model was calibrated for the FUAMPEA cultivar and implemented using a Python-based neural network post-processor. Training was conducted on data from 1990--2017 and testing on data from 2018--2021. Results show that the CGRCM effectively captures biomass responses to drought, temperature and heat stress. The model achieved strong agreement with observed yields, with an MAE of 134.2, an RMSE of 153.6 and a prediction accuracy of 91.4% for Makurdi, and an MAE of 109.4, an RMSE of 113.7 and a prediction accuracy of 93.5% for Mokwa. Bootstrap confidence interval, paired t-test and Diebold-Mariano tests confirmed that the CGRCM performed better, demonstrating its reliability as a scalable and data-efficient tool for crop-growth prediction."
    },
    {
        "DOI": "10.1175/JAMC-D-25-0166.1",
        "Title": "Applications of the World Wide Lightning Location Network (WWLLN) Thunder Hour Climatology",
        "Year": 2026,
        "Abstract": "Abstract Thunder hours represent both a historic measure of lightning occurrence and a metric of thunderstorm frequency that is comparatively less sensitive to variations in the detection capabilities of a lightning location system. The World Wide Lightning Location Network (WWLLN) has monitored global lightning since late 2004, and this paper introduces a WWLLN thunder hour climatology for 201324, when the number of global sensors remained largely consistent. WWLLN reports nearly half as many thunder hours as the Geostationary Lightning Mappers (GLMs) even though its detection efficiency for all flashes is much lower, and the geographic distribution of thunder hours is similar to other global ground-based networks. WWLLN thunder hours are also compared with convective precipitation hours derived from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) (IMERG). Various applications of thunder hours are explored, including variations related to diurnal, seasonal, and interannual cycles [El NinoSouthern Oscillation (ENSO)]. Brief case studies illustrate a variety of factors affecting thunderstorm occurrence and timing, including coastlines, terrain, and island size in the geographically complex Philippines. Thunder hour persistence provides a metric for understanding event duration, including geographic regions where the most frequent and most sustained thunderstorm hazards occur at different times of year. Comparisons with monthly fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) fields quantify the relationship between thunder hours and measures of regional temperature, moisture, and instability."
    },
    {
        "DOI": "10.5194/ACP-26-6781-2026",
        "Title": "Future volcanic eruptions may delay the recovery of lower stratospheric ozone over Antarctica and Southern Hemisphere mid-latitudes",
        "Year": 2026,
        "Abstract": "Abstract. Sporadic explosive volcanic eruptions can inject large amounts of sulfur into the stratosphere, which forms volcanic sulfate aerosols with the potential to affect stratospheric ozone chemistry. Future volcanic eruptions have been represented in climate projection studies with varying degrees of realism despite their potential importance for polar ozone recovery. Climate projections typically use a constant volcanic forcing based on a historical average, which very likely underestimates the magnitude of future volcanic forcing and ignores the sporadic nature of volcanic eruptions. In this study, we use stochastic volcanic eruption scenarios and a plume-aerosol-chemistry-climate model (UKESM-VPLUME) to assess the effect of future volcanic sulfur injections on lower stratospheric ozone recovery over Antarctica and Southern Hemisphere mid-latitudes. We find that sporadic eruptions can delay Antarctic total column ozone recovery by up to five years, though this delay is relatively small when compared with the long-term ozone recovery timescale. Large-magnitude eruptions occurring before mid-century can, however, episodically cause more substantial delays in the recovery. Based on a composite analysis we show that the ozone response to volcanic sulfate aerosols over Antarctica and Southern Hemisphere mid-latitudes weakens over the 21st century due to declining chlorofluorocarbon concentrations. Overall, our findings underscore the need for fully interactive volcanic aerosol-chemistry coupling to assess the resilience of the Antarctic ozone layer in response to future volcanic eruptions and other stratospheric perturbation events. Our results also support previous calls for sustained monitoring of stratospheric composition and ozone-depleting processes to better anticipate and attribute changes in ozone recovery."
    },
    {
        "DOI": "10.1088/3033-4942/AE4FEB",
        "Title": "Large variability in satellite-based estimates of irrigation water use",
        "Year": 2026,
        "Abstract": "As competition for water resources intensifies, especially in water-scarce regions, there is a growing need to manage usage effectively, particularly irrigated agriculture. However, data on agricultural use and abstractions are often unavailable or only at coarse spatial resolutions. Water by crops can be physically characterised the landscapes rate of evapotranspiration (ET). Satellite-based monitoring actual ET provides one potential solution this gap, but significant knowledge gaps remain about uncertainty satellite-based estimates irrigation (IWU) their associated implications policy management. In model-intercomparison study, we attempt address relevance model choice IWU assessing variability resulting from different IWU. We utilised six datasets OpenET five precipitation estimate field-level over 6 years high plains aquifer, United States. Results reveal substantial estimates, field seasonal scales, which reduces when aggregated spatially temporally. ET, rather than precipitation, was primary driver estimates. These findings highlight challenges using satellite fine temporal scales areas where supplements rainfall. Aggregating emphasises importance choices both farm regional levels."
    },
    {
        "DOI": "10.5194/AMT-19-3095-2026",
        "Title": "A hybrid optimal estimation and machine learning approach to predict atmospheric composition",
        "Year": 2026,
        "Abstract": "Abstract. We present a HYbrid REtrieval Framework (HYREF) that predicts subcolumn carbon monoxide (CO) concentrations from Cross-track Infrared Sounder (CrIS) observations, trained to replicate the TRopospheric Ozone and its Precursors from Earth System Sounding (TROPESS) retrievals based on optimal estimation (OE). Unlike the OE algorithm, which produces retrievals for only a small fraction of available CrIS observations due to computationally expensive but physically accurate radiative transfer, the addition of machine learning (ML) techniques enables full coverage by providing high-resolution predictions for every valid CrIS sample. Importantly, in addition to CO concentrations, TROPESS-HYREF also predicts key retrieval diagnostics, namely column averaging kernels, degrees of freedom, and retrieval errors, that are essential for meaningful comparison with other observations, models, and ingestion into data assimilation. The framework is designed to emulate and extend the OE retrieval, rather than replace it, by providing full spatial coverage and enhanced resolution consistent with the underlying physical solution. The new framework achieves excellent performance with correlation coefficients r>0.99 and a bias <0.1 % when benchmarked against an independent test set, and reproduces fine-scale spatial patterns in CO fields observed during a major wildfire over North America. A scale analysis reveals substantial variability in CO concentrations below the nominal 0.80 resolution of the TROPESS OE retrieval, which TROPESS-HYREF successfully resolves. Inference is computationally efficient, with daily global predictions completed in minutes on a single compute node. By filling observational gaps while maintaining consistency with the OE retrieval, this fusion of OE-derived physical information and ML-driven efficiency provides a practical pathway to high-resolution atmospheric CO monitoring with robust diagnostics."
    },
    {
        "DOI": "10.1038/S41598-026-48173-3",
        "Title": "Identification of dust aerosol source areas and transmission paths along the ChinaPakistan Economic Corridor",
        "Year": 2026,
        "Abstract": "Dust aerosols significantly impact the global climate, ecological environment and human health. Clarifying distribution changes, sources, transport routes of dust along ChinaPakistan Economic Corridor is important for assessing regional atmospheric environments ensuring sustainable development. This study uses MODIS aerosol data, meteorological reanalysis data from 2003 to 2023, HYSPLIT model reveal characteristics trends aerosols, quantify distributions estimate emissions. Results 1Dust exhibit significant seasonal variations: summer (0.66)> spring (0.58)> autumn (0.37)> winter (0.29), high-value areas are concentrated in eastern southern Pakistan. The proportion with increasing optical depth (DAOD) 2.92%. 2Pakistans Balochistan Sindh Provinces, Chinas Xinjiang Kashgar Region, main sources. emissions natural surfaces high spring, while those anthropogenically influenced peak summer. 3In air mass pathways dominated by short-distance southeastward (75%), long-distance northeastward occurs across all four seasons."
    },
    {
        "DOI": "10.1029/2025GL120799",
        "Title": "Strong Ventilation Drives the Aerosol Pollution Receptor Center and Its Interannual Variability in Central China's TwainHu Basin",
        "Year": 2026,
        "Abstract": "Abstract This study investigates the unique winter phenomenon in central China's TwainHu Basin where high aerosol optical depth (AOD) centers coincide with a regional nearsurface wind speed maximum. Based on multidecadal reanalysis data sets, satellite retrievals, ground observations, and FLEXPARTWRF simulations, we demonstrate that strong ventilationdriven regional aerosol transport dominates the formation and interannual variability of highAOD centers. The synergistic effects of the anomalous synopticscale conveyor belt, intensified boundarylayer ventilation, and the basin's funnel topography enhance regional aerosol transport and convergence, while high humidity and stable stratification aloft further promote vertical redistribution and accumulation. During highAOD years, regional transport contributes 56.46% to PM 2.5 concentrations, and ventilation coefficients explain 68% of AOD variance. A Ushaped relationship emerges between AOD interannual variability and ventilation, indicating a shift from localaccumulationdominated mild pollution to transportdriven severe pollution. These findings challenge the conventional stable synoptic mode paradigm and advance understanding of receptorregion pollution dynamics. , Plain Language Summary In central and eastern China, air pollution research has traditionally emphasized stable meteorological conditions associated with topographic weakwind zones. However, the TwainHu Basin exhibits a distinctive high windhigh pollution pattern in winter, challenging this conventional view. Multisource observations and model simulations reveal that aerosol pollution is primarily driven by strong ventilationinduced regional transport rather than local accumulation over TwainHu Basin. Specifically, anomalous transportfavorable circulation, enhanced boundarylayer ventilation, and the basin's funnellike topography synergistically promote regional aerosol convergence and vertical redistribution, along with high humidity and stable stratification aloft, leads to interannual positive AOD anomalies. The ventilation coefficient explains 68% of the interannual variance in positive AOD years, and nonlocal sources contribute 57% of PM 2.5 , underscoring the dominance of regional transport in modulating air quality in receptor regions. These findings provide critical evidence for crossregional air quality management. , Key Points High wintertime AOD and PM 2.5 coincide with surface wind maxima over the TwainHu Basin Regional aerosol transport controls the HighAOD receptor center and its interannual variability The AOD interannual variability exhibits a Ushaped dependence on ventilation coefficients"
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.109096",
        "Title": "Coupling of rainband structure and intraseasonal monsoon variability shaping rainfall distribution during weak tropical cyclone Lupit (2021)",
        "Year": 2026,
        "Abstract": "This study investigates the extreme rainfall associated with the landfall of tropical cyclone (TC) Lupit, with emphasis on the multi-scale interactions between the TC and the East Asian summer monsoon. The event resulted from constructive coupling between synoptic-scale TC rainband dynamics and intraseasonal monsoon variability. Moisture transport was dominated by enhanced southwesterly within a reversed monsoon trough, with the primary inflow entering through the southern sector of the TC and peaking concurrently with the rainfall maximum. Wavelet analysis reveals that synoptic-scale disturbances (<10 days) directly triggered extreme precipitation at both the Southern Center (SC) and Northern Center (NC), yet their effectiveness depended strongly on the phase of the 3060-day oscillation. Rainfall maxima occurred only when these high-frequency systems coincided with the positive phase of the low-frequency mode, which intensified moisture advection from the South China Sea. Vertical structural analyses further show that the SC was embedded within the persistent ascending branch of the 3060-day disturbance, whereas the NC experienced deep, organized convection as Lupit approached, characterized by strengthened radial inflow and concentrated updraft cores aligned with the outer rainbands. These results highlight that accurate prediction of such extreme rainfall requires not only reliable forecasts of TC track and structure but also realistic representation of intraseasonal monsoon conditions, particularly the configuration of the reversed monsoon trough that regulates moisture supply and convective organization."
    },
    {
        "DOI": "10.1186/S40562-026-00475-0",
        "Title": "Marine heatwave in the Bohai-Yellow Seas enhanced the July 2025 rainstorm in Beijing and its surrounding areas",
        "Year": 2026,
        "Abstract": "Abstract Beijing and its surrounding areas (BSA) experienced a record-breaking rainstorm from July 23 to 29, 2025, with the area-averaged accumulated precipitation reaching 210.4 mm. Combining high-resolution simulations and Lagrangian trajectory analysis, we demonstrate that sequential tropical cyclones established a relay-like moisture conduit from the Pacific to BSA and maintained the long-lasting event. Moisture budget for rainfall-related particles revealed that terrestrial evaporation over eastern China contributed the most to this event, while the western North Pacific and the Bohai-Yellow Seas acted as key secondary moisture sources. Meanwhile, a concurrent marine heatwave in the Bohai-Yellow Seas (BYS) amplified the event by supplying additional moisture via enhanced evaporation. Sensitivity experiments show that the moisture from the BYS would be suppressed by about 25% without the heatwave, and the area-averaged accumulated rainfall would also be reduced by 22 mm (16.2%). Overall, this study highlights compound tropical cyclone-marine heatwave events as unignorable amplifiers of rainfall extremes."
    },
    {
        "DOI": "10.1016/J.JASTP.2026.106800",
        "Title": "Multiplatform observations and WRF-Based diagnosis of an extreme pre-monsoon convective outbreak over the Delhi National Capital Region, India",
        "Year": 2026,
        "Abstract": "In early May 2025, the Delhi National Capital Region (NCR), India experienced a high-impact convective storm that produced intense pre-monsoon rainfall, resulting in fatalities and widespread urban disruption. This study presents a comprehensive analysis of the exceptionally rare convective event of 12 May 2025, during which the Safdarjung Observatory (28.58 N, 77.20 E), New Delhi, recorded 77 mm of rainfall, the third highest daily total since 1885. A multi-platform observational and modelling framework is employed, integrating INSAT-3DR imager and sounder observations, GPM IMERG precipitation estimates, Doppler Weather Radar (DWR) reflectivity, radiosonde soundings, ERA5 reanalysis, lightning observations, and Weather Research and Forecasting (WRF) model simulations. Diagnostic analyses indicate that the event was initiated by the interaction of an eastward-propagating Western Disturbance with pronounced local thermodynamic instability. Satellite observations revealed deep convective development, characterized by cloud-top brightness temperatures below 230 K and suppressed outgoing longwave radiation (<150 W m2). Radiosonde measurements revealed a highly unstable pre-convective environment, characterized by Convective Available Potential Energy (CAPE) values exceeding 2300 J kg1. DWR observations documented convective towers reaching altitudes above 12 km, while INSAT-3DR sounder-derived total precipitable water exhibited a sharp increase (20 mm) in the lower troposphere (900700 hPa), indicating abundant moisture availability. ERA5 outputs and WRF model simulations further corroborated these findings, showing substantial moisture convergence in the lower levels, which played a crucial role in the storm's rapid intensification. Hydrometeor profiles from WRF showed enhanced mixing ratios in the mid-to-upper troposphere (510 km). Apparent heat source (Q1) and apparent moisture sink (Q2) profiles from the model confirmed that latent heat release and moisture condensation are primary contributors to convective development, with maxima occurring in the mid-troposphere (700850 mb). The results demonstrate that integrated observational analyses combined with numerical modelling are essential for characterizing severe pre-monsoon convection over the NCR. In a warming climate, the frequency and intensity of short-duration extreme rainfall events are projected to increase, disproportionately affecting densely populated urban regions. These findings reinforce the need for climate-informed, impact-based early warning systems and adaptive forecasting frameworks to limit growing societal and infrastructural risks associated with convective extremes."
    },
    {
        "DOI": "10.1109/LGRS.2026.3680662",
        "Title": "Retrieving Ice Water Path Using GMI Brightness Temperature Data Based on the New-Constructed ARM Database",
        "Year": 2026,
        "Abstract": "Using neural network to retrieve ice water path (IWP) from spaceborne microwave radiometer brightness temperatures is a new technical method for remote sensing cloud microphysical parameters, but constructing high-quality retrieval database remains key limitation its application. To this end, study proposes construction driven by the All-Day Radiance Matching IWP Construction algorithm. This algorithm used extend spatial coverage of CloudSat-derived satellite track both sides orbit. Designed day and night conditions, aims cover swath range Global Precipitation Measurement (GPM) Microwave Imager (GMI). Based on database, built with has following performance metrics: root mean square error (RMSE) 610.41 g/m2, absolute percentage (MAPE) 48.7%, coefficient determination 0.729, Pearson correlation 0.855. The results indicate that model established using constructed can effectively stably GMI data."
    },
    {
        "DOI": "10.1007/S41748-026-01164-W",
        "Title": "Compound Heatwaves in North Africa: Mechanisms and Model Capabilities",
        "Year": 2026,
        "Abstract": "Abstract Climate hazards driven by extreme events have recently gained attention due to their severe environmental and socio-economic impacts. While independent daytime nighttime heatwaves been widely studied, compound (CHWs) remain poorly documented in Africa. This study provides the first comprehensive assessment of summer CHWs North Africa, identifying corresponding dominant atmospheric surface drivers using reanalysis data CORDEX-CORE regional climate simulations for period 19792023. Compound show strong spatial variability across occurring more frequently along populated coastal regions less often over mountainous areas such as Atlas. These are associated with pronounced temperature anomalies; they tend develop under a persistent dipole configuration at 500 hPa, characterized ridge located either western part region (Morocco) or eastern sector (Egypt). circulation pattern favors subsidence adiabatic warming, while warm air advection from central Sahara further increases near-surface temperatures. Enhanced downward longwave radiation emerges radiative contribution whereas solar plays comparatively secondary role. The models capabilities shows systematic biases representation these events, generally underestimating occurrence Mediterranean overestimating them Sahara. Our analysis indicates that is strongly influenced model than driving global model. findings help advance understanding heatwave dynamics Africa showcase need improve models. By characterizing mechanisms related this helps support risk adaptation, particularly light increasing impacts heat extremes on public health, water resources, agriculture, energy systems region. Graphical characterization (i.e., combined extremes) nine simulations. occur 12 times per year, last 35 days, exhibit anomalies reaching + 12 C during 5 night, enhanced regions. hPa circulation, high-pressure leading warming. Near surface, enhances heating, role maintaining high models biases, African coasts Sahara, results mainly controlled improves supports better heat-risk modeling"
    },
    {
        "DOI": "10.1038/S41561-026-01981-8",
        "Title": "Evapotranspiration declines prolonged by deforestation and fire in South American biomes",
        "Year": 2026,
        "Abstract": "Evapotranspiration strongly couples land and atmosphere to regulate water, carbon energy fluxes across tropical South America. Ongoing deforestation fires reduce the capacity of deep-rooted trees recycle moisture, while intensifying droughts further alter timing magnitude evapotranspiration. Here we present a high-resolution, data-constrained hydrological modelling analysis isolate effects anthropogenic disturbances on evapotranspiration vegetation function Amazon adjacent biomes from 2003 2020. We find that declines persist 2122% longer than those caused by fire or drought alone. When these stresses co-occur, losses intensify 36% 66% average impact individual stressors. Across neighbouring biomes, grasslands savannas in Cerrado are most vulnerable droughts, with recovery often exceeding seven years, Pantanal wetlands recover rapidly due sustained moisture availability. Furthermore, productivity under compounding despite concurrent greening trends. Our findings reveal recurrent human erode ecosystem resilience, threatening long-term ecological stability. Isolating footprint is pivotal guide sustainable land-use transitions preserve landatmosphere coupling Americas ecosystems. Human-driven prolong American ecosystems, disrupting water balance according remote sensing data."
    },
    {
        "DOI": "10.1029/2025JG009240",
        "Title": "Tracking Water Dynamics of a Temperate Forest Under Drought and NonDrought Conditions Using Active and Passive Microwave and Optical Remote Sensing",
        "Year": 2026,
        "Abstract": "Abstract Water status, water dynamics, and ecohydrological resilience of a protected German beech forest during the 20182020 multiyear drought are assessed using over six years of multifrequency remote sensing data, integrating active, passive, and optical sensors with varying canopy penetration depths, highlighting the importance of monitoring forests under extreme conditions. In this study, we investigate Sentinel1 Cband backscatter (S1 0 ) and the relative water content estimated from vegetation optical depth (RWC VOD ) of AMSR2 (X and Cbands) and SMAP (Lband) within the soilplantatmosphere system (SPAS). In addition, time series are intercompared and examined through correlation and sensitivity analyses applying tailored environmental and newly developed hydrological selection strategies. Our results show that S1 VH is most influenced by leaf area index and thus leaf biomass when sensed during dense vegetation (leafon), nofrost, and very wet conditions ( r = 0.94). In contrast, during sparse vegetation (leafoff), nofrost, stable drydown, and extremely dry conditions, S1 VH is very sensitive to topsoil moisture ( r = 0.91). Due to increased microwave attenuation, resulting in reduced S1 VH backscatter, an anticyclical behavior (negative correlations) is observed between almost all SPASbased variables/proxies and S1 VH during leafon conditions. Conversely, this reverses to a cyclical behavior (positive correlations) during leafoff conditions. Our results reveal that X and Cband RWC VOD effectively detect drought onset by capturing fast water content changes in leaves and twigs of the top canopy due to shallow sensing depth, while Lband RWC VOD captures legacy effects after repetitive droughts through slower water content changes in branches and trunks of lower tree compartments. , Plain Language Summary Continuous forest monitoring is becoming increasingly important due to climate change and extreme events. The 20182020 multiyear drought caused severe damage to forests in Central Europe. Using satellite remote sensing data, we analyze the water status and water dynamics of a protected German beech forest during this drought. Sensors onboard active microwave satellites transmit longwave electromagnetic radiation toward the Earth's surface, which can penetrate clouds and operate during daytime and nighttime. These microwaves interact with objects on the Earth's surface and are scattered back to the satellite sensor. Passive microwave sensors do not actively send electromagnetic radiation, but passively measure the natural emission. Our results show that active microwave data are most influenced by leaf biomass when sensed during dense vegetation (leafon) and very wet conditions. In contrast, these data are very sensitive to topsoil moisture during sparse vegetation (leafoff) and extremely dry conditions. Our results reveal that passive microwave data of shorter wavelengths can detect drought onset by capturing fast water content changes in leaves and twigs of the top canopy. Conversely, data of longer wavelengths capture legacy effects from past drought conditions through slower water content changes in branches and trunks of lower tree compartments. , Key Points Sentinel1 VH is sensitive to Leaf Area Index under wet and leafon conditions, and to topsoil moisture under dry and leafoff conditions X and Cband Relative Water Content can capture a drought onset through fast water content changes in leaves and twigs of the top canopy Lband Relative Water Content can capture drought legacy effects via slow water content changes in branches and trunks of the lower canopy"
    },
    {
        "DOI": "10.1080/02626667.2026.2654775",
        "Title": "Disentangling a century of water harvesting development in the Paraguayan dry Chaco flatlands",
        "Year": 2026,
        "Abstract": "Improving water harvesting in flat drylands is one of the great challenges that humanity will face coming years. Based on a remote sensing approach, we mapped and determined drivers systems (ponds ~1 ha) distribution Mennonite colonies Paraguayan Chaco. A total 59,251 ponds were detected. The average pond density was for every 4 km2, showing very heterogeneous spatial (14% sampled territory has no ponds). Pond mainly explained by mean annual precipitation (36.01% increment MSE), while soil variables (slope, texture) showed explanatory power. Our findings show decision where to build strategically made based production development needs, without considering environments natural capacity generate water (catchment areas). This ability makes Chaco as interesting unique world."
    },
    {
        "DOI": "10.1109/TGRS.2026.3677267",
        "Title": "A Hybrid Physics-Guided Machine Learning for Soil Moisture Monitoring and Drought Assessment From CYGNSS in Complex Terrain",
        "Year": 2026,
        "Abstract": "Accurate soil moisture estimation is critical for drought monitoring, water resource management, and agricultural planning from the Cyclone Global Navigation Satellite System (CYGNSS). However, conventional approaches often struggle with spatial heterogeneity seasonal variability in complex landscapes. This study develops a three-layer hybrid framework integrating Physics-guided principles, machine learning, optimization to improve across Eastern China's diverse hydroclimatic regions. The sequentially combines empirical baseline, machine-learning correction module, coherence layer leverage complementary strengths modelling paradigms. Performance was evaluated using internal CYGNSS consistency checks external Soil Moisture of China (SMCI) situ datasets, covering 41,948 samples 4,025 locations during 2021 summer monsoon season. achieved R2 = 0.653 0.474 validation. It significantly outperformed individual model components (p < 0.001) maintained temporal stability successfully captured drought-to-wet transition while reducing RMSE by 26.4% when compared baseline models. Spatially, it showed consistent performance regions, optimal results areas (RMSE / 0.046 m3/m3). Error distribution improved substantially, 60.3% predictions within 0.05 m3/m3 reference values. also demonstrated multi-scale indices, supporting its potential operational monitoring landscapes early warning."
    },
    {
        "DOI": "10.1175/WAF-D-25-0182.1",
        "Title": "On the Representation of Convectively Coupled Kelvin Waves in Operational Forecast Models: An Object-Tracking Perspective",
        "Year": 2026,
        "Abstract": "Abstract Accurately forecasting convectively coupled Kelvin waves (CCKWs) remains a major challenge, as many models struggle to realistically simulate their structure and propagation. However, previous studies have often focused on a limited set of models or relied on diagnostics that obscure individual wave characteristics. It also remains unclear how well models represent interactions between CCKWs and other tropical waves, such as easterly waves (EWs). In this study, an object-based tracking framework is used to evaluate forecasts of CCKWs and EWs across nine operational models. These include traditional physics-based models and ECMWFs new Artificial Intelligence Forecasting System (AIFS). Forecast skill is assessed as a function of observed wave attributes, life cycle phase, and environmental context. All nine models are found to underestimate CCKW strength and misrepresent the vertical structure, with the largest errors occurring during wave growth. More skillful models exhibit better vertical coherence between Kelvin wavefiltered rainfall and divergence, suggesting that forecast errors are linked to deficiencies in representing convective coupling. Forecast models also frequently miss CCKWEW interactions, and captured interactions are typically understrengthened. Despite these challenges, AIFS forecasts of EWs and CCKWs compare favorably to those of physics-based models. While the original analysis period overlaps the AIFS training window, we find that this skill persists for forecasts outside that period, highlighting the potential of data-driven systems for tropical wave prediction. These results underscore persistent challenges in CCKW forecasting and motivate further work to better understand the representation of tropical wave interactions in numerical models. Significance Statement Waves in the tropical atmosphere, including convectively coupled Kelvin waves (CCKWs), play a key role in shaping local weather. While previous studies have shown that CCKWs are hard to forecast, most have used a limited range of models and have not directly tracked individual waves. Here, we use a wave-following framework to evaluate how well nine different weather models forecast CCKWs. All models underestimate CCKW strength, especially during growth stages, and poorly represent interactions between CCKWs and easterly waves. Better-performing models show a stronger connection between the CCKWs convection and atmospheric circulation (convective coupling). These results highlight that persistent shortcomings in representing CCKWs are tied to convective coupling and suggest these errors may affect high-impact weather forecasts."
    },
    {
        "DOI": "10.1038/S44407-026-00075-4",
        "Title": "Air quality and health benefits achievable by mitigating Indian coal-fired power plant SO2 emissions",
        "Year": 2026,
        "Abstract": "Sulfur dioxide (SO 2 ) emissions from coal-fired power plants (CFPPs) are a major precursor of secondary fine particulate matter (PM 2.5 ), which has adverse health impacts. In India, the number CFPPs increased in recent decades. However, potential air quality and benefits fully mitigating these SO remain poorly explored. Here, for first time, we estimate reductions ambient PM concentrations by conducting nested regional chemical transport model simulations, integrated with novel satellite-derived CFPP-SO emission catalogue. We find that India could reduce exposure 0.3-12 gm -3 annually through mitigation emissions, preventing 124,564 (95% uncertainty intervals: 103,388-145,740) deaths annually. Additionally, states levels 0.1-13.6 ppb, males, people other backward classes, deprived subgroups expected to experience greater than their demographic counterparts. This study emphasises urgent need mitigate Indian particularly hotspots where CFPP contributes significantly achieve substantial improvements public disparity benefits."
    },
    {
        "DOI": "10.5194/ACP-26-4727-2026",
        "Title": "Microphysical properties of various precipitation systems worldwide classified via objective methods based on dual-frequency precipitation radar observations",
        "Year": 2026,
        "Abstract": "Abstract. Microphysical properties play crucial roles in physical processes related to the development of precipitation. In this study, Global Precipitation Measurement (GPM) dual-frequency precipitation radar (DPR) data were processed to demonstrate the microphysical properties of different precipitation systems (PS) that are objectively classified with the k-means clustering algorithm. Four types of regular/non-extreme PS (high-latitude shallow PS, subtropical shallow PS, moderate PS, deep PS) and four types of extreme PS (extreme deep PS, strong PS, extreme strong PS, and marine extreme PS) were recognized. These eight types of PS exhibit differences in spatial-temporal features and convection characteristics, such as storm height, rain intensity, and vertical structures. For example, the extreme strong PS, with the highest radar echo top and largest mean mass-weighted mean diameter are mainly located over tropical continents, whereas high-latitude shallow PS have the least precipitation rate and mean normalized intercept parameter values. The relationships between convection features and microphysical properties also vary among the eight types of PS. For extreme PS, maximum precipitation rate near the surface generally exceeds 100 mm h1 and balanced breakup and coalescence processes play a dominant role compared with non-extreme PS. In contrast, the coalescence processes dominate near the surface in two types of shallow PS. These results highlight the diversity of global precipitation microphysics and emphasize the necessity of global studies to increase the understanding of precipitation processes."
    },
    {
        "DOI": "10.5194/ACP-26-6929-2026",
        "Title": "Measurement report: Insights into the high temporal variability of atmospheric carbon dioxide (CO2 ) at a suburban station in the Indo-Gangetic Plain",
        "Year": 2026,
        "Abstract": "Abstract. The unusual weather patterns and large anthropogenic emissions over the Indo-Gangetic Plain (IGP) make it a significant hotspot of greenhouse gases like carbon dioxide (CO2). Given the significance of the IGP and highly populated Delhi National Capital Region (Delhi-NCR), a GHG observatory was established at a suburban monitoring station in Sonipat, Haryana (28.95 N, 77.10 E; 228 m a.s.l.), about 45 km north of the Delhi state boundary. Using a laser-based cavity ring-down spectroscopy (CRDS) technique, we measured CO2 mole fraction from February 2023 to January 2025. An annual average CO2 mole fraction of 440.8 19.7 parts per million (ppm) was recorded in 2024, which includes a strong seasonal variability, ranging from 422.6 23.3 ppm during the monsoon (JuneSeptember) to 456.4 30.8 ppm in post-monsoon (OctoberNovember). A strong CO2 diurnal amplitude of 29 ppm in May and 63 ppm in October was observed mainly due to seasonal changes in boundary layer mixing (faster in May than October) and biospheric activity (weaker in May than October). Further investigation of the drivers of strong seasonal and diurnal CO2 variability over IGP revealed a strong contrast to other global monitoring stations in the same latitude band. A strong correlation between CO2 and methane (CH4) indicated a co-located emission source, while the strong positive correlation between CO2 and carbon monoxide (CO) during post-monsoon emerges due to emissions from biomass burning. We demonstrated that the high temporal CO2 variability in the IGP region is driven by the complex interplay of local anthropogenic and biomass burning emissions, biospheric fluxes, and prevailing meteorology."
    },
    {
        "DOI": "10.1007/S41651-026-00265-4",
        "Title": "A Time-Explicit Spatiotemporal Modelling Framework for Landslide Susceptibility Driven By Cumulative Precipitation and Runoff Concentration",
        "Year": 2026,
        "Abstract": "Landslide prediction is a core issue in disaster risk management. In rainfall-driven mountainous regions, landslide susceptibility evolves dynamically in response to changing hydro-meteorological conditions, yet most existing approaches remain static or retrospective and lack explicit temporal predictability. This study proposes a spatiotemporal modelling framework that formulates landslide susceptibility as a time-evolving system state driven by precipitation. The framework follows a two-stage workflow, in which dynamic environmental forcing is forecast prior to susceptibility estimation. A U-Net spatiotemporal model is first employed to predict short-term precipitation patterns, which are then integrated with static environmental attributes within an Extreme Gradient Boosting (XGBoost) susceptibility model. Model interpretability is enhanced using SHAP to quantify the relative contributions of dynamic and static predictors. The framework is demonstrated in Nepal using ten years of daily precipitation data to generate temporally explicit landslide susceptibility maps. The results show that the model achieves an accuracy of 90.54%. The proposed approach supports time-explicit landslide susceptibility prediction and provides a practical tool for hazard management and early-warning decision-making in rainfall-driven mountainous regions."
    },
    {
        "DOI": "10.1021/ACSEARTHSPACECHEM.5C00373",
        "Title": "Enhancing WRF-Chem Simulations of Nitrogen Dioxide, Formaldehyde, and Ozone Using Satellite Retrievals and Top-Down Emission Estimates over East Asia",
        "Year": 2026,
        "Abstract": "Nitrogen oxides (NO x ) play a central role in tropospheric chemistry by regulating the atmospheric oxidation capacity. In sun-lit atmosphere, NO x, together with volatile organic compounds (VOC), contributes to accumulation of ozone, which has adverse effects on human health. this study, top-down emissions over East Asia during MayJune 2016 were estimated utilizing satellite-observed nitrogen dioxide column densities and chemical transport model simulations. The 5070% lower than bottom-up emission inventory across most regions China (reference year: 2010). This is associated reductions between 2010 2016, as well uncertainties estimates for given year. Applying simulations improved both absolute values spatial patterns 2 ), bringing them closer surface-level concentrations satellite observations, although some biases remained Seoul other South Korea. Surface ozone simulated using increased, showing much better agreement observations Beijing North Plains based inventory. Meanwhile, model-simulated levels overestimated Shanghai (Yangtze River Delta, YRD) Chengdu (Southwestern China, SWC). Additional sensitivity experiments indicated that also formaldehyde (YRD) (SWC), likely due excessively high anthropogenic biogenic VOC emissions, respectively. These results suggest reducing can be effective mitigating urban pollution studied region."
    },
    {
        "DOI": "10.1038/S41467-026-73063-7",
        "Title": "Vegetation responses to air dryness amplify future land surface warming",
        "Year": 2026,
        "Abstract": "Temperature exerts a first-order control on vegetation photosynthesis and transpiration. Yet most studies investigating temperature impacts plants rely near-surface air temperature, rather than canopy temperature-the actually experience. Because directly regulates ecosystem function, it provides more accurate measure of vegetation-climate interactions. Combining Earth System Model (ESM) simulations satellite observations in dual emergent constraint, here we show that is projected to increase substantially (~0.11-degrees or 16% their difference) over the 21st century. The ESM ensemble median fails capture these stronger increases majority vegetated regions. We find largest difference between are predicted occur regions where elevating moisture stress-particularly rising vapor pressure deficit-increasingly constrains growth This implies future warming will impose constraints plant function currently estimated. Relying alone therefore lead systematic underestimation effects photosynthesis, growth, land carbon sink. Accurate representation ESMs thus essential improve projections responses feedbacks climate change."
    },
    {
        "DOI": "10.1038/S41467-026-73395-4",
        "Title": "Differentiable land model reveals global environmental controls on latent ecological functions",
        "Year": 2026,
        "Abstract": "Abstract The spatial distributions of plant functional traits observed today are living imprints of current environmental gradients and past selection, offering insight into how plants have adapted to their environments. What remains insufficiently understood is how traits combine and coordinate across environments, and whether such coordination reflects organizing principles in ecology that can improve modeling of ecosystem functional diversity and decadal-scale carbon exchange. Here we present DifferLand, a differentiable hybrid model that learns high-dimensional, coordinated environmenttrait relationships directly from multi-modal satellite and in situ observations. DifferLand reveals a small number of latent axes that represent how suites of plant traits jointly shape vegetation dynamics and carbonwater fluxes, enabling the model to capture both long-term adaptation patterns and short-term responses to meteorological variability, and to outperform models that rely solely on plant functional types in spatial generalization. The spatialization network learns nonlinear interactions between plant functional attributes and environmental gradients, organizing latent ecological parameters that represent functional traits at the global scale. This latent environmenttrait structure reveals large-scale patterns of ecosystem functional diversity and improves the spatial generalization of terrestrial biosphere models."
    },
    {
        "DOI": "10.1134/S0001433825701506",
        "Title": "Gross Primary Production Estimation of the Leningrad Region Ecosystem Based on OCO-2 Satellite Data",
        "Year": 2025,
        "Abstract": "Abstract In the Russian Federation, carbon test sites, including representative ecosystems characteristic of territory our country, have been established to implement climate-active gas monitoring measures and study greenhouse absorption potential. Quantifying gross primary production (GPP) understanding processes that influence it are necessary CO2 by northwest ecosystem. This is one goals Ladoga site, planned for construction in 20242025 Leningrad Region. GPP Region 20142022 determined using solar-induced chlorophyll fluorescence (SIF) data measured an OCO-2 satellite equipment. found exhibit annual cycle with maximum values JuneJuly, consistent results independent studies. Over period under consideration, growth rate was positive amounted 0.08 0.02 gC m2 day1 year1. The site capacity obtained this 0.12.3 ktCO2 can be used as a priori estimates ground-based measurements at well potential on territory."
    },
    {
        "DOI": "10.1007/S00382-026-08152-W",
        "Title": "Observational characteristics of cloud-radiation-precipitation during 2019 drought period in Yunnan of Southwest China",
        "Year": 2026,
        "Abstract": "The Yunnan region of Southwest China has experienced frequent droughts in recent years. However, current studies have limited understanding of the characteristics and potential feedback mechanisms of cloud-radiation processes during these drought periods. This study analyzes cloud-radiation-precipitation changes during the extreme drought period from April to June 2019 in Yunnan, using reanalysis data and satellite-derived data. The study explores the changes in cloud-radiation throughout the rainy season and their relationship with regional precipitation and potential feedback effects. The results indicate that dominant anomalous subsidence and reduced water vapor transport were the primary causes of severe drought in Yunnan from April to June 2019, leading to a cumulative decrease in precipitation of 149.11 mm. During the drought period, Yunnan region experienced particularly strong subsidence and significant reductions in water vapor inflow. Compared to historical data, the optical thickness of clouds in the region decreased by 0.9, the liquid water path was reduced by 12.69 g m 2, and the effective radius of liquid water particles also declined 0.89 $$\\upmu\\text{m}$$. These changes hindered cloud formation, resulting in decreased cloud cover and a weakened cloud radiative cooling effect. It is found that total cloud cover during the extreme drought period decreased by 8.54% relative to historical averages. Surface shortwave cloud radiative forcing decreased by 17.78 W m 2, and net surface cloud radiative forcing dropped by 14.82 W m 2, with an average surface temperature increase of 1.33 C. The diminished cooling effect of clouds results in more solar radiation reaching the surface, causing an anomalous rise in surface temperature. This, in turn, led to decreased soil moisture and reduced local water vapor evaporation, further intensifying and sustaining the drought in Yunnan."
    },
    {
        "DOI": "10.5194/ACP-26-4509-2026",
        "Title": "Inferring drivers of tropical isoprene: competing effects of emissions and chemistry",
        "Year": 2026,
        "Abstract": "Abstract. Isoprene is the most significant non-methane hydrocarbon by total emissions and an important control on the tropospheric oxidative capacity. In the atmosphere, isoprene is oxidized by the hydroxyl radical (OH) on the order of hours depending on local OH concentrations. Using isoprene retrievals from the Cross-track infrared sounder (CrIS), we monitor global isoprene column variability and observe differing isoprene column responses to El Nino-Southern Oscillation across three tropical regions: Amazonia, the Maritime Continent, and equatorial Africa. We find correlations between isoprene column variability and temperature over Amazonia, which suggests that isoprene emissions drive Amazonian isoprene variability (emissions-controlled). In the Maritime Continent, we find strong correlations between isoprene columns, precipitation and soil moisture, as well as an anti-correlation between isoprene and formaldehyde retrievals. These correlations suggest that isoprene columns may be modulated by non-anthropogenic NOx emissions, namely soil and biomass burning NOx (chemistry-controlled), although convection and lightning NOx may also modulate isoprene column retrievals if the lofted isoprene flux is large enough. In equatorial Africa, both biomass burning and temperature can explain isoprene variability during different periods, representing an intermediate regime with contributions from emissions and chemistry. We suggest that these isoprene regimes are caused by differences in the dynamic temperature and oxidant range between the three regions, and we specifically highlight oil palm plantations in the Maritime Continent as an area of co-located isoprene and soil NOx fluxes. By leveraging CrIS isoprene retrievals, we can study interactions between VOC and NOx sources over tropical areas with few in-situ observations."
    },
    {
        "DOI": "10.1029/2025JD045067",
        "Title": "Stratospheric Intrusions Over Hong Kong: Impact and Seasonality Based on LongTerm Ozonesonde Records",
        "Year": 2026,
        "Abstract": "Abstract Stratospheric intrusions (SI) is a natural source of tropospheric O 3 , yet quantifying their contribution remains challenging in the Pearl River Delta (PRD) of China, a region characterized by high anthropogenic emissions, distant from the stratospheretotroposphere transport (STT) hotspots, but vulnerable to the topographic effect of the Tibetan Plateau (TP). Combining ozonesondes, reanalysis and trajectory simulations, we find that tropopause foldings are the primary trigger for SI, facilitating the injection of stratospheric airmasses, while the TP promotes their downstream transport. Furthermore, we establish an SI climatology by detecting the distinct SI chemical features from longterm ozonesondes with sufficient constraints. SI events are most frequent and deepest in winter, but induce larger O 3 enhancements in the upper troposphere in summer. SI seasonality is jointly controlled by largescale STT background and local conditions. These findings can advance our understanding of stratospheric impact on city clusters distant from STT hotspots. , Plain Language Summary An important source of tropospheric O 3 is the downward transport from the stratosphere. However, quantifying the amount of stratospheric O 3 injected into the troposphere is challenging due to the complexity of tracking descending stratospheric air. This is especially true in the Pearl River Delta (PRD) of southern China, owing to its high pollution levels, its distance from stratospheretotroposphere transport (STT) hotspots, and its vulnerability to the downstream transport of stratospheric O 3 influenced by the Tibetan Plateau (TP). Using vertical O 3 observations and other auxiliary data, we track the dynamical and chemical evolution of typical stratospheric intrusion cases. We find that tropopause foldings are the primary trigger of stratospheric airmasses to enter the PRD region, while the TP promotes their downstream transport. Stratospheric intrusions occur more frequently in winter, but can induce larger O 3 enhancements in the upper troposphere in summer, due to different atmospheric circulation patterns and local factors such as convection and the TP's topographic effect. This research can help us understand the stratospheric influence in regions distant from STT hotspots. , Key Points Stratospheric intrusions can substantially enhance tropospheric O 3 in the Pearl River Delta (PRD) region of southern China Tropopause folding and topographic effect of the Tibetan Plateau jointly facilitate the intrusion of stratospheric air reaching the PRD SI occur more frequently and deeply in winter, but induce larger O 3 enhancements in the upper troposphere in summer"
    },
    {
        "DOI": "10.1109/JSTARS.2026.3691675",
        "Title": "Incremental Learning for Passive Microwave Precipitation Retrievals Using Advanced Technology Microwave Sounder",
        "Year": 2026,
        "Abstract": "Spaceborne passive microwave (PMW) radiometry is central to global precipitation monitoring, yet retrieval uncertainties remain substantial, particularly for cross-track sounders whose variable footprints and channel configurations are optimized atmospheric temperature moisture profiling rather than precipitation. Consequently, existing operational products often exhibit angular-dependent biases, limited effective swath utilization, unrealistic rainfall probability distributions, systematic misclassification of phase. These limitations further compounded by the scarcity globally accurate representative observations, as training data from Dual-frequency Precipitation Radar (DPR) Cloud Profiling (CPR) spatially sparse, lack uniform coverage, heterogeneous error characteristics across regimes. To address these challenges, this study presents a supervised algorithm that incrementally trains an ensemble extreme gradient-boosted decision trees augmenting base learners with pre-training on reanalysis post-training coincident DPR CPR observations matched Advanced Technology Microwave Sounder (ATMS). By transferring prior information posterior constraints radar adopting sequential detection-estimation strategy phase rate retrieval, proposed approach yields retrievals full ATMS largely free persistent deficiencies in current Global Measurement (GPM) products. In particular, method resolves bimodal artifacts mitigates high-latitude snowfall including overestimation Arctic underestimation Antarctic. Validation against independent Multi-Radar Multi-Sensor (MRMS) over Contiguous United States (CONUS) demonstrates improved performance detection estimation relative both GPM PMW"
    },
    {
        "DOI": "10.1007/S10705-026-10497-X",
        "Title": "Cover crop decomposition and nitrogen release over three years of precipitation variability in the Western Corn Belt",
        "Year": 2026,
        "Abstract": "Cover crops can cycle nitrogen (N) in corn (Zea mays L.) production systems by absorbing this highly mobile nutrient during the winter and early spring and releasing it during the growth of the next crop. To better understand the N dynamics in a no-till cover crop-corn cropping system, we measured the decomposition and N release of three cover cropscereal rye (Secale cereale L.), hairy vetch (Vicia villosa Roth), and a rye-vetch mix in 2021, 2022, and 2023 in Nebraska. We also measured corn yield without N fertilizer inputs following the cover crops. In 2021 and 2023, the cereal rye and mix treatments produced more biomass than hairy vetch, while in 2022, the cover crop biomass production was similar across treatments. In all three years, hairy vetch had a higher rate of biomass decomposition and N release, followed by mixture and cereal rye treatments. Between 50 and 67% of the initial N content was released by all the cover crop treatments yearly. The rainfall variability over the years affected the percentage of biomass decomposed and N release, as well as the time of N release during the corn growing season. There were no differences in corn yield between cover crop and no cover crop treatments. The N release was likely not only dependent on the chemical composition of the cover crops, but also on the rainfall distribution. In addition, none of the cover crops decreased corn yield compared to no cover crop."
    },
    {
        "DOI": "10.1007/S11869-026-01996-5",
        "Title": "Decoupling anthropogenic and biogenic influences in the formation of HCHO over two hotspot regions of India",
        "Year": 2026,
        "Abstract": "Tropospheric formaldehyde (HCHO) serves as an effective tracer of volatile organic compound (VOC) oxidation and provides insight into regional photochemical regimes. In this study, satellite-derived HCHO column densities from the Ozone Monitoring Instrument (OMI) are analysed over the Indian subcontinent for the period 20072021 to examine spatial patterns, seasonal variability, long-term trends, and transport-driven enhancements. Seasonal mean HCHO fields are used to identify persistent hotspot regions, while day-to-day variability is evaluated using back-trajectory, residence-time, and potential source contribution function (PSCF) analyses at two hotspot locations in eastern (Nandapura, Odisha) and southern (Kuttampuzha, Kerala) India. The results reveal pronounced spatial and seasonal contrasts in HCHO distribution, with enhanced summer concentrations over forested regions and coastal zones, and reduced wintertime levels. Summer-to-winter HCHO ratios indicate strong biogenic influence across both hotspots, while trend analysis highlights divergent long-term behaviour, with a sustained increase in HCHO at Nandapura and comparatively stable or declining levels at Kuttampuzha. Analysis of the HCHO-NOx ratio demonstrates clear regional differences in ozone sensitivity, identifying NOx-limited conditions over the Malabar coast and VOC-limited to transitional regimes over eastern India, with distinct seasonal shifts at Nandapura. Trajectory-based diagnostics show that extreme HCHO events at both locations are associated with air masses that experience prolonged residence over emission-rich regions, including forested areas, coastal zones, shipping corridors, and power sector. Our results indicate that residence time within these source regions, rather than transport pathway alone, plays a critical role in determining peak HCHO column enhancements. Overall, this study demonstrates how long-term satellite observations, when combined with transport and chemical regime diagnostics, can be used to disentangle the roles of biogenic emissions, anthropogenic influence, and atmospheric transport in controlling HCHO variability and ozone sensitivity over India."
    },
    {
        "DOI": "10.48048/TIS.2026.12518",
        "Title": "Spatial Classification of Diurnal Precipitation Cycle in the Tropical and Subtropical Regions Based on the Circular Statistical Analysis",
        "Year": 2026,
        "Abstract": "The impact of climate and weather on human existence is of utmost importance, as it shapes several aspects, such as human evolution, migration patterns, and social progress. Observing and comprehending precipitation patterns, especially the diurnal cycle, are imperative for effectively managing everyday activities. This study thoroughly examines diurnal precipitation patterns in tropical and subtropical regions through the utilization of a parametric approach based on the bimodal von Mises distribution. The research effectively creates a spatial classification criterion for diurnal precipitation, offering a simple but helpful tool for meteorological and climatological investigations. It helps identify the spatial extents of convective and stratiform precipitations and reveals the physical mechanisms behind the precipitation, such as over the complex mountaintop terrains. The user-friendliness and capacity to elucidate the bimodal characteristics of diurnal precipitation patterns also render this criterion a significant asset for academia and practitioners. HIGHLIGHTS Introduces a circular statistical framework using the bimodal von Mises distribution to model diurnal precipitation patterns across tropical and subtropical regions. Demonstrates that the proposed method effectively captures both unimodal and bimodal rainfall variations, outperforming traditional harmonic approaches, particularly in representing asymmetric or dual-peak behaviors. Provides a spatial classification of diurnal precipitation into classes covering convective, orographic, and stratiform regimes, revealing clear landocean and topographic contrasts. Shows that continental regions exhibit afternoon and evening convective peaks, while oceanic areas are dominated by nocturnal and early-morning stratiform rainfall. Offers a quantitative tool for climatological and hydrological applications, supporting improved weather prediction, water-resource management, and climate-resilience planning. GRAPHICAL ABSTRACT"
    },
    {
        "DOI": "10.1109/SIGNASS67826.2026.11479974",
        "Title": "Smart Forecasting of Millimeterwave Window Frequency in Tropical Africa Using Machine Learning",
        "Year": 2026,
        "Abstract": "The millimeter-wave frequency band is essential for modern-day mobile communication, radar, and other applications throughout equatorial Africa. However, while propagating through the atmosphere, millimeter waves suffer from strong atmospheric absorption due to water vapour (22.235 GHz) oxygen (60 absorption. These effects are highly variable, influenced by temperature density, which determine location of window frequencies ( $\\mathrm{f}_{\\text{windows }}$) , regions where attenuation minimum. In order identify in 20-200 GHz range have least amount signal attenuation; this comprehensive research looks at meteorological surface data fourteen tropical African nations with latitudes ranging xmlns:xlink=\"http://www.w3.org/1999/xlink\">$7.37^{\\circ} \\mathrm{N}$ xmlns:xlink=\"http://www.w3.org/1999/xlink\">$6.37^{\\circ} \\mathrm{S}$. We examined seasonal variations between wet dry seasons, paying particular attention how affected minimum vapor levels. Sophisticated machine learning models were employed, including Temporal Convolutional Networks (TCN), Neural (CNN), Long Short-Term Memory (LSTM), Recurrent (RNN), Feedforward (FNN) increase prediction accuracy develop dependable forecasting capabilities identification accurate around 30, 94, 140 GHz. Results show that }}$ ) 30 vary 31.09 Gabon (wet season) 31.30 Kenya Tanzania (dry season). LSTM model performed superbly during correlation coefficients (R2) above 0.999 Root Mean Square Error (RMSE) values as low 0.0019 forecasts."
    },
    {
        "DOI": "10.1038/S41467-026-71969-W",
        "Title": "Strengthening influence of atmospheric rivers on global snow depth dynamics",
        "Year": 2026,
        "Abstract": "Atmospheric rivers (ARs), narrow zones of intense water vapor transport in the Earths atmosphere, play a pivotal role driving heavy precipitation and temperature anomalies. Snowpack dynamics, essential for global availability, are sensitive to variations temperature. However, influence ARs on snowpack dynamics remains unclear. Here, we assess how affect snow depth worldwide explore underlying physical mechanisms. Our results reveal that drive strong intra-seasonal variabilitygenerally increasing winter spring decreasing it summer (with declines exceeding 15% Temperate regions) autumn. Active El Nino-Southern Oscillation can amplify this influence. On interannual timescales, more frequent associated with reduced but increased other seasons. Snowfall emerges as primary factor explaining changes related ARs. This study provides crucial advancement understanding complex climate-snowpack relationship underscores need represent AR-snowpack interactions Earth system models. The authors show atmospheric (ARs) contribute summer, seasons snowfall being"
    },
    {
        "DOI": "10.1016/J.APR.2026.103070",
        "Title": "Deep-learning for urban air quality: Downscaling satellite nitrogen dioxide with ground observations over Delhi, India",
        "Year": 2026,
        "Abstract": "Delhi is a critical global hotspot for Nitrogen Dioxide (NO2), fueled by dense traffic and industrial hubs. Effective monitoring requires high-resolution data to capture local gradients, yet long-term analysis is often hindered by the coarse resolution of historical Ozone Monitoring Instrument (OMI) products (25 km). This study develops deep learning models to downscale OMI Multi-Decadal Nitrogen Dioxide and Derived Products from Satellites (MINDS) data specifically for the Delhi metropolitan region. By integrating high-resolution Tropospheric Monitoring Instrument (TROPOMI) data and Central Pollution Control Board (CPCB) ground observations, we bridge the gap between historical records and modern precision. Optimized using the 2024 monitoring cycle, our architecture accurately reconstructs NO2 spatial patterns across extreme seasonal shifts, from post-monsoon pollution peaks to summer transitions. This approach provides a high-fidelity framework for analyzing long-term urban air quality trends at a granular scale. To achieve this, we implemented and evaluated two models: 1) Gated Fusion Downscaling Predictor (GFDP) and 2) a hybrid model that comprises Inverse Distance Weighting with Deep Neural Networks (IDW + DNN). Quantitative evaluation against withheld ground-based data confirmed high model accuracy, yielding a normalized root mean square error (NRMSE) of 0.05, a mean bias of 0.03, and a normalized mean absolute error (NMAE) of 0.04. This advancement is essential for monitoring long-term NO2 trends, provides improved spatial characterization of NO2 distribution, enabling more accurate identification of localized pollution hotspots and supports better interpretation of air quality patterns in complex urban environments."
    },
    {
        "DOI": "10.5194/ACP-26-4341-2026",
        "Title": "Impacts of the Icelandic Holuhraun volcanic eruption on cloud properties using regional model cloud-aerosol simulations",
        "Year": 2026,
        "Abstract": "Abstract. Aerosol-cloud interactions remain a significant uncertainty in climate prediction, largely due to the complexity of measuring and modelling these processes. Volcanic eruptions, such as the Holuhraun event in 2014, offer valuable opportunities to study these interactions by introducing substantial aerosol perturbations. In this study, we investigate the impacts of the Icelandic Holuhraun volcanic eruption on cloud properties using the CASIM cloud microphysics model and the UKCA-GLOMAP aerosol microphysics model within the high-resolution regional model of the UK Met Office Unified Model. For a four-week simulation, our findings indicate a more than 80 % increase in droplet number concentration during the eruption with reductions in cloud droplet size, both of which are statistically significant at the 0.05 level in t-tests. In contrast, the effects of the volcanic eruption on liquid water path and cloud fraction are not generally significant. During the third week of September, neither satellite observations nor model simulations show significant impacts of the volcanic plume on cloud properties when comparing in-plume to out-of-plume properties. Our simulations suggest that the volcanic aerosol effect during this period was masked by factors affecting the out-of-plume atmospheric conditions, such as natural meteorological variability or non-volcanic aerosols possibly originating from Europe. When volcano on/off simulations are examined, the droplet number increase and the reduction in droplet size remain evident, indicating that these effects are still active. This highlights the crucial role of realistic models in revealing aerosol-cloud interactions that can be obscured in observations due to environmental and meteorological factors."
    },
    {
        "DOI": "10.1111/PHP.70108",
        "Title": "Methods for substituting onsite ambient ultraviolet radiation measurements for personal exposure studies",
        "Year": 2026,
        "Abstract": "Abstract Measurements of personal ultraviolet radiation (UVR) exposure are a helpful tool in estimating people's UVR exposure. Gained values are valid for the corresponding location, time and date only. Assessment of personal UVR exposure as well as its translation to other locations, times and dates require ambient UVR as a reference. In some cases, UVR measurements are not available at the study site for practical reasons. Therefore, we have investigated alternative methods to substitute onsite measurements of ambient erythemally weighted daily radiant exposure. These methods comprise the assumption of spatial persistency of measurements from distant highgrade instruments, model calculations including clouds (TEMIS) and satellite measurements (OMI). Evaluation was done by substituting measurements of a highgrade instrument operated in Vienna, Austria. Our results show that up to a distance of 82 km the assumption of spatial persistency delivers lowest uncertainties. For larger distances, TEMIS performs better. Substituting with OMI carries the highest uncertainty but is the only method with global coverage, and therefore the only applicable method in large parts of the world. For correction of altitude, an increase of +14%/1000 m was found for clear sky, but for allsky, monthly medians up to +40%/1000 m were found."
    },
    {
        "DOI": "10.5194/ACP-26-5333-2026",
        "Title": "Isotopic apportionment of sulfate aerosols between natural and anthropogenic sources in the outflow of South Asia",
        "Year": 2026,
        "Abstract": "Abstract. Sulfate aerosols cool the climate and thus temporarily mask climate warming, but at a cost to air quality. Their short atmospheric lifetime leads to heterogeneous global coverage, with sulfate concentrations over South Asia being especially elevated and continuing to increase. It remains challenging to constrain the relative importance of different emission sources due to poor observational coverage and uncertainties in bottom-up technology-based emission estimates. The stable sulfur isotope composition (34S-SO42-) quantitatively distinguishes natural and anthropogenic sources. This study aimed to constrain the sources of sulfate arriving at the Maldives Climate Observatory Hanimaadhoo (MCOH), which is ideally situated for intercepting the outflow from airsheds over the Indian subcontinent. The results show that anthropogenic sources of sulfate contributed 93 14 %, 87 10 %, and 66 12 % in winter (post-monsoon), spring (pre-monsoon), and summer (monsoon), respectively. There was also a moderate to strong correlation (r2= 0.75, p0.05, n=7) between continental anthropogenic (winter and spring) sulfate (34S) and black carbon aerosols from fossil fuel combustion (pinpointed by 14C). This study provides improved constraints on sulfate sources for South Asia a key region for aerosol pollution and aerosol masking of climate warming."
    },
    {
        "DOI": "10.1007/S41976-026-00278-Z",
        "Title": "Validation of Satellite-based and Gridded Precipitation Products for Gap-filling in Precipitation Series in the Eastern Amazon",
        "Year": 2026,
        "Abstract": "Abstract Accurate precipitation measurement is essential for climate, hydrological, and agronomic studies. However, in regions such as the Amazon, scarcity of rain gauges frequent gaps historical series pose a significant challenge long-term analyses. This study evaluated performance satellite gridded estimates gap-filling daily rainfall data recorded between 2019 2024. The observed dataset was obtained from micrometeorological tower installed an oil palm-based Agroforestry System (AFS) Eastern Amazon. evaluation employed widely recognized statistical metrics, coefficient determination (R 2 ), root mean square error (RMSE), absolute (MAE), percent bias (PBIAS), Nash-Sutcliffe efficiency (NSE), Willmotts index agreement (d). Additionally, cumulative curves different databases were compared with to identify over- or underestimation trends. results showed that, among tested databases, NASA Power (NP) exhibited best terms consistency lower bias, making it most suitable filling series. analyses highlighted importance careful selection alternative ensure continuity quality remote tropical regions, aspect hydrological modeling"
    },
    {
        "DOI": "10.1017/JOG.2026.10154",
        "Title": "The Python Energy Balance model for Snow and Ice (PEBSI): application and tradeoff analysis on Gulkana Glacier, Alaska",
        "Year": 2026,
        "Abstract": "Abstract Glacier energy-balance models offer mechanistic insights into glacier mass balance under a changing climate, yet their considerable data requirements hinder large-scale applications. Here we present the open-source Python Energy Balance model for Snow and Ice (PEBSI), which includes physically based albedo evolution using Snow, Aerosol Radiative (SNICAR) model. PEBSI is calibrated validated robust in situ from Gulkana Glacier, Alaska 2000 to 2024. Simulations forced with original bias-corrected climate reanalysis show that statistically downscaling observations necessary reproduce summer (mean absolute error [MAE] = 0.75 m w.e. vs 0.22 w.e., respectively). A grid search across two parameters, precipitation factor densification parameter, reveals tradeoffs performance compared seasonal end-of-winter snow density depth. No single combination of parameters minimizes all errors, underscoring inherent overparameterization challenges translating coarse scale. The successfully simulates 2024 melt season, agreeing surface-height change (MAE 0.48 m) 0.066) observations. Moving forward, provides unique opportunities quantify feedbacks impact on future loss."
    },
    {
        "DOI": "10.5194/WES-11-1251-2026",
        "Title": "Biases in preconstruction estimates of wind plant annual energy production",
        "Year": 2026,
        "Abstract": "Abstract. Estimating the energy yield of a wind plant during the preconstruction phase is a historically difficult task, even with industry improvements in these estimations. We build on prior research comparing the realized energy production of wind plants and their estimated annual energy production P50 values (median energy production), using owner-provided energy production and losses. We produced similar results to prior studies but with a slightly increasing bias of overestimating median energy production (a bias between realized and estimated energy production of 7.4 % to 6.6 %, depending on the scenario, as opposed to 6.7 % to 5.5 % from earlier studies). In addition to assessing annual energy production P50 bias, we compared both the 1-year and the long-term annual energy production P90 and uncertainty energy yield assessment estimates to the observed long-term-corrected energy production. We found that neither the energy yield assessment uncertainty nor the P90 is conservative enough compared to the observed distribution of prediction errors, suggesting significant room for improvement in the energy yield assessment process."
    },
    {
        "DOI": "10.1038/S41598-026-49693-8",
        "Title": "A simple statistical technique to improve seasonal predictions of northeast monsoon rainfall over India using selective ensemble mean",
        "Year": 2026,
        "Abstract": "The predictability of northeast monsoon rainfall (NEMR) has been poorly studied. It is noted that the September-initialized multi-model ensemble (MME) seasonal forecast systems shows modest skill in predicting NEMR over southern peninsula India for October-November-December (OND) season. This study proposes an approach to improve by selecting MME members effectively capture correlation between and major modes tropical climate variations (viz., El Nino-Southern Oscillation Indian Ocean Dipole) during OND Both deterministic probabilistic verification results indicate selected outperforms full NEMR, with improved skill. improvement can be attributed part external forcing due sea surface temperature realistic representation atmospheric processes members. Composite analysis extreme positive negative precipitation seasons region streamfunction anomalies realistically. failed correct phases unrealistic response equatorial Pacific."
    },
    {
        "DOI": "10.1007/S00376-026-5582-Y",
        "Title": "Different Aerosol Impacts on the Vertical Structure of Cold-Topped Convective Precipitation under Varying Meteorological Conditions in the Sichuan Basin",
        "Year": 2026,
        "Abstract": "Aerosols influence convective precipitation through both radiative and microphysical effects, yet their impacts are strongly modulated by meteorological conditions. Using six years of precipitation profiles, this study examines how aerosols affect the vertical structure of cold-topped convective precipitation over the Sichuan Basin. Results show that under low convective available potential energy (CAPE) conditions, both precipitation top height (PTH) and precipitation rate exhibit a non-monotonic response to aerosol optical depth (AOD), first increasing and then decreasing. By contrast, under high CAPE conditions, PTH still increases and then decreases with AOD, whereas precipitation rate reaches a minimum at moderate AOD, indicating a decoupling between cloud depth and precipitation. This behavior is likely driven by stronger dynamical lifting that rapidly transports numerous small droplets aloft, where they freeze to form abundant small ice crystals. These crystals are inefficient at growing into precipitation-sized particles, thereby weakening rainfall despite deeper cloud development. It is also notable that, while evaporation is evident in the lower troposphere, precipitation at different layers and at the surface still responds consistently to AOD. Overall, the results highlight the strong dependence of aerosol-precipitation interactions on meteorological conditions and underscore the need for further investigation."
    },
    {
        "DOI": "10.1029/2025GL119983",
        "Title": "Uniformity in Heavy Precipitation Microphysics During the Northward Advancement of Summer Monsoon in China Unveiled by Objective Weather Typing",
        "Year": 2026,
        "Abstract": "Abstract The microphysical evolution of the East Asian summer monsoon precipitation during its northward advance across China remains unclear, due to the mixing of diverse weather systems in past studies. Applying objective synoptic classification to a decade of satellite observations, we isolate canonical monsoontype heavy precipitation across South, East, and North China. We find its microphysics are highly uniform to first order, consistently exhibiting maritimelike high concentrations of smalltomedium raindrops through dominated warmrain accretion process. This uniformity arises from a consistent synoptic environment of deep moisture transport. In contrast, nonmonsoon systems (e.g., cold troughs which comprise >50% of heavy precipitation in North China) favor icephase processes and produce larger raindrops. Merging these regimes biases domainwide statistics, explaining prior reports of regional disparity. Our findings underscore the necessity of synoptic pattern classification to accurately characterize monsoon precipitation microphysics and to improve the capacity of regionspecific quantitative precipitation estimation and modeling. , Plain Language Summary The summer monsoon brings heavy rainfall from southern to northern China. We investigate whether the size and number of raindrops changes as it moves north. Using a weatherpattern filter and 10year satellite radar data, we examine only rain purely related to monsoon circulation. We show that the monsoontype heavy rain characteristics stay remarkably consistent all the way north: It is always made of many smalltomedium sized raindrops, like warm tropical ocean rains. However, in North China, over half of heavy rainfall events come from nonmonsoon weather systems, which create larger raindrops. Blending these types distorts the true picture of monsoon precipitation microphysics. Distinguishing weather types is therefore crucial for accurate rainfall measurement and prediction across China. , Key Points Statistically, monsoon rainfall over China exhibits uniform maritimelike microphysics through efficient warmrain processes Cold troughrainfall largely distort microphysical statistics, explaining prior observational discrepancies and regional variations Synoptic weather classification is essential for accurate interpretation of precipitation microphysics"
    },
    {
        "DOI": "10.5194/ACP-26-4153-2026",
        "Title": "Emerging low-cloud feedback and adjustment in global satellite observations",
        "Year": 2026,
        "Abstract": "Abstract. From mid-2003 to mid-2024, a global decrease in low-cloud amount enhanced the absorption of solar radiation by 0.220.07 W m2 per decade (1 range), accelerating the energy imbalance trend during that period (0.44 W m2 per decade). Through controlling factor analysis, here we show that the low-cloud trend is due to a combination of cloud feedback and adjustments to greenhouse gases and aerosols (respectively 0.090.02, 0.050.03, and 0.030.03 W m2 per decade), which jointly account for 74 % of the trend. The contribution of natural climate variability is weak but uncertain (0.010.08 W m2 per decade), owing to a poorly constrained trend in boundary-layer inversion strength. Importantly, the observed low-cloud radiative trend lies well within the range of values simulated by contemporary global climate models under conditions close to present day. Any systematic model error in the representation of present-day global energy imbalance trends is thus likely to originate in processes unrelated to low clouds."
    },
    {
        "DOI": "10.1175/JHM-D-25-0179.1",
        "Title": "Extreme Rainfall from Tropical Cyclones is Revealed by Kilometer-Scale Downscaling in Southeast Africa",
        "Year": 2026,
        "Abstract": "Abstract Tropical cyclones in southeast Africa pose significant flood risks, driven by high winds that can cause storm surge flooding, and extreme rainfall often leading to devastating inland impacts. Understanding current and future flood risk from extreme rainfall in the region is hindered by the limited observational network. Although reanalysis and satellite rainfall data improve spatial coverage, their coarse resolution restricts the accurate representation of intense convective rainfall associated with tropical cyclones. To address this limitation, fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) reanalysis data were dynamically downscaled to a convection-permitting resolution using the Conformal Cubic Atmospheric Model (CCAM) from 2014 to 2023. This study assesses the ability of kilometer-scale CCAM simulations to improve the characterization of extreme rainfall from six impactful tropical cyclones affecting Malawi, Madagascar, and Mozambique. Comparisons are made against reanalysis data, satellite-derived rainfall, and rain gauge observations. Results show that CCAM captures higher rainfall totals and improves the underestimation bias of extreme hourly rainfall present in the other datasets. Additionally, CCAM reveals detailed rainband structures, including evidence of double rainbands within the eyewall, and inner and outer spiral rainbands. This work has demonstrated that the current reanalysis is not sufficient to properly characterize flood risk from tropical cyclones. The kilometer-scale model provides a more realistic simulation of flood-relevant rainfall. These advancements are essential for understanding and managing flood risk under current and future climate scenarios, supporting adaptive actions to mitigate tropical cyclone impacts. Significance Statement This study emphasizes the critical importance of high-resolution approaches for accurately assessing flood risks related to tropical cyclones in southeast Africa. The use of convection-permitting resolution significantly improves the representation of extreme convective rainfall. Results indicate that this approach captures extreme rainfall from tropical cyclones more accurately than existing reanalysis and satellite datasets, offering better insights into the complex rainband structures associated with cyclones. These advancements are important for informing effective flood risk management strategies and supporting adaptive measures in response to changing climate conditions. Such progress is essential for safeguarding vulnerable communities in Malawi, Madagascar, Mozambique, and beyond."
    },
    {
        "DOI": "10.1007/S10346-026-02766-1",
        "Title": "Evolution mechanism of the flash flood-debris flow disaster chain triggered by high-elevation shallow landslides: a case study of the Huangya Gully event in Yuzhong, China, on August 7, 2025",
        "Year": 2026,
        "Abstract": "With increasingly frequent extreme rainfall events driven by global climate change, flash flood-debris flow disasters triggered by high-elevation shallow landslides are becoming more prevalent, yet their evolution mechanisms remain unclear. This study investigates the catastrophic flash flood-debris flow event that occurred on August 7, 2025, in Huangya Gully, Yuzhong County, Gansu Province, China. By integrating multi-source remote sensing, field investigation, and numerical simulation, the evolution mechanism of the disaster chain induced by high-elevation sources was systematically revealed. The results show that abundant antecedent rainfall is the critical precondition for quasi-instability of shallow landslide clusters. Dense pre-event vegetation intensified rainwater infiltration through root-induced preferential seepage pathways. This process increased the slope weight and reduced the soil shear strength, ultimately triggering widespread shallow landslides. The dynamic inversion using the Massflow model indicates that the mobilized mass reached an initial velocity of 24 m/s. Through intense basal erosion, lateral scouring, and dam-break amplification effects, the material volume increased by approximately 32.40% along the path. This study further distinguishes the fundamental differences in causation and evolution mechanisms between this disaster chain, characterized by \"cluster occurrence, shallow failure, and rapid fluidization,\" and traditional chains caused by \"single, deep-seated, debris-fluidized\" high-elevation landslides. Finally, based on the revealed evolution mechanism, a comprehensive prevention strategy of \"source regulationprocess attenuationrisk avoidance\" is proposed. The findings provide an important scientific basis for the prevention and control of such flash flood-debris flow disasters."
    },
    {
        "DOI": "10.1088/2515-7620/AE6D88",
        "Title": "Response of phytoplankton under varying environmental conditions in Cabo Verde: a seasonal and interannual analysis",
        "Year": 2026,
        "Abstract": "Abstract Phytoplankton are the foundation of marine ecosystems, yet their response to environmental variability in oligotrophic regions remains insufficiently quantified. This study examines the seasonal and interannual variability of chlorophyll-a (Chl-a) in the Cabo Verde region (19982024) and evaluate its relationship with Saharan dust, sea surface temperature (SST), and wind speed. Chl-a displays a double-peaked seasonal cycle, with a primary winter peak (0.37 mgm 3 ) driven by to strong winds and low SST, and a secondary early-summer peak (0.40 mgm 3 ) coinciding with increased dust loading. Principal component analysis reveals a dominant windthermalbiological mode explaining 48.55% of the variance, while dust process constitute a secondary, seasonally modulated influence. Dry deposition is positively associated with Chl-a during winter and fall, whereas wet deposition during the summer rainy season coincides with Chl-a minima. ENSO (Nino 3.4) shows a moderate negative correlation with Chl-a in winter ( r 0.31), while the North Atlantic Oscillation exhibits a moderate positive correlation ( r +0.28); both relationships are statistically significant ( p < 0.05). These results indicate a consistent, though not very high, association, suggesting that while large-scale climate modes contribute to Chl-a variability, additional factors likely play an important role. Recent warming (20202024) reveal strong SST anomalies and suppressed productivity, consistent with stratification-driven nutrient limitation. These integrated findings highlight the dominant role of physical forcing and the conditional influence over of atmospheric inputs on Cabo Verde marine ecosystem."
    },
    {
        "DOI": "10.1029/2025JA034841",
        "Title": "Semidiurnal Tide Modulation by Ozone and Nonlinear Interaction With Planetary Wave During the 2024 Southern Hemisphere Sudden Stratospheric Warmings",
        "Year": 2026,
        "Abstract": "Abstract We investigate the neutral wind and semidiurnal tide (SDT) variations in the mesosphere and lower thermosphere (MLT) during two consecutive minor Southern Hemisphere (SH) sudden stratospheric warmings (SSWs) that occurred unusually early in JulyAugust 2024. Zonal and meridional winds from four meteor radar stations at 5070S were analyzed. Zonal winds reversed from eastward to westward between 80 and 100 km altitude during both events, showing a more distinct reversal in the second event. The SDT amplitudes increased and exhibited longitudinal differences around the second event. To elucidate the mechanisms responsible, we analyzed ozone observations from Aura/MLS along with MERRA2 shortwave heating. Positive ozone anomalies at 10 hPa (32 km) in the SH polar region around each event coincide with enhanced SDT amplitudes from meteor radars. In addition, the shortwave heating rate shows an enhanced 12hr component at SH highlatitudes above 40 km during these events, supporting an ozonerelated radiative contribution to the SDT variability. Using phasedifferences from longitudinally separated meteor radars, we estimated the zonal wavenumber. Based on this analysis, we propose that nonlinear interaction between the quasi16day zonal wavenumber2 planetary wave (Q16DW2) and the migrating semidiurnal tide (SW2) contributed to the observed longitudinal differences in SDT amplitude. Furthermore, nonlinear advection associated with Q16DW2SDT interactions is examined and shows clear longitudinal differences that lead to longitudinal asymmetry in SDT amplitude. These findings show the strong modulation of the SDT by SH SSWs and underscore the combined roles of ozone variability and nonlinear wave interactions in modulating upperatmospheric tidal responses. , Plain Language Summary Sudden Stratospheric Warming (SSW) is a phenomenon in which temperatures in the polar stratosphere rise rapidly due to interactions between planetary waves (PWs) propagating from the troposphere and the background wind flow. While SSWs occur approximately once every 2 years in the Northern Hemisphere, they are extremely rare in the Southern Hemisphere (SH). In JulyAugust 2024, two rare consecutive SH SSWs occurred, making them the earliest events ever recorded during the satellite era. Given the unusual nature of these events, we investigated how winds and atmospheric tides in the mesosphere and lower thermosphere (MLT) responded using four meteor radars located near Antarctica. We found that the semidiurnal tide (SDT) amplitude increased during these events, exhibiting distinct longitudinal asymmetry. To elucidate the mechanisms driving these tidal changes, we examined satellitebased ozone observations from Aura/MLS and applied a phasedifferencing technique to paired meteor radar sites located at similar latitudes but separated by 150 in longitudes. The results indicate that both ozonerelated radiative forcing and nonlinear interactions between PWs and SDT could contribute to the observed enhancement and longitudinal asymmetry in SDT amplitude. These results can improve our understanding of how stratospheric disturbances influence upper atmospheric dynamics on a global scale. , Key Points Meteor radars observe wind and semidiurnal tide (SDT) variations during the 2024 southern hemisphere sudden stratospheric warmings SDT zonal wavenumber and propagation direction are determined from phase differences between longitudinally separated meteor radars SDT variability is likely influenced by stratospheric ozone and nonlinear interaction with quasi16day planetary waves"
    },
    {
        "DOI": "10.1029/2025JD045291",
        "Title": "Stratospheric Constituent and Temperature Responses to the Hunga Volcanic Eruption and QBO in the Southern Hemisphere Low to Midlatitudes",
        "Year": 2026,
        "Abstract": "Abstract Stratospheric temperature, ozone, and constituent observations show significant perturbations in the 12 years following the January 2022 Hunga volcanic eruption. This study uses GSFC2D model simulations forced with satellitebased Hunga aerosol and water vapor anomalies to investigate the resulting impacts in the Southern Hemisphere stratosphere (tropics to midlatitudes) during the 214 years following the eruption. The relative impacts of the volcanic forcings are quantified and compared with dynamical changes caused by the quasibiennial oscillation (QBO), which exerts a substantial influence. Largest volcanicdriven chemical and radiative changes occur during 2022 and gradually diminish thereafter. Significant model enhancements in HNO 3 and ClO, and corresponding reductions in NO 2 and HCl, are driven by the Hunga aerosol via heterogeneous reactions, and the Hunga water vapor perturbation via gasphase reactions. However, for model ozone, the net volcanic chemical impacts are small. For total column ozone, the aerosolinduced effects are mostly negative with maximum changes of 3 DU, while the water vaporinduced impacts are mostly positive with maximum changes of +1 DU. Simulated total ozone anomalies driven by transport are significantly larger (512 DU), consistent with previous studies. Model temperature anomalies are driven mainly by a combination of the QBO (12 K) and radiative cooling of the Hunga H 2 O plume (23 K). The Hunga aerosol had a minor impact on stratospheric temperature, causing a net warming of at most +0.5 K. , Plain Language Summary Stratospheric ozone protects life on Earth from harmful ultraviolet radiation. Ozone, along with water vapor and atmospheric particles (aerosols), are key components that control the temperature and chemistry of the atmosphere. The January 2022 eruption of the Hunga underwater volcano in the South Pacific caused a large increase in water vapor and a moderate increase in aerosols in the stratosphere. Observations show significant changes in stratospheric temperature, ozone, and chemical constituents in the Southern Hemisphere following the eruption. In this study, we simulate the volcanic water vapor and aerosolinduced changes in stratospheric temperature and chemistry in the 214 years following the eruption. The simulations are in good agreement with the observations and help isolate the various chemical and radiative processes caused by the volcanicdriven changes and separate these from nonvolcanic changes in atmospheric circulation. The largest Hungadriven impacts occurred in 2022 and gradually diminished thereafter. Temperature and several nitrogen and chlorine chemical species were significantly impacted by the volcanicdriven changes. However, the net chemical impact on ozone caused by Hunga was small compared to nonvolcanic atmospheric circulation processes. , Key Points Observations show significant anomalies in stratospheric temperature, ozone, nitrogen, and chlorine species following the Hunga eruption QBO transport variability is an important driver of stratospheric anomalies in the Southern Hemisphere tropics to midlatitudes Magnitudes of the volcanic chemical perturbations vary with constituents, but the net chemical impact on ozone is small (13 DU)"
    },
    {
        "DOI": "10.5194/ACP-26-7081-2026",
        "Title": "Long-term ozone formation sensitivity in China: spatiotemporal evolution and machine learning attribution",
        "Year": 2026,
        "Abstract": "Abstract. Accurate diagnosis of ozone (O3) formation sensitivity (OFS) is essential for designing effective precursor-control strategies, yet long-term, observation-based, and interpretable national-scale assessments remain limited. Here, we combined OMI satellite observations of tropospheric nitrogen dioxide (NO2) and formaldehyde (HCHO) with ground-based O3 measurements to derive region-specific FNR (HCHO / NO2) threshold ranges from the relationship between FNR and O3 exceedance probability. Based on these thresholds, we characterized the spatiotemporal evolution of OFS across China during the warm season (AprilSeptember) from 2005 to 2023 and coupled Random Forest (RF) with SHapley Additive exPlanations (SHAP) to quantify contributions from emission-related and meteorological factors. The results reveal a clear phase reversal in OFS over China. From 2005 to 2012, the national area fraction of nitrogen oxides (NOx)-limited regimes decreased by 7.9 %, while transitional and volatile organic compound (VOC)-limited regimes increased by 5.6 % and 2.3 %, respectively. After 2013, this pattern reversed, and by 2023 NOx-limited regimes expanded to 76.5 % of the polluted area, whereas VOC-limited regimes accounted for only 2.8 %. Regionally, after 2013, both BeijingTianjinHebei (BTH) and Fenwei Plain (FWP) exhibited transitions between transitional and NOx-limited regimes, while the Yangtze River Delta (YRD) showed a shift toward NOx-limited regimes. Sichuan Basin (SCB) remained predominantly transitional, and Pearl River Delta (PRD) also shifted toward NOx-limited regimes. SHAP analysis shows emission-related variables contributed 50.1 %69.4 % of total importance, exceeding meteorological factors, which increased after 2013. Overall, OFS evolution is primarily associated with emission changes but increasingly modulated by meteorological conditions under cleaner conditions."
    },
    {
        "DOI": "10.1007/S00704-026-06256-1",
        "Title": "Paradigm shift in rainfall observations: a review of high-resolution satellite based precipitation products",
        "Year": 2026,
        "Abstract": "Satellite-based precipitation products (SPPs) are an important method for measuring and analyzing rainfall, especially in areas with limited rain-gauge networks. These products offer more reliable coverage and are valuable for various environmental applications, such as precipitation analysis, hydrological modeling, and drought monitoring. Unlike ground-based rain gauges, SPPs provide more evenly distributed coverage over large areas using sophisticated infrared and microwave instruments to detect precipitation. A number of satellite-based rainfall products have been developed and successfully implemented, including GSMaP, PERSIANN, TRMM, CHIRPS, and GPM IMERG. However, there are several drawbacks in employing SPPs, such as lacking the capacity to measure the temporal and spatial variability of the daily precipitation rate due to instrument uncertainties, particularly over large areas. SPPs may be inaccurate due to variations in the quality of data collected directly from rain gauges. Hence, it is essential to understand the limitations and potential biases of these products. This comprehensive review aims to address the challenges in obtaining reliable precipitation data, particularly in developing countries, and investigates the potential of SPPs as a solution by emphasizing their importance for accurate precipitation estimation for climate change analysis, extreme event prediction, and hydrological impact assessment. The methodology for selecting relevant literature on satellite-based high-resolution precipitation products is discussed, along with the characteristics and applications of various SPPs. This highlights the need for future research to improve the accuracy and reliability of SPPs. Overall, SPPs offer a valuable substitute to traditional rain gauge data; however, their limitations should be carefully considered when interpreting and utilizing the data for different applications."
    },
    {
        "DOI": "10.1109/SOUTHEASTCON63549.2026.11476667",
        "Title": "A Python-Based Workflow for Producing Soil Electromagnetic Properties",
        "Year": 2026,
        "Abstract": "This paper presents a Python-based workflow to model the complex permittivity of soil utilizing Geographic Information Systems datasets. The incorporates texture, moisture, organic matter, bulk density, and temperature estimate volumetric water content via Campbell's retention curve applies recommendation ITU-R P.527-6 compute across frequency range. Results demonstrate workflow's utility for electromagnetic modeling, with potential applications in wireless communication remote sensing."
    },
    {
        "DOI": "10.1007/S11069-026-08143-4",
        "Title": "Assessing future heat wave patterns in India: insights from a high-resolution regional climate model",
        "Year": 2026,
        "Abstract": "Considering the growing risk of extreme heat events, this study evaluates the mid-twenty-first century (20412060) projections of heatwave characteristics and its associated atmospheric dynamics over India using high-resolution (20-km) simulations from the RSM-CCSM4 regional climate model (RCM). Evaluation of the present-day simulation of the RCM indicates reasonable fidelity in simulating the 2 m air temperature, 2 m specific humidity, and heatwave characteristics compared with ERA5 reanalysis. Projections show an earlier onset, delayed cessation, and increased frequency of the heatwaves, particularly over central and northwest India, implying a higher risk of prolonged extreme heat. During heatwave days, temperature and humidity increase across India, with maximum warming over western Indo-Gangetic Plain (IGP) and peninsular India. Similarly, projected humidity change is maximum along the eastern coast, central India and eastern IGP. The projected zonal wind anomalies over northwest India show a strengthening negative anomaly over 2030N in mid-twenty-first century, along with a notable northward shift in the mid- to upper-tropospheric levels, suggesting a northward shift of subtropical westerly jet in the future climate. Future projections also show (a) amplified mid-tropospheric geopotential height anomalies and associated anticyclonic circulation during heatwave events over northwest India, (b) increased persistence of these anomalies resulting in a greater number of 2-day to a 4-day heatwave events, and (c) the heatwaves which are more frequent during the dry phase of the intraseasonal oscillations is projected to intensify in the future. Overall, the projected intensification of heatwaves highlights the need to prepare and implement locally relevant adaptation strategies under a warming climate."
    },
    {
        "DOI": "10.1007/1345_2026_311",
        "Title": "Assessing the Contribution of GRACE Data to Snow Water Equivalent Estimation in Comparison with Conventional Snow Measurement Techniques",
        "Year": 2026,
        "Abstract": "Abstract Snow Water Equivalent (SWE) is a key indicator of seasonal water availability and climate variability in high-latitude regions. Conventional SWE monitoring methods, such as ground-based snow courses and passive microwave retrievals, are limited by sparse coverage and uncertainties related to snowpack properties. The Gravity Recovery and Climate Experiment (GRACE) mission offers a unique, basin-integrated perspective by measuring mass changes directly from space, providing an independent approach to large-scale SWE estimation. In this study, we evaluate the performance of GRACE-derived SWE across four high-latitude river basins (Yenisei, Ob, Mackenzie, and Yukon) over the period 20032022. The average monthly GRACE RL06 mascon water-storage anomalies from Jet Propulsion Laboratory (JPL), the Center for Space Research (CSR) at the University of Texas at Austin, and Goddard Space Flight Center (GSFC) were used in this study. SWE anomalies were derived by isolating the cold-season signal and restricting the analysis to areas identified by removing all the non-snow components using the hydrological models. SWE estimates were compared against long-term in-situ snow-course observations, multiple Hydrological Models, and passive microwave product. Results show that GRACE reliably captures the seasonal accumulationmelt cycle and interannual variability, achieving robust correlations ranging from 0.81 to 0.90 against in-situ observations and 0.71 to 0.92 during seasonal peaks. Following our refined processing workflow, basin-averaged root-mean-square errors (RMSE) were reduced to a range of 23.4 to 41.7 mm. The analysis reveals that while hydrological models typically return to a fixed seasonal baseline, the GRACE-derived signal reflects total integrated mass changes, capturing interannual deviations and deep storage fluctuations particularly in the North American basins that are absent in process-based models. GRACE also demonstrates sensitivity to extreme snow years that are underestimated by conventional datasets. These findings confirm the added value of GRACE gravimetry for SWE assessment, particularly at basin scales where traditional observations are sparse."
    },
    {
        "DOI": "10.1007/S00376-025-5512-4",
        "Title": "Synergistic Effects of El NinoSouthern Oscillation and Arctic Oscillation on the Winter Northern Hadley Circulation Edge",
        "Year": 2026,
        "Abstract": "The Hadley circulation edge (HCE) is an important component of the HC, modulating the subtropical high-pressure distribution and global precipitation pattern. This study investigates the synergistic influence of winter El Nino and Arctic Oscillation negative phase (AO) on the locations of the northern HCE (NHCE). Results show that both El Nino and AO are individually associated with equatorward NHCE shifts, but their concurrent occurrence results in a significantly amplified displacement, reaching 1.34 latitude. Mechanistic analysis indicates that El Nino and AO jointly modulate the variations in eddy momentum flux (EMF), meridional temperature gradient (MTG), subtropical tropopause height (STH), and baroclinic instability criterion (BIC). Under the co-occurrence of El Nino and AO, anomalous EMF divergence is observed over 1530N, thereby causing an equatorward shift of the convergence center. The El Nino related warming over 530N and AO related cooling over 30-55N weaken the MTG, combined with a significant decrease in STH over 3035N and enhanced BIC in the latitudinal belt of 2535N. These anomalies together contribute to the equatorward displacement of the NHCE. The synergistic impacts of El Nino and AO are further established by using 195080 datasets. The ridge regression analysis shows that variations in MTG are the dominant contributor to the anomalous NHCE shift. This study underscores the importance of the synergistic effects of climate variabilities in shaping the variations of NHCE latitude, which is important for the crucial role of NHCE latitude in impacting global climate."
    },
    {
        "DOI": "10.5194/ESSD-18-3109-2026",
        "Title": "State-of-the-art hydrological datasets exhibit low water balance consistency globally",
        "Year": 2026,
        "Abstract": "Abstract. The proliferation and diversification of hydrological datasets have significantly advanced hydrological research. However, the coherence across these datasets remains poorly understood, hindering the comparability of findings derived from different data sources and variables. Here, we demonstrate that state-of-the-art hydrological datasets exhibit overall low consistency when evaluated through the lens of water balance specifically, the relationship between variations in soil moisture and the difference between precipitation, evapotranspiration, and runoff. Our analysis reveals that satellite-based precipitation datasets generally show the highest consistency, while gauge-based datasets perform better in densely monitored regions of the Northern Hemisphere. For evapotranspiration, runoff, and soil moisture, reanalysis datasets demonstrate broader areas of higher consistency compared to gauge- or satellite-based products. Spatial patterns of consistency for most assessed datasets are strongly influenced by aridity and temperature, which affect measurement and modelling accuracy. Notably, dataset consistency has improved significantly in northern mid-latitudes over recent decades, likely reflecting advancements in observational technologies and the effects of climate warming. These findings underscore the importance of continued efforts to enhance dataset coherence and reliability for robust hydrological assessments."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108980",
        "Title": "Increasing carbon emissions despite declining burned area in the Northern Hemisphere wildfires",
        "Year": 2026,
        "Abstract": "Under climatic warming, global wildfires have become more frequent, causing extensive burned area (BA) and carbon emissions (CEs). However, the relationships between these two key wildfire characteristics remain insufficiently explored at the global scale. Using satellite and reanalysis data, this study indicates a pronounced and persistent decline in global BA during 20012022, whereas global CEs exhibit only a weak downward trend with substantial interannual variability. This apparent decoupling is particularly evident in the Northern Hemisphere, where BA decreases but CEs increase significantly after 2010. The BA reduction is primarily driven by central and southern Africa, whereas the rise in CEs is dominated by northeastern Siberia, northwestern Canada, and western North America, whose combined emissions can offset the decrease in Africa. These three mid- to high-latitude regions thus exhibit exceptionally high CE-to-BA ratios (1836 Tg C / Mha), greatly exceeding the global mean (45 Tg C / Mha). Correspondingly, global CEs peak only during JulySeptember, while BA shows double peaks in JulySeptember and NovemberFebruary, driven by the out-of-phase seasonal cycle of African wildfire activity. The interannual increases and seasonal variations in these regions are closely linked to intensified near-surface heat and drought conditions, further amplified by anomalously high geopotential height and reduced water vapor transport under internal climate variability. These findings highlight the critical role of mid- to high-latitude regions in global wildfire activity."
    },
    {
        "DOI": "10.1016/J.CATENA.2026.110193",
        "Title": "Climate-dominated, LUCC-enhanced waterecosystem responses in the humid temperate Tumen River basin",
        "Year": 2026,
        "Abstract": "Transboundary basins increasingly face coupled pressures from climate variability and land-use change, yet their combined impacts on waterecosystem dynamics in humid regions remain poorly constrained. Here, we investigate these interactions in the humid temperate Tumen River Basin of Northeast Asia during 20012024. We integrate GRACE-based terrestrial water storage (TWS), MODIS land-cover, vegetation, and land-surface temperature products, ERA5-Land climate reanalysis, and topographic information, and combine these datasets with a VHIILR framework, ridge-regression decomposition, random forest analysis with SHapley Additive exPlanations (SHAP), and GeoDetector. We show that basin-mean TWS has increased significantly since 2002 under a wetter climate, while vegetation health has improved mainly where cropland was converted to shrubland or forest, but shows weak or inconsistent gains after cropland-to-grassland conversion. Interannual TWS anomalies are dominated by climate variability, yet land-use/cover change exerts a persistent secondary control: a characteristic \"cropland shrubland forest\" succession, with the most pronounced signals occurring on steep, erosion-prone slopes, is associated with enhanced water retention, soil moisture and greening, without the strong watervegetation trade-off reported for semi-arid afforestation. Together, these results support a climate-dominated, LUCC-enhanced mechanism of waterecosystem co-evolution in humid temperate basins and highlight the potential of well-designed restoration programmes to deliver concurrent benefits for terrestrial water storage and vegetation resilience."
    },
    {
        "DOI": "10.1021/ACSEARTHSPACECHEM.6C00055",
        "Title": "Spatiotemporal Variations in Fossil vs Non-Fossil Source Contributions to Carbonaceous Aerosols across Diverse Indian Environments: An Assessment via Radiocarbon",
        "Year": 2026,
        "Abstract": "Carbonaceous aerosols (CAs) are a dominant component of particulate matter over South Asia, yet the relative contributions fossil-fuel combustion and nonfossil sources remain poorly constrained across Indias diverse emission environments. Here, we present comprehensive assessment CA using dual-carbon isotopes ( 14 C 13 C), complemented by chemical composition (OC, EC, WSOC), aerosol mass spectrometer (AMS)-derived oxidation indicators f 43 44 ). Aerosol samples were collected eight geographically distinct locations spanning Indo-Gangetic Plain (IGP), semiarid western India, southern Himalayan foothills, Bay Bengal during postmonsoon, winter, spring, summer seasons. Radiocarbon-derived fractions total carbon bio_TC ) ranged from 0.39 to 0.86 (mean = 0.71 0.13), with highest values observed postmonsoon paddy-residue-burning period IGP (up 0.82 at Patiala), reflecting overwhelming dominance biomass-burning-derived carbon, lowest Delhi (0.45) Ahmedabad (0.49), indicating clear seasonal shift toward fossil-fuel-dominated CAs. Stable isotope signatures ( together ), reveal pronounced regional contrasts in aging secondary processing, aged, highly oxidized biomass-burning prevailing northern northeastern India fresher primary emissions dominating sites. Comparison MERRA-2 reanalysis reveals systematic underestimation TC concentrations all sites seasons bias 42%), most severe PRB winter pointing deficiencies fire inventories transport representation current products. These findings provide robust, observation-based constraints on evolution offering critical inputs for refining inventories, improving model performance, designing targeted mitigation strategies reducing air-quality climate impacts carbonaceous Asia."
    },
    {
        "DOI": "10.1371/JOURNAL.PGPH.0006328",
        "Title": "Investigation of an outbreak of acute algal-associated dermatoses among artisanal fishermen in Senegal: A one health approach",
        "Year": 2026,
        "Abstract": "In November 2020, an alert for a mysterious disease among fishermen was issued. Fishermen are particularly subjected to dermatoses due to their constant contact with seawater, fish, crustaceans, and fishing equipment that may contain harmful agents. The study aimed to examine the alert, identify the causative agent and suggest preventive and control measures. This was a cross-sectional study of dermatoses in Dakar (Senegal) from October 11 to November 30, 2020, using quantitative and qualitative methods within a One Health approach. The investigation included bacterio-virological, anatomopathological and toxicological examinations. Data were analyzed using Epi info and QGIS (case mapping), We observed all confidentiality measures during the study. A total of 555 cases were diagnosed with an attack rate of 5.4% among fishermen and no deaths were reported. There was a delay in epidemic detection and notification. The epidemic was most prevalent among people from coastal areas. Average age of cases was 22 9 years, and all were male and artisanal fishermen by profession. Patients presented with fever (16%), cutaneous pain (100%) and mucocutaneous lesions (100%) consisting of vesicles, papules and ulcerations localized on exposed areas of the body, external genitalia and oral mucosa, with severe cases (8%). Toxicology revealed the presence of a toxic alga ( V. rugosum ) in marine equipments. The notion of a sea trip in the 2448 hours before the onset of the disease was found in 92%. Majority of cases (74%) did not have full personal protective equipment (PPE). The proportion of people without full protection was 83% among those who developed severe forms. People without full protection were more exposed to severe forms than those with full PPE; (OR = 1.818; 95% CI [0.829 - 3.988]). The investigation has linked the epidemic to a probable algal origin. We need to promote the use of personal protective equipment and improve the early warning and notification system."
    },
    {
        "DOI": "10.5194/AMT-19-2737-2026",
        "Title": "Super-resolution localization and quantification of SO2 emissions over India using TROPOMI observations",
        "Year": 2026,
        "Abstract": "Abstract. India has high sulfur dioxide (SO2) emissions, primarily due to its extensive coal-fired power sector. SO2 column observations from Sentinel-5P Tropospheric Monitoring Instrument (TROPOMI) enables observation-based emission estimates using inversion techniques. Among inversion methods, the flux-divergence method is particularly sensitive to point source emissions and well-suited for estimating SO2 emissions in India. However, when applied to satellite observations, this method tends to spatially spread calculated emissions into neighboring grid cells around the source. This spreading effect weakens the emission signal at the exact source location, making precise quantification of emissions more difficult. In this paper, we design a sharpening algorithm to reverse the spreading and sharpen the emission signals while conserving total mass of the emissions. We apply the algorithm on gridded SO2 emissions at a high spatial resolution of 0.025 0.025 ( 2.5 km 2.5 km) derived from TROPOMI observations that have a typical mean footprint size of 6.0 km 6.0 km. After sharpening, the effective spatial resolution of the emissions matches the grid cell resolution. Emissions from point sources increase at their exact locations, while emissions in neighboring grid cells decrease. In the resulting SO2 emission inventory, about 80 % of coal-fired power plants with capacities above 100 MW are detected at their correct location, while the remaining 20 % fall below the detection threshold. The detected power plants account for 99 % of India's total coal-based power generation. We also identify twenty two previously unreported SO2 point sources, including coal-based thermal power plants, cement factories, crude oil production facilities, chemical fertilizers factory, and copper, steel, and aluminum industries. This sharpening algorithm improves emission detection and can also be extended to other pollutants emitted by point sources to enhance the accuracy of emission inventories."
    },
    {
        "DOI": "10.5194/TC-20-2703-2026",
        "Title": "A spatiotemporal analysis of errors in InSAR SWE measurements caused by non-snow phase changes",
        "Year": 2026,
        "Abstract": "Abstract. Spatially distributed measurements of snow water equivalent (SWE) in mountainous terrain are not currently feasible from existing satellite platforms. The NISAR satellite has the potential to provide high resolution (80 m) SWE measurements on a 12 d orbit cycle over many of Earths snowy regions, which would represent a new era of spaceborne snow monitoring. The most promising approach for NISAR SWE measurements uses interferometric synthetic aperture radar (InSAR) techniques to derive the 12 d change in SWE (SWE) from the change in phase between two SAR acquisitions. However, many non-snow factors can also change in this 12 d period which subsequently modulate the SAR phase. These non-snow factors can vary differently in both space and time, and in turn introduce spatially and temporally variable errors into InSAR-derived SWE measurements. Here we explore the effects of six non-snow factors that can affect InSAR phase: electron content of the ionosphere, atmospheric water vapor, atmospheric pressure, soil permittivity, vegetation permittivity, and surface deformation. We show how these factors affect phase-based SWE measurements at 13 SNOTEL stations across the western US, as well as regionally across North America. We consider errors resulting from individual 12 d periods, as well as the cumulative effects of the errors when a timeseries of SWE measurements is integrated to derive peak seasonal SWE. Ionosphere effects result in the largest cumulative error at all SNOTEL stations in our analysis, with changes in the total electron content resulting in phase changes equivalent to 0.2710.414 m of SWE, or more than 500 % larger than the median 1 April SWE at some shallow snow stations. When ionosphere effects are removed, the remaining cumulative error ranges from 0.0740.022 m of SWE, equivalent to 0 %89 % of 1 April SWE. Relative error results are affected primarily by differences in peak SWE rather than differences in absolute error values. For a randomly selected 12 d period, exceedance probability analysis shows that there is a 50 % chance the ionospheric component introduces an error larger than 0.211 m into the overall SWE measurement, while the remaining five components have a 50 % exceedance probability of 0.031 m. We also find that individual error components can show offsetting effects, where positive and negative biases partially cancel out to lower the total cumulative error. Accurate SWE measurements using NISAR data will not be possible unless ionospheric effects can be appropriately addressed. Removal of other error sources requires careful consideration of the SWE monitoring application: for tracking seasonal SWE accumulation in areas with deeper snowpacks, correcting some errors but not others may actually decrease accuracy by removing offsetting cumulative effects. For individual 12 d periods, wet and dry tropospheric effects (due to changes in water vapor and pressure) should be removed for accurate interpretation of spatial patterns of snow accumulation at basin to range scales, and site-specific factors should be considered to assess the relative influence of vegetation, soils, and surface deformation."
    },
    {
        "DOI": "10.1029/2025JD045744",
        "Title": "Machine Learning Assessment of Aerosol and Meteorological Impacts on Atmospheric Boundary Layer Height in Eastern China: Highlighting the Role of Aerosol Hygroscopicity",
        "Year": 2026,
        "Abstract": "Abstract The planetary boundary layer height (PBLH) regulates air pollutant dispersion, yet the combined effects of aerosols and meteorology remain insufficiently quantified. Here, an interpretable XGBoost SHAP framework is applied to daytime (08:0020:00 BJT) PBLH across the BeijingTianjinHebei (BTH) and Yangtze River Delta (YRD) regions using ERA5 and MERRA2 reanalysis data for 20132023. The mean PBLH in BTH (812 m) is about 125 m higher than in YRD (687 m), largely due to contrasting humidity conditions (46.3% vs. 68.6%). Meteorology dominates PBLH variability, with surface net solar radiation (SSR) and nearsurface wind speed correlating positively, whereas relative humidity (RH) is the opposite. Among aerosol factors, singlescattering albedo (SSA) exerts the strongest influence (10.9% in BTH; 10.8% in YRD). In BTH, absorbing aerosols are associated with lower PBL more strongly than scattering aerosols, whereas in YRD neither type exerts dominant control. High RH enhances the hygroscopic growth of scattering aerosols, increasing the extinction efficiency and SSA and weakening the positive SSAPBLH relationship, especially in YRD. Componentspecific analysis shows that hygroscopic components are identified as the major contributors to scattering extinction, with the sulfate effect markedly amplified under humid conditions. Seasonal analysis shows that thermal variables (SSR, RH) dominate the model in summer, while dynamic factors are more influential in winter, especially over BTH. These findings provide quantitative evidence for the coupled impacts of aerosols and meteorology, especially hygroscopic growth, on boundary layer dynamics, offering a scientific basis for regionspecific air quality management in eastern China. , Plain Language Summary The evolution of the planetary boundary layer height (PBLH) is influenced by various factors such as meteorological fields and aerosols. Therefore, it is crucial to analyze the influence mechanisms of different parameters. This study applies an interpretable machine learning model to explore the effects of different meteorological elements and aerosol parameters on the daytime boundary layer in eastern China using a reanalysis data set. Results show that meteorological factors dominate PBLH variability, while single scattering albedo (SSA) is the most important among aerosol parameters. Meanwhile, this study indicates that scattering aerosols exhibit stronger hygroscopicity compared to absorbing aerosols under a high humidity environment, which can lead to the weakening of the positive correlation between SSA and PBLH. The PBLH in the Yangtze River Delta region is approximately 125 m lower than that in the BeijingTianjinHebei region, which is largely related to the significant humidity difference. These findings are of great significance for clarifying the boundary layer development mechanism. , Key Points Atmospheric Boundary Layer height is primarily driven by meteorology, with single scattering albedo as a significant aerosol factor High relative humidity weakens the correlation between single scattering albedo and planetary boundary layer height Hygroscopic components significantly enhance scattering extinction under humid conditions, impacting the planetary boundary development"
    },
    {
        "DOI": "10.5194/AMT-19-2197-2026",
        "Title": "Cloud liquid water path detectability and retrieval accuracy from airborne passive microwave observations over Arctic sea ice",
        "Year": 2026,
        "Abstract": "Abstract. Clouds are critical in the Arctic's water balance and energy budget. Especially, the cloud liquid water path (CLWP) modifies the cloud radiative properties and affects the surface energy balance. Spaceborne microwave radiometers provide a high sensitivity to CLWP at pan-Arctic scales, but extracting this information over sea ice requires separation of surface and cloud emission. Here, we assess CLWP detectability and retrieval accuracy over sea ice from a physical optimal estimation retrieval applied to airborne passive microwave observations during the HALO(AC)3 campaign. Reference data on surface temperature, young ice fraction, hydrometeor occurrence, and cloud liquid layers are available from collocated airborne instruments. The retrieval estimates CLWP and five surface parameters by inverting a forward operator consisting of the Snow Microwave Radiative Transfer (SMRT) and Passive and Active Microwave radiative TRAnsfer (PAMTRA) models. We find a consistent representation of sea ice and snow emission from 22183 GHz under clear-sky conditions in both observation and state space. The CLWP detectability, defined as the 95th percentile of retrieved CLWP under clear-sky conditions, is about 50 g m2 in the Central Arctic and increases towards the marginal ice zone up to 350 g m2. The CLWP retrieval accuracy increases with increasing CLWP, with a relative root mean squared error below 50 % for CLWP above 100 g m2. Retrieval uncertainties occur due to ambiguities between cloud liquid water emission and scattering in the snowpack and emission by newly formed sea ice. We further analyze the impact of surface melt and a rain-on-snow event associated with the warm air intrusion on the surface parameters. Finally, we show CLWP distributions along the flight track for all airborne observations in comparison to ERA5 for different cloud regimes. The retrieval algorithm enhances the understanding of Arctic clouds and allows for an improved use of passive microwave satellite data in polar regions."
    },
    {
        "DOI": "10.1038/S41586-026-10474-Y",
        "Title": "Uncertain dynamic response of mid-latitude winter precipitation",
        "Year": 2026,
        "Abstract": "Understanding changes in precipitation is crucial for society and ecosystems1,2. Studies have documented the respective contributions of anthropogenic forcing and internal variability to precipitation trends3,4, yet discrepancies persist between observed and simulated patterns. In Northern Hemisphere winter, these mismatches are often attributed to unforced internal variability that dominates observed trends5. However, growing evidence also indicates that climate models underestimate the total response of precipitation to human forcings6, 78. Here we show that the thermodynamic contribution is broadly reproduced by climate models, whereas the dynamic contribution can diverge more substantially. Our approach disentangles the anthropogenic forced thermodynamic and dynamic components from internal variability in winter precipitation trends (19502022) to investigate their contribution to the trend discrepancies. In the Mediterranean, the forced dynamic signal from model simulations explains only about 10% of the observed dynamic trend, making detection challenging. Under continued anthropogenic emissions, the projected circulation response intensifies and more closely resembles observed trend patterns. Although internal variability in the observed record may contribute to this similarity, the results indicate an uncertain yet potentially emerging role of dynamic response in shaping regional winter precipitation trends. A reliable representation of the forced large-scale circulation response in climate models remains key for increasing confidence in regional precipitation projections."
    },
    {
        "DOI": "10.1175/JAS-D-25-0161.1",
        "Title": "Precipitation on the Manus Island of Papua New Guinea and Ice Cloud Radiative Effects in the Surrounding Atmosphere in Boreal Winters",
        "Year": 2026,
        "Abstract": "Abstract The longwave cloud radiative effect (LW CRE) plays a critical role in regulating tropical moist convection by modifying the atmospheric energy budget. While previous studies have linked LW CRE to the maintenance of convective quasi equilibrium (CQE) on large scales, its influence at shorter time scales and on individual convective events remains less explored. In this study, we analyze the relationship between precipitation and LW CRE on daily time scales at a location over the equatorial western Pacific. We focus on how LW CRE shapes the thermodynamic environment prior to deep convection. The present results show that enhanced LW CRE and planetary boundary layer (PBL) moisture increase column-integrated moist static energy (MSE) before heavier rainfall events, creating conditions more favorable for deep convection. Notably, LW absorption at the cloud base heats the lower portion of cirrus clouds, and the radiative heating enhances the mid- to upper-tropospheric MSE. In addition, increased humidity in the lower free troposphere above the PBL is found to increase the height of anvil cloud tops through minimizing buoyancy reduction during the mixing of rising plumes/parcels with the ambient environment. These findings contribute to a process-level perspective on how LW CRE shapes tropical moist convection."
    },
    {
        "DOI": "10.1029/2025JG009163",
        "Title": "Regionalized Determinants of Dryland Forest Gross Primary Productivity",
        "Year": 2026,
        "Abstract": "Abstract Ecosystems may exist in alternative stable states and thereby extremely differ in ecosystem structure and functions, including gross primary productivity (GPP), which is crucial for assessing an ecosystem's ability to capture atmospheric carbon dioxide, especially in the context of climate change. This study applied the alternative stable states theory to evaluate GPP in global dryland forests, and analyzed multiyear average GPP data alongside environmental factors such as the Aridity Index and mean annual precipitation. Here, we found the existence of alternative stable states of GPP along the aridity gradient. The mean values of GPP were 893 and 1,540 gC m 2 year 1 under lower and higher branches of alternative stable states, respectively, compared to the current mean value of 1,203 gC m 2 year 1 . Notably, we observed striking regional disparities in GPP, with Africa and Oceania predominantly in the higher alternative stable state, while North America and Asia were in the lower alternative stable state. However, GPP, along with mean annual precipitation, did not exhibit alternative stable states, but a significant variation during the medium range of mean annual precipitation (241402 mm year 1 ). The relationships between GPP data and environmental factors were consistent across different forest types. This study sheds light on dryland forest productivity and indicates adaptive management strategies that should be used to bolster ecosystem function in the context of climate change. , Plain Language Summary Dryland forests, accounting for 25% of global forests, are vital for capturing carbon and helping to slow climate change. Yet these ecosystems do not respond to climate in simple ways: under a similar dryness condition (aridity index, AI), forests can exist either in a lowerproductivity or a higherproductivity state. In this study, we analyzed 20 years of global satellite data to explore how forest productivity (gross primary productivity, GPP) changes with AI and precipitation (mean annual precipitation). We discovered that along the AI gradient, forest productivity tends to split into two distinct levels, that is, about 893 gC m 2 year 1 and about 1,540 gC m 2 year 1 . However, distinct forest productivity states do not exist along the precipitation gradient. We found that the global patterns also hold across forest type models, but not across climate zones. These findings highlight a potential vulnerability of global dryland forests: as climate conditions change, dryland forests may abruptly switch from higher to lower productivity states, dramatically affecting carbon cycling. Recognizing and managing these alternative stable states will be crucial for conserving dryland forests and strengthening their role in climate change mitigation. , Key Points Global dryland forest GPP exhibited alternative stable states along the AI gradient Patterns of dryland forest GPP differ among climatic zones, but not ecosystem types"
    },
    {
        "DOI": "10.1038/S41597-026-07165-8",
        "Title": "CANSD v1.0: the first map of agricultural nitrogen surplus across China from 2000 to 2022",
        "Year": 2026,
        "Abstract": "Under the dual challenges of global climate change and agricultural sustainability, spatially precise characterization nitrogen surplus has become a key technical bottleneck in balancing food security water environmental protection. Existing national datasets remain restricted to individual species or loss pathways, with no unified, long-term, continuous gridded product for total surplus-particularly at scale China. Compounding these limitations, current modeling approaches are plagued by coarse statistical resolution, short temporal coverage, overly simplified process representations that distort realistic migration cycling pathways. To address critical gaps, this study constructs 0.5 spatial resolution dataset China period 2000-2022 (CANSD v1.0). The grid-scale (NS) is operationally defined as variable quantifies heterogeneity resolution. Gridded values post-processed rescaling align prefecture-level accounting, while realizing based on inherent cycling. This approach overcomes mismatch between administrative statistics field-scale processes integrating machine learning downscaling, thereby mitigating potential circular causality enhancing cross-scale transfer patterns. Our framework provides practical basis foundation evidence-based management risk mitigation."
    },
    {
        "DOI": "10.1038/S43247-026-03505-Z",
        "Title": "Regional aerosol hygroscopicity influences radiative forcing globally",
        "Year": 2026,
        "Abstract": "Abstract Aerosol hygroscopicity is a critical parameter for predicting radiative forcing and climate sensitivity, particularly under sub-saturated regimes where it drives complex aerosolwater interactions. Here, we show that externally mixed aerosols exert a stronger influence on direct radiative forcing than is currently represented in models. Incorporating our findings into radiative forcing calculations indicates a stronger aerosol cooling effect, especially at suburban sites, highlighting the importance of representing regional differences in mixing state. The conventional bulk-chemistry approach, which assumes volume-based mixing with limited spatial variability, exhibits low predictive performance for aerosol hygroscopicity (R2 0.61) at urban and suburban sites. Using an interpretable machine learning framework trained on geographically diverse, region-specific datasets can capture this variability with higher accuracy (R2 0.97), identifying key chemical compositional and mixing-state drivers."
    },
    {
        "DOI": "10.1175/JHM-D-25-0162.1",
        "Title": "PERSIANN-U-Net: A Global Deep Learning Framework for Near-Real-Time Precipitation Estimation Using Infrared Data",
        "Year": 2026,
        "Abstract": "Abstract Access to high-quality, high-resolution, near-real-time precipitation data is essential for hydrological and meteorological research and disaster response. Traditional tools such as rain gauges and radar networks, though effective, are limited by sparse coverage in remote areas and radar constraints such as beam blockage and increasing beam height with range, which reduce near-surface accuracy. Satellite observations address these challenges by providing global coverage with fine spatial and temporal resolution. Many precipitation products combine geosynchronous thermal infrared (IR) and passive microwave (PMW) data. PMW sensors offer detailed atmospheric profiles but are restricted to infrequent overpasses and increasing reliance on smaller satellites with higher-frequency channels, which are less sensitive to liquid precipitation. In contrast, IR sensors provide consistent, high-frequency global observations, making them valuable for near-real-time estimation. Recent advances in deep learning, particularly convolutional neural networks (CNNs), have further improved satellite precipitation retrievals. This study introduces Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-U-Net (PU-Net or PERSIANN V3), a quasi-global algorithm covering 60N60S that combines IR data, monthly climatology, and the U-Net architecture to produce half-hourly precipitation estimates at 0.04 resolution. The product is evaluated against Hydro Estimator (HE), Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), and PERSIANN Dynamic InfraredRain Rate (PDIR-Now) for 202223. Results show that PU-Net closely matches its training target, IMERG V07 Final, at the global scale, and its performance is further evaluated against Stage IV as a reference over contiguous United States (CONUS). Training PU-Net on IMERG (201621) leverages a high-quality, integrated PMWIRgauge precipitation product while developing an IR-based framework not reliant on PMW availability. By operating on a single global image, PU-Net avoids tile partitioning and blending steps, reducing edge discontinuities, and produces more spatially consistent precipitation fields across hemispheres. Significance Statement Reliable and timely precipitation data are critical for weather forecasting, flood monitoring, and climate research. However, traditional tools like rain gauges and radar systems are limited by sparse coverage in remote or data-scarce regions and require substantial infrastructure. This study presents Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-U-Net (PU-Net or PERSIANN V3), a quasi-global (60S60N) precipitation estimation model that leverages deep learning with satellite infrared imagery and monthly climatology to generate near-real-time rainfall estimates at 0.04 and 30-min resolution. Compared to existing satellite products, PU-Net shows closer agreement with the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) Final across global and regional domains. Its global architecture provides a consistent and scalable framework for near-real-time precipitation estimation and long-term climate data record development."
    },
    {
        "DOI": "10.1007/S00343-026-5295-1",
        "Title": "Research on the response of the upper ocean to typhoons",
        "Year": 2026,
        "Abstract": "Typhoon, as one of the most destructive weather systems, causes significant impacts on the marine environment. We established a multi-dataset case-comparative framework by integrating Argo buoy observations, HYbrid Coordinate Ocean Model (HYCOM) reanalysis data, and Integrated Multi-satellitE Retrievals for GPM (IMERG) satellite precipitation data, to elucidate the upper ocean response mechanisms to four distinctive typhoons in the northwest Pacific (2021-2024). Our analysis revealed several key mechanistic insights. Results show that the HYCOM model can simulate the temperature and salinity trends, but systematically underestimates precipitation dilution (with salinity deviations of 0.1-0.2) and mixing intensity (with temperature deviations of 0.4-1.2 C), due to insufficient parameterization of the wind-wave-current coupling process. After typhoon passage, sea surface temperature decreases sharply by 1.5-3 C due mainly to the enhanced vertical mixing, while salinity decreases by 0.1-0.6 as a result of the combined effects of precipitation dilution and mixing with deep high-salinity water. The mixed layer depth (MLD) increases from 40 to 100 m. The temperature recovers gradually within 10 d due to solar shortwave radiation and advection, while salinity recovers more slowly due to its conservative nature. A notable asymmetry was observed, and the Ekman suction driven by strong winds was enhanced on the right side of the typhoon track, leading to a significantly deeper disturbance depth compared to the left side. Significant thermal anomalies were observed in the subsurface (80-500 m), resulting from the competing effects of vertical mixing and Ekman pumping. The cold wake is primarily governed by wind-driven vertical mixing, while its spatial pattern is co-regulated by precipitation-induced freshwater input. Ekman transport plays a critical role in subsurface heat redistribution. These findings underscore the importance of multi-platform observations for understanding complex ocean responses and highlight key areas for improving coupled model parameterizations."
    },
    {
        "DOI": "10.1029/2026JA035039",
        "Title": "Biennial Signature in the Equatorial Upper Mesospheric DW1 Amplitudes During Boreal Fall Equinox and Its Relationship With Stratospheric Ozone",
        "Year": 2026,
        "Abstract": "Abstract Interannual variability of the migrating diurnal tide (DW1) in the equatorial upper mesosphere lower thermospheric region has been studied using longterm (20032024) temperature data obtained from the Sounding of Atmosphere using Broadband Emission Radiometry (SABER) instrument on board the ThermosphereIonosphereMesosphere Energetics and Dynamics (TIMED) satellite. The DW1 tidal amplitude exhibits consistent biennial variability in the recent years during the boreal fall equinox months (SeptemberOctober). It is interesting to note that the stratospheric ozone volume mixing ratio (vmr) at 31 hPa obtained from the Microwave Limb Sounder instrument on board the Aura satellite also shows similar biennial variability. However, the positive anomaly of DW1 coincides with the negative anomaly in Total Column Water Vapor (TCWV), obtained from the European Centre for Medium Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) data sets. Besides, during SeptemberOctober, the TCWV shows a low value close to its seasonal minimum, while the ozone vmr is larger, comparable to its seasonal maximum. Biennial variability is also observed in the heating rates of four lowest order classical Hough modes, calculated from ozone values at 31 hPa and in their corresponding tidal amplitudes, estimated using Laplace Tidal Equation, with (1, 1) tidal mode showing the maximum amplitude. Hence, it is suggested that the biennial variability in the DW1 tide at 100 km observed consistently during boreal fall equinox months in the recent years is likely driven by the (1, 1) tidal mode variability generated due to the stratospheric ozone heating at 31 hPa. , Plain Language Summary The migrating diurnal (DW1) tidal amplitude in the equatorial upper mesosphere lower thermospheric (UMLT) region shows a biennial variability in the recent years (since 2016) during the months of September and October. Since the DW1 tide is principally forced by infrared radiation (IR) absorption by tropospheric water vapor and ultraviolet radiation (UV) absorption by stratospheric ozone, the recent changes in the lower and middle atmospheric chemical constituents in connection to the biennial variability in the DW1 tide have been investigated. The study reveals that a biennial signature is present in stratospheric ozone with larger values in the years when the DW1 amplitude is larger. Unlike the stratospheric ozone, the Total Column Water Vapor (TCWV) exhibits anticorrelation with the DW1 amplitude. The stratospheric ozone heating rates of four lowest order classical Hough modes and their corresponding amplitudes at 31 hPa calculated using Laplace Tidal Equation, show similar biennial variability to that observed in the DW1 tide in the UMLT region, with the (1, 1) mode showing the maximum amplitude. Therefore, it is suggested that the (1, 1) tidal mode generated from stratospheric ozone heating most likely governs the DW1 amplitude variability in the UMLT region during the months of September and October. , Key Points Both stratospheric ozone and upper mesospheric migrating diurnal tide (DW1) amplitude exhibit biennial variability during boreal fall equinox An increase in the DW1 amplitude is noted, when the total column water vapor decreases and the stratospheric ozone increases The (1, 1) mode forced by stratospheric ozone heating possibly acts as a major driving force for the DW1 tide, when the water vapor is less"
    },
    {
        "DOI": "10.1007/S00703-026-01147-6",
        "Title": "Influence mechanism of eastward-moving mesoscale gravity waves on Southwest vortex and its precipitation",
        "Year": 2026,
        "Abstract": "ERA5 reanalysis data, GPM satellite precipitation products, and radar mosaic combination reflectivity (RMCR) data were used to analyze the influence mechanism of mesoscale gravity waves (MGWs) on the Southwest vortex (SWV) and its precipitation during a rainstorm process that occurred in the Sichuan Basin (SCB) from 30 Jun to 1 Jul 2020. The rainstorm was affected by the SWV, upper trough, and the MGW with a wavelength of 150200 km and a period of 45 h. The MGW was initiated by the combined effects of the shear instability mechanism, the non-equilibrium flow mechanism, and steep slope terrain. The updraft of the SWV was coupled with the updraft phase of the MGW, resulting in transient, periodic deep convection. The large-value area of equivalent potential temperature corresponding to water vapor convergence caused by the eastward-moving MGW resulted in the enhancement of equivalent potential temperature in the upper troposphere. A critical layer and strong vertical wind shear formed in the upper troposphere caused the reflection of the MGW, promoting downward energy and phase propagation of the MGW. Meanwhile, the strong vertical wind shear in the lower troposphere also led to the reflection of the MGW, transporting energy upward in front of the eastward propagation path of the MGW, thereby enhancing convection and precipitation in the front of the eastward-moving SWV. A comparative analysis of two rainstorm cases indicates that when the MGW is phase-coupled with the SWV, cold cloud processes and warm cloud processes jointly dominate rainstorm occurrence."
    },
    {
        "DOI": "10.1029/2025EA004745",
        "Title": "Refine the Uncertainty of GPM IMERG Precipitation Product Accounting for the Inherent Error From Rain Gauges Estimations",
        "Year": 2026,
        "Abstract": "Abstract Satellite precipitation retrieval accuracy assessment requires reliable ground validation, yet conventional approaches using rain gauges as truth neglect representativeness errors inherent in pointtoarea approximations. This study uses 7,253 rain gauges (20202024) over the Jianghuai monsoon region to quantify these errors and reassess Integrated Multisatellite Retrievals for GPM (IMERG) performance. We show that at least 16 gauges per 0.2 grid are required for reliable areamean precipitation estimates. Analysis reveals dual dependence of gauge representativeness errors on gauge density (n, number of gauges per grid cell) and rainfall intensity (RR): (a) errors decay exponentially with increasing n, following root mean square error (RMSE) = ae bn , where a and b are fitted coefficients; (b) errors increase with RR when n is held constant. Parameterized relationships enable error quantification across density gradients. Direct IMERGgauge comparisons show that seasonal mean differences are negatively correlated with gauge density (Pearson's r = 0.33, p < 0.01), indicating that sparse gauge networks are a primary driver of apparent discrepancies. Error decomposition using gauge uncertainties yielded bounded IMERG retrieval errors (RMSE B _min/max). Applying the same framework to KlingGupta efficiency (KGE) revealed similarly improved IMERG performance after removing gaugeinduced uncertainties, reinforcing the internal consistency of our analysis. Crucially, incorporating gauge errors reduced significant discrepancy frequency by 16%/6%/16%/17% across seasons, proving that traditional methods overestimate IMERGgauge deviation occurrence by 6%17%. This establishes gauge density as critical accuracy determinant, provides robust errorquantification framework, and reveals that terraincomplexity misinterpretations arise when disregarding representativeness errors, with implications for global satellite precipitation validation. , Plain Language Summary Accurately measuring rainfall from satellites requires reliable groundbased validation, but traditional approaches often assume that individual rain gauges perfectly represent surrounding areas. In reality, this pointtoarea mismatch introduces errors, especially when gauges are sparse. Using China's exceptionally dense national rain gauge network of about 76,000 instruments, with a focus on the Jianghuai monsoon region, we quantified how gauge spacing and rainfall intensity jointly influence measurement uncertainty. Our results show that many gauges are needed within each grid cell to obtain reliable averages, and that errors become larger during heavier rainfall. By applying this improved understanding, we reassessed the performance of the widely used IMERG satellite product. We found that many of the apparent discrepancies between IMERG and ground observations were actually caused by limited gauge density rather than problems with the satellite itself. When accounting for gaugerelated uncertainties, IMERG's accuracy improved substantially and false indications of bias were reduced by up to 17%. These findings highlight the critical role of gauge density in satellite validation and provide a new framework for interpreting differences between satellite and ground rainfall measurements. Ultimately, this work helps ensure more accurate assessments of satellite precipitation data, with broad implications for weather and climate studies worldwide. , Key Points Based on a highdensity observational network, an exponential relationship between gauge density and representativeness error is identified Gauge errors depend on gauge density and rainfall intensity, limiting reliability of satellite validation Incorporating gauge errors reduces IMERGgauge discrepancy frequency by up to 17%, showing traditional methods overestimate deviations"
    },
    {
        "DOI": "10.5194/HESS-30-1755-2026",
        "Title": "Exploring groundwater-surface water interactions and recharge in fractured mountain systems: an integrated approach",
        "Year": 2026,
        "Abstract": "Abstract. This study presents an integrated approach to map groundwater-surface water (GW-SW) interactions in a scarcely anthropized Mediterranean mountain catchment (Ussita) characterized by fractured limestone rocks with complex spatial-temporal patterns of hydrological processes. Understanding GW contributions to streams like the Ussita is crucial for addressing environmental challenges, including water resources management and evaluating ecological flows to protect aquatic ecosystems. The use of traditional hydrological techniques, such as discharge measurements along various stream reaches, combined with hydrochemical-isotopic analyses and innovative thermal drone surveys, enabled us to quantify the specific contributions of different limestone aquifers to sustaining streamflow. Integrating satellite-based meteorological datasets with in-situ observations further helped to constrain the water budget and assess the extent of the recharge area. Hydrogeological analyses also revealed that snowmelt contributes about 18 % to aquifer recharge, an important consideration for GW availability in the face of future spatial-temporal changes in snow patterns. These findings can support further studies in other catchments by guiding and optimizing field campaigns to identify site-specific conditions responsible for GW inflow, from point sources to stream reaches. Moreover, the results can help optimize resource management, mitigate climate-related risks, and support the long-term sustainability of both upstream and downstream socio-ecological systems."
    },
    {
        "DOI": "10.1080/22797254.2026.2641527",
        "Title": "Downscaling of soil moisture in the Wujiang River Basin based on integrated decision tree-based machine learning",
        "Year": 2026,
        "Abstract": "Soil moisture (SM) is a crucial variable for agriculture, drought monitoring and hydrological modelling. While microwave remote sensing provides large-scale SM data, its coarse spatial resolution limits fine-scale applications. This study compares three decision tree-based ensemble machine learning algorithms: random forest (RF), light gradient boosting (LightGBM) extreme (XGBoost), to downscale the 25-km European Space Agency Climate Change Initiative Moisture (ESA CCI SM) 1-km in Wujiang River Basin, China. Models were evaluated using randomly temporally stratified test sets validated against situ observations. The results revealed that R2 values of prediction models ranged from 0.6694 0.7163, with RMSE 0.01650.0178 m3/m3 Bias 00.0001 m3/m3, LightGBM performing relatively better. Evaluation independent year-stratified seasonal differences among models. Before after downscaling, SM's distribution remained largely consistent responded well precipitation. RF model achieved highest accuracy (RMSE = 0.0189 0.003 m3/m3) strongest correlation site observations (R 0.6714). downscaled aligns better measured data proposed downscaling framework supports hydrological, agricultural environmental"
    },
    {
        "DOI": "10.1016/J.ENVRES.2026.124580",
        "Title": "Gap-filling XCO2 variability: A conditional framework to estimate XCO2 in data-sparse regions using dense TROPOMI NO2 observations over China",
        "Year": 2026,
        "Abstract": "Accurate monitoring of fine-scale anthropogenic carbon dioxide (CO2) variability is crucial for urban emission assessment but remains constrained by the sparse spatial sampling of dedicated satellites like Orbiting Carbon Observatory-2 and -3 (OCO-2/3). Nitrogen dioxide (NO2), co-emitted with CO2 and observed daily by TROPOspheric Monitoring Instrument (TROPOMI), offers a potential pathway to inform CO2 patterns where direct measurements are lacking. This study addresses a central challenge in leveraging this link: rather than seeking a universal relationship, we rigorously define the specific spatiotemporal conditions under which TROPOMI NO2 can reliably estimate XCO2 variability in data-sparse regions over China (20202024). Our analysis reveals a fundamental decoupling in long-term trends (NO2 stable, XCO2 rising) yet identifies a significant positive correlation (r = 0.344, p = 0.008) in their short-term, de-seasonalized relative changes. Crucially, this correlation is strongly conditional: it peaks during winter (r > 0.60) and is significantly enhanced within intense emission hotspots like the Yangtze River Delta. Building on this empirical mapping, we develop an integrated framework that quantifies the modulation by season and region. A proof-of-concept application demonstrates its potential for generating observationally informed estimates of XCO2 changes in areas unsampled by current satellites. Our work provides the essential foundation for developing reliable, context-aware gap-filling methods, directly addressing a key limitation in high-resolution urban carbon monitoring."
    },
    {
        "DOI": "10.1109/JSTARS.2026.3682497",
        "Title": "Vegetation Water Content Estimation Using Optical and Microwave Remote Sensing",
        "Year": 2026,
        "Abstract": "Accurate estimation of vegetation water content (VWC) is crucial for understanding terrestrial ecosystem dynamics. However, conventional remote sensing retrieval methods struggle to capture the complex, nonlinear relationships between VWC and variables, such as optical depth (VOD), leaf area index (LAI), height (VH), across diverse land cover types. While machine learning provides potential solutions, models with fixed architectures often exhibit poor generalization. This study introduced a novel framework using conditional adaptive neural network (CANN), which dynamically tailored its parameters input data characteristics. The model predicted VOD, LAI, VH, International Geosphere-Biosphere Programme type inputs. Results show that proposed estimates in this were highly consistent reference VWC, by achieving correlation coefficient (R) 0.94 [root-mean-square error (RMSE) = 1.13 kg/m2] against LFMC-VWC (VWC derived Globe-Live Fuel Moisture Content) an R 0.95 (RMSE 2.48 kg/m2) normalized difference (NDVI)-VWC NDVI). In addition, temporal dynamics CANN-VWC closely followed SMAPVEX situ measurements, exhibiting more competitive performance than NDVI-VWC estimates."
    },
    {
        "DOI": "10.3390/MATH14081384",
        "Title": "Scalar-on-Function Regression with Replicated Error-Prone Functional Covariates",
        "Year": 2026,
        "Abstract": "In this article, we study scalar-on-function regression with functional covariates observed through replicated measurements subject to measurement error. Treating replicated curves as surrogates of an underlying latent process, the proposed framework resolves the identifiability issues commonly encountered in functional measurement error models. Through functional principal component analysis, the model is represented as a finite-dimensional hierarchical linear measurement error model. Parameter estimation is carried out using an expectation-maximization algorithm, and alternative correction strategies based on adjusted regression calibration and simulation extrapolation are also considered for comparison. Simulation studies demonstrate the advantages of explicitly accounting for measurement error in terms of bias reduction and estimation stability. An application to soybean yield prediction in Illinois, using meteorological variables contaminated by measurement error, illustrates the practical value of the proposed approach."
    },
    {
        "DOI": "10.1038/S41467-026-70647-1",
        "Title": "Tropical cyclone rainfall extends inland",
        "Year": 2026,
        "Abstract": "Tropical cyclone (TC) rainfall, which is typically more intense over the ocean, has increasingly caused devastating floods in coastal regions recent decades. Regions beyond 100 km inland from coastlines often lack adequate preparedness for TC-induced flooding, underscoring need to assess whether global shifts terrestrial TC particularly heavy have occurred. Here, we show that rainfall extended globally 1980 2023. Specifically, along continental coasts of Northern Hemisphere, landward extent (30 mm per 3 h) increased at a rate 3.8 1.8 decade (95% CI). Notably, statistical significance this trend robust, regardless spatial constraints on or trajectories TCs. Observations and model simulations suggest nearshore sea-surface temperature (SST) warming closely linked extension, likely by amplifying landocean contrast terms friction-related dynamical responses. Coastal urbanization may further enhance extension when coupled with SST warming. As cities continue extend inland, could exacerbate population exposure potential flood risk. This study shows tropical Hemisphere since 1980. Nearshore drives expansion, effect. These findings highlight increasing risk populations as grow."
    },
    {
        "DOI": "10.1029/2025JD045445",
        "Title": "2025 California Wildfire Observations Using Single FieldofView Sounder Atmospheric Products (SiFSAP)",
        "Year": 2026,
        "Abstract": "Abstract Southern California wildfires result in a significant loss for Los Angeles and San Diego County from January 7 to 31, 2025. Palisades Fire and Eaton fires, the two largest fires that caused most of that damage, were observed by the infrared sounder CrIS and microwave sounder ATMS aboard a polarorbit satellite of the National Oceanic and Atmospheric Administration, that is, NOAA20. The high spatial resolution Single Fieldofview Sounder Atmospheric Products (SiFSAP) retrieved from CrIS and ATMS measurement have been used to illustrate the emission and transport of the plume generated by the wildfires. The CO total column and UV aerosol index (UVAI) observed by TROPOMI, CO total column observed by AIRS, the aerosol optical thickness (AOT) provided by VIIRS, and wind provided by ERA5 were used to illustrate plume produced by wildfires. Furthermore, cloud optical depths and top pressures retrieved by SiFSAP and VIIRS were compared to assess the IR sounder's capability to retrieve clouds. Results indicated that the SiFSAP data products effectively capture not only large scale dry airmass transport but also the movement of wildfiregenerated plumes containing high CO concentrations. The study shows that the SiFSAP CO retrieved from IR measurements is less affected by aerosols as compared to solar measurements like TROPOMI. This study demonstrates that the SiFSAP products provides a highquality data set for wildfire observations and disaster observations. , Plain Language Summary The two largest fires in Southern California wildfires, Palisades Fire and Eaton fires, were observed by infrared sounder CrIS and microwave sounder ATMS aboard on satellite NOAA20 from January 7 to 31, 2025. The SiFSAP retrieved from combined CrIS and ATMS measurement have been used to illustrate the emission and transport of the plume generated by the wildfires. The SiFSAP's air temperature, relative humidity, CO total column, and cloud properties, combined with the CO total column observed by AIRS, CO total column and UVAI observed by TROPOMI, the aerosol optical thickness provided by VIIRS, and wind provided by ERA5 are used to illustrate the wildfire emission and transport. The result shows that SiFSAP provides a higher spatial resolution observation for the wildfire measurements when compared with AIRS's science team level2 product. It also indicates the ability of the SiFSAP to observe wildfire emission in the presence of firerelated aerosols. This study demonstrates that the SiFSAP products provides a highquality data set for wildfire observations and disaster observations. , Key Points SiFSAP is able to detect CO enhancements in smoke plumes from wildfires during 2025 January California wildfires SiFSAP CO and relative humidity, combined with VIIRS/TROPOMI observations, and ERA5 wind fields provides a broad view of satellitebased wildfire observations The SiFSAP cloud products matches very well with cloud observed by NOAA20/VIIRS"
    },
    {
        "DOI": "10.1038/S41467-026-69028-5",
        "Title": "Plant diversity within communities, not among them, stabilizes grassland productivity across spatial scales",
        "Year": 2026,
        "Abstract": "Evidence shows that local functional trait composition and diversity along the fast-slow leaf economics spectrum can predict temporal stability of community productivity in response to environmental changes. However, it remains unclear whether these relationships persist at larger spatial scales. Combining a field survey plant with remote sensing estimates primary across large gradient 235 grasslands Qinghai-Tibet Plateau Inner Mongolia Plateau, we find species richness contributes stabilizing productivity, while destabilizing scale. In contrast, no evidence variation among communities While between conditions differ two regions, overall positive are consistent both Our study offers insights into how traits mediate effects factors (e.g., precipitation) on ecosystem contrasting"
    },
    {
        "DOI": "10.3390/RS18040630",
        "Title": "Satellite-Based Assessment of Spatially Heterogeneous XCO2 and Marine pCO2 Trends (20152020)",
        "Year": 2026,
        "Abstract": "Satellite remote sensing has revolutionized the monitoring of atmospheric carbon dioxide (CO2) concentrations, yet its integration into studies of airsea CO2 flux dynamics remains limited. Leveraging high-resolution observations from the Orbiting Carbon Observatory 2 (OCO-2) and Copernicus Marine Environment Monitoring Service (CMEMS), this study investigated the spatiotemporal heterogeneity of atmospheric column-averaged CO2 (XCO2) and sea surface partial pressure of CO2 (pCO2) between 2015 and 2020. Our analysis reveals pronounced latitudinal gradients, with the Northern Hemisphere exhibiting stronger seasonal XCO2 variability (5.67 0.42 ppm annual amplitude) compared to the Southern Hemisphere (1.2 0.18 ppm). Notably, the XCO2 growth rate was marginally higher in the Southern Hemisphere (2.48 ppm yr1) than the Northern Hemisphere (2.39 ppm yr1), while coastal regions showed elevated atmospheric CO2 concentrations, but slower pCO2 increases relative to the open ocean, suggesting a buffering capacity of marginal seas. Furthermore, we identified distinct seasonal phasing between land and ocean XCO2, with oceanic signals lagging terrestrial ones by approximately one month. These findings highlight the utility of satellite data in resolving fine-scale airsea carbon flux dynamics and provide critical insights into how heterogeneous atmospheric CO2 changes propagate across marine systems."
    },
    {
        "DOI": "10.5194/ACP-26-3783-2026",
        "Title": "Global NO2 changes between 2019 and 2024 as observed by TROPOMI in urban areas and emerging hotspots",
        "Year": 2026,
        "Abstract": "Abstract. We present a global assessment of space-based urban nitrogen dioxide (NO2) observations from 2019 to 2024 using annual and monthly mean tropospheric vertical column densities (VCDs) from the TROPOspheric Monitoring Instrument (TROPOMI). Across 11 500 cities defined by the Global Human Settlement Layer-Settlement Model (GHS-SMOD), we find population-weighted annual mean urban NO2 VCDs were lower in 2024 than 2019 in Europe (13 %) and Asia and Oceania (17 %), with seasonal decomposition indicating that annual changes are largely driven by concentration decreases during NovemberMarch. Aggregated urban VCD changes in North America, South America and Africa were statistically insignificant, though numerous individual cities exhibited significant changes. Of larger cities, Tehran had the largest annual mean NO2 VCD (> 30 1015 molecules cm2) and Seoul experienced the largest reduction (9.4 1.0 % yr1; p < 0.001). We then calculate NO2 VCD urban enhancements (VCDENH) by removing background concentrations from urban signatures and compare VCDENH to changes in nitrogen oxide (NOx) emissions from two emissions inventories, highlighting regions with potential inventory discrepancies. We find VCDENH changes exceed changes in inventory NOx emissions in Europe, North America and Asia and Oceania, with worse agreement in the Global South. We further identify changes in NO2 near fossil fuel operations and note conflict-related changes in NO2, highlighting the responsiveness of satellite NO2 to certain societal disruptions. This work demonstrates the value in space-based remote sensing being an accountability agent for air pollution emissions on a global scale and to identify changes in NO2 in otherwise unmonitored regions."
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108762",
        "Title": "Contrasting microphysics and environmental drivers of weak and intense convection-induced extreme precipitation: Insights from GPM DPR observations",
        "Year": 2026,
        "Abstract": "Under global warming, extreme precipitation events (EPEs) have become frequent worldwide, with unclear microphysical differences in convective clouds. The weak and intense convection-induced EPEs (WeEPEs and InEPEs, respectively) over South China are identified with 10-year spaceborne dual-frequency precipitation radar observations. Results suggest that most EPEs occur in morning and nighttime. The distribution of WeEPEs (land-prevalent) and InEPEs (ocean-prevalent) is regulated by ocean thermal gradients, moisture transport, and orographic lifting. Weak sea surface temperature (SST) gradients and southerly moisture transport generate offshore moisture centers. The humid environment in the ocean may help the formation of WeEPEs. Conversely, strong SST gradients, coupled with stronger southerly moisture transport form inland moisture centers, with orographic forcing enhancing upward motion for strong convection. Weak convection generates extreme precipitation through synergistic increase in particle size and concentration below the 0 C level, with high concentration being more critical. For oceanic events, InEPEs show a slightly larger particle diameter increase (0.11 mm) below the 0 C level. However, WeEPEs have a much greater concentration increase (4.34 dB), resulting in a larger reflectivity factor increase (7.58 dBZ). For the most extreme precipitation, even if one of the warm-rain process and the ice-phase process dominates, the other still plays a significant role. Across eventsof different regions, continental WeEPEs reach peak rates through ice-particle melting (16.1 mm/h) and warm-rain coalescence to enhance particle size (70.6 mm/h); marine InEPEs (102.02 mm/h) rely on high liquid water path and efficient coalescence for maximum rates (warm-rain contribution: 43.3 mm/h)."
    },
    {
        "DOI": "10.1002/ASL.70002",
        "Title": "Evaluation of Precipitation Observations Across Ecuador",
        "Year": 2026,
        "Abstract": "ABSTRACT Accurate precipitation observations are crucial for hydrological and climate monitoring, forecasting and research. However, sparse networks in regions with complex topography, like Ecuador, limit data availability. To address this gap, products such as ERA5 reanalysis, produced by ECMWF within the Copernicus Climate Change Service, and satellitebased datasets, including IMERG (Integrated Multisatellite Retrievals for GPM) and MSWEP (MultiSource WeightedEnsemble Precipitation), offer nearrealtime monitoring and spatial coverage in datascarce areas. This study evaluates these three precipitation products over Ecuador using qualitycontrolled station data (19802024) to assess biases, extreme event detection, and the impact of topography. The results show that: (1) IMERG is the most skillful overall in estimating precipitation, particularly in lowland areas, though it declines in mountainous regions; (2) ERA5 and MSWEP underestimate precipitation in the Amazon, while ERA5 overestimates in highaltitude regions; (3) during the 1998 El Nino event, all products had challenges in capturing localized heavy precipitation, although ERA5 consistently captured but overestimated coastal heavy precipitation; (4) For 99th percentile precipitation extremes, ERA5 overestimated precipitation by 5.0 mm/day, while IMERG overestimated by 2.5 mm/day. This study highlights the need to analyse highaltitude precipitation estimates carefully and adapt biasadjustment methods, providing insights for climate monitoring in tropical mountain regions."
    },
    {
        "DOI": "10.1007/S00382-026-08102-6",
        "Title": "Mesoscale Convective Systems in Northeastern North America: identification and evaluation with the convection-permitting version of the Canadian Regional Climate Model",
        "Year": 2026,
        "Abstract": "Extreme precipitation events are often associated with mesoscale meteorological phenomena, such as convective systems (MCS). Convection-permitting models (CPMs), which operate at high spatial resolution, have enhanced our ability to represent atmospheric processes phenomena. This study aims identify and evaluate MCSs in northeastern North America over the 2015-2022 period using various observational model-based products. A tracking algorithm is used characterize ERA5 reanalysis, satellite-based (IMERG) data, radar observations (STAGE-IV MRMS), two simulations performed sixth version of Canadian Regional Climate Model (CRCM6). These horizontal grid spacings 12 km 2.5 (CRCM6-12 CRCM6-2.5, respectively), higher-resolution (2.5 km) simulation operating CPM mode. Radar indicate that occur most frequently from May September typically initiate early afternoon. The lower resolution products (IMERG, CRCM6-12, ERA5) underestimate both mean occurrence interannual variability compared reference dataset STAGE IV-MERGIR, biases - 17%, 50%, 88% standard deviations 23, 17, 5.8 per year, respectively (reference deviation = 32 year). CRCM6-2.5 model configuration accurately reproduces key MCS characteristics, including their intra-annual occurrence, size, duration, intensity, diurnal cycle, contribution total extreme precipitation. Notably, contribute more than significantly improves representation finer scales lower-resolution products, although it slightly overestimates comparison observations. Supplementary Information: online contains supplementary material available 10.1007/s00382-026-08102-6."
    },
    {
        "DOI": "10.1371/JOURNAL.PCLM.0000820",
        "Title": "High-resolution regional climate modeling over Myanmar using WRF: Historical validation and future projections under different shared socioeconomic pathways",
        "Year": 2026,
        "Abstract": "Myanmar is one of the most vulnerable countries to climate change, and its complex geography together with heterogeneous climate and precipitation patterns present major challenges for producing reliable climate change projections. In light of these challenges, high-resolution regional climate models are essential for improving our understanding of climate change and to provide a knowledge base for adaptation strategies. We employed the Weather Research and Forecasting (WRF) model to simulate the present climate (19812010), a mid of century (20312060) and an end of century (20712100) climate for the SSP2-4.5 and SSP5-8.5 scenarios. We tested out different domain settings and show that large domains are needed to accurately model the climate and, particularly, precipitation in Myanmar. The past climate is validated against station data and satellite based products, and the model demonstrates good skill in representing the climate over Myanmar, with the exception of a dry bias in the southern Ayeyarwady Delta. Generally, the model underestimates precipitation at the end of the rainy season in October, which is related to a mismatch in the atmospheric circulation, moisture availability, and therefore, moisture transport into Myanmar. The climate projections show distinct increases in 2m-temperature, with warming of 0.9 to 2.7 C for the mid-century in an SSP2-4.5 to end of the century under the SSP5-8.5. Our simulations project that in April the temperature in the Dry Zone in the centre of the country increases disproportionally with a warming of up to 3.6 C for the SSP5-8.5 end of century simulation, while for all other scenarios the strongest increase is found in May. Changes in precipitation show a non-significant wetting in the Dry Zone and a significant drying in the Shan Hills and the Tanintharyi Region for the two periods in the SSP2-4.5 and the mid-century simulation under SSP5-8.5 scenario. For the end of century simulation under the SSP5-8.5 pathway a general wetting of the north western part including the Dry Zone in the range of 40 to 60% is projected. Even if the annual sum shows an increase in precipitation, this is not true for all the months. Especially, January, July, August and November are months which are projected to have less precipitation in all future scenarios compared to present climate."
    },
    {
        "DOI": "10.1007/S00382-025-07955-7",
        "Title": "The subseasonal evolution of Indian rainfall dipole and its local impact in recent decades",
        "Year": 2026,
        "Abstract": "Over the past two decades, the summer monsoon rainfall has shown decreasing trends over central India, with the Indo-Gangetic plains showing the most prominent changes. In particular, Bundelkhand, a subregion in the Gangetic plains, has experienced an increase in the frequency and severity of meteorological droughts. We analyse long-term rainfall data revealing two types of droughts in Bundelkhand: Type-1 droughts that coincide with the large-scale Indian monsoon droughts, and Type-2 droughts, which are localised to Bundelkhand. Our focus in this work is on the spatio-temporal evolution of rainfall during the latter category of droughts. We find that there is a distinct dipole structure in rainfall on a subseasonal scale, characterised by increased rainfall in the western India region and a decreased rainfall in east central India during July and August. Furthermore, we analyse the upper- and lower-level atmospheric circulation changes responsible for the subsidence during Type-2 droughts. The rainfall deficit during July and August appear to be associated with a midlatitude stationary Rossby wave which induces an anomalous anticyclone over western North Pacific which drives easterlies over the south China Sea into east central India, that in turn reduce the regional moisture influx. At the same time, the Rossby wave also induces an anomalous cyclonic circulation over northwest India and Pakistan, increasing moisture convergence from the Arabian Sea, leading to enhanced convection over west India. During September, the high-pressure region over Tibetan Plateau migrates further southward into central India, increasing (decreasing) subsidence (convection). Taken together, a combined effect of intraseasonal variations in the midlatitude jet and the circulation over western North Pacific and Tibetan Plateau are responsible for the observed rainfall dipole pattern. At the downstream end, we find that the enhanced soil stress (dryness) owing to reduced rain may have a role to play in the much-reported depletion of groundwater storage in northwest India in general."
    },
    {
        "DOI": "10.34133/REMOTESENSING.1028",
        "Title": "Divergent Responses of Multi-frequency Vegetation Optical Depth Products to Climate Variations in China",
        "Year": 2026,
        "Abstract": "Vegetation optical depth (VOD) has been widely assessed for satellite monitoring of vegetation carbon and water status under different environmental conditions. However, abilities of multi-frequency VODs to reflect the integrated dynamics in vegetation status under changing climate are still underinvestigated, especially in China, which has experienced substantial vegetation greening since 2000. To fill this gap, this study examines 7 VOD products for their capabilities to detect vegetation status changes under climate variations from 2012 to 2022 in China. We find divergent responses of multi-frequency VODs to climate variations in China, and the retrieval frequency is the most important factor, followed by the underlying retrieval algorithms. All 7 VOD products show stronger responses to water limitations (atmospheric and soil water stress) than temperature across different plant functional types in China, suggesting high potential of VOD to track vegetation water status changes. We also find that the capabilities of VODs to represent vegetation responses to climate variations are affected by vegetation growth limitations, showing consistent responses for ecosystems dominated by either temperature or water. Most importantly, we find that VODs capture well the carry-over effects of climate on vegetation dynamics in China, with X- and C-band VODs showing stronger abilities than L-band VODs, especially for ecosystems located in Chinas arid and semiarid regions. These findings address the divergent capabilities of multi-frequency VOD products to capture the vegetation responses to climate variations across China, promoting ecological applications of different VOD products in regional studies."
    },
    {
        "DOI": "10.1080/2150704X.2026.2644223",
        "Title": "Long-term assessment of cyclonic disturbances over the North Indian Ocean (18912019) using a cloud-based platform with special reference to cyclone Fani",
        "Year": 2026,
        "Abstract": "Tropical cyclones (TCs) pose a serious threat to Indias 5400 km long continental coastline strip. The historical data (18912019) claims that four eastern states experience cyclonic storms more frequently than other coastal states. analysis of shows the Bay Bengal (BoB) has faced TCs Arabian Sea (AS), with frequency ratio approximately 3:1. Most in BoB occur during pre-monsoon and post-monsoon seasons powerful winds, huge storm surges, significant rains. This study also reveals an increasing intensification rate severe over both AS, supported by regression moving average trend lines. Fani, extremely (ESCS), was unique because being rare summer cyclone. It one three most destructive last 150 years. Sentinel-1 Synthetic Aperture Radar (SAR) dataset been used Google Earth Engine (GEE) environment determine amount inundation West (203.30 km2) Odisha (129.81 km2). So, this recent article successfully presents overview North Indian Ocean (NIO) provides detailed account ESCS Fani."
    },
    {
        "DOI": "10.1038/S41559-026-02997-4",
        "Title": "Increase in plant reliance on past precipitation associated with greening and drying",
        "Year": 2026,
        "Abstract": "Water is indispensable for life on Earth. Plants use water either from recent precipitation (within a month) or from past precipitation stored in deeper soil (PP; at least a month ago) to maintain metabolism and growth. It is widely known that plants tend to rely more on PP to buffer against short-term rainfall deficits. However, how this reliance has changed in response to global change remains unclear. Here we develop a novel framework to estimate temporal changes in plant reliance on PP during the past four decades. Observational data reveal that 42% of tropical and subtropical natural ecosystems have experienced a significant increase in plant reliance on PP over the period 19822021 (P < 0.05). Such an increase is consistent with greening during the late growing season in drylands and drying during the wet-to-dry transitional period in non-drylands, when short-term precipitation fails to meet plant water demand. Adaptive changes in root depth and species composition may further facilitate this change in PP reliance, especially in drylands. Our study highlights the importance of PP in ecosystem functioning and implies an increasing ecosystem resilience to climate variability."
    },
    {
        "DOI": "10.1088/1755-1315/1586/1/012047",
        "Title": "Evaluation of ERA5, FLDAS and AIRS temperature data in Central Java and Its utilization as a basis for climate-based policy",
        "Year": 2026,
        "Abstract": "Abstract Increasing global temperatures over recent decades are evidence of global warming. Central Java Province, Indonesia, located in the equatorial region, may also be impacted by global warming. To investigate the rise in air temperature, long-term climate analysis is necessary, utilizing accurate air temperature data. Therefore, this study examines the accuracy of gridded surface air temperature datasets, ERA5, FLDAS, and AIRS, against observational data obtained from six meteorological stations representing the northern coastal areas, central highlands, and southern coasts of Central Java. The Bilinear interpolation method was applied over the period 2009-2024. Statistics evaluation used to prove bias, mean absolute error (MAE), mean absolute percentage error (MAPE), and root mean square error (RMSE). Results show that FLDAS has the highest accuracy, with MAE, MAPE, and RMSE of 0.79C, 2.92%, and 0.91C, for all regions. ERA5 and AIRS show comparable accuracy. MAE, MAPE, and RMSE of ERA5 are 1.54C, 5.59%, and 1.76C, respectively while for AIRS are 1.33C, 5.19%, and 1.75C, respectively. The best agreement with observations data in the highland and southern coast regions with the MAE and RMSE of 1.54C and 1.61C, respectively. However, AIRS data has a large error in highland areas with MAE, MAPE, and RMSE of 3.35C, 14.45% and 3.46C, respectively. FLDAS and ERA5 tend to underestimate, as shown by the bias of -0.77C and -1.52C, respectively. Scatter plot, annual cycle, and Taylor diagram analyses consistently supported the statistical findings, highlighting that FLDAS and ERA5 more accurately represented seasonal and spatial temperature variations compared to AIRS for all area conditions. Furthermore, ERA5 offers the longest and most continuous historical record, making it more suitable for studies of long-term climate change and global warming. The findings highlight the importance of selecting datasets based on both accuracy and data availability for climate research and policy development in Indonesia."
    },
    {
        "DOI": "10.1038/S41597-026-06565-0",
        "Title": "A Benchmark Dataset for Satellite-Based Estimation and Detection of Rain",
        "Year": 2026,
        "Abstract": "Abstract Accurately tracking the global distribution of precipitation is essential for both research and operational meteorology. Satellite observations remain the only means of achieving consistent, global precipitation monitoring. While machine learning has long been applied to satellite-based precipitation retrieval, the absence of a standardized benchmark dataset has hindered fair comparisons between methods. To address this, the International Precipitation Working Group has developed SatRain, the first AI benchmark dataset for satellite-based detection and estimation of rain. SatRain integrates multi-sensor satellite observations from the primary platforms used in precipitation remote sensing with high-quality reference precipitation estimates derived from gauge-corrected ground-based radar composites over the conterminous United States. It offers a standardized evaluation protocol and out-of-distribution testing data from Asia and Europe to enable robust and reproducible comparisons across machine learning approaches. In addition to algorithm evaluation, the diversity of sensors and inclusion of time-resolved geostationary observations make SatRain a valuable foundation for developing next-generation AI models to deliver more accurate global precipitation estimates."
    },
    {
        "DOI": "10.5194/GMD-19-1683-2026",
        "Title": "GHGPSE-Net: a method towards spaceborne automated extraction of greenhouse-gas point sources using point-object-detection deep neural network",
        "Year": 2026,
        "Abstract": "Abstract. Point sources account for a large portion of anthropogenic greenhouse gas (GHG) emissions. Timely detection, localization, and quantification of these emissions are critical for supporting carbon neutrality efforts. Spaceborne monitoring satellites can provide essential concentration data for identifying point sources. However, existing methods often require human intervention and typically detect plume masks instead of source locations, limiting their utility for regulatory applications. In this study, we present GHGPSE-Net, a deep learning method for greenhouse gas point source extraction. GHGPSE-Net simultaneously performs detection, localization, and quantification of emissions, eliminating the need for traditional segmentation steps. To train and evaluate the model, we construct synthetic datasets using an atmospheric transport model and validate its accuracy against radiosonde profiles and satellite observations. GHGPSE-Net demonstrates desirable performance in the simulation data across detection (F1-score of 0.96), subpixel-level localization and quantification (Pearson's correlation of 0.99, root mean square error of 89.9 tCO2 h1), tested on ideal instrument of 0.5 km 0.5 km resolution with retrieval noise of 1.5 parts per million (ppm). The results also demonstrate considerable generalization of the proposed model when tested using two independent datasets. On the identified sources from OCO-3 spaceborne observations, GHGPSE-Net achieves a detection precision of 0.89, localization accuracy of 3.02 km, and a Pearson's R of 0.59 for quantification. The proposed method and datasets provide a valuable foundation for future research towards rapid and automated GHG point source extraction, offering critical data to support swift responses to abnormal emission events."
    },
    {
        "DOI": "10.1175/JCLI-D-24-0664.1",
        "Title": "Moisture Transport from Tropical Oceans Drives Summertime Rainfall Variability over Southwest North America",
        "Year": 2026,
        "Abstract": "Abstract Southwest North America (SWNA) is prone to hydroclimate extremes, with the west part (termed region 1) featuring a Mediterranean-type climate and the east part (termed region 2) featuring a monsoonal climate. Despite extensive research, a critical aspect of the SWNA hydroclimateatmospheric moisture variabilityis not well understood. Here, we examine the leading sources and physical processes related to SWNA summer atmospheric moisture variability with a combined LagrangianEulerian approach. In a semi-Lagrangian moisture-tracking model, transport from tropical oceans is found to be a substantial source of summer atmospheric moisture variability over SWNA. The subtropical Pacific accounts for 74%/58% of atmospheric moisture variations in region 1 during wet/dry extremes of MayJune. While both oceanic and land sources contribute to moisture fluctuations in region 1 during JulyAugust and region 2 during May, the tropical Atlantic becomes the leading (83%/70% for wet/dry extremes) source for region 2 during JuneAugust. The moisture transport variations are primarily attributed to wind anomalies, while oceanic moisture anomalies can be important. Consistent with the Lagrangian analysis, an Eulerian moisture budget analysis shows that moisture advection is critical for the growth of atmospheric moisture anomalies over SWNA. The dynamic component of moisture advection boosts atmospheric moisture over the coastal ocean (SWNA) via anomalous southerlies (easterlies), while the thermodynamic component transports moisture anomalies farther inland via climatological southwesterlies. The decline of atmospheric moisture over SWNA occurs with peak precipitation triggered by wind convergence. These results enhance our understanding of SWNA summer moisture variability, with implications for the associated rainfall predictability."
    },
    {
        "DOI": "10.1002/QJ.70124",
        "Title": "Does vertical wind shear increase tropical cyclone rain?",
        "Year": 2026,
        "Abstract": "Abstract Changes in tropical cyclone (TC) rain induced by vertical wind shear (VWS) have significant implications. Using a 26year stateoftheart precipitation dataset, this study provides a systematic analysis of the responses of TC rain to VWS. Results reveal an unexpected VWSinduced rain volume enhancement despite reduced TC intensity, with rain volume up to 23% higher in high versus lowshear conditions. The responses are spatially asymmetric: rainfall increases in the outer region but decreases in the inner core, and enhancements downshear generally outweigh suppressions upshear, yielding a net increase in rain production. Beyond the mean response, VWS also modifies rainfall extremes and storm structure. It reduces the maximum azimuthal mean rain rate, whereas the maximum local rain rate remains largely unchanged and even intensifies slightly in the strongest TCs. The radii of rainfall maxima expand outward with shear, and the peak local rain rate tends to converge with the azimuthal mean maximum at high shear. When adjusted by storm intensity, stronger shear enables higher rain rates, larger rain areas, and greater rain volumes for the same TC intensity. These results challenge the conventional view of shear as purely detrimental to TCs, revealing a dual role: VWS weakens winds but enhances rainfall, potentially mitigating wind damage while amplifying flood risk. This tradeoff underscores the need to account for shearinduced hydrological impacts in TC hazard assessment and prediction."
    },
    {
        "DOI": "10.1109/JSTARS.2025.3650418",
        "Title": "Satellite-Based Fraction of Available Water Reveals Soil Moisture Deficits Preceding Major Wildfires",
        "Year": 2026,
        "Abstract": "Wildfires, exacerbated by climate change, land-use alterations, and extreme weather conditions, can have catastrophic impacts on both people ecosystems. Recent research highlights the role of soil moisture (SM) as a predisposing factor to large fires, yet critical thresholds remain poorly characterized across different data sources. Volumetric SM measurements differ in magnitude dynamic range spatial extents satellite products, making direct comparisons challenging. To address this, we calculated Fraction Available Water (FAW), which ranges from 0 1 varies wilting point field capacity. Using observations Soil Moisture Ocean Salinity (SMOS), Global Change Observation Mission for Water, Active Passive (SMAP), change initiative (CCI) programs, explored antecedent conditions south-central Chile that favored an fire spread early February 2023, when over 240 000 ha burned just four days. Our analysis showed FAW was lownot only days immediately before but throughout preceding month. Critical emerged multiple revealing plant stress (FAW < 0.50) drought 0.20). Even drier 0.10) were widespread, affecting nonburned areas reducing constraints region. findings demonstrate derived including SMOS, SMAP, CCI, provide robust framework identifying levels may predispose wildfire danger."
    },
    {
        "DOI": "10.5194/AMT-19-211-2026",
        "Title": "Hydrometeor partitioning ratios for dual-frequency space-borne and polarimetric ground-based radar observations",
        "Year": 2026,
        "Abstract": "Abstract. Conventional radar-based hydrometeor classification algorithms identify the dominant hydrometeor type within a resolved radar volume, while newer techniques estimate the proportions of individual hydrometeor classes (hydrometeor partitioning ratios, HPRs) within a mixture. These newer algorithms (HMCP) are based on dual-polarization measurements from ground-based radars (GR), while to date no comparable algorithms for space-borne radars (SR) with dual-frequency capabilities exist. This study (1) further improves HPR estimates based on GR dual-polarization measurements, (2) exploits the combination of dual-frequency SR and dual-polarization GR to introduce HPRs based on dual-frequency observations only, and (3) evaluates GR- and SR-based HPR retrievals. To achieve these objectives, dual-polarization measurements of NEXRAD's GRs are matched with those of the dual-frequency precipitation radar of the Global Precipitation Measurement Core satellite. All matched volumes are represented by averaged dual-frequency and dual-polarization observations and several hundred GR sub-volumes classified with standard hydrometeor classification. The latter are used to calculate quasi-HPRs (qHPRs). qHPRs and averaged dual-frequency and dual-polarization variables of the training dataset are used to derive covariances and centroids for each hydrometeor class. They serve as the basis for dual-frequency and dual-polarization based HPR retrievals within HMCP and are applied to the test dataset. The ensuing evaluation of HPR retrievals is performed with the qHPRs of the test dataset. HPRs show for most hydrometeor classes high correlations with the qHPRs and confirm the overall good HMCP performance. However, dual-polarization based classification performance is superior to dual-frequency ones. Both underestimate snow, overestimate graupel, and result in low correlations for big drops."
    },
    {
        "DOI": "10.18280/EESRJ.120402",
        "Title": "Modelling of Temperature Forecasting Using Statistical Method for Ilorin Over a Period of Three Decades",
        "Year": 2025,
        "Abstract": "This study uses Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis data from 1990 to 2024 model forecast monthly surface temperatures over Ilorin, Nigeria.Finding the best statistical capture seasonal short-term temperature variations in a tropical climate was goal.A stationarity test using Augmented Dickey-Fuller (ADF) approach (p = 6.07 10; statistic -4.7749)was first step time series confirm suitability autoregressive modeling.Two models were fitted: an automatically chosen ARIMA (2,0,2)(2,0,1) with lowest Akaike Information Criterion (AIC) of 1036.006 Seasonal Autoregressive Integrated Moving Average eXogenous regressors (SARIMAX) (1,1,1)(1,1,1) AIC 842.975.With intercept 178.51 K, latter showed strong dependence through significant moving-average terms.The model's predictive reliability validated by diagnostic tests (Ljung-Box Jarque-Bera), which verified acceptable residual behavior.The results show that data-driven can produce precise medium-term forecasts successfully climatic variability.These findings offer useful basis agricultural planning, environmental management, regional adaptation regions West Africa."
    },
    {
        "DOI": "10.5194/WES-11-51-2026",
        "Title": "Model sensitivity across scales: a case study of simulating an offshore low-level jet",
        "Year": 2026,
        "Abstract": "Abstract. In this study, a seven-member ensemble of mesoscale-to-microscale simulations with varying sea surface temperature (SST) is conducted for a case in which an offshore low-level jet was observed via floating lidar. The performance of each SST setup in reproducing the physical characteristics of the observed low-level jet is compared across the mesoscale and microscale domains. It is shown that the representation of low-level shear, jet-nose height, and hub-height wind speed are generally improved when moving from mesoscale to microscale. Specifically, low-level shear is improved in the microscale by reducing near-surface wind speeds and lowering the jet-nose height to be closer to that observed. Counterintuitively, the sensible heat flux on the mesoscale domains is more negative than on the microscale domains, which would indicate a more stable boundary layer with higher shear; however, the low-level shear in the mesoscale is weaker than that of the microscale domains. This indicates over-mixing of the (planetary boundary layer) PBL scheme in the mesoscale domains and/or the overprediction of surface drag in the microscale domain. We analyze performance considering a real-world scenario in which the computational burden of running an ensemble of large-eddy simulations (LESs) limits a study to performing a mesoscale ensemble to select the best model setup that will drive a single LES run. In the context of this study, the best model setup is subjective and weighs model performance in the physical representation of the low-level jet as well as the model surface forcing through the temperature gradient between air and sea. The expectation of this approach is that the best-performing setup of the mesoscale simulations will produce the best result for the microscale simulations. It is shown that there are large fundamental changes in the characteristics of the low-level jet as well as in the surface forcing conditions between the mesoscale and microscale domains. This results in a non-linear ranking of performance between the mesoscale domains and the microscale domains. While the best-performing mesoscale setup is also deemed to produce the best results on the microscale, the second-best-performing mesoscale setup produces the worst results on the microscale."
    },
    {
        "DOI": "10.1016/J.JASTP.2026.106730",
        "Title": "Long-term air quality forecasting in Korba, India (20252047): A hybrid model using 44-year satellite data",
        "Year": 2026,
        "Abstract": "This study employs a hybrid Temporal Convolutional Network (TCN) and Transformer model to forecast air quality trends in Korba, Chhattisgarh, a critically polluted industrial hub dominated by extensive coal mining (including Gevra, Dipka, and Kusmunda mines), multiple thermal power plants, aluminium smelters, and cement production, from 2025 to 2047. Utilizing 44 years of satellite-derived data (19802024), the model integrates meteorological variables, vegetation indices (NDVI and NDBI), and coal mining metrics. Under a moderate policy change scenario, it predicts gradually rising pollutant levels: PM10 (3284 g/m3), PM2.5 (1033 g/m3), SO2 (7.2512 g/m3), NO2 (5.259 g/m3), and AQI (45110, Moderate per CPCB Delhi standards), with seasonal patterns showing reduced concentrations during monsoon due to rainfall washout and elevated levels in summer and winter owing to limited atmospheric dispersion. The model demonstrated strong performance (R2 = 0.750.91; RMSE = 1.0911.91), effectively capturing both short- and long-term trends driven by industrial emissions and environmental factors. A sensitivity analysis further revealed the model's robust response to 1020 % variations in key drivers, with the most decisive influence from coal production increases, which could be elevated by 2030 % and add 1015 AQI points, while reduced rainfall, higher temperatures, and lower NDVI amplified dust resuspension and secondary pollutant formation. Uncertainty analysis identified high-risk periods, including elevated PM2.5 variability in 20252026 and AQI in 20332038. Validation against ground-truth data from Urja Nagar, Rampur stations, and MODIS Satellite-derived AQI (From JanuarySeptember 2025; R2: 0.72, 0.61, and 0.58) confirmed forecasted AQI (30164, mostly Moderate), posing potential respiratory risks to vulnerable groups upon prolonged exposure. These projections highlight escalating public health threats, particularly respiratory and cardiovascular diseases, underscoring the need for urgent interventions, such as stricter emission controls, a transition to renewable energy sources, the adoption of an air quality health index, and enhanced reforestation for dust mitigation. This work offers a robust, data-driven baseline and scalable framework for sustainable air quality management in industrial regions, aligning with India's vision for balanced development by 2047."
    },
    {
        "DOI": "10.1080/02626667.2025.2608147",
        "Title": "Controls on lag time in Philippine catchments identified using rainfallrunoff modelling and a generalized additive model (GAM)",
        "Year": 2026,
        "Abstract": "Understanding the controls upon lag time, can improve river and flood management decision-making. This study investigates relative importance of catchment characteristics in explaining time variability across Philippines. Numerically simulated 5-year return period times for 291 catchments were analysed using a generalized additive model (GAM) to capture non-linear relationships with location, geology, climate, topography, land use. The is representative moderate response, as varies little periods. Correlation analysis recursive feature elimination guided variable selection, while bootstrapping assessed stability uncertainty. Ten significant on identified, relief ratio, cover index, area most influential. GAM achieved an R2 0.77 explained 84% deviance. Land emerged only anthropogenically modifiable control, highlighting key lever. National hydrological observations are needed further support calibration."
    },
    {
        "DOI": "10.1186/S13021-025-00388-Z",
        "Title": "Tree size-dependent effects of tree diversity on aboveground biomass during development in subtropical coniferous forests",
        "Year": 2026,
        "Abstract": "The biodiversity and ecosystem functioning (BEF) relationships can offer insights for management of forest plantations. Most evidences on BEF from field observations experiments focus short-term ( 50 years) development. However, how tree diversity aboveground biomass co-evolve during long-term development remain poorly understood. Addressing this knowledge gap improve the diversity-based carbon sequestration. We employed a process-based Ecosystem Demography model (ED-2.2) to simulate three categories attributes over approximately one century (2004-2100) in subtropical coniferous forests China. These included: (1) plant functional type (PFT) metrics (Simpson, Shannon-Wiener, Pielou evenness metrics); (2) structural DBH [standard deviation (SD), coefficient variation (CV), Gini (Gini)]; as well (3) stock (AGBS) production (AGBP). modeling results were evaluated using inventory. showed that PFT (particularly Simpson's Shannon-Wiener diversity) (notably SD DBH) generally exhibited U-shaped hump-shaped trajectories, respectively. AGBS increased gradually before slightly declining, while AGBP rose rapidly then entered gradual decline. Notably, seasonal warming precipitation stress may lead severe mortality, potentially altering trajectories AGBS/AGBP forests. strengthened time century. Among six metrics, (95%) (79%) demonstrated highest proportion significant across years. stand density mean size followed Yoda's power law (- 1.44 vs. - 1.50). effects predominantly negative concentrated first half period simulation. In contrast, primarily positive, especially period. Mean emerged stronger driver than density, with detected 67% 56% cases, To some extent, modulated relationships. This study provides succession modelling evidence, highlighting importance size-dependent processes shaping"
    },
    {
        "DOI": "10.1007/S44393-026-00011-5",
        "Title": "Study on a Typhoon-Induced Gravity Wave Captured by High-Altitude Radiosonde Observations in Okinawa in July 2024",
        "Year": 2026,
        "Abstract": "Abstract Understanding the characteristics of atmospheric gravity waves driving stratospheric circulation, which directly influence tropospheric weather, is of considerable importance. However, in-situ observation datasets in the middle and upper stratosphere remain limited. To investigate gravity wave activity in this part of the atmosphere, we conducted high-altitude radiosonde observations using large rubber balloons over Okinawa, Japan, in July 2024, obtaining wind velocity and temperature data. During the observation, a distinctive fluctuation was observed on July 25 as Typhoon Gaemi passed southwest of Okinawa. In this study, we investigate the characteristics of the observed fluctuation using hodograph and ray tracing analyses. The results suggest that the fluctuation was a gravity wave with a large vertical wavelength ~ 11 km, originating near the center of Typhoon Gaemi and propagating upward and east-northeast direction. Furthermore, the typhoon-induced gravity waves were modulated by the easterly mean wind field, suggesting that their vertical propagation characteristics differed between the eastern and western sectors of the typhoon."
    },
    {
        "DOI": "10.34194/5F80B592",
        "Title": "Operational Flood Forecasting System in Denmark  Integrating Groundwater and Surface-water",
        "Year": 2026,
        "Abstract": "Most operational flood forecasting systems provide predictions of pluvial and fluvial floods, often neglecting groundwater flooding. Groundwater-induced floods can occur when prolonged rainfall, high river stages or elevated sea levels raise the groundwater table above the surface of the land, often occurring in low-lying areas or areas with specific soil and land-surface conditions. This study presents an operational, national-scale, integrated flood forecasting system that combines surface water and groundwater components such as river discharge and high groundwater levels to assess flood risk in Denmark. The system has been proven to effectively capture peak river flows and elevated groundwater levels, as it did across the country during the winter of 2024, and provide local-scale insights, as exemplified during a specific flood event in Varde, west Denmark. This study demonstrates how groundwater flooding, often neglected in operational forecasting, can be effectively incorporated at a national scale to support more informed flood management."
    },
    {
        "DOI": "10.1007/S11431-025-3075-7",
        "Title": "Ensemble optical flow algorithms for intra-day solar irradiance forecasting with Fengyun-4A",
        "Year": 2026,
        "Abstract": ""
    },
    {
        "DOI": "10.1038/S41467-026-69480-3",
        "Title": "Hydroclimate shapes photosynthetic sensitivity to cloud cover across global terrestrial ecosystems",
        "Year": 2026,
        "Abstract": "Abstract Vegetation photosynthesis primarily depends on surface energy and water availability, both of which are simultaneously regulated by clouds through radiation and precipitation, respectively. However, the net impact of cloud-induced changes in surface solar radiation and precipitation on photosynthesis remains elusive. Here, using observational- and model-based datasets spanning the past few decades, we show that, consistently across scales from site-level eddy covariance measurements to global-scale gridded datasets, the sensitivity of photosynthesis to cloud cover is spatially shaped by the hydroclimate, as quantified by the humidity index (mean annual precipitation-to-evapotranspiration ratio). Specifically, we find that in water-limited arid regions, clouds promote photosynthesis through increased precipitation, with a delayed effect typically within one month, whereas in energy-limited humid regions, they inhibit photosynthesis almost instantaneously by blocking sunlight. An annual scale spatially resolved sensitivity metric of photosynthesis to cloud cover is further examined to estimate potential changes in vegetation productivity driven by clouds. The findings indicate that, under a warming climate, particularly in the Coupled Model Intercomparison Project Phase 6 ssp585 scenario (20152099), gross primary productivity is projected to decline in arid regions and increase in humid regions due to changes in cloud cover, suggesting an exacerbation of regional disparities in ecosystem functions."
    },
    {
        "DOI": "10.1016/J.ASR.2026.02.088",
        "Title": "Assessing the impacts of long-term weather variability and urban development on crop production to analyze food security in Iran",
        "Year": 2026,
        "Abstract": " This study employed an integrated remote-sensing approach on the cloud-free Google Earth Engine (GEE) platform to assess the impacts of long-term weather trends and human activities, in particular, urban development on crop production to analyze food security in Iran from 2000 to 2022. A range variables such as air temperature (AT), actual evapotranspiration (AET), precipitation, snow cover, built-up area, surface water, and groundwater storage (GWS) were analyzed using pre-processed products from different resources (e.g., ERA5-Land) available within the GEE environment. Relationships between these variables and croplands were then examined using the Mann-Kendall (MK) non-parametric trend test to evaluate their effects on crop production. Our findings revealed correlation coefficients of −0.14, 0.20, 0.35, and 0.30 between croplands and AT, AET, precipitation, and snow cover, respectively. Furthermore, strong correlations between croplands and built-up areas (−0.80), surface water (0.78), and GWS (0.75) were found. The results suggest that precipitation and snow cover had moderate corelations with croplands, indicating that their decline between 2000 and 2022 affected crop production in Iran. In contrast, AT and AET showed weaker correlations with croplands. Urban expansion, however, had the most significant negative impact on crop production, while the reduction in crop production was also linked to a decrease in surface and groundwater resources over the same period. These factors contributed to a reduction in crop area by 7892.26 km2, with a marked decline starting in 2007. Before this pivotal year, the average crop area was around 78,568.72 km2, which dropped to 71,755.45 km2 afterward. In conclusion, the findings underscore those variables (built-up areas, surface water, and GWS) had the negative influence on crop production, posing a direct threat to food security in the country. "
    },
    {
        "DOI": "10.1038/S41467-025-67082-Z",
        "Title": "Global risk pooling mitigates financial risk from drought in hydropower-dependent countries",
        "Year": 2026,
        "Abstract": "Abstract More than 50 countries rely on hydropower for over 25% of their electricity generation, making them vulnerable to drought and resulting revenue losses. Governments can offset financial losses for publicly-owned hydropower generators, but this can create fiscal pressures and lead to negative consequences, such as lower bond ratings. Index-based financial instruments, used to manage weather-related risk, offer an alternative, though data collection and index design are challenging. Using remotely sensed hydrometeorological data, we develop index insurance contracts to manage drought-related financial risk for hydropower-dependent countries. Low correlations in drought across these countries allow cost reductions when risks are pooled. Pooling the contracts yields average savings of 54% compared to individual risk management via reserves. These findings indicate that pooled index insurance can strengthen financial resilience in countries dependent on hydropower and support governments in mitigating drought-related economic risks."
    },
    {
        "DOI": "10.1007/S10762-025-01106-Z",
        "Title": "Physics-Informed Deep Learning for Adaptive Atmospheric Compensation in Terahertz Satellite Communication Networks",
        "Year": 2026,
        "Abstract": "Terahertz (THz) satellite communications face challenges due to atmospheric attenuation and distortion caused by molecular interactions, including absorption and scattering. This research introduces a novel Physics-informed deep learning (PIDL) framework for adaptive atmospheric compensation in terahertz (0.110 THz) satellite communication networks. The framework integrates Maxwell's equations for electromagnetic propagation and HITRAN-derived molecular absorption models directly into neural network architectures to overcome THz signal degradation caused by atmospheric attenuation and group velocity dispersion (GVD). This enables real-time adaptation to atmospheric variations, increasing the data transmission rate and compensating for atmospheric effects, making THz satellite systems feasible. Through extensive validation using 225,000 atmospheric samples and real satellite data in various climatic conditions, the framework achieves an unprecedented performance of > 95% GVD compensation, sub-millisecond adaptation latency (515X faster compared to existing methods), 40% energy efficiency improvement, and 1.48 terabit per second (TB/s) average data rate with zero-shot generalisation to unseen atmospheric conditions. Utilising global atmospheric profiles and experimental satellite measurements, PIDL enables real-time physical consistency without external reference, thereby opening the door to THz satellite deployment for 6G and beyond wireless networks."
    },
    {
        "DOI": "10.1126/SCIADV.ADV7998",
        "Title": "Forest loss intensifies meteorological drought in more than half of Earths climate zones",
        "Year": 2026,
        "Abstract": "Global forest loss increases the risk of meteorological drought by altering surface energy balances. While local impacts on temperature and precipitation are known, the extent and underlying mechanisms of its influence on meteorological drought remain unclear. Here, we analyzed 3696 paired forest loss and intact sites across boreal, temperate, and tropical zones. Forest loss intensified meteorological drought in more than 52% of affected regions. Drought prevalence in boreal zones rose by 5% over 20 yearsthree times greater than in tropical zonesbecause of reduced latent heat flux and increased surface albedo, which together suppressed convective rainfall. In contrast, tropical forests demonstrated greater ecological resilience, mitigating ~40% of meteorological drought intensification. Notably, forest lossinduced meteorological drought may further evolve into more severe agricultural and hydrological droughts. Therefore, we recommend implementing strategies tailored for each climate zone, including native forest conservation, proactive ecological restoration, and connectivity enhancement, to effectively reduce drought risk. , Forest loss increases the risk of meteorological drought more in boreal zones than in the tropics."
    },
    {
        "DOI": "10.1038/S44304-026-00184-W",
        "Title": "Severe rapid indian monsoon weakening due to emissions from extreme Canadian wildfires",
        "Year": 2026,
        "Abstract": "August 2023 was the driest Indian monsoon in recorded history, causes for this extended precipitation deficit an otherwise typical remaining unclear. Given that monsoonal is highly sensitive to northern hemispheric aerosol abundances, here we investigate whether smoke emitted by unprecedented Canadian wildfires occurred during same season contributed anomaly. We conducted ensemble simulations with a state-of-the-art Earth System Model and very similar anomaly observed when accounting fire smoke, as opposed not it. The mechanism proposed based on model findings generation of pronounced low-level pressure over Asian continent following smoke-related cooling, which led weakened winds reduced moisture transport region, thus diminishing precipitation. modelled supported comparisons radiosonde, reanalysis satellite data performed. Wildfires are becoming more severe climate change, our study highlights potential large-scale wildfire events impact crucial meteorological phenomena regions far away from emission source."
    },
    {
        "DOI": "10.1186/S40645-025-00794-4",
        "Title": "Changes in ambient ozone concentrations from urban to remote areas in Japan during the COVID-19 pandemic period in April and May, 2020",
        "Year": 2026,
        "Abstract": "Abstract During the COVID-19 pandemic period, anthropogenic activities were severely restricted, leading to a substantial reduction in emissions. The effects of the emission reduction on air quality have been reported worldwide, but the focus has been over urban areas. Tropospheric ozone (O 3 ) increased due to the weakening of the NO titration effect caused by the large decrease in NO x emissions. In this study, we considered the time lag in the emission decreases due to COVID-19 over East Asia and evaluated O 3 changes in April and May, 2020 over the whole of Japan, from urban to remote areas, with different timescales. The decreases in O 3 concentration were evaluated according to both ground-based observation and air quality modeling in April and May, with a greater change observed in May. The air quality modeling result demonstrated that meteorological changes, particularly solar radiation and relative humidity, could explain around 50% of the O 3 change in April and 7080% in May. The domestic anthropogenic emission changes during COVID-19 led to a decrease in O 3 over the whole of Japan; however, the monthly mean O 3 concentration increased and the maximum O 3 concentration peaks decreased over urban areas. The effects of emission changes were smaller than the effects of meteorology in April and May. To understand the worsening of O 3 pollution in East Asia, we clarified the need to evaluate O 3 abatement strategies, focusing on the characteristics of different areas (urban and remote) and timescales (mean and maximum) based on the lessons from the COVID-19 pandemic."
    },
    {
        "DOI": "10.1080/13545701.2025.2589122",
        "Title": "Gender Difference in the Effects of Air Pollution on Labor Supply: Evidence from the United States",
        "Year": 2025,
        "Abstract": "This article revisits the question of gender inequality in labor market using pollution as driving force. The study analyzes a causal estimation effect air on supply across US counties for years 200619. To address endogeneity and selection into market, implements Heckman-2SLS strategy thermal inversion strength an instrumental variable. Using hours worked measure supply, findings show that 1 percent increase PM2.5 concentration reduces womens working by 2.1 compared to no reduction men. These adverse impacts are stronger among racial minority women, married women with dependents such children or parents household. Surprisingly, unlike group, unmarried do not exhibit men.HIGHLIGHTS Gender disparities due evident even contexts United States.Moderate increases county-level reduce more than mens.Marital status dependent responsibilities contribute observed disparity.High childcare costs limit mothers ability work response shocks.The gap is pronounced lower baseline levels."
    },
    {
        "DOI": "10.1038/S41598-026-37219-1",
        "Title": "Role of Arabian Sea warm pool and atmospheric instability in triggering a monsoonal MCC over Peninsular India",
        "Year": 2026,
        "Abstract": "Mesoscale Convective Complexes (MCCs) significantly contribute to the global hydrological cycle and about 50% of total monsoon rainfall over Indian region through extreme flood events. An event triggered devastating landslides widespread damage Wayanad in Western Ghats, India, during 29-30 July 2024. This study investigates MCC responsible for using satellite, in-situ, reanalysis datasets. The system exhibited deep organized convection, strong low-level moisture transport, upper-level divergence within a highly unstable atmospheric environment. anomalously persistent warm pool southeastern Arabian Sea, sustained break phase, created favorable thermodynamic conditions elevated sea surface temperatures latent heat fluxes. findings underscore growing role ocean-atmosphere coupling monsoonal convective extremes highlight need improved mesoscale modeling early warning frameworks topographically complex regions like Ghats."
    },
    {
        "DOI": "10.3390/HYDROLOGY13020052",
        "Title": "Daily and Monthly Scale Comparisons of Three Gridded Precipitation Datasets over the British Columbia Province, Canada",
        "Year": 2026,
        "Abstract": "Understanding the characteristics of precipitation datasets in a given region is crucial for hydrological studies. This study focuses on the British Columbia (BC) Province in Canada and evaluates the statistical characteristics of precipitation data from three gridded precipitation datasets: the Pacific Climate Impacts Consortiums northwestern North America meteorological dataset (PNWNAmet), Global Precipitation Measurement (GPM), and Global Precipitation Climatology Centre (GPCC). These precipitation datasets at both daily and monthly scales were compared with point observation data from the Global Historical Climatology Network (GHCN). For the daily-scale comparison of three precipitation datasets, seven indices of extreme precipitation were computed at ten observation points. Out of eleven locations for the monthly analysis, GPCC showed the lowest RMSE at six locations (five of them were in the northern to central BC), and PNWNAmet showed the lowest RMSE at four locations (three of them were in the southern BC), suggesting GPCCs superior agreements with GHCN at the northern and central part of BC and PNWNAmets better agreements with GHCN at the southern part of BC. The comparison of monthly precipitation averaged over BC showed that PNWNAmet offers higher monthly precipitation than GPCC and GPM, while the variability of annual precipitation among the three datasets is similar. Spatial analysis of precipitationelevation relationships revealed the value of considering both elevation and distance from the coast in evaluating the precipitationelevation relationships."
    },
    {
        "DOI": "10.1073/PNAS.2524123123",
        "Title": "Projecting nitrous oxide over the 21st century, uncertainty related to stratospheric loss",
        "Year": 2026,
        "Abstract": "Extending the N 2 O lifetime derived from Microwave Limb Sounder satellite observations, we find a mean value of 117 y and a likely decrease of 1.4 0.9% per decade over the period 2004 to 2024. This trend is consistent with the previously published 2004 to 2021 value of 2.1 1.2% per decade. A more careful analysis of uncertainty now provides a more robust likely (one-sigma) range. From analyses of a range of factors controlling the N 2 O lifetime, we find that the decrease in lifetime can be explained by recent changes in stratospheric circulation and temperature. Projection of the lifetime change to 2100 shows that this effect is comparable to differences across the shared socioeconomic pathways used for climate projections and cannot be ignored. An updated evaluation of the N 2 O chemical feedbacks shows that this effect produces a relatively small shift in atmospheric abundance over the 21st century, but still an important shift, 11%, in the global warming potential of N 2 O."
    },
    {
        "DOI": "10.1038/S41597-026-06585-W",
        "Title": "A new, long-term root zone soil moisture dataset for operational agricultural drought monitoring over Africa",
        "Year": 2026,
        "Abstract": "Abstract Quantifying root zone soil moisture (RZSM) is critical for assessing water availability to crops and identifying agricultural drought across Africa, with access to reliable and timely RZSM data essential for informed decision-making. While rainfall is frequently used to assess crop growing conditions, it alone may not reliably reflect crop water availability due to the impact of evapotranspiration on soil moisture and rainfall not always reflective of concurrent soil water content at rooting depth. To provide robust information on agricultural drought, this paper describes a new, operational RZSM dataset, called TAMSAT soil moisture (TAMSAT-SM), available from 1983-present at 0.25 spatial resolution. TAMSAT-SM is derived using the JULES land surface model, forced with TAMSAT rainfall estimates and other meteorological variables from the NCEP reanalysis, and tuned to SMAP satellite soil moisture observations. Comparison against other RZSM products and independent satellite-derived vegetation health data show TAMSAT-SM can reliably capture the spatial and temporal RZSM and vegetation health patterns across Africa and can be a useful tool to support existing agricultural drought monitoring efforts."
    },
    {
        "DOI": "10.3847/1538-3881/AE45FD",
        "Title": "Identifying Exoplanets with Deep Learning. VI. Enhancing Neural Network Mitigation of Stellar Activity RV Signals with Additional Metrics",
        "Year": 2026,
        "Abstract": "Abstract The measurement of exoplanet masses using the radial velocity (RV) technique is currently limited by stellar activity, which introduces quasiperiodic variability signals that must be modeled and removed to enhance the sensitivity of the RV measurements to exoplanet signals. Neural networks have previously been demonstrated effective in modeling stellar activity signals in HARPS-N solar data using white light cross correlation functions (CCFs). Building on this work, we train a neural network on 6 yr of HARPS-N solar data with additional parameters commonly associated to stellar activity, including chromatic CCFs, line shape metrics, spectral activity indicators, total solar irradiance (TSI) light curves from SORCE and TSIS-1, and TSI time derivatives. Our results show that parameters such as the bisector inverse slope and Na D equivalent widths (EWs) do not significantly improve the neural networks ability to predict activity-induced RV variations compared to using the white light CCFs alone. However, parameters such as unsigned magnetic flux, the TSI and its time derivative, S-index, H EW, chromatic CCFs, contrast, and FWHM do improve the neural network's ability to predict RV scatter. Our new model reduces the RV scatter in a held-out test set from 147.1 cm s 1 to 93.3 cm s 1 , consistent with supergranulation noise levels reported in previous studies. These results suggest that finding effective tracers for (super)granulation will be critical to train models capable of further mitigating RV jitter, and necessary for characterizing Earth analogs."
    },
    {
        "DOI": "10.1016/J.ACCRE.2025.04.010",
        "Title": "Global prevalence of compound heatwaves from 1980 to 2022",
        "Year": 2025,
        "Abstract": "Global warming has led to increasing occurrence of hot extremes, yet our understanding the compound heatwaves (CHW) both day and nightthe most threatening harmful typeremains limited. Here we use air temperature from ERA5-Land datasets analyze key characteristics global CHW 1980 2022. Our results demonstrate a pronounced increase in CHW, with an annual cumulative intensity rising by 3.32 C per decade ( p < 0.001), approximately four times greater than increases observed individual heatwave types daytime (0.73 decade, 0.001) nighttime (0.78 respectively. High latitudes Northern Hemisphere, particularly Arctic regions, have experienced highest (>10 decade), especially since 2005. Moreover, interannual variations are closely linked major climate modes, displaying strong region-specific connections varied lagged effect, ENSO PDO tropical regions. Altogether, these reveal unexpected prevalence recent decades, emphasizing urgent need address its potential adverse impacts on human ecosystem well-being."
    },
    {
        "DOI": "10.5194/AMT-19-249-2026",
        "Title": "An algorithm to retrieve peroxyacetyl nitrate from AIRS",
        "Year": 2026,
        "Abstract": "Abstract. Herein, we describe an approach to retrieve free tropospheric columns of peroxyacyl nitrates (PANs) from radiances observed by the Atmospheric Infrared Sounder (AIRS). AIRS has provided daily global coverage since its launch in 2002, making the AIRS data a valuable long term record. Although the instrument is very radiometrically stable, the radiance noise level is large enough to present a challenge when retrieving a weak absorber such as PAN. To address this, spectral windows were selected to minimize interference from other species as much as possible and a set of filters was developed to predict whether a PAN value retrieved from AIRS is within 0.2 ppb or 50 % of what would be retrieved from the Cross-track Infrared Sounder (CrIS) and to remove spurious signals caused by specific surface features or clouds. We show that AIRS is capable of retrieving PAN plumes with very high concentrations of PAN (such as those from significant wildfires) that have similar spatial extent as seen by CrIS and that PAN retrieved from AIRS has good correlation with CrIS given sufficient averaging. We conclude with recommendations for users to help ensure that these data are used appropriately."
    },
    {
        "DOI": "10.1002/LNO.70275",
        "Title": "Extreme wildfire conditions shift coastal phytoplankton community structure in California",
        "Year": 2026,
        "Abstract": "Abstract Extreme wildfires have increased in frequency and intensity globally, particularly in the Western United States. Here we examine how the 2020 Lightning Complex Fires in California influenced coastal phytoplankton communities using monitoring network data products in the Monterey Bay, the ocean region closest to this large fire. We observed no clear response in ocean chlorophyll a , often considered a proxy for total phytoplankton biomass, during and after the fires. However, using phytoplankton community composition observations we detected a shift in phytoplankton size and taxonomic structure that coincided with the timing of the fires. Small centric diatoms initially dominated, followed by a proliferation of chainforming diatoms, including Asterionellopsis , Skeletonema , Hemiaulus , Leptocylindrus , Thalassionema , and Thalassiosira . Crosscorrelation analysis and generalized additive models identified wildfire aerosols (PM2.5) as a significant predictor of these diatom blooms, though the precise mechanisms remain uncertain. We speculate that a combination of nutrient deposition, light limitation from smoke shading, interactions with oceanographic conditions, and differential mortality due to grazing or toxicity drove the observed phytoplankton shifts. This study provides rare observational evidence linking extreme wildfires to changes in coastal phytoplankton communities and underscores the need for sustained ocean monitoring, rapidresponse sampling, and mechanistic studies to unravel these complex wildfireocean interactions."
    },
    {
        "DOI": "10.5194/GMD-19-543-2026",
        "Title": "Evaluation of coupled and uncoupled oceaniceatmosphere simulations using icon-2024.07 and NEMOv4.2.0 for the EURO-CORDEX domain",
        "Year": 2026,
        "Abstract": "Abstract. Evaluation results from the reanalysis-driven (ERA5/ORAS5) simulation for the years 19792021 with a regional coupled oceanatmosphere model (ROAM) are presented. The coupled setup portrayed here is one of the first regional climate modeling systems to couple the ICON atmosphere model in climate limited-area mode (CLM) with the ocean model NEMO for the North and Baltic Sea (NBS), using a flux-based OASIS3-MCT coupling approach. Along with the simulation using the coupled model configuration ROAM-NBS, the simulations with the uncoupled components (ICON-CLM and NEMO-NBS, respectively) are analyzed and compared with various observational datasets. ROAM-NBS complements atmosphere-only climate projections with the same atmospheric model and setup, which will all be published in accordance with EURO-CORDEX specifications. Climate projections by ROAM-NBS will enrich the data available to support the German Strategy for Adaptation to Climate Change (DAS), especially for our target region, which are the German national waters. In general, the mean model climate is well represented by all setups. The sea surface temperature (SST) bias is, on average, about 0.5 K. Differences in fluxes and precipitation over the ocean between the coupled and uncoupled simulations are largely related to SST differences. However, the mean influence on the land areas is negligible. The evaluations of ocean variables indicate a strong agreement between ROAM-NBS and NEMO-NBS. Compared to observations, both simulations overestimate sea ice concentration and extent. Mean temperature and salinity profiles in the Baltic Sea are generally reproduced by both simulations, with biases in the deeper layers. Major inflow events are captured but underestimated. Sea surface height and storm surge highly coincide with observational data, with NEMO-NBS slightly outperforming ROAM-NBS in terms of correlation. The marine heat wave (MHW) evaluation against observations in the North and Baltic Sea demonstrates that the simulations capture the inter-annual variability of MHW characteristics. Overall, the coupled simulation demonstrates adequate performance for both the atmosphere and the ocean, and the setup will be used to produce coupled regional climate projections for Europe. However, bias correction for the deeper Baltic layers remains necessary for further applications, and future work will focus on refining the setup for this region."
    },
    {
        "DOI": "10.1038/S44304-026-00172-0",
        "Title": "Widespread forest disturbance from windthrow in central African rainforests",
        "Year": 2026,
        "Abstract": "Abstract Natural disturbances are major drivers of tropical forest dynamics, yet their role in Central Africas old-growth rainforests, the worlds second largest tropical forest block, remains poorly quantified. Here we present the first regional assessment of windthrow, the uprooting or breakage of trees by wind. Using Landsat imagery from 2019 to 2020, we detected 74 windthrow events 30 ha, collectively affecting ~18,600 ha. These events were concentrated in eastern regions where mesoscale convective systems and extreme rainfall are most frequent. Sizes of windthrow events followed a Weibull distribution, with a single 3974 ha event accounting for one fifth of the total affected area. Event orientations aligned with prevailing storm outflows, and their timing coincided with peaks in extreme rainfall. For a subset of seven events with adequate temporal coverage before and after disturbance, near-infrared reflectance returned to pre-disturbance levels within months, indicating a rapid vegetation regrowth. Together, these findings show that windthrow is an important disturbance agent in Central Africa and must be considered in assessments of forest resilience under intensifying storm regimes."
    },
    {
        "DOI": "10.5194/WCD-7-247-2026",
        "Title": "A new index used to characterise the extent of Antarctic marine coastal winds in climate projections",
        "Year": 2026,
        "Abstract": "Abstract. Antarctic marine coastal near-surface winds play a key role in Southern Ocean circulation. Using the ERA5 reanalysis dataset, this paper develops directional wind constancy as a tool for identifying key features in these winds and their relationship with the mid-latitude westerly jet. In particular, the Antarctic coastal wind boundary (ACWB), defined as the minimum offshore directional constancy boundary, is shown to be a useful way to define the marine near-coastal region where the Antarctic topography plays an important role in influencing the wind direction. We show that, while the ACWB is linked to large-scale modes of atmospheric circulation through its close association with variability in the mid-latitude westerly jet, it also highlights key regions where topographically-influenced, meridional flows are dominant. These meridional flows are not identified in current regional climate indices. Future changes in the ACWB are examined using CMIP6 projections for a high emissions scenario. This indicates that by the end of this century the ACWB is projected to shift poleward by about 60 km, less than the 130 km shift in the mid-latitude westerly jet, indicating a reduction in the extent of the circumpolar trough."
    },
    {
        "DOI": "10.1038/S41467-026-68989-X",
        "Title": "Pantropical moist forests are converging towards a middle leaf longevity",
        "Year": 2026,
        "Abstract": "Leaf longevity is a fundamental plant trait that largely explains ecosystem functional dynamics in global pantropical moist forests. However, the signs, magnitudes, and mechanisms of spatiotemporal variations leaf with ongoing climate change are still lacking. Using both ground measurements gridded age-dependent area index data, we map continental-scale variability annual mean across forests over 20012023. We find biome-dependent converging trend under change. In Amazon tropical Asia long (> ~1.8 years), decreases due to rising temperature intensified atmospheric dryness. contrast, an increasing observed Congo subtropical where have short (<~1.8 years). These responses cause convergence into middle range, maximization traits, photosynthesis, species evenness, which expected better resist variability. Our study provides emerging evidence for large-scale structural adaptions helpful predicting climate-driven risks stability. The leaves determines overall duration photosynthesis plants. This suggests drives toward intermediate ranges, which, by altering traits enhancing photosynthetic capacity, strengthens stability closely linked vegetation diversity."
    },
    {
        "DOI": "10.1038/S41597-026-06604-W",
        "Title": "A Machine Learning approach for Total Water storage anomaly eXtension back to 1980 (ML-TWiX)",
        "Year": 2026,
        "Abstract": "Abstract We present ML-TWiX, a global dataset of monthly total water storage anomalies (TWSA) reconstructed from 1980 to 2012, provided on a 0.5 0.5 global grid. While the GRACE and GRACE Follow-On satellite missions have provided valuable observations of global TWSA, their combined record spans just over two decades, limiting their utility for long-term climate and hydrological studies. ML-TWiX extends the GRACE-era record into the pre-GRACE period by learning from global hydrological and land surface model simulations using an ensemble of three machine learning models: Random Forest, XGBoost, and Gaussian Process Regression. The three machine learning models were independently used to reconstruct TWSA, and their outputs were subsequently combined through ensemble averaging to produce a unified product with spatially explicit uncertainty estimates. We validated ML-TWiX against multiple independent datasets, including satellite laser ranging, storage deduced from the water mass balance closure, and global mean sea level budget estimates. It provides a continuous reconstruction of global TWSA, enabling a wide range of applications in hydrology, climate science, and water resource assessment."
    },
    {
        "DOI": "10.5194/HESS-30-525-2026",
        "Title": "Joint calibration of multi-scale hydrological data sets using probabilistic water balance data fusion: methodology and application to the irrigated Hindon River Basin, India",
        "Year": 2026,
        "Abstract": "Abstract. Hydrological data sets have vast potential for water resource management applications; however, they are subject to uncertainties. In this paper, we develop and apply a monthly probabilistic water balance data fusion approach for automatic bias correction and noise filtering of multi-scale hydrological data. The approach first calibrates the independent data sets by linking them through the water balance, resulting in hydrologically consistent estimates of precipitation (P), evaporation (E), storage (S), irrigation canal water imports (C), and river discharge (Q) that jointly close the basin-scale water balance. Next, the basin-scale results are downscaled to the pixel-scale, to generate calibrated ensembles of gridded Precipitation (P) and Evaporation (E) that reflect the basin-wide water balance closure constraints. An application to the irrigated Hindon River basin in India illustrates that the approach generates physically reasonable estimates of all basin-scale variables, with average standard errors decreasing in the following order: 21 mm month1 for storage, 10 mm month1 for evaporation, 7 mm month1 for precipitation, 4 mm month1 for irrigation canal water imports, and 2 mm month1 for river discharge. Results show that updating the original independent data with water balance constraint information reduces uncertainties by inducing cross-correlations between all independent variables linked through the water balance. In addition, the introduced approach yields (i) hydrologically consistent gridded P and E estimates that fuse information from prior (original) data across different land use elements and (ii) statistically consistent random errors that reflect the model's confidence about P and E estimates at each grid cell. The analysis also shows a long-term decreasing trend in groundwater, which is better captured by the more severe decline from GRACE JPL mascon than GRACE Spherical Harmonic data. This finding points towards the possible sustainability issues for irrigation in the basin and requires further validation using piezometer groundwater-level measurements. Future opportunities exist to further constrain the generated water balance variables and their associated errors within process-based models and with additional data."
    },
    {
        "DOI": "10.1029/2025JD044278",
        "Title": "KernelBased Estimation of Stratospheric Aerosol Radiative Effects From Volcanic and Wildfire Events",
        "Year": 2026,
        "Abstract": "Abstract To facilitate the quantification of the stratospheric aerosol direct radiative effect (ARE), this study develops a suite of aerosol kernels based on ModernEra Retrospective Analysis for Research and Applications, Version 2 reanalysis data. The kernels comprise a fivedimensional data set that includes latitude, longitude, time, wavelength, and radiative forcing scenarios. They quantify the sensitivity of topofatmosphere (TOA) radiative fluxes to changes in stratospheric aerosol optical depth (AOD), and distinguish between scattering and absorbing aerosols. Bandbyband radiative kernels are developed to capture the spectral dependence of ARE, while adjusted kernels account for stratospheric temperature responses. Additionally, an analytical kernel is introduced, enabling the estimation of broadband radiative kernel values from boundary conditions such as TOA insolation, reflectance, and stratospheric AOD. Using these kernels, the stratosphere AREs of the 2022 Hunga volcanic eruption and the 2020 Australian wildfire are estimated. The Hunga eruption resulted in a global mean cooling effect of approximately 0.4 W/m 2 throughout 2022. In contrast, the Australian wildfire induced a global mean instantaneous ARE of +0.3 W/m 2 and a stratosphereadjusted ARE of 0.04 W/m 2 . Validation against radiative transfer model calculations confirms the accuracy of our kernelbased estimates. The results demonstrate the significance of spectral dependencies in stratospheric ARE and highlight the distinct radiative sensitivities of stratospheric aerosols compared to their tropospheric counterparts. The developed radiative kernels provide an efficient and versatile tool for assessing the climatic impacts of stratospheric aerosols. , Plain Language Summary Stratospheric aerosols influence the Earth's energy balance by scattering and absorbing solar radiation, making it crucial to accurately measure their radiative impact. However, quantifying the aerosol radiative impact is computationally expensive if using radiative transfer models. In this work, we develop a set of aerosol radiative kernels that can provide a flexible and efficient means for calculating the radiative effects of stratospheric aerosols. The kernels have been demonstrated to effectively quantify the radiative impacts of stratospheric aerosols resulting from wildfire and volcanic eruption events. , Key Points A global data set of radiative sensitivity kernels is developed to quantify the stratospheric aerosol radiative effect (ARE) The stratospheric aerosol kernels capture the spatiotemporal variations in ARE values of volcanic eruptions and wildfire events well Using bandbyband aerosol kernels is recommended to improve constraints on aerosol wavelength dependency"
    },
    {
        "DOI": "10.1007/S11356-026-37623-0",
        "Title": "Assessing environmental impacts and ecosystem services of Hops crop in Galicia, NW Spain: critical contributors for sustainable cultivation strategies",
        "Year": 2026,
        "Abstract": "Abstract Hops (Humulus lupulus) is a crop of great interest due to its use in the brewing industry; however, the environmental impacts associated with its cultivation remain largely unexplored. Therefore, this study provides the first evaluation of the environmental performance of hops cultivation using the Life Cycle Assessment methodology, with a case study of Galicia, NW Spain. The system boundaries, following a cradle-to-farm-gate approach, included all field operations throughout its entire lifespan (20 years), with primary data obtained directly from farmers. Impact categories such as Global Warming, Eutrophication, Ecotoxicity and Water Scarcity were considered. The main environmental hotspots identified were irrigation, due to the high crop water requirements, followed by agrochemical production and the associated on-field emissions. The results showed impacts of 3.05 kg CO2 eq in global warming, 35.35 g SO2 eq in terrestrial acidification or 70.3 m 3 in water scarcity per kilogram of dry hop cones. Agricultural activities exert pressure on our natural ecosystems in different ways, for example, globally through the propagation of emissions or locally through the degradation of species-rich landscapes, with plants being the most affected taxon in this case. Moreover, two ecosystem services were assessed: pollination and soil erosion. Pollinator presence was found to be constant throughout spring and summer, with a ratio close to 50% of the regional maximum, while the effects of soil erosion control were estimated at 838 over the 20-year lifespan of the crop. With these results, the environmental performance of hop cultivation can be significantly improved, paving the way for more sustainable products within the brewing industry."
    },
    {
        "DOI": "10.5194/AMT-19-119-2026",
        "Title": "GEMS ozone profile retrieval: impact and validation of version 3.0 improvements",
        "Year": 2026,
        "Abstract": "Abstract. This study presents the first comprehensive description of the operational GEMS (Geostationary Environment Monitoring Spectrometer) ozone profile retrieval algorithm and evaluates the performance of the reprocessed version 3.0 dataset. The retrieval operates in the 310330 nm spectral range and yields total degrees of freedom for ozone ranging from 1.5 to 3. Although the vertical sensitivity is limited, GEMS achieves an effective vertical resolution of 510 km and is capable of separating tropospheric and stratospheric ozone layers. This work highlights significant algorithmic and calibration improvements in version 3.0. Radiometric offsets in irradiance measurements are corrected using a scaling factor derived from the average ratio to a solar reference, while residual wavelength-dependent biases in the normalized radiance are further mitigated through soft calibration. In addition, shift corrections are applied separately to irradiance and radiance wavelengths. As a result, version 3.0 significantly reduces spectral fitting residuals, lowering them from 0.8 % in version 2.0 to 0.2 % under nominal conditions. This improvement also mitigates altitude-dependent oscillating biases observed in the previous version (+40 DU in the troposphere, 20 DU in the stratosphere). The version 3 ozone profiles show agreements within 10 DU of ozonesonde observations, with a mean bias of 7.7 % in tropospheric ozone columns and within 5 % in the stratosphere. Furthermore, the retrievals capture day-to-day vertical ozone variability, as demonstrated by comparisons with daily ozonesonde launches in February and March 2024. Integrated ozone columns derived from the profiles also show improved consistency with ground-based total ozone measurements, yielding a mean bias of 3.6 DU and outperforming the GEMS operational total column ozone product."
    },
    {
        "DOI": "10.5194/ACP-26-607-2026",
        "Title": "Global transport of stratospheric aerosol produced by Ruang eruption from EarthCARE ATLID, limb-viewing satellites and ground-based lidar observations",
        "Year": 2026,
        "Abstract": "Abstract. The Atmospheric LIDar (ATLID) instrument of the ESA's Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) satellite mission launched in May 2024 provides high-resolution vertical profiling of aerosols and clouds at 355 nm. Fully operational since July 2024, ATLID has been witness to a significant perturbation of stratospheric aerosol budget following the eruptions of Ruang volcano (Indonesia) in late April 2024. Using ATLID together with limb-viewing satellite instruments (OMPS-LP and SAGE III), we quantify the stratospheric aerosol perturbation generated by the Ruang eruption and characterize the global transport of volcanic aerosols. To evaluate the ATLID performance in the stratosphere, its data are compared with collocated ground-based lidar observations at various locations in both hemispheres and overpass-coordinated balloon flights carrying AZOR backscatter sonde. The intercomparison with suborbital observations suggests excellent performance of ATLID in the stratosphere and proves its capacity to accurately resolve fine structures in the vertical distribution of stratospheric aerosols. Using various satellite observations, we show that Ruang's eruptive sequence in April 2024 produced eruptive columns reaching 25 km altitude, and resulted in a doubling of the tropical stratospheric aerosol abundance for several months. The eruption timing in austral Fall and its high-altitude reach fostered efficient poleward transport into the southern extratropics during austral Winter 2024. By the time of the austral Fall 2025, the sulphate aerosols from Ruang have spread across the entire Southern hemisphere and were most probably entrained by the 2025 Antarctic polar vortex, potentially enhancing the polar stratospheric cloud occurrence."
    },
    {
        "DOI": "10.3389/FSUFS.2025.1716065",
        "Title": "Distribution, conservation, and indigenous knowledge of finger millet germplasm in different agroecologies in Uganda",
        "Year": 2026,
        "Abstract": "Introduction Crop improvement is crucial in addressing food and nutritional security, as it requires a wide range of genetic diversity to serve as germplasm during breeding. Finger millet is an underutilized yet climate-resilient crop with valuable genetic variation that can be leveraged to enhance food security and improve nutritional quality. Methods This study examines varietal diversity, farmers preferred attributes, varietal distribution, production environments, and traditional conservation practices of finger millet germplasm across six agroecological regions (Mid-Northern, Northern, West Nile farmlands; Southern dryland and highlands, Western highlands, and Karamoja drylands) in Uganda. Data was collected between June 2020 and February 2021 through household surveys, key informant interviews, and field observations. Results Most agroecologies were highly to moderately suitable for finger millet production, and farmers utilized traditional knowledge to select and conserve millet germplasm for present and future purposes. Over 90% of the varieties collected were landraces exhibiting wide variability, providing desirable traits necessary for improving finger millet. A total of 460 landrace accessions were collected, and 198 distinct local names were documented across ethnic groups, depending on morphology, maturity, and cultural significance. Farmer selection and conservation of finger millet focused on taste (38.6%), drought tolerance (31.9%), pest and disease tolerance (14.1%), and early maturity (12.4%), confirming the role of preferential traits in addressing food and nutrition security. Conservation practices include sharing seeds with neighbors or relatives, replanting stored seeds, and selecting and storing seeds in designated areas, such as farm stores or rooftops. Over 72.1% of the seed was from farmer-saved sources, underscoring the important role of farmers in maintaining varietal diversity. Correlation analysis showed significant associations between soil characteristics, agroecology, seed sources, and farmer preference. PCA grouped varietal adoption drivers into environment factors, market/consumption attributes, and seed system/conservation practices. However, threats such as labor demands, drought, pests, diseases, aging farmers, and the replacement of millet with maize and rice pose a risk of genetic erosion. Conclusion The abundance of landraces presents a rich genetic pool for breeding and conservation. Integrating both in situ and ex situ conservation strategies is recommended to safeguard finger millet diversity to support food and nutrition security."
    },
    {
        "DOI": "10.1016/J.SOFTX.2026.102538",
        "Title": "SEISMO-VRE: A tool for a multiparametric and multidisciplinary study of an earthquake",
        "Year": 2026,
        "Abstract": "The study of earthquake preparation phases often relies on fragmented approaches, limiting reproducibility and comparison between methods. To address this, we developed a Virtual Research Environment (VRE) for multiparametric multidisciplinary investigations. Built as Jupyter Notebook with MATLAB Python kernels, the VRE integrates seismic, geodetic, atmospheric, ionospheric data into unified automated workflow. Users can define spatial, temporal other parameters to retrieve process across layers. Its effectiveness is demonstrated through analysis 2016 Central Italy 2025 Marmara earthquakes, where tool proved capability easy reproduce cross-domain results."
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.135146",
        "Title": "Remote control of North China autumn rainfall by Tibetan Plateau soil conditions",
        "Year": 2026,
        "Abstract": "Schematic diagram illustrating how the large-scale atmospheric circulation associated with TP subsurface soil anomalies modulates precipitation over NC. Enhanced intensify turbulent heating and elicit a barotropic thermal equilibrium response. The anomalous from primarily excites significant Rossby wave train that propagates toward NC, resulting in lower-tropospheric low, upper-tropospheric high configuration Moist flows Bay of Bengal western Pacific, combined favorable vertical motion, also provide conducive environment for temperature moisture modulate NC autumn rainfall. rainfall via waves, enhancing ascent supply. Warmer soils increase intensity, not frequency, heavy conditions offer predictive potential prediction. Autumn North China impacts agriculture water resources, yet its drivers remain poorly understood. Here, we reveal Tibetan Plateau significantly Chinas total extreme rainfall, exerting stronger influence. explain quarter East Asian variability. enhance surface heating, triggering excite downstream propagation. trains generate upper-level high-pressure low-level low-pressure strengthen convergence China. Moreover, warmer events by increasing intensity rather than frequency. Statistical deep learning analyses demonstrate persistent nature anomalies, offering seasonal weather forecasts. Overall, these findings establish as key regulator providing critical insights regional climate prediction management."
    },
    {
        "DOI": "10.1080/07011784.2026.2616002",
        "Title": "Assessment of snow water equivalent characteristics in time and space over the Mackenzie River basin",
        "Year": 2026,
        "Abstract": "This study quantifies the spatiotemporal variability of snow water equivalent (SWE) using a set complementary statistical approaches across site, grid, and sub-basin scales. It incorporates in-situ measurements, remote sensing, reanalysis, machine learning-derived datasets for period 20002020 Mackenzie River Basin (MRB). Employing comprehensive evaluation framework, we assessed performance multiple gridded SWE against area-averaged observations at both lowland highland sites over time, ERA5-Land reanalysis. Spatiotemporally, reproduce well-observed seasonal interannual cycles patterns spatial variability; however, random forest (RF) model demonstrates superior accuracy reliability, achieving highest correlations (R2 = 0.72 R2 0.95) lowest errors (RMSE 43.1 37.2 mm) when compared to respectively. Annual variations were prominent, with peak values exceeding 700 mm in regions during late winter early spring, reflecting regions climatic conditions. The RF feature importance principal component analysis indicate that snow-related parameters, including depth, density snowfall are primary drivers estimates MRB. Our results reveal slight, though statistically insignificant, decreasing trend (for example, Sens slope 0.301 mm/year p-value 0.26 sites), which could suggest potential impacts climate change on accumulation. These findings underscore critical role basins hydrological processes future resources. insights provided further enhance our understanding variations, providing basis improved inputs numerical hydrologic modeling within basin."
    },
    {
        "DOI": "10.1007/S00445-025-01929-7",
        "Title": "Seismological observations of the 2011 Nabro, Eritrea eruption: implications for eruptive deep closure and volcano-tectonic interactions",
        "Year": 2026,
        "Abstract": "Abstract Understanding the eruptive processes of an active volcano is integral to eruption prediction and hazard mitigation, and co-eruptive earthquakes are potentially a vital indicator. However, co-eruptive seismicity is hard to detect and interpret given the elevated level of tremors, scarcity of seismic stations, and large explosive events. We used a single station at Nabro Volcano, Eritrea, starting on 23 June, 11 days after the onset, which provided a suitable dataset to study co-eruptive seismicity and volcano-tectonic interactions. We detected and classified the events according to their waveform features: short-duration mid-crustal events, long-duration events, and events clustered at approximately an S-P traveltime difference consistent with the vent location, subdivided into the events with large P/S ratios and the ordinary ones. Overall, the daily Nabro seismicity rate has a strong relationship to the daily SO 2 emission rate. We observed seismic patterns linked to the vent openness and eruptive styles. Seismically recorded events at the vent decrease when the strength of the ash eruption increases, which suggests that stronger ash eruption unclogs the vent, reduces stress accumulation in the plumbing system, and discourages earthquake nucleation. We also discovered a strong linkage between the SO 2 eruption and a seismically inferred deep magma reservoir. Activation of deep-seated earthquakes accompanying a decline in SO 2 flux is observed in two transitional ash eruptions. In addition, we observe an abrupt increase in microseismicity near Lake Afrera following the cessation of the eruption, which suggests the interactions between the volcano and the magmatic-enriched rift zone. The observation, in summary, reveals the closure of an open system, which is closely tracked by collapse-related mid-crustal seismicity."
    },
    {
        "DOI": "10.3390/RS17030560",
        "Title": "WindRAD Scatterometer Quality Control in Rain",
        "Year": 2025,
        "Abstract": "Rain backscatter corrupts Ku-band scatterometer wind retrieval by mixing with the signatures of the (backscatter measurements) on the sea surface. The measurements are sensitive to rain clouds due to the short wavelength, and the rain-contaminated measurements in a wind vector cell (WVC) deviate from the simulated measurements using the wind geophysical model function (GMF). Therefore, quality control (QC) is essential to guarantee the retrieved winds quality and consistency. The normalized maximum likelihood estimator (MLE) residual (Rn) is a QC indicator representing the distance between the measurements and the wind GMF; it works locally for one WVC. JOSS is another QC indicator. It is the speed component of the observation cost function, which is sensitive to spatial inconsistencies in the wind field. RnJ is a combined indicator, and it takes both local information (Rn) and spatial consistency (JOSS) into account. This paper focuses on the QC for WindRAD, a dual-frequency (C and Ku band) rotating-fan-beam scatterometer. The Rn and RnJ have been established and thoroughly investigated for Ku-band-only and combined CKu wind retrieval. An additional 0.4% of WVCs are rejected with RnJ, as compared to Rn for both Ku-band-only and combined CKu wind retrievals. The number of accepted WVCs with high rain rates (>7 mm/h) is reduced by half, and the wind verification with respect to ECMWF winds is generally improved. The C-band measurements are little influenced by rain, so the Ku-based Rn is more effective for the combined CKu wind retrieval than the total Rn from both the C and Ku bands. The rejection rate of the combined CKu retrievals reduces by about half compared to the Ku-band-only retrieval, with similar wind verification statistics. Therefore, adding the C band into the retrieval suppresses the rain effect, and acceptable QC capabilities can be achieved with fewer rejected winds."
    },
    {
        "DOI": "10.23851/IJES.V2I1.21",
        "Title": "Spatial and Temporal Evaluation of the Frequency of Thunderstorms and their Effects on PM2.5 Distribution over Baghdad and Basra Airdromes",
        "Year": 2026,
        "Abstract": "Particulate matter 2.5 is one of the primary components of air pollution. Sources of PM2.5 may be natural or anthropogenic. The main sources of the pollutants in Iraq include burning natural gas, oil, and power plants. The study is based on archived datasets from the Iraqi Meteorological Organization and Seismology and satellite data available from ECMWF and NASA for a ten-year period in Baghdad and Basra Airdromes. The spatial and temporal analysis showed that the highest frequency of thunderstorms occurs in April about 64.03%. The highest annual frequency of thunderstorms was for Baghdad Airport station in 2018 and 2002 (20 days) and Basra Airport station in 2003 and 2018 (13 days). The PM2.5 concentration at Baghdad Airport and Basra Airport stations prior to, during, and following the storm was of the category D of the air quality indices. The concentration kept growing without addressing the causes. It looks like the rise in PM2.5 concentrations that thunderstorms increased in the station is caused by arid thunderstorms and the storm's downdrafts, which lift dust upwards. This leads to PM2.5 concentrations remaining in the atmosphere for a longer period. As the Normalized Difference Dust Index results indicate, 9095% of Baghdad is situated in areas with moderate dust levels for the period 2000-2019, while 95% or more of Basra is situated in areas with moderate to high dust levels for the same period."
    },
    {
        "DOI": "10.5194/ACP-26-3743-2026",
        "Title": "Tropical stratospheric upwelling as seen in observations of the tape recorder signal",
        "Year": 2026,
        "Abstract": "Abstract. Tropical upwelling constitutes the ascending branch of the global mean stratospheric circulation and governs the thermal and chemical properties of the tropical stratosphere. A lack of direct observations and a spread in upwelling structure across the modern reanalysis creates difficulties in determining variability and long-term changes of tropical upwelling. We have derived time series of effective vertical transport in the tropical lower and middle stratosphere from MLS and SWOOSH water vapour for 20052020 and 19952020. Our calculated upwelling is found to be in the range of 0.210.33 mm s1 for 7328 hPa in very good agreement with reanalysis vertical velocities (ERA5, JRA-3Q, MERRA-2) and other observation-based estimates (ANCISTRUS). We show that interannual variations of upwelling in the middle stratosphere are dominated by the QBO signal, which explains a large fraction of the upwelling anomalies. In the lower stratosphere, tropospheric modes of variability also play a role with the QBO and ENSO being equally important for explaining interannual variability. Individual peaks of strongly enhanced upwelling in the lower stratosphere in 2000/01 and 2011/12 cannot be explained by QBO or ENSO variability and coincide with known drops in water vapour and cold point temperatures. We use independent observational data to show that tropical upwelling is anticorrelated with long-lived stratospheric tracers such as ozone as expected, lending confidence to the derived values. A reduction in variability is observed for 20162020 in our calculated upwelling and observed ozone, which is consistent with the disruption to regular QBO variability over this period."
    },
    {
        "DOI": "10.5194/WCD-7-65-2026",
        "Title": "Storm Boris (2024) in the current and future climate: a dynamics-centered contextualization, and some lessons learnt",
        "Year": 2026,
        "Abstract": "Abstract. The response of mean and extreme precipitation to anthropogenic global warming stems both from warming of the troposphere and dynamical changes in the large-scale circulation, especially upward motions. The interaction between these two components complicates future projections and makes the attribution of extreme precipitation events challenging, both using conditional (e.g., analog-based) and unconditional (e.g., extreme value theory-based) methods. In this study we reflect upon this problem and propose some possible solutions to tackle it starting from the case study of Storm Boris, that led to major floods over central Europe in mid-September 2024. The first step is the identification of key circulation features associated with the event, whose representation is deemed crucial to obtain realistic analogs: the presence of a slow-moving, upper-level potential vorticity (PV) cutoff, the peculiar track of the surface cyclone associated with Boris, and the presence of anomalously strong forcing for ascent. Circulation analogs of Boris are then identified in a large ensemble of present-day and future-climate simulations with the CESM1 model, to understand how Boris-like storms will change in an end-of-the-century high warming scenario. We find that the combined use of upper-level PV and of a surface cyclone identification algorithm substantially improves the quality of the analogs, both in terms of the large-scale flow pattern and the precipitation associated with the cyclone. Analogs of Boris restricted to the same season in a warmer climate feature on average less precipitation, due to an overall weakening of upper-level-driven ascent over Europe. However, analogs of Boris not restricted to the same season show a seasonality shift: they become less frequent at the end of the warm season and more frequent in the shoulder seasons when the dynamical and thermal conditions of September in the present-day climate can be recovered again , and exhibit an increase in mean precipitation in the warmer climate. The results obtained from the analog-based approach are then compared with an unconditional, statistics-based approach focusing only on the seasonal and yearly maxima of precipitation: the latter approach allows to recover the expected intensification of extreme precipitation in a warmer climate at the price, however, of considering events that do not necessarily have the same dynamics as Storm Boris. The sensitivity of attribution outcomes with respect to implicit and explicit methodological choices is discussed in detail. The systematic comparison of different approaches, the two-step methodology to obtain more reliable analogs of heavy precipitation events, and the focus on process understanding are key ingredients of this study, with general implications for investigating the role of climate change for specific weather extremes."
    },
    {
        "DOI": "10.1038/S41598-026-42647-0",
        "Title": "Comparing novel backward hydrological models for watershed-scale precipitation estimation: an evaluation of inverted PDM and Kirchner-hybrid structures",
        "Year": 2026,
        "Abstract": "Accurate precipitation estimation is critical for water resource management, yet it remains a challenge in data-scarce regions. This study develops and evaluates two novel bottom-up hydrological models daily watershed-scale estimation: an inverted Probability Distributed Model (PDM) hybrid Soil Moisture to Rain (SM2RAIN)-Kirchner model. These new structures were systematically compared against multiple SM2RAIN configurations suite of benchmark Global Gridded Precipitation Products (GGPPs) over the Walnut Gulch Experimental Watershed, Arizona, USA. The results demonstrate that locally calibrated backward significantly outperform established GGPPs, with Kirchner model driven by Merged via Modified Collocation (SMMC) achieving highest performance (KGE = 0.62). A key finding was successful validation structures, PDM proving be robust approach 0.55). Furthermore, revealed insight regarding input data: spatially integrated SMMC product led more generalizable than one in-situ observations, which caused overfitting some structures. While excelled quantitative accuracy, they less skillful at event detection highlighting important trade-off application-specific selection. work introduces viable field confirms high-accuracy estimates are achievable regions using merged, globally available datasets."
    },
    {
        "DOI": "10.1109/TVT.2026.3659498",
        "Title": "Analysis of Path Losses on Terahertz Band for Non-Terrestrial Networks",
        "Year": 2026,
        "Abstract": "With the ongoing evolution of global connectivity, demand for reliable, high-speed communication links in scenarios such as in-flight internet and remote areas has intensified. As a result, research increasingly focused on millimeter wave (mmWave)/terahertz (THz) band non-terrestrial networks (NTNs) due to its potential support ultra-high data rates. To assess feasibility, this paper presents comprehensive assessment path losses their impact NTN operating within THz band. It examines key impairments affecting signal propagation, including atmospheric absorption other losses. A detailed investigation is conducted different across diverse geographical locations, primarily focusing adverse weather conditions while also considering clear skies, with seasonal variations integrated link reliability. The findings reveal that, ground-to-space links, frequencies above 300 GHz experience significantly high losses, whereas satellite-to-airborne up 1000 can be achieved."
    },
    {
        "DOI": "10.1029/2025GL120299",
        "Title": "Tropical Pacific Sea Surface Temperature Gradient Biases Shape PresentDay and Future Precipitation Projections Over Southern Hemisphere Midlatitudes",
        "Year": 2026,
        "Abstract": "Abstract Climate models exhibit significant biases in simulating presentday tropical Pacific sea surface temperature (SST) patterns, particularly the zonal SST gradient, which may contribute to uncertainties in precipitation projections over midlatitude populated regions. Biases in the simulated tropical Pacific SST gradient across CMIP6 models significantly influence presentday and future winter precipitation over South America through a stationary wave pattern resembling the PacificSouth American (PSA2) mode. Models with a weakerthanobserved SST gradient simulate a deeper trough east of South America, resulting in stronger wetting trends over northern Argentina. Applying observational constraints reduces uncertainties in projected precipitation trends by approximately 31%. For Tasmania and New Zealand, SST gradient biases impact the simulation of presentday winter precipitation, but are not well correlated with future precipitation projections. Our findings highlight the critical need to accurately represent the tropical Pacific SST gradient and its associated atmospheric circulation features for reliable regional climate simulation. , Plain Language Summary Climate models often struggle to accurately represent the presentday sea surface temperature (SST) difference between the west Pacific warm pool and east Pacific cold tongue (defined as tropical Pacific SST gradient). The tropical Pacific SST gradient differs among models and consequently may affect their representation of presentday and future precipitation. In this study, we examine how models' varying representation of the tropical Pacific SST gradient affects precipitation over populated midlatitude regions in the Southern Hemisphere. Using CMIP6 climate models, we find that models with a weakerthanobserved SST gradient tend to simulate a deeper lowpressure trough east of South America, which in turn influences precipitation over South America both in the presentday climate and under global warming. By selecting models that closely match the observed tropical Pacific SST gradient and associated atmospheric circulation features, we reduce the uncertainty in projected future precipitation by approximately 31% in northern Argentina. The SST gradient biases in models also affect their representation of presentday precipitation in Tasmania and New Zealand, but do not as clearly affect future precipitation projections. Our findings underscore the importance of correcting these tropical Pacific SST biases in climate models to enhance the accuracy of their representation of regional precipitation. , Key Points Tropical Pacific sea surface temperature (SST) gradient bias in CMIP6 models affects presentday and future precipitation over Southern Hemisphere midlatitude lands SST gradient bias affects precipitation by modifying atmospheric circulation near South America via a PSA2like stationary wave pattern Applying observational constraints reduces uncertainties in projected precipitation trends by approximately 31% in northern Argentina"
    },
    {
        "DOI": "10.1007/978-3-031-84736-3_15",
        "Title": "Al-Based LVBs Water Storage Products",
        "Year": 2026,
        "Abstract": ""
    },
    {
        "DOI": "10.1038/S43016-026-01322-3",
        "Title": "Ozone pollution reduction partially offsets the negative impact of climate change mitigation efforts on global hunger",
        "Year": 2026,
        "Abstract": "Abstract Studies warning of the potential negative effects of climate mitigation on food security through the competing use of land for bioenergy and afforestation have overlooked the impact of reduced ozone and its potential enhancement of crop yields. Here we use six global agro-economic models to compare the impacts of climate change with climate mitigation policy and ozone reduction on agriculture. We find that ozone reduction could reduce the negative impact of a 1.5 C-consistent climate change mitigation policy on global hunger by 15% in 2050. Sub-Saharan Africa and India, where hunger is most severe, account for 56% of this global reduction. Our findings indicate that the negative effects of climate mitigation on global hunger could be partially offset by the ozone reduction impact."
    },
    {
        "DOI": "10.3390/RS18050782",
        "Title": "Improving the Data Consistency Between GPM and Weather Radar with Advection Correction",
        "Year": 2026,
        "Abstract": "Multi-instrument synergistic observation is vital for studying cloud and precipitation physics. However, using the nearest scan time for matching inevitably introduces temporal mismatches. Here we employ three advection correction methods for temporal matching in weather radar and spaceborne radar observations: LucasKanade (LK), Variational Echo Tracking (VET), and Anisotropic Diffusion (AD). These methods calculate the movement speed of the storms using optical flow methods, and then determine their positions based on the elapsed time between instruments. Next, we conducted a quantitative assessment of the performance of these three methods based on the consistency of storm morphology and rainfall rates. Our results demonstrate that all three advection correction methods effectively reduce the discrepancies in morphology and rainfall rate among multi-source data. Without correction, the Coincidence Rate (CR) and Structural Similarity (SSIM) were 30.96% and 0.689 in the US and 29.44% and 0.670 in China, respectively. In comparison, applying the LK, VET, and AD methods increased those indices to 32.94%, 32.72%, 32.85% and 0.718, 0.715, 0.716 in the US, and 31.34%, 31.17%, 31.24% and 0.696, 0.694, 0.693 in China, respectively. The rainfall rate inconsistencies were also effectively reduced after advection correction. The performances among the three methods were similar. Overall, the LK method performed slightly better than AD, followed by VET."
    },
    {
        "DOI": "10.3390/ATMOS17010094",
        "Title": "Bayesian Model Averaging Method for Merging Multiple Precipitation Products over the Arid Region of Northwest China",
        "Year": 2026,
        "Abstract": "Accurate precipitation estimation is essential for hydrological modeling and water resource management in arid regions; however, complex terrain and sparse meteorological station networks introduce substantial uncertainties into gridded precipitation datasets. This study evaluates the performance of nine widely used precipitation products in the arid region of Northwest China (ARNC) at both the meteorological station scale and the sub-basin scale, and applies the Bayesian Model Averaging (BMA) approach to merge multi-source precipitation estimates. The results reveal pronounced spatial heterogeneity and significant differences in performance among datasets, with the Integrated Multi-Satellite Retrievals for the Global Precipitation Measurement mission performing best at the station scale and the Famine Early Warning Systems Network Land Data Assimilation System performing best at the sub-basin scale. Compared with individual products, the BMA-merged precipitation demonstrates substantial improvements at both scales, providing higher coefficients of determination and agreement indices, and lower relative mean absolute error and relative root mean square error, indicating enhanced accuracy and robustness. The BMA-merged precipitation product generally exhibits superior and more spatially consistent performance than the individual datasets across the ARNC, thereby providing a more reliable basis for regional hydrological and climate-related applications. The merged dataset shows that the mean annual precipitation in the ARNC during 20002024 is approximately 230.4 mm, exhibiting a statistically significant increasing trend of 1.4 mm per year, with the strongest increases occurring in the Tianshan and Qilian Mountains. This study provides a reliable foundation for hydrological modeling and climate-change assessments in data-limited arid environments."
    },
    {
        "DOI": "10.1029/2024WR039170",
        "Title": "Meteorological to Agricultural Drought Transitions Compounded by Heat Waves in Historical and Future Climates",
        "Year": 2026,
        "Abstract": "Abstract Meteorological droughts (persistent precipitation deficits) often, but not always, transition into agricultural droughts (persistent soil moisture deficits). The intensity of agricultural drought, however, can vary for a given precipitation deficit due to a number of catalyzing cofactors beyond precipitation such as atmospheric evaporative demand and temperature. In this study we use Earth System Model data to quantify (a) how warm temperature anomalies affect this evolution from meteorologicaltoagricultural drought and (b) how the evolution of droughts from historical and future climate scenarios differ. We benchmark these results against observational data and use a multimodel ensemble to quantify agreement on future drought propagation. Broadly speaking, drought temperatures in the upper third of local distributions correspond with shifts on the order of 5 percentile of the soil moisture distribution. We would expect today's meteorological droughts to propagate into agricultural droughts roughly one drought classification more severe in the SSP37.0 scenario in most regions. Even regions with increases in precipitation are likely to see more intense meteorologicaltoagricultural drought propagation by the end of the 21st century. Models disagree on drought propagation changes in Africa for the same precipitation deficit, but suggest that all historical droughts would have had worse agricultural droughts in Europe and Eastern North America if they happened under SSP37.0. When accounting for precipitation changeswhich tend toward more frequent accumulated precipitation deficitsthe increased severity of meteorologicaltoagricultural drought evolution leads to predictions of major increases in moderate to extreme (D1D3) drought events in all regions globally by the end of the century. , Plain Language Summary Crops and natural ecosystems respond to dry soils rather than to low rainfall directly. Warm temperatures in particular are important for determining how much low rainfall will dry soils. We use Earth System Models and satellite data to determine how important warm temperatures are for drying soils when rainfall is low. We find that a oneinthree warm month corresponds to a shift in agricultural drought by 121 drought classification levels (D0D4). Models predict this effect to be twice as large for all droughts by the end of century, even if rainfall was unchanged. While it is not clear if Africa will follow this trend, all other regions should expect substantially drier soils in the future versus today for the same rain deficit. In all regions, the frequency of agricultural droughts in D1D3 Drought Monitor categories (moderate to severe drought) is expected to increase by 40% or more by end of century. , Key Points We highlight a bivariate drought evolution (meteorological to agricultural) globally in historical and future climate conditions A multimodel ensemble predicts the same precipitation deficit will evolve into more severe agricultural drought in the future due to heat Nearly all regions should expect more frequent level D0D3 droughts by end of century, regardless of precipitation changes"
    },
    {
        "DOI": "10.1175/MWR-D-25-0108.1",
        "Title": "Influences of Desert Afforestation on Boundary Layer Convergence Lines and Related Convection and Convective Precipitation over a DesertOasis Border",
        "Year": 2026,
        "Abstract": "Abstract Land-use and land-cover changes significantly influence weather and climate by altering surface albedo, roughness, heat flux, and boundary layer dynamics. These changes can modify convection and precipitation through shifts in near-surface temperature, moisture, and atmospheric instability. Vegetation heterogeneity across a desertoasis divide has been observed to generate boundary layer convergence lines (BLCLs) and initiate convection. Afforestation may affect these behaviors through landair interaction and boundary layer processes. Since 1986, the Hetao area of the Yellow River in northern China, where an irrigated oasis is surrounded by extensive deserts, has undergone afforestation for over 30 years. This study examines how this desert afforestation may affect BLCLs and their associated convection and precipitation through ensemble simulation by perturbing a composite of 34 cases in the synoptic pattern that has the most frequent formation of BLCLs and their associated convection initiation (CI). The results indicate that the afforestation weakens the desertoasis contrast in 2-m air temperature and moisture, causing BLCLs to form later, attain maximum length later, and weaken. Although delaying and weakening convective triggering, the afforestation increases the near-surface water vapor content and vertical transport of water vapor, and thus the resultant larger instability, which accelerates CI, intensifies and spatially broadens convection, and causes more frequent and heavier convective precipitation in the Hetao area. This study suggests that afforestation can enhance convection and precipitation at the desertoasis boundary even though it seems to do the opposite by delaying and weakening convection triggers. These findings have important implications for weather forecasting, land management, and climate adaptation in arid and semiarid regions. Significance Statement Afforestation in the Hetao region of the Yellow River has significantly altered local atmospheric processes by reducing the desertoasis contrast in surface air temperature and moisture. This study reveals a dual effect: Afforestation delays and weakens boundary layer convergence lines (BLCLs), which typically act as triggers for convection and whose delay is expected to have a negative impact on convection initiation (CI). However, afforestation paradoxically accelerates CI by enhancing near-surface moisture content and vertical transport. This improved understanding of the complex impacts of afforestation on CI offers valuable insights for accurate predictions of convective weather in heterogeneous vegetated regions, supporting regional climate adaptation strategies and sustainable land management in arid and semiarid environments."
    },
    {
        "DOI": "10.1007/S10489-025-06959-4",
        "Title": "A hybrid machine learning model for flood prediction with recursive feature elimination informed by training performance",
        "Year": 2026,
        "Abstract": ""
    },
    {
        "DOI": "10.1038/S41597-026-06895-Z",
        "Title": "Global 0.25-degree gridded Snow water equivalent data derived from machine learning using in-situ measurements",
        "Year": 2026,
        "Abstract": "Abstract In this study, we developed a machine learning-based global daily SWE product (SWEML) with 0.25 (~25 km) resolution for 19802020. Using k-means clustering, in-situ SWE measurements were grouped into 14 clusters, and a random forest model was trained on 11,687 grid points with meteorological forcing and terrain attributes. SWEML was compared with ten reference datasets representing diverse approaches, including landatmosphere reanalysis without data assimilation (DA), systems incorporating DA, snow model simulations of varying complexity driven by reanalysis forcing with or without DA, and remote sensing products. The overall root mean square error (RMSE) and bias were 10.33 mm and 7.13 mm, respectively. Notably, SWEML achieved high accuracy in high-elevation regions such as the Rocky Mountains, with an RMSE of 7.30 mm and correlation coefficient of 0.98. It also agreed with the Gamma airborne SWE over North America and showed similar spatial patterns and peak SWE time series of the Andes Snow Reanalysis. These results highlight the robustness of SWEML in regions with and without training data."
    },
    {
        "DOI": "10.3390/HYDROLOGY13020061",
        "Title": "Exploring the Seven Climate Zones of China: How Soil Moisture and Vapor Pressure Deficit Influence Vegetation Productivity",
        "Year": 2026,
        "Abstract": "Reduced soil moisture (SM) together with elevated vapor pressure deficit (VPD) suppresses gross primary productivity (GPP) and thus weakens the capacity of the terrestrial carbon pool. Against the backdrop of global climate change, soil and atmospheric drought exert a more profound impact on vegetation growth, and their combined impacts remain unclear. Based on multi-source remote sensing observations and reanalysis datasets, three vegetation remote sensing indices, GPP, SIF, and NDVI (collectively referred to as Vegetation Remote Sensing Indices, VSI), are employed in this study to assess the relative impacts of soil and atmospheric drought on terrestrial vegetation. First, Copula-based conditional probabilities are applied to identify which factor (reduced SM or high VPD) plays a dominant role under conditions of declining vegetation productivity and to determine their corresponding thresholds. Furthermore, the underlying driving mechanisms are elucidated by utilizing Structural Equation Modeling (SEM) for path analysis to clarify how climatic factors indirectly affect vegetation productivity by influencing SM and VPD. The results suggest that vegetation growth in Chinas different climatic zones is affected by distinct factors. Specifically, SM is the primary factor influencing vegetation productivity, dominating 71.16% of the nations vegetated areas. Its influence is particularly pronounced in arid and semi-arid regions. In contrast, the impact of VPD is predominantly concentrated in semi-humid plain regions. Furthermore, the critical thresholds for SM in different climate zones are identified: the threshold averages approximately 0.33 m3/m3 in humid and plateau regions and 0.13 m3/m3 in arid and semi-arid regions. The SEM analysis further reveals the complex pathways by which climatic variables influence vegetation growth. In SM-dominated regions, higher SM directly promotes vegetation growth; in VPD-dominated regions, drier air imposes a stronger suppression on vegetation growth. Nonetheless, the plateau temperate semi-arid zone demonstrates distinct hydrometeorological characteristics. Attributed to the regions unique hydrometeorological conditions, the negative effects of higher VPD are generally outweighed by the favorable conditions for photosynthesis with which it co-occurs. These findings clarify the intricate impacts of SM and VPD on vegetation productivity, providing a foundational framework for the development of tailored ecological management strategies and drought early warning systems."
    },
    {
        "DOI": "10.1021/ACS.EST.5C13946",
        "Title": "Temporal Evolution and Origin of Radionuclide Fallout and Contaminants Recorded in Sediment from the Kerguelen Archipelago Fjord System",
        "Year": 2026,
        "Abstract": "Deposition of heavy metals and persistent organic pollutants (POPs) in fjord systems during ice melting was investigated with a sediment core collected in Table Fjord from Kerguelen Island (49 33.8 S69 13.9 E) situated in the Southern Indian Ocean. Multiple radionuclides (210Pbex, 137Cs, 240Pu/239Pu) were used to establish an accurate age-depth model and show the occurrence of French nuclear weapon test fallout in this remote region for the first time. Environmental changes related to the retreat of the Cook Ice Cap since the 1960s were found to be one of the major factors dominating the dynamics of anthropogenic lead deposition flux in the fjord through the release of long-range transported legacy anthropogenic lead. The released legacy anthropogenic lead was likely transported across the proglacial Ampere Lake by a hypopycnal plume to the fjord. Backward trajectories and lead stable isotopic signatures suggest the southern part of South Africa as a major source of anthropogenic lead transported to the Kerguelen Archipelago. In contrast, contamination by arsenic, molybdenum, antimony, and POPs was found to be more recent (since 2001). Fractionation of rare earth elements was observed in the sediment due to the formation of proglacial Ampere Lake, which acts as a sediment trap."
    },
    {
        "DOI": "10.1029/2025GL119159",
        "Title": "Wildfire IgnitionDay Vapor Pressure Deficit Trend and Its Weakening Atmospheric Circulation Control Over the Western United States",
        "Year": 2026,
        "Abstract": "Abstract Vapor pressure deficit (VPD) is a key fire weather indicator linked to increased burned areas in western US. Despite a strong increase in regional VPD due to climate change, we find no significant trend in VPD on fire ignition days (VPD F ). This discrepancy is due to a decreasing climatological mean (VPD Fm ), driven by the expansion of fires into higherlatitude and higheraltitude regions with climatologically lower VPD, and a relatively stable anomaly (VPD Fa ). The weak trend in this VPD Fa is, in turn, a result of two opposing trends: a significant increase in the thermodynamic contribution from background warming, offset by a weakening of the dynamic contribution from circulation patterns. This lowering of the circulation bar for ignition, where less extreme weather is now sufficient to start large fires, explains the observed spatial expansion of fire risk, meaning regions historically less prone to fires now face heightened risk. , Plain Language Summary Wildfires in the western United States (WUS) are strongly influenced by vapor pressure deficit (VPD), a measure of how hot or dry the air is. Previous studies have shown that higher VPD values are linked to larger burned areas. While VPD has been increasing overall due to climate change, our study finds that the VPD specifically associated with wildfire ignitions has not shown a significant increasing trend. This is because recent fires are occurring more often in higherlatitude and higheraltitude regions, where VPD is climatologically lower. Even after accounting for fire locations, the intensity of firerelated surface weather conditions has not increased as much as overall VPD. Our analysis of atmospheric circulation patterns suggests that wildfires are now happening under weather conditions that were historically less favorable for fire in the northern WUS. As a result, regions that were previously less prone to wildfires are now facing a greater fire risk. , Key Points Wildfires in the Western US are expanding into regions with historically lower vapor pressure deficit (VPD) Despite overall climatic drying, the specific VPD on fire ignition days has not significantly increased Increased background aridity now allows weaker circulation patterns to trigger wildfires"
    },
    {
        "DOI": "10.1016/J.GSD.2026.101592",
        "Title": "Synergizing machine learning and hydrological model to enhance water availability and demand forecasting in Godawari, Nepal",
        "Year": 2026,
        "Abstract": "Nepal's peri-urban regions, including Godawari, face escalating water scarcity due to evolving demographics, groundwater overuse, recharge failures, and climate variability, aggravated by sparse observational data. In response, this study develops an innovative hybrid framework synergizing the Water Evaluation and Planning (WEAP) system with a two-stage Multilayer Perceptron Artificial Neural Network (MLP-ANN) to enhance forecasting of water availability and demand in Godawari Municipalitya rapidly urbanizing critical groundwater recharge zone within the Kathmandu Valley. The WEAP model was uniquely validated against Groundwater Storage Anomaly (GWSA) derived from GRACE and GLDAS data, ensuring reliability under data-scarce conditions, typically in Nepalese municipalities like Godawari. From 2020 to 2050, ANN forecasted Terrestrial Water Storage Anomaly (TWSA) decline of 35.5 mm/year (3.4 MCM/year for the municipal area of 96.08 km2), while WEAP simulated a GWSA loss of 86 MCM (2.9 MCM/year) under Business-As-Usual (BAU) scenario. Together, these results indicate that groundwater depletion is the primary component of system-wide total storage loss. Although WEAP projected modest water demand growth (1 MCM under BAU), groundwater depletion patterns are consistent with reduced recharge from urbanization and increased impermeable surfaces, though extraction data are unavailable to conclusively rule out pumping contributions. Scenario analyses further revealed that GWS losses were relatively higher under rapid urbanization compared to sustainable development scenarios, underscoring the critical need for sustainable interventions. This dual-model synergistic approach strengthens predictive confidence, reveals groundwater vulnerabilities, and establishes a robust foundation for evidence-based, sustainability-oriented planning to achieve Sustainable Development Goals (SDGs), offering replicable strategies for similar data-scarce, water-stressed regions."
    },
    {
        "DOI": "10.5194/ACP-26-647-2026",
        "Title": "Northern Hemisphere stratospheric temperature response to external forcing in decadal climate simulations",
        "Year": 2026,
        "Abstract": "Abstract. To predict the future state of the Earth system on multiyear timescales, it is crucial to understand the response to changing external radiative forcing (CO2 and Ozone). Analyzing the Northern Hemisphere (NH) winter stratospheric polar vortex temperature, we found a general temperature decrease in the reanalysis data (19822020), the expected trend with increasing CO2, except for a sharp warming during the period 19922000. Results from 1 GEOS-MITgcm coupled general circulation model simulations of past decades show a similar increase in the NH polar stratospheric temperature during 19922000 and a decrease during 20002020. To isolate the influence of external forcing, we conducted a series of 30-year-long perpetual time-slice experiments in which the external forcing for a particular year is held fixed at its values for 1992, 2000, and 2020. Each simulated year of these perpetual experiments is forced with the CO2, Ozone, anthropogenic aerosol emissions, and trace gases of that year, but none of the simulations include any explosive volcanic forcing. The increasing and then decreasing temperature trend is also manifest in the CMIP6 historical simulations performed with models that include a well-resolved stratosphere. The configuration of the perpetual experiments rules out a direct response to volcanic emissions or a change in the phase of decadal modes of variability as explanations for the warming rather than the expected cooling behavior. Analysis of the temperature budget showed (only significant terms are discussed) that the polar stratospheric temperature behavior is dictated by meridional eddy transport of heat resulting from changes in CO2 and Ozone over the past decades."
    },
    {
        "DOI": "10.1007/S00703-026-01136-9",
        "Title": "Dynamics and thermodynamics of extreme rainfall event over different subregions of Himachal Pradesh during 810 July, 2023",
        "Year": 2026,
        "Abstract": "The summer monsoon of 2023 witnessed catastrophic rainfall occurrences to the mountainous regions of Himachal Pradesh (HP), prompting an investigation into the underlying cause of the anomalous rainfall observed from 8 to 10 July, 2023. This study analyses the spatial and temporal characteristics of the extreme rainfall by utilising half hourly GPM_IMERG data (0.1 0.1) and ERA 5 data (0.25 0.25). Dynamic and thermodynamic conditions across four subregions, representing different mountainous elevations were analysed during the extreme rainfall event. Four subregions were identified based on the intensity of maximum rainfall exceeding 50 mm h 1. Subregional analysis revealed that rainfall in subregions 1 and 2 was predominantly influenced by low level convergence, whereas subregions 3 and 4 were primarily driven by low level cyclonic vorticity. The timing of maximum vertical motion exhibiting varied lead times, 7 h prior to rainfall in subregions 1 and 2 and 12 to 14 h in subregions 3 and 4. These findings suggest a longer atmospheric response time in elevated regions compared to lower regions. While dynamic forcing played a dominant role in driving extreme rainfall in higher elevations, a combination of dynamic and thermodynamic processes governed the intense rainfall in lower subregions."
    },
    {
        "DOI": "10.1175/JHM-D-25-0054.1",
        "Title": "The Impact of the MJO on Climate in Hawaii",
        "Year": 2026,
        "Abstract": "Abstract Among the most remote places on Earth, the Hawaiian Islands rely heavily on local water resources. Therefore, a deep understanding of rainfall variability is critical to ensure the habitability and sustainability of life on the archipelago. While climate variability has been studied at interannual and interdecadal scales, less is known at shorter scales. Here, we investigate the impact of the MaddenJulian oscillation (MJO) on rainfall in Hawaii by leveraging data from various sources, utilizing a newly released high-resolution precipitation dataset. Our results indicate that rainfall increases significantly during MJO active phases, especially on the islands windward sides, while suppressed phases lead to drier conditions. Other climatic variables are also affected by the MJO, with active phases characterized by cooler temperatures, higher humidity, and stronger winds from a predominantly northeasterly direction. These impacts appear linked to the Rossby waves generated by the MJO propagating in the central North Pacific and to a strengthening of the local Hadley circulation during active MJO phases. These findings offer meaningful insights that may benefit stakeholders across the state by improving understanding of short-term climate variability. Significance Statement Understanding the drivers of rainfall variability in Hawaii is critical for managing the islands ecosystems and water resources. This study provides the first quantitative assessment of how the MaddenJulian oscillation (MJO), the leading mode of intraseasonal tropical variability, influences rainfall and temperature in Hawaii. Active MJO phases are associated with statistically significant increases in rainfall and enhanced extreme precipitation, while suppressed phases correspond to significantly drier and warmer conditions. Because the MJO is a predictable source of variability on subseasonal time scales, these relationships indicate periods of enhanced forecast skill at lead times of up to 4 weeks. Linking large-scale tropical variability to local hydroclimate impacts supports improved subseasonal outlooks and more informed water resource and hazard planning in Hawaii."
    },
    {
        "DOI": "10.5194/AMT-19-529-2026",
        "Title": "Validation of SNPP OMPS limb profiler version 2.6 ozone profile retrievals against correlative satellite and ground based measurements",
        "Year": 2026,
        "Abstract": "Abstract. The Ozone Mapping and Profiler Suite Limb Profiler (OMPS LP) was launched onboard the Suomi National Polar-orbiting Partnership (SNPP) satellite in 2011 and began routine science operations in April 2012. The OMPS LP uses measurements of scattered solar radiation in the ultraviolet, visible and near infrared wavelengths to retrieve high vertical resolution profiles of ozone from 12 km (or cloud tops) up to 57 km. In mid-2023, version 2.6 of the OMPS LP ozone profile retrievals was released, featuring improvements in calibration, retrieval algorithm, and data quality. We evaluate OMPS LP version 2.6 ozone retrievals using correlative data from other satellite instruments and ground based data for the period April 2012 to April 2024. Our results show agreement between OMPS LP and all correlative data sources between 20 and 50 km at all latitudes with differences of less than 10 %, with OMPS generally exhibiting a negative bias, except between 32 and 38 km in the tropics and southern mid-latitudes, where the bias is positive. In the tropics and southern mid-latitudes the differences between OMPS LP and MLS, and OMPS LP and SAGE III/ISS are less than 5 % between 20 and 45 km. Above 50 km, the agreement with MLS is still on the order of 5 % or better. Larger positive biases, up to 35 %, are seen in the upper troposphere lower stratosphere layer ( 15 to 20 km) between approximately 40 S and 40 N. We find that OMPS version 2.6 ozone exhibits the same seasonal cycle as compared to all correlative measurement sources and our analysis shows that there is no significant seasonal bias in OMPS LP. We find drifts relative to correlative observations at all latitude bands of less than 2 % per decade (1 % per decade) between 25 and 50 km for the 20122024 period, with larger drifts above 50 km and below 20 km. These drifts vary between correlative measurements and straddle the zero line, we therefore conclude that there is no significant systematic drift in OMPS LP version 2.6 ozone for the period 2012 to 2024. The drift results represent an improvement in the long term stability of version 2.6 ozone over that of version 2.5."
    },
    {
        "DOI": "10.5194/AMT-19-993-2026",
        "Title": "Solar Backscatter Ultraviolet (BUV) retrievals of mid-stratospheric aerosols from the 2022 Hunga Eruption",
        "Year": 2026,
        "Abstract": "Abstract. On 15 January 2022, a highly explosive eruption of the submarine Hunga volcano (Kingdom of Tonga) generated the largest stratospheric hydration event ever observed and the largest aerosol perturbation since the 1991 Pinatubo eruption. Here, we develop a novel method for satellite retrieval of stratospheric aerosol optical depth (AOD) and layer peak height (zp) using solar backscattered ultraviolet (BUV) radiation; this is made possible by the exceptional mid-stratospheric altitude of the Hunga aerosols. We analyze BUV observations of the Hunga stratospheric aerosol cloud on 17 January 2022 (47 h after the eruption), using BUV band 1 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) on board the ESA/Copernicus Sentinel-5 precursor (S5P) satellite, and the Ozone Mapping and Profiling Suite- Nadir Profiler (OMPS-NP) on board the National Oceanic and Atmospheric Administration (NOAA)-20 satellite. We retrieve AOD and zp by fitting hyperspectral BUV radiance ratios in a narrow spectral window restricted to 289296 nm, chosen in order to reduce interference from tropospheric clouds while highly sensitive to stratospheric aerosols located above ozone peak altitude. The retrieval employs radiative transfer calculations from the Vector Linearized Discrete Ordinate Radiative Transfer (VLIDORT) forward model. We assume a single Hunga aerosol layer composed of polydisperse sulfuric acid spherical particles embedded in a Rayleigh atmosphere with a known ozone profile. The ozone profile is supplied from a version of the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2) Stratospheric Composition Reanalysis of the Microwave Limb Sounder (MLS) on board NASA Earth Observing System-chemistry (EOS Aura) satellite produced by NASA's Global Modeling and Assimilation Office using a stratospheric chemistry model and MERRA-2 meteorology. We also include a sulfur dioxide SO2 layer, which coincides spatially with the retrieved aerosol vertical profile, and with the total loading normalized to the stratospheric SO2 vertical column density from the operational TROPOMI SO2 product. We validate our AOD retrievals against ground-based AErosol RObotic NETwork (AERONET) direct-sun AOD measurements, and zp retrievals against Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) overpasses using Lagrangian trajectory modeling. We estimate the total Hunga stratospheric wet aerosol mass (sulfuric acid solution droplets, including water uptake) to be Maer0.50.05 Tg. This value is consistent with our previous BUV estimates of Hunga SO2 emissions ( 0.40.5 Tg SO2) and rapid conversion of SO2 to sulfate aerosol. Based on these BUV retrievals we can also estimate the sulfuric acid (H2SO4) mass fraction w0.4 and H2SO4/H2O solution density: 1.34 g cm3. These new values represent an extreme departure from the stratospheric background sulfate aerosol (Junge layer), which is typified by values of w0.75 and 1.7 g cm3 supported by decades of observations of the lower stratosphere during both quiescent and volcanically impacted periods. The new low values, inferred from BUV observations and backed up by microphysical modeling, are a result of the uniquely water-rich conditions in the early Hunga plume. Relative humidity in the plume, as modeled by the NASA Goddard Earth Observing System Chemistry-Climate Model with the Community Aerosol and Radiation Model for Atmospheres (CARMA), reached values as high as 60 %, compared to background values closer to 1 %. These findings are unique in the long-term observational record of the stratosphere; similar relative humidities only otherwise occur in overshooting clouds or cold winter hemisphere vortices."
    },
    {
        "DOI": "10.1007/978-981-95-5311-2_6",
        "Title": "Milk Matters: Enhancing Early Childhood Nutrition Through Dairy in Central Madagascar",
        "Year": 2026,
        "Abstract": "Renewed interest in the significance of Animal-Source Foods (ASF) in addressing a variety of health issues in developing countries, most notably stunted growth in children, has arisen recently. Although ASF products constitute an important source of proteins and essential nutrients for young children, empirical evidence on the relationship between ASF production and child growth is limited, especially studies using a longitudinal cohort. This study contributes to the literature by unveiling the multiple causal mechanisms between ASF production and child health, using lagged individual-level indicators. Leveraging on an eight-round panel dataset from central Madagascar, a region with relatively low milk intake and high stunting rates, findings indicate that only lagged milk production is positively and significantly associated with the height-for-age Z-score (HAZ) and stunting of children under 5 years of age. The possible transmission channels at the household level are sustained frequency and probability of milk consumption, and improved welfare, suggesting that production of milk and dairy products is an important driver of long-term growth gains among children in this region."
    },
    {
        "DOI": "10.5194/AMT-19-421-2026",
        "Title": "Retrieval of aerosol composition from spectral aerosol optical depth and optical properties using a machine learning approach",
        "Year": 2026,
        "Abstract": "Abstract. Accurate aerosol composition retrievals support radiative forcing assessment, source attribution, air quality analysis, and improved modeling of aerosolcloudradiation interactions. Aerosol retrievals based solely on visible-wavelength aerosol optical depth (AOD) observations provide limited spectral sensitivity, which may be insufficient to reliably distinguish among aerosol types with similar optical properties. In this study, we present a new retrieval framework that combines multi-wavelength AOD observations from both the visible and shortwave infrared spectrum, enhancing aerosol type discrimination. A neural network forward model trained on simulations from the Model for Optical Properties of Aerosols and Clouds (MOPSMAP), which relates aerosol optical properties to spectral AOD, is embedded in an optimal estimation method (OEM) to retrieve aerosol composition. This machine learning-based forward model achieves computational efficiency without making compromises in accuracy. The neural network forward model achieves a mean R2 of 0.99 with root-mean-square error below 0.01. The retrieval resolves up to four independent aerosol components, with degrees of freedom for signal about 3.75. We apply this hybrid method to ground-based observations, including data from the Aerosol Robotic Network (AERONET) and Fourier Transform Infrared spectrometer (FTIR) measurements. The retrieved aerosol compositions are consistent with physical expectations and validated through backward trajectory analysis."
    },
    {
        "DOI": "10.1016/J.JAG.2026.105142",
        "Title": "Three distinct types of heavy snowfall clouds Revealed by spaceborne Dual-Frequency Precipitation Radar observations (20142023)",
        "Year": 2026,
        "Abstract": " Global heavy snowfall is classified into three types using Gaussian Mixture Model. Topographic type dominates over highland with the smallest D m . Continental-like shows intermediate characteristics. Oceanic ocean largest All differ in vertical structure, environment, and temporal variations. This study examines global (snow rate > 2.5 mm h 1 ) 10 years (20142023) of observations from Precipitation Measurement (GPM) Dual-frequency Radar (DPR). Heavy cases are categorized distinct an Expectation-Maximization (EM) approach based on liquid-equivalent mass-weighted diameter (D normalized intercept parameter (logN w ). The first represents majority regions characterized by smaller snow particles higher particle number concentrations, along lower hydrometeor content colder environments, indicating limited growth particles. second exhibits slightly larger logN than predominantly occurs continental regions. third oceans explains contribution to volumetric snowfall, relatively content. Based characteristic occurrence frequency distributions each type, referred as topographic (THS), continental-like (CHS), oceanic (OHS), respectively. exhibit pronounced seasonality, whereas their diurnal variation weak. enhances understanding characteristics environmental differences among types, which expected support improvements satellite retrievals microphysical parameterizations numerical models."
    },
    {
        "DOI": "10.1007/S10333-026-01058-7",
        "Title": "Spatiotemporal assessment of actual evapotranspiration using remote sensing-based PySEBAL model over lowland rice irrigation scheme in the Philippines",
        "Year": 2026,
        "Abstract": "In irrigated agriculture, the estimation of actual evapotranspiration (ETa) is significant to effective water resource management. The dearth of actual ETa observations, particularly in developing countries, is a crucial limitation in attaining water security for food production. Furthermore, ETa estimation often involves high-cost instruments and labor-intensive routines, and since ETa is highly variable over space and time, extrapolated point ETa data is insufficient in representing large areas such as irrigated cropping systems. This study used the Python-based Surface Energy Balance Algorithm for Land (PySEBAL) model assimilated with remotely sensed datasets to quantitatively assess ETa over the NIA Magat River Integrated Irrigation System (MARIIS) Division IV service areas in the Philippines during dry (DS) and wet (WS) cropping seasons from 2015 to 2023. The model estimated a higher average ETa during WS (5.54 mm/day) compared to DS (4.23 mm/day). The seasonal values were found to be associated with the meteorological parameters and the prevailing climate in the area. Spatial analysis indicates that areas near the boundary line and built-up areas have lower ETa values, while areas near main and lateral canals have higher ETa values, implying that areas near the water supply tend to have higher ETa values. Sound estimation of ETa is crucial in comprehensive water accounting, particularly for efficient water allocation and distribution. The results of the study are valuable for management operations and in promoting effective water utilization."
    },
    {
        "DOI": "10.5194/ISPRS-ANNALS-X-5-W4-2025-197-2026",
        "Title": "Assessment of Groundwater Potential in North-Central Palawan using Remote Sensing and Geophysical Analysis of Fractured Basement Aquifers",
        "Year": 2026,
        "Abstract": "Abstract. Aquifers are geologic units capable of storing and transmitting water from surface runoff or precipitation. Porosity and permeability are key factors in determining whether rocks can function as aquifers. While aquifers typically occur in sedimentary deposits, sufficiently fractured crystalline units can also serve as groundwater reservoirs. This is especially true in regions underlain by igneous and metamorphic rocks. Such is the case in Palawan, an island province within a microcontinental block in the Philippine archipelago. The municipality of Puerto Princesa in Palawan is a major eco-tourism hub with a growing population. However, water scarcity during the dry season significantly disrupts local livelihoods. This study aims to evaluate the groundwater potential of North-Central Palawan using remote sensing and geophysical analysis. SRTM and Landsat-9 images were used in the delineation of surface lineament features, while subsurface lineaments were identified from edge detection enhancements of gravity anomaly data. By integrating lineament density data with elevation, lithology, and rainfall data, a groundwater potential map was generated using normalized weight factors derived from an Analytic Hierarchy Process. Results indicate that areas with low elevation and a broad sedimentary cover have very high groundwater potential. In contrast, igneous and metamorphic rocks generally exhibit low potential, except where dense fracturing is present. Statistical analysis using the chi-square test reveals a significant association between well depth and groundwater potential, which validates the model generated in this study. The results underscore the practical advantages of non-invasive geophysical and remote sensing means in characterizing key groundwater sites for future drilling."
    },
    {
        "DOI": "10.1073/PNAS.2523388123",
        "Title": "Human-induced biospheric carbon sink: Impact from the Taklamakan Afforestation Project",
        "Year": 2026,
        "Abstract": "The Taklamakan Desert, one of the worlds largest and driest deserts, has traditionally been considered a biological void. Here, we demonstrate that large-scale ecological restoration is transforming this hyperarid environment into a carbon sink. By analyzing satellite and ground-based data, we find strong seasonal dynamics: During the wet season (Jul to Sep), precipitation increases to 16.3 mm/mo, enhancing vegetation coverage and photosynthetic activity and drawing down atmospheric CO 2 by approximately three parts per million (ppm) relative to the dry-season levels. Long-term trends reveal significant increases in vegetation cover (6.8 10 4 /y) and photosynthetic activity (6.1 10 3 W/m 2 /sr/m/y), accompanied by a strengthening net CO 2 uptake (NEE trend: 5.2 10 12 kg/m 2 /s/y). These changes are spatially concentrated along the desert margins and their timing aligns with implementation of Chinas Three-North Shelterbelt Program. Our results provide the direct evidence that human-led intervention can effectively enhance carbon sequestration in even the most extreme arid landscapes, demonstrating the potential to transform a desert into a carbon sink and halt desertification. This underscores the critical role of dryland restoration in global carbon management strategies and highlights the Taklamakan Desert as a model for climate change mitigation through nature-based solutions and ecological engineering."
    },
    {
        "DOI": "10.5194/GMD-19-523-2026",
        "Title": "The ISIMIP groundwater sector: a framework for ensemble modeling of global change impacts on groundwater",
        "Year": 2026,
        "Abstract": "Abstract. Groundwater serves as a crucial freshwater resource for people and ecosystems, playing a vital role in adapting to climate change. Yet, its availability and dynamics are affected by climate variations, changes in land use, and abstraction. Despite its importance, our understanding of how global change will influence groundwater in the future remains limited. Multi-model ensembles are powerful tools for impact assessments; compared to single-model studies, they provide a more comprehensive understanding of uncertainties and enhance the robustness of projections by capturing a range of possible outcomes. However, to date, no ensemble of groundwater models has been available to assess the impacts of global change. Here, we present the new Groundwater sector within ISIMIP, which combines multiple global, continental, and regional-scale groundwater models. We describe the rationale for the sector, the sectoral output variables that underpinned the modeling protocol, and showcase current model differences and possible future analysis. Currently, eight models are participating in this sector, ranging from gradient-based groundwater models to specialized karst recharge models, each producing up to 19 out of 23 modeling protocol-defined output variables. To showcase the benefits of a joint sector, we utilize available model outputs of the participating models to show the substantial differences in estimating water table depth (global arithmetic mean 6127 m) and groundwater recharge (global arithmetic mean 78228 mm yr1), which is consistent with recent studies on the uncertainty of groundwater models, but with distinct spatial patterns. We further outline synergies with 13 of the 17 existing ISIMIP sectors and specifically discuss those with the global water and water quality sectors. Finally, this paper outlines a vision for ensemble-based groundwater studies that can contribute to a better understanding of the impacts of climate change, land use change, environmental change, and socio-economic change on the world's largest accessible freshwater store groundwater."
    },
    {
        "DOI": "10.1007/S00382-026-08071-W",
        "Title": "Impacts of cold surges on the synoptic changes of the western North Pacific anticyclone in winter",
        "Year": 2026,
        "Abstract": "The western North Pacific anticyclone (WNPAC) is an important system influencing the winter weather and climate in East Asia. Its formation and maintenance mechanisms have been extensively discussed across subseasonal and interannual timescales. However, the mechanisms regarding the synoptic variations of the WNPAC, which are closely linked to extreme weather, remain less well understood. In this study, an extreme case of WNPAC development is examined to investigate the key factors driving its changes on the synoptic timescale. Results show that synoptic variations of the WNPAC can be driven by the cold surge in East Asia. The WNPAC forms during the outbreak (cooling) period of cold surges and further strengthens in decay (warming) period, suggesting a complex influence of cold surges on WNPAC evolution. The formation of WNPAC in the cooling period is primarily attributed to the dynamic processes associated with the southward expansion of Siberian High. While during the warming period, thermal effects dominate the strengthening and vertical deepening of the WNPAC. Using a simplified two-layer diagnostic framework, we demonstrate that adiabatic heating in the mid-troposphere and diabatic heating in the lower troposphere jointly contribute to the WNPAC intensification during the warming period. Futher analyses of large-scale circulation reveal that the mid-tropospheric adiabatic heating in warming period is also indirectly linked to cold surge, as the intrusion of cold air into the tropics enhances subsidence within a local meridional circulation."
    },
    {
        "DOI": "10.1016/J.LANPLH.2025.101379",
        "Title": "Zero-emissions vehicle adoption and satellite-measured NO2 air pollution in California, USA, from 2019 to 2023: a longitudinal observational study",
        "Year": 2026,
        "Abstract": "Background Electrifying the transportation sector is a key climate-change mitigation strategy. Reductions in exhaust emissions have anticipated air quality co-benefits; yet, evidence primarily based on projections. Using observed data California, USA, we aimed to investigate whether reductions from transition zero-emissions vehicles (ZEVs: battery electric, plug-in hybrid, and hydrogen fuel cell) were detectable using Tropospheric Monitoring Instrument (TROPOMI) satellite measurements of nitrogen dioxide (NO 2 ) pollution. Methods In this longitudinal observational study, combined 2019 2023 annual light-duty ZEV registrations 1692 California ZIP code tabulation areas (ZCTAs; cross-walked codes) with mean TROPOMI-measured NO . We used linear mixed-effects models assess association between within-ZCTA changes changes, adjusting for temporal trends time-varying potential confounding, or excluding 2020. positive control analyses, related internal combustion engine vehicle ground-truth ZEVs concentrations 123 Environmental Protection Agency monitors 2012 2023. Findings The median increase was 272 (IQR 18 839). A 200 associated 110% (95% CI 119 100) decrease average main findings supported by sensitivity analyses (132% [143 121] when year 2020), analysis (087% [176 003] ground-level monitors), (080% [063 097] per 800 number vehicles). Interpretation natural experiment, found that increases pollution measured replicated monitors. This work serves as proof-of-principle future satellite-measured quantify effects efforts combustion-related within USA internationally. Funding National Institutes Health, Institute Health Sciences, Aeronautics Space Administration Air Quality Applied Sciences Team, Atmospheric Composition Modeling Analysis Program."
    },
    {
        "DOI": "10.1038/S44458-025-00002-W",
        "Title": "Human contributions to evapotranspiration mitigate swings in dry-to-wet year transitions",
        "Year": 2026,
        "Abstract": "Abstract Californias food and economic security depends on water availability, particularly under increasingly extreme climate scenarios. A key component of the water balance is evapotranspiration, the combination of soil and surface evaporation and plant transpiration. Evapotranspiration is influenced by natural drivers (e.g., climate, vegetation cover) and human intervention (e.g., irrigation, land management). Here, we analyze the transition between one of Californias driest years (2022) to an exceptionally wet year (2023) to assess evapotranspiration responses to climate extremes. Despite increased precipitation, total statewide evapotranspiration changed less than 10%. In 2022, human contributions accounted for 30% of statewide evapotranspiration and 80% in managed lands. In 2023, natural evapotranspiration increased, and human contributions fell by 30%, yet still comprised nearly 50% of evapotranspiration in managed areas. Our findings underscore the enduring role of human activity on Californias hydrology, even during wet years, and demonstrate a framework to separate natural and anthropogenic controls on evapotranspiration."
    },
    {
        "DOI": "10.1007/S41976-025-00265-W",
        "Title": "Explainable AI  Based Study of the Interactions between Remote Sensing and Ground-Truth Climate Variables and Lake Chads Level Fluctuations",
        "Year": 2026,
        "Abstract": "Research area: Lake Chad (Republic of Chad). Purpose: To identify significant remote sensing and ground-truth climate factors and their interactions and contributions in predicting remote sensing and ground-truth lake levels. A comparative analysis from 2013 to 2021 using Linear model (LM), regression tree (RT), random forest (RF), and gradient boosting regression (GBR) shows that GBR outperforms other methods for both remote sensing and ground-truth data. Ground-truth lake level regressed on ground-truth features ($${R}^{2}$$= 71%, $$MAE$$= 0.23, $$MSE$$= 0.09, $${CV}_{MSE}$$= 0.12) outperforms that regressed on remote sensing features ($${R}^{2}$$= 64%, $$MAE$$= 0.27, $$MSE$$= 0.11, $${CV}_{MSE}$$= 0.15). Local Interpretable Model-agnostic Explanations and SHapley Additive exPlanations based on GBR reveal that ground-truth air temperature influences the most ground-truth lake level: higher temperatures decrease predictions, while lower temperatures increase them. Remote sensing precipitation also significantly affects ground-truth lake level: higher precipitation reduces predictions, while lower amounts increase them. Air temperature emerges as the most critical factor, whether from remote sensing or ground-truth data. Precipitation and evaporation are 90% clustered, irrespective of the data source. These findings provide valuable insights for decision-makers regarding the impacts of climate change and water resource management. Further studies are necessary for validation purposes."
    },
    {
        "DOI": "10.1175/JCLI-D-25-0226.1",
        "Title": "The Strength of Coupling to the Southern Ocean Modulates Tropical Eastern Pacific Variability and Forced Response",
        "Year": 2026,
        "Abstract": "Abstract Despite rising global-mean temperatures, large parts of the Southern Ocean and tropical eastern Pacific Ocean have cooled during the satellite era. These regions may be linked by teleconnections, with Southern Ocean cooling contributing to tropical eastern Pacific cooling. We demonstrate that, on average, state-of-the-art Earth system models (ESMs) underestimate the magnitude of interaction between the Southern Ocean and tropical eastern Pacific Ocean. The strength of the teleconnection is shown to be mediated by the magnitude of the positive cloudsea surface temperature (SST) feedback in the subtropical eastern Pacific Ocean and the strength of the windevaporationSST (WES) feedback. We link excessive precipitation in the tropical Pacific south of the equator to the strength of the Southern Oceaneastern Pacific teleconnection. This model bias, known as the double intertropical convergence zone (ITCZ), is shown to be related to erroneous convection south of the equator, weakened cross-equatorial trade winds, and unfavorable meteorological conditions for marine boundary layer subtropical clouds. We postulate there is a two-way interaction, in which a double-ITCZ occurs with weaker cloudSST and WES feedbacks, which in turn impact local SSTs and amplify the double-ITCZ. Models with a stronger Southern Ocean to tropical Pacific teleconnection tend to exhibit more multidecadal variability in the Walker circulation, ITCZ, and westeast equatorial SST gradient, as well as greater delayed warming in the tropical eastern Pacific Ocean resulting from delayed Southern Ocean warming under greenhouse gas forcing. These results provide insight into why ESMs struggle to replicate observed tropical Pacific temperature trend patterns and point to ITCZ location as a key target for improvement in future model development. Significance Statement The key advancement of this study is to demonstrate that, on average, state-of-the-art Earth system models underestimate the magnitude of interaction between the Southern Ocean and tropical east Pacific. As a result, historical cooling in the Southern Ocean may explain a larger fraction of observed east Pacific cooling than previously appreciated. Initial evidence suggests unrealistic precipitation simulated by models in the southeast equatorial Pacific may result in a blocking of high latitude influence due to its impact on the magnitude of the cloudSST feedback and response of easterly trade winds. These results improve our understanding of the processes controlling the Southern Oceaneastern Pacific teleconnection and provide a guide for future model development and climate trend attribution."
    },
    {
        "DOI": "10.5194/GMD-19-327-2026",
        "Title": "Development of CAS-ESM_MMF: improving East Asian summer precipitation simulation with a Multiscale Modeling Framework",
        "Year": 2026,
        "Abstract": "Abstract. Traditional global climate models (GCMs) exhibit substantial biases in simulating precipitation over East Asia, largely due to uncertainties in convection parameterizations. To address this issue, we implement a Multiscale Modeling Framework (MMF), which explicitly resolves convection in a cloud resolving model, into the atmospheric component of the Chinese Academy of Sciences Earth System Model (CAS-ESM). Simulations using CAS-ESM with and without MMF reveal that the MMF implementation significantly reduces the wet bias around the Tibetan Plateau and the dry bias over South China and Southeast Asia. The intensityfrequency characteristics of precipitation are more realistically represented in the MMF version. In addition, the CAS-ESM with MMF better captures the monthly evolution of precipitation and simulates a more realistic seasonal migration of the East Asian rainband, albeit with a somewhat step-wise progression. Further enhancement is achieved by incorporating a convective momentum transport (CMT) parameterization, typically neglected in previous MMF implementations. This inclusion leads to a smoother northward migration of the rainband, more consistent with observations. Comparison with ERA5 reanalysis suggests that this improvement is associated with a more accurate simulation of the western Pacific subtropical high. These results demonstrate that MMF, especially when combined with CMT, substantially improves the simulation of East Asian precipitation. This modeling advancement offers a promising approach for evaluating regional precipitation responses to future climate change."
    },
    {
        "DOI": "10.1038/S41612-026-01336-5",
        "Title": "East Asian Meiyu variability reflected in precipitation oxygen isotopes via western Pacific subtropical high",
        "Year": 2026,
        "Abstract": "Abstract It remains uncertain whether precipitation oxygen isotopes ( 18 O) reliably capture East Asian Meiyu monsoon variability. Analyzing daily 18 O across the Yangtze-Huai River Basin from 28-34N, we reveal a distinct spatial dichotomy. In the middle and northern Meiyu regions, 18 O robustly tracks Meiyu precipitation. Conversely, the southern Meiyu margin is decoupled from Meiyu variability, primarily reflecting upstream convection processes further south. We identify the western Pacific subtropical high (WPSH) as the central driver, creating a dynamic dipole: its northwestward extension enhances moisture transport and deep convection along its northwestern flank (driving isotopic depletion in the northern Meiyu region), while imposing subsidence and convective inhibition under its body (suppressing isotopic depletion in the southern Meiyu region). Importantly, these mechanisms persist on interannual timescales. Consequently, while northern 18 O records effectively capture Meiyu variability, southern records reflect distinct vertical constraints, necessitating spatially differentiated paleoclimate interpretations."
    },
    {
        "DOI": "10.1016/J.SRS.2026.100402",
        "Title": "Enhancing water science in Earths second lung: AI-generated centenary hydrological insights from two decades of satellite data in the Congo Basin",
        "Year": 2026,
        "Abstract": "This contribution showcases advanced artificial intelligence applications that transform over 20 years of terrestrial water storage anomaly (TWSA) observations from the Gravity Recovery and Climate Experiment (GRACE) its follow-on (GRACE-FO) mission into a comprehensive 100-year dataset for Congo Basin. We develop CM-RecNet, climate-memory hybrid model, to reconstruct basins TWSA period 1923-2024. CM-RecNet combines two RecNet deep learning modelsone capturing climate-driven another memory effectsfused via multilayer perceptron. The model achieves strong performance, with correlation coefficient (CC), NashSutcliffe Efficiency, normalized root mean square error 0.82, 0.70, 0.20 during testing period, respectively. Our reconstruction aligns well observed runoff (CC > 0.6 at most stations), Normalized Difference Vegetation Index (CC=0.71), balance budget (CC=0.69). In addition consistency existing reconstructions, exhibits heightened capacity capture climate variability. innovative approach enables access previously unavailable data within Basin, necessary understanding critical challenges associated change anthropogenic activities."
    },
    {
        "DOI": "10.1029/2025GL119923",
        "Title": "Bulk RadiativeConvective Equilibrium Is Common Over MidLatitude Land",
        "Year": 2026,
        "Abstract": "Abstract Radiativeconvective equilibrium (RCE) is commonly used as an approximation of the time and spaceaveraged tropical atmosphere. We examine two reanalyses to assess the extent to which columnintegrated radiative cooling balances convective heating (bulk RCE) in the tropics and at higher latitudes. Our analysis shows that bulk RCE is a reasonable approximation of the tropics over ocean, but not land. Surprisingly, bulk RCE is often a reasonable approximation in midlatitudes, especially over land. These findings are explained by a simple argument. Over land, the ground heat flux is small, and bulk RCE arises when the topofatmosphere net radiative flux is small, which occurs in midlatitudes. Over ocean, the same mechanism applies but the ocean heat flux can be substantial and causes deviations relative to land. We conclude that bulk RCE is a surprisingly useful approximation of midlatitude land climate, which permits the development of simple theory for land climate, more broadly. , Plain Language Summary Radiativeconvective equilibrium (RCE) is a simple model used to understand the atmosphere. In RCE, the sun heats the surface which in turn heats the atmosphere through convection. This heating is balanced by radiation that is emitted by the atmosphere to space. Understanding where, and why, RCE is a good approximation of the Earth gives us a better understanding of the applications of simple RCE models. Typically, RCE is thought to apply in the tropics where there is abundant convective heating. We show that verticallyaveraged radiative cooling approximately balances convective heating over midlatitudes, particularly over land. We explain this result using a simple energy balance argument. This simplified picture of the atmosphere helps us understand midlatitude climate over land, where much of the Earth's population is located. , Key Points Radiativeconvective equilibrium (RCE) is a useful simplification of the atmosphere, usually employed in the tropics We show that bulk RCE is also a good approximation of annuallyaveraged midlatitude land surfaces This result is explained by a simple energy balance argument"
    },
    {
        "DOI": "10.5194/AMT-19-793-2026",
        "Title": "Mapping stratospheric nitric acid (HNO3 ) patterns in the extratropics with nadir-viewing infrared sounders  a retrieval perspective",
        "Year": 2026,
        "Abstract": "Abstract. With this paper, we aim to demonstrate how stratospheric HNO3 can be retrieved from nadir hyperspectral infrared (IR) measurements such that it is largely uncorrelated with tropospheric HNO3 and most other interfering signals. This is achieved by decomposing the set of HNO3 sensitive channels into orthogonal vectors that isolate the stratospheric HNO3 signal for use in the retrieval. Such a nadir-IR HNO3 product could add useful information to the monitoring of some stratospheric chemical processes affecting ozone in the extratropics, especially once the state-of-the-art Microwave Limb Sounder (MLS) on Aura is decommissioned in 2026. Nitric acid is typically used as indicator species for heterogeneous chemical processing inside the winter polar stratospheric vortices. The proposed stand-alone stratospheric HNO3 retrieval would be an improvement over the only other nadir-IR HNO3 product available today, namely FORLI (Fast Optimal Retrieval on Layers for IASI), which is a stratosphere + troposphere correlated profile retrieval affected by uncertainty in tropospheric water vapor at the time of measurement. We demonstrate the potential of this new stratospheric nadir-IR HNO3 retrieval strategy using the Community Long-term Infrared Microwave Combined Atmospheric Processing System (CLIMCAPS) as the retrieval framework with measurements from CrIS (Cross-track Infrared Sounder) on the Joint Polar Satellite System 1 (JPSS-1) during the Northern Hemisphere winter of 2019/2020. Future work will focus on optimizing and validating CLIMCAPS HNO3 retrievals for operational deployment."
    },
    {
        "DOI": "10.1038/S43247-025-02977-9",
        "Title": "Variations in land-atmosphere coupling during drought-heatwave events",
        "Year": 2026,
        "Abstract": "Droughts and heatwaves are linked through different land-atmosphere coupling pathways. While high temperatures depleted soil moisture (SM) characterize all drought-heatwave events, latent heat flux (LHF) reveals the dominant forcing mechanism driving these events. Our grid-based analysis of six events since 2000 shows spatially inhomogeneous associated with surface partitioning. Atmospherically driven regimes, characterized by increased LHF following hot temperature anomalies, accounted for majority 2022 East Asia event (64.8%). Land surface-driven exhibiting deficits dry SM were most prevalent in 2023 Central America (45.4%). Using a medium-range forecast model, we reproduced both showed that water-limited (2023 America) case exhibits lead-time predictability improvement about 2-3 days relative to energy-limited (2022 Asia) case. These results highlight limits domain-averaged model potential improve forecasted drought-heatwaves when incorporate regime-based characteristics. Compound exhibit two distinct land-driven regimes being more predictable than atmosphere-driven ones, based on analyses extreme past decades."
    },
    {
        "DOI": "10.1029/2025JD045635",
        "Title": "Distinct Impacts of Aerosol Size on AerosolCloud Interactions Over Ocean and Land Regions in Eastern China",
        "Year": 2026,
        "Abstract": "Abstract Representation of aerosolcloud interactions (ACI) remains one of the largest uncertainties in climate models and our understanding of climate change. Using multisource cloud, aerosol, and meteorology data during summer of 20152024, this study investigates ACI from the perspective of aerosol size (denoted by Angstrom exponent, AE) over the ocean and land in eastern China. Our findings reveal that at a fixed cloud water path, the cloud droplet effective radius (CER) increases with the aerosol index (AI) under highAE conditions (finemode aerosols), while CER decreases with increasing AI when AE is below 1.4 (coarsemode aerosols) in both regions. We interpret the opposite correlations as arising from aerosol sizedependent regulation of cloudnucleating ability, which leads to distinct dominant cloud microphysical processes. Over land, smaller aerosols with lower cloudnucleating ability lead to weaker competition for water vapor and the collisioncoalescence process becomes dominant due to the enhanced turbulence as aerosols increase. Conversely, activation efficiency is significantly stronger for coarsemode aerosols over the ocean and the competition effect becomes the dominant process. In addition, the dominant aerosol size decreases as cloud top pressure increases over land, leading to a transition in the CERAI relationships from negative to positive. The link between lower cloud tops and finer aerosols is consistent with the enhanced radiative stabilization induced by a higher proportion of fine aerosols (often lightabsorbing). In contrast, AE values over the ocean remain consistently low, resulting in persistent negative correlations. Despite variations in meteorological conditions, the opposite correlations under dominant coarse and finemode aerosol conditions still exist. , Plain Language Summary Influence of aerosols on clouds during summer over ocean and land regions in eastern China is investigated using multisource data from 2015 to 2024. Results suggest that at a fixed cloud water path in both regions, the cloud droplet effective radius (CER) increases (decrease) with the aerosol index (AI) under the high (low) Angstrom exponent (AE) condition. To explain the opposite correlations, we propose a plausible mechanism linking the critical role of aerosol particle size to cloudnucleating ability and the subsequent distinct dominant microphysical process, with collisioncoalescence dominating over land where finemode aerosols prevail, and vapor competition dominating over the ocean where coarsemode aerosols are more abundant. The quantified relative contributions of condensation and coalescence growth for different AE conditions, the first indirect effect, and the CERAI correlations stratified by cloud top pressure further support this theoretical inference. In addition, the more stable thermal structure induced by more small aerosols over land makes the cloud tops lower. Despite differences of local meteorological conditions, the impact of aerosol size on the ACI proved to be significant across both regions. Our findings highlight the critical role of aerosol size in modulating ACI. , Key Points Both the cloud droplet effective radius and its relationship with the aerosol index over land/ocean are influenced by aerosol particle size The cloud droplet effective radius and the aerosol index show a positive (negative) correlation for fine (coarse) mode dominant aerosols Although meteorology also affects the cloud droplet effective radius, aerosol particle size has a robust impact on aerosolcloud relationship"
    },
    {
        "DOI": "10.1109/RPIC67987.2025.11260742",
        "Title": "Assessing the Accuracy of Global Meteorological Datasets for Climate-Sensitive Disease Vector Modelling: Insights from Argentina",
        "Year": 2025,
        "Abstract": "The increasing availability of global meteorological data has enabled it to be used as input for multiple environmental and climate-driven models. However, the accuracy these datasets can vary depending on climatic variable, region, spatial scale considered. This study evaluated performance two datasets, NCEP GDAS/FNL GPM_3IMERGDL, in Argentina between 2015 2024. These were selected based their use a population dynamics model Aedes aegypti. Five variables analysed: minimum, mean maximum temperature; precipitation; relative humidity. Modelled compared with observations from 18 weather stations across 12 Argentinian zones. Temporal analysis included calculating daily bias evaluating seasonal differences using linear mixed Spatially, metrics (correlation, RMSE bias) relationship geographical (latitude, longitude altitude) assessed regression. Significant inter-seasonal biases detected all three temperatures Thermal showed strong correlation ($\\mathrm{R}>0.8$) low error (RMSE 4.3 $6.0{ }^{\\circ} \\mathrm{C}$). Precipitation humidity exhibited weaker fit xmlns:xlink=\"http://www.w3.org/1999/xlink\">$\\mathbf{R}$ 0.38 0.68, respectively) greater variability. Regression revealed that altitude was main driver variation temperature error, whereas latitude influenced precipitation error. findings highlight need validate relation temporal context application. Future work could explore how different inputs influence predictions climate-sensitive"
    },
    {
        "DOI": "10.1016/J.JHYDROL.2026.134956",
        "Title": "The interaction between vegetation greenness and hydro-climatic factors in China",
        "Year": 2026,
        "Abstract": "The interactions between vegetation and hydro-climatic factors play a critical role in key geophysical processes, including carbon and hydrological cycles. However, the interaction between vegetation and hydro-climatic factors in China remains unclear. Here, nonlinear Granger causality tests were employed to analyze the bidirectional relationships between vegetation and key hydro-climatic variables from 2002 to 2021. The results indicate that temperature was the dominant Granger-causal factor influencing vegetation growth (43.49 % of grid cells), followed by terrestrial water storage (16.49 %), soil moisture (11.44 %), precipitation (10.92 %), and solar radiation (3.11 %). In the reverse direction, vegetation exerted the strongest feedback on terrestrial water storage (31.18 %) and precipitation (28.77 %), with weaker effects on soil moisture (20.68 %), solar radiation (9.67 %), and temperature (3.79 %). Bidirectional Granger causality was observed in 33.57 %46.77 % of the assessed areas, with grasslands showing the highest proportion of significant causal relationships. These findings improve our understanding between vegetation and hydro-climatic factors across China, providing valuable insights that can guide the refinement and optimization of these processes in ecological and hydrological models."
    },
    {
        "DOI": "10.5194/ACP-26-33-2026",
        "Title": "NMVOC emission optimization in China through assimilating formaldehyde retrievals from multiple satellite products",
        "Year": 2026,
        "Abstract": "Abstract. Non-methane volatile organic compounds (NMVOCs) are key precursors of ozone and secondary organic aerosols. As one of the worlds largest NMVOC emitters, accurate emission inventories are essential for understanding and mitigating air pollution in China. Commonly-used inventories (e.g., MEIC) are largely based on bottom-up methods, which often fail to capture the spatiotemporal variability of NMVOC emissions, resulting in significant model-observation mismatches. This study evaluates the shape factor, filtered data volume, and monthly mean biases of OMI, OMPS, and TROPOMI formaldehyde products, with the latest OMPS and TROPOMI retrievals offering substantially higher effective spatiotemporal coverage. Monthly NMVOC emissions over China in 2020 are then optimized by independently assimilating formaldehyde retrievals either from OMPS or from TROPOMI, using a self-developed 4DEnVar assimilation emission inversion system. The OMPS- and TROPOMI-driven assimilation yields consistent seasonal and regional increments in NMVOC emissions in general, but distinctions are also notable. A consistency analysis is introduced to assess the reliability of these two posterior emissions. Highly consistent increments are obtained in the North China Plain (MayJune), the Yangtze River Delta and Pearl River Delta (JanuaryMarch, OctoberDecember), and the Sichuan Basin (January, JuneDecember). These adjustments significantly improve surface ozone simulations, with 81.25 % of consistent cases demonstrating reduced biases and an average RMSE reduction of 24.7 %. These findings highlight the effectiveness of OMPS and TROPOMI formaldehyde assimilation, coupled with consistency analysis, in refining NMVOC emission estimates and enhancing ozone simulation accuracy. Similar promising results are achieved in the OMPS/TROPOMI-based NMVOC emission inversion in 2019."
    },
    {
        "DOI": "10.1109/TGRS.2026.3658136",
        "Title": "Short-Term SAR Change Detection for Soil Moisture Retrieval: A Case Study Over Danish Test Sites",
        "Year": 2026,
        "Abstract": "This paper investigates the use of a constrained short-term change detection (STCD) method for field-scale soil moisture retrieval from Sentinel-1 synthetic aperture radar (SAR) backscatter. estimates changes in surface dielectric properties by analyzing backscatter ratios between consecutive acquisitions, under assumption that fluctuates more rapidly than vegetation and/or roughness changes. The inversion scheme integrates ancillary data, namely coarse-scale product, texture maps, and field capacity, to constrain solution minimize outliers. influence key parameters, including window size, was examined. Additional corrections are introduced handle limitations arising crop type or measurement depth, seasonal adjustment model. approach is validated using situ data multiple test sites Denmark. Results show STCD achieves strong agreement with ground-based observations, yielding an overall correlation R = 0.72, low bias (-0.26%), RMSE 4.32%. These findings support potential SAR scalable, high-resolution monitoring agricultural landscapes where variability well characterized."
    },
    {
        "DOI": "10.5194/ACP-26-5249-2026",
        "Title": "Evaluation of stratospheric transport in three generations of Chemistry-Climate Models",
        "Year": 2026,
        "Abstract": "Abstract. The representation of stratospheric transport in Chemistry-Climate Models (CCMs) is key for accurately reproducing and projecting the evolution of the ozone layer and other radiatively relevant trace gases. We evaluate stratospheric transport in CCMs that have participated in three model intercomparison initiatives (CCMVal-2, CCMI-1, and CCMI-2022) over the last 15 years using modern satellite datasets and reanalyses. Key long-standing model biases persist across generations, with some worsening in recent simulations. Transport remains overly fast in the models, with a global mean age of air young bias of 1 year for the CCMI-2022 median. It is argued that this bias could be associated with too fast tropical upwelling in the lower stratosphere and possibly to excessive vertical diffusion, with mixing biases being more uncertain. In the springtime southern polar stratosphere, the final warming is delayed ( 3 weeks), downwelling is underestimated ( 25 %), and the depth of the ozone minimum is overestimated ( 10 DU) on average in the most recent models. The tropopause is too high in all generations, and the tropical cold point tropopause is too warm in the latest generation ( 12 K). Long-term trends in transport over 19801999 are consistent across model generations and highlight the crucial role of ozone depletion in contributing to accelerate the Brewer-Dobson circulation and delaying the southern polar vortex breakdown."
    },
    {
        "DOI": "10.1080/15481603.2026.2653089",
        "Title": "Assessment of adjacency-effect correction for satellite-derived reflectance and water quality in agriculturally impacted rivers: a case study in Eastern Canada",
        "Year": 2026,
        "Abstract": "The adjacency effect (AE) is a major challenge for optical remote sensing of small inland waterbodies. We evaluated satellite-based retrieval accuracy two rivers in agriculturally intensive Eastern Ontario, Canada, namely the Ottawa River and its tributary, South Nation River. Satellite-derived reflectance water quality parameters, with without AE correction, were compared to situ measurements. Applying T-Mart correction Sentinel-2 MSI Landsat OLI/OLI-2 imagery significantly improved ACOLITE-derived reflectance, reducing average RMSE bias metrics across all bands by 19.4 %, 32.2 24.1 respectively, largest improvements red-edge near-infrared bands. Turbidity was well retrieved using 705 nm band, achieving an 5 FNU 272 range. While minimally affected this it reduced more 783 band 48.5 which important estimating turbidity highly turbid waters. also chlorophyll-a concentrations (Chl-a) colored dissolved organic matter absorption (CDOM) at 440 nm, though performance remained relatively poor (RMSE 30 g/L Chl-a 1.26 m1 CDOM within respective ranges 288 2.05.2 m1). Simulated based on measurements helped explain these outcomes. Further may require enhanced atmospheric visible range, while accurate likely requires hyperspectral sensors high signal-to-noise ratios between 600 800 where spectra optically complex waters are sensitive changes Chl-a."
    },
    {
        "DOI": "10.1029/2025JD043630",
        "Title": "Trend of North African Dust Storms and Potential Link to Climate Change",
        "Year": 2026,
        "Abstract": "Abstract Over recent decades, North African dust storms have undergone marked variability, reflecting complex interactions between regional climate processes and environmental change. Using four decades (19842023) of visibilitybased observational records, we examine regional and seasonal trends in dust storm frequency across the Sahel and the Sahara, capturing their distinct dust dynamics. Results reveal a significant decline in dust activity in both regions, most pronounced during premonsoon (MAM) and monsoon (JJA) seasons in the Sahel, and during postmonsoon (SON) and dry season (DJF) in the Sahara. Integrating surface observations with local meteorology (precipitation, surface wind speed, vegetation) and climate indices (AMO, NAO, MEI), we find the Atlantic Multidecadal Oscillation (AMO) as the primary driver, with regionspecific effects: in the Sahel, AMOdriven warming and rainfall increase vegetation, suppressing dust; in the Sahara, AMO intensifies the Saharan Heat Low (SHL) and elevates temperatures, modulating dust through atmospheric stability and wind patterns. Local meteorology further differentiates responses, with precipitation and Leaf Area Index (LAI) dominating dust variability in the Sahel, while SHL strength and surface winds are most influential in the Sahara. By explicitly separating the Sahel and Sahara and integrating multiple drivers, this study provides a more spatially resolved understanding of dustclimate link and suggests continued declines in North African dust storm activity under future warming. These findings offer critical constraints for improving dust emission projections in climate models. , Plain Language Summary This study is the first to analyze North African dust storms by separately considering the Sahara and Sahel, enabling a direct comparison of their distinct dust dynamics. By integrating longterm surface observations with both local meteorology (rainfall, temperature, surface wind, vegetation) and largescale climate drivers, we identify the dominant mechanisms controlling dust variability, providing new, spatially resolved insights into dustclimate interactions. Dust storms across North Africa, affect air quality, climate, and livelihoods. Using horizontal visibility observations from 1984 to 2023, we find a significant decline in dust storms in both regions, with strongest reductions during premonsoon and monsoon (MAMJJA) in the Sahel and postmonsoon and dry season (SONDJF) in the Sahara. Largescale climate patterns, particularly the Atlantic Multidecadal Oscillation (AMO), emerged as major drivers. In the Sahel, positive AMO phases are likely to produce warmer, wetter conditions that suppress dust through increased soil moisture and vegetation; in the Sahara, AMO lead to intensify the Saharan Heat Low and alters wind patterns, modulating dust emissions. These findings indicate that, with ongoing warming and the AMOdust relationship, North African dust storms may continue to decline, with important implications for future air quality, regional climate, and global dust transport. , Key Points This study provides the first regionally separated assessment of longterm dust storm trends over the Sahara and Sahel Dust storm activity has declined significantly across both regions since the mid1980s, with pronounced seasonal contrasts Largescale climate variability, particularly the AMO, controls dust activity through regionspecific effects on synopticscale drivers"
    },
    {
        "DOI": "10.1029/2025GL120318",
        "Title": "Apparent Global Increase in Cloud Droplet Number Concentration After 2022 Attributed to MODIS Orbital Drift",
        "Year": 2026,
        "Abstract": "Abstract A longterm, consistent satellite record of cloud droplet number concentration ( N d ) is essential for understanding aerosolcloud interactions and their climate effects. However, the Aqua MODISretrieved N d exhibits an unexpected and substantial increase over the nearglobal oceans after 2022, contradicting the expected decline from continued emission reduction efforts. Here we demonstrate that this surge is not physical but largely an artifact of sensor orbital drift, which alters viewing geometry and solar illumination. By leveraging concurrent SuomiNPP VIIRS observations unaffected by drift, we developed an empirical correction that removes this artificial signal and quantified global mean N d artificial biases of +2.4 cm 3 in 2023 and +5.0 cm 3 in 2024. These biases substantially distort the N d trends, reversing the previously decreasing global N d trend into an apparent strong rise after 2022. These findings highlight the critical need to correct for such artifacts when constructing satellitebased climate data records. , Plain Language Summary Satellite measurements of cloud droplet number concentration ( N d ) are essential for understanding how human activities influence clouds and climate. The Aqua MODIS satellite has provided one of the longest continuous records of cloud properties since 2002. However, after 2022, the MODIS data suddenly show a strong global increase in N d , which cannot be explained by known physical processes. We found that this apparent rise is mainly an artifact caused by the satellite's orbital drift, which changes the time and angle of sunlight when MODIS observes the Earth. By comparing MODIS data with those from the SuomiNPP VIIRS satellite, whose orbit has remained stable, we developed a correction that removes the artificial increase. The results show that orbital drift introduced N d biases of +2.4 cm 3 in 2023 and +5.0 cm 3 in 2024. These findings emphasize the need to carefully monitor satellite orbit changes to maintain the reliability of longterm climate data. , Key Points Aqua MODISretrieved cloud droplet number concentration ( N d ) shows a substantial increase after 2022 The post2022 N d rise primarily results from Aqua's orbital drift rather than physical changes Aqua orbital drift induces global mean N d biases of +2.4 cm 3 in 2023 and +5.0 cm 3 in 2024"
    },
    {
        "DOI": "10.1029/2025JD045882",
        "Title": "Nationwide Overestimation of Black Carbon Emissions During Clean Air Action Identified by Assimilation Inversion",
        "Year": 2026,
        "Abstract": "Abstract An accurate estimate of black carbon (BC) emission is critical, as BC represents one of the most important shortlived climate forcers. The widely used BC emission inventories were developed using either bottomup or topdown approaches, both of which have large uncertainties. The challenges of the bottomup approach include uncertainties in emission factors for different fuel types and combustion technologies. Conversely, topdown BC emission inversion relies primarily on satelliteretrieved aerosol absorption optical depth, which has significant limitations in quantifying BCspecific contributions. The China Atmospheric Monitoring Network, established by the China Meteorological Administration, provides groundbased hourly BC observations and a valuable opportunity to constrain BC emissions. This study presents the first application of these nationwide BC observations in emission inversion during the Clean Air Action (20132017), achieved using the 4DEnVar assimilation technique. Validation against independent observations demonstrates significant improvements in posterior estimates, reducing the root mean square error by 36.7%. Compared to the posterior, widely used bottomup inventories (e.g., MEIC) overestimate China's total BC emissions by 36.7%, with overestimations ranging up to 80.6% in the North China Plain (averaged between 2013 and 2017). In terms of climate impact, MEICbased estimates yield an 18.7% higher direct radiative effect on average, while CMIP6 historical estimates further exaggerate BCinduced forcing by a factor of 1.7. Additionally, our inversion reveals that annual total BC emissions declined markedly by 28.1% during the Clean Air Action, from 1.24 to 0.89 Tg. These findings are critical for quantifying the role of BC in the regional and global climate. , Plain Language Summary Black carbon (BC), or soot, is one of the major contributors to climate warming. However, scientists have struggled to accurately measure how much BC is released into the atmosphere. Traditional estimation methods either rely on calculations based on fuel use and combustion technology (which are highly uncertain) or on satellite measurements (which can't reliably distinguish BC from other pollutants). This study takes a new approach by using data from groundbased monitoring stations across China that directly measure BC in the air every hour. We applied these measurements to improve emission estimates during China's Clean Air Action period (20132017). Our results show that previous estimates significantly overestimated BC emissions in China by 36.7% on average, and by as much as 80.6% in some regions like the North China Plain. These overestimates led to inflated calculations of BC's warming effect on the climate, with some estimates being 1.7 times higher than reality. We also found that China's BC emissions dropped dramatically by 28.1% during the 5year Clean Air Action, falling from 1.24 to 0.89 million metric tons annually. These more accurate measurements are essential for understanding BC's true role in regional and global climate change. , Key Points MEIC overestimated China's black carbon emissions by 36.7% BC's climate warming effect in CMIP6 was overstated by up to 75.7% China's BC emissions declined 28.1% during the Clean Air Action from 2013 to 2017"
    },
    {
        "DOI": "10.1029/2025EF006503",
        "Title": "LongTerm Snow Avalanche Trends in High Mountain Asia: Climatic Drivers and Impacts",
        "Year": 2026,
        "Abstract": "Abstract Devastating snow avalanches are frequent in High Mountain Asia (HMA) yet remain undocumented with climate change impact drivers poorly understood. Here we introduce the first record of 60 million avalanche deposits across 10,701 small catchments, compiled from 33 years of Landsat data from 1990 until 2022 using a snow index. Potential damages from avalanches in areas at risk in HMA include nearly 20% of the buildings and up to 22% of the road network annually blocked by deposits temporarily disconnecting villages from food, energy, medicine, and communication infrastructures. Across 85% of HMA, no longterm trends of deposits were detected due to variable snow and temperature during winter. Nonetheless, in 15% of the 214 larger aggregated catchments comprising HMA, the number of deposits increased by 10 every year. Multivariate analysis among these increases of deposits and winter snow and temperature parameters from reanalysis data revealed that a few areas of western HMA experienced increases in snow water equivalent (5 mm in three decades) and air temperature (2C) contributing to the increase of avalanche activity. There, a decrease in snowfall of 50 mm, with an increase of rainfall, contributed to the formation of weak and unstable snowpacks. Most deposit trends could not be explained by snowtemperature variables because of the complex and variable interactions between avalanches and climate. These results call for an adoption of mitigation measures in HMA to address avalanche impacts on infrastructure and human lives, especially in areas where avalanche occurrence may increase with time due to climate tendencies. , Plain Language Summary Snow avalanches are dangerous hazards in mountainous regions, particularly in populated valleys of resourcepoor High Mountain Asia (HMA) where homes are close to hillslopes. In HMA, avalanche occurrence and the levels of community exposure are poorly understood, contributing to casualties and damages. Analysis of historical and recent satellite imagery revealed 60 million avalanche deposits, enabling the identification of exposed houses, roads, and other critical human activities and exposures, as well as the temporal evolution of avalanche occurrence. This assessment classifies the level of exposure to important infrastructure throughout the entire HMA and informs where avalanche activity has changed with time related to recent localized climate evolutions, namely increases in rainfall, temperature, and snow water equivalent, leading to wetter and unstable snowpack conditions. , Key Points Sixty million avalanches were mapped in the High Mountains of Asia over 33 years using Landsat archives Nearly 20% of buildings in High Mountain Asia (HMA) face frequent nearby avalanches; 22% of roads are blocked yearly, risking homes and village access The number of avalanches per year rose in 15% of the catchments in western HMA, due to wetter snowpack conditions and warmer temperatures"
    },
    {
        "DOI": "10.1016/J.ATMOSRES.2026.108912",
        "Title": "Interannual variability of spring dust column mass concentration over Central Asia: Associations with multiple covarying meteorological factors",
        "Year": 2026,
        "Abstract": "Central Asia (CA) is a globally important region for dust activity. However, the interannual variability of spring dust column mass concentration (DUCMASS) and its associations with multiple meteorological factors remain insufficiently understood. Based on multi-source datasets for 19802024, this study systematically examines these aspects using Empirical Orthogonal Function (EOF) decomposition and regression analyses. The results show that DUCMASS exhibits pronounced interannual fluctuations, with an average coefficient of variation of 18.5% and an interannual variance contribution of 43.7%. The first mode (EOF1) corresponds to a Tarim Basin monopole pattern, while the second mode (EOF2) represents a Tarim BasinCaspian Sea dipole pattern. EOF1 and EOF2 explain 40.8% and 23.1% of the interannual variance, respectively. 10 m winds, lower- to mid-tropospheric circulation anomalies, dust emission and transport, vertical motion, temperature, precipitation, and soil moisture covary and collectively shape the DUCMASS spatial pattern over CA in each mode. North Atlantic tripole sea surface temperature (SST) anomalies can trigger NAO-like patterns, which, through atmospheric baroclinic and barotropic processes, produce circulation anomalies consistent with EOF1. The wave activity fluxes associated with EOF1 follow two primary pathways, whereas those associated with EOF2 follow a single dominant pathway, suggesting that Rossby wave propagation sustains the circulation anomalies over CA. EOF1 is primarily linked to Siberian sea-level pressure (SLP) anomalies, NAO-like anomalies over the Atlantic, and tripole SST anomalies, while EOF2 is significantly associated with North Atlantic and Pacific bipolar SST anomalies. The maximum absolute correlation coefficient reaches 0.48. These findings provide insights into the interannual variability of dust activity in CA and offer a theoretical foundation for its prediction."
    },
    {
        "DOI": "10.1038/S44360-026-00105-1",
        "Title": "Global health benefits and cost-effectiveness of indoor air purification to mitigate PM2.5 from wildfire smoke",
        "Year": 2026,
        "Abstract": ""
    },
    {
        "DOI": "10.5194/HESS-30-1813-2026",
        "Title": "From grid to ground: how well do gridded products represent soil moisture dynamics in natural ecosystems during precipitation events?",
        "Year": 2026,
        "Abstract": "Abstract. Soil moisture (SM) is a critical variable governing landatmosphere interactions and influencing ecohydrological and climatic processes. Despite substantial progress in estimating SM through remote sensing and land surface models, considerable uncertainties still remain, especially in near-natural and poorly monitored ecosystems interacting with deeper soil layers. In this study, the performance of four state-of-the-art gridded SM products (SMAP-L4, GLDAS-Noah, ERA5 and ERA5-Land) is evaluated against in situ observations at ten natural monitoring sites in central and southern Chile, covering different hydroclimatic conditions (five semi-arid and five humid sites). The evaluation is performed at a 3-hourly temporal resolution, using well-known statistical metrics of performance, including unbiased root mean square error, modified KlingGupta efficiency (KGE), deseasonalised Spearman's rank correlation coefficient, and percent bias, each applied separately for surface soil moisture (SSM) and root zone soil moisture (RZSM). Finally, the dynamic SM responses to precipitation events is evaluated using rising time and amplitude SM signatures during the first and the most intense precipitation events of the year. Our results show that ERA5 and ERA5-Land consistently outperform SMAP-L4 and GLDAS-Noah on most metrics and in most regions, with ERA5-Land being particularly strong in humid areas. However, SMAP-L4 achieved the best SSM performance in selected northern arid locations, based on KGE; while GLDAS-Noah performed the worst overall, with the exception of moderate correlation values in southern RZSM. During the first precipitation event of the year, all products systematically overestimated both rising times and amplitudes in the arid north, indicating challenges in capturing SM responses under dry antecedent conditions. In contrast, all the gridded products aligned more closely with in situ measurements during intense precipitation events, particularly in humid regions. Our findings suggest that both ERA5 and ERA5-Land are valuable datasets for monitoring SM variability in near-natural and data-scarce ecosystems, while highlighting the value of event-based SM signatures to complement traditional performance metrics. Finally, we recommend the use of the deseasonalised Spearman rank correlation to better detect inconsistencies in temporal dynamics, especially in regions with strong seasonal cycles, such as arid environments."
    },
    {
        "DOI": "10.1002/GDJ3.70070",
        "Title": "MERRA2_CNN_HAQAST_PM25: Hourly BiasCorrected PM2.5 Datasets for Global Air Quality Assessment",
        "Year": 2026,
        "Abstract": "ABSTRACT This product provides MERRA2 biascorrected global hourly surface total PM2.5 mass concentration with the exact horizontal spatial resolution as MERRA2, covering a temporal range from 2000 to 2024. It is derived using a machine learning (ML) approach with a convolutional neural network (CNN) method. It is specifically developed for the NASA Health and Air Quality Applied Sciences Team (HAQAST). The dataset consists of two parameters: MERRA2_CNN_Surface_PM25 and QFLAG. MERRA2_CNN_Surface_PM25, a 3dimensional variable (time, latitude, longitude), represents the surface PM2.5 concentrations in g/m 3 . QFLAG denotes the quality of data at each grid point, where four indicates the highest quality and 1 indicates the lowest quality. It is recommended to use QFLAG values of 3 and 4 for quantitative analysis."
    },
    {
        "DOI": "10.1007/S12524-025-02405-7",
        "Title": "Spatio-Temporal Trends and Hotspot Analysis of Black Carbon over India (19802023)",
        "Year": 2026,
        "Abstract": "This study analyzes Black Carbon (BC) concentrations in India from 1980 to 2023 using MERRA-2 data, revealing an average rise from 0.54 g/m3 in the 1980s to 1.64 g/m3 in 2023, with an annual increase of 0.028 g/m3. Notably, BC concentrations peaked at1.64 g/m3 in 2016 but have declined since then, partly due to emission regulations on vehicles and biomass burning. There has been a significant upward trend since 2001, linked to industrial growth, urbanization, and increased emissions. Seasonal patterns show elevated BC during winter, with peaks at 2.06 g/m3 in the Indo-Gangetic Plain, particularly in December and January, attributed to long-range transport from this region. Northern and eastern India face high concentrations, while central regions like Madhya Pradesh and Chhattisgarh exhibit significant BC hotspots. In contrast, southwestern and western India show cold spots influenced by meteorological factors and emissions. Targeted pollution control measures are essential for specific seasons and regions in India. The country has a low density of air quality monitoring stations, particularly for black carbon measurement. Additionally, compiling a comprehensive emissions inventory is critical for effective policy-making, though data collection issues remain."
    },
    {
        "DOI": "10.1029/2025GL120933",
        "Title": "Global Diurnal Variation Characteristics of Aerosol Optical Depth From 32 Years of AERONET Observations",
        "Year": 2026,
        "Abstract": "Abstract Aerosols are ubiquitous microscopic particles in the atmosphere, and their diurnal variation characteristics reflect shortterm atmospheric changes that are crucial for climate monitoring and prediction. However, satellite, groundbased, and reanalysis systems cannot simultaneously provide observational authenticity together with full temporalspatial continuity. Using 32 years of hourly groundtruth Aerosol Optical Depth (AOD) data from the global AERONET network, we identify eight representative modes of AOD diurnal variability through cluster analysis. The dominant diurnal patterns are strongly influenced by land cover and aerosol type. Comparison with MERRA2 reanalysis shows that only 12.7% of stations exhibit consistent allday diurnal AOD variability with AERONET observations. These results provide new constraints for understanding global aerosol diurnal behavior and offer guidance for satellite temporal sampling strategies and the improvement of satellitebased AOD retrievals. , Plain Language Summary Aerosols are tiny particles in the atmosphere that influence Earth's radiation balance and energy exchange within the climate system. Understanding how aerosol levels change throughout the day is important for improving climate monitoring and satellite observations. In this study, we analyzed 32 years of hourly aerosol optical depth measurements from the worldwide AERONET groundbased network. Using a clustering method, we identified eight representative patterns that describe typically daily changes in aerosol levels. These patterns vary across different land surface types and aerosol environments. Comparisons with the MERRA2 reanalysis show that agreement with AERONET observations occurs mainly in the morning over natural surfaces and in the afternoon over anthropogenic regions. These results help explain global differences in aerosol daily variability and provide guidance for improving the temporal sampling and interpretation of satellite aerosol observations. , Key Points Eight distinct global modes of Aerosol Optical Depth (AOD) diurnal variability are identified from 32 years of AERONET observations Land cover strongly modulates the shape and amplitude of AOD diurnal cycles MERRA2 reproduces morning AOD variability better than afternoon, highlighting the importance of observational temporal sampling"
    },
    {
        "DOI": "10.7780/KJRS.2025.41.6.19",
        "Title": "Analysis of the November 2023 to March 2024 Marine Heatwave in the Java Sea Using Satellite and Profiling Floats Data",
        "Year": 2025,
        "Abstract": ""
    },
    {
        "DOI": "10.5194/AMT-19-2657-2026",
        "Title": "Study on the life cycle of an ice cloud system over the Taklamakan desert using multi-source data",
        "Year": 2026,
        "Abstract": "Abstract. Using a coherent Doppler wind lidar, the whole process of formation and decomposition of an ice cloud event was recorded in Minfeng (37.06 N, 82.69 E) on the southern edge of the Taklamakan Desert (TD) from 5 to 6 February 2022. Combined with ERA5 and MERRA-2 reanalysis data, FY-4A and Himawari-8 meteorological satellite data, local meteorological data, and HYSPLIT model, the evolution process of ice clouds affected by the wind profile, dust aerosol, turbulence, temperature, humidity, and terrain was analyzed. The results show that the uniquely relatively enclosed basin topography of the TD, coupled with the feeble turbulence and robust downdrafts at night, constrains the upward supply of water vapor and dust aerosols. As a result, the base height of the ice clouds is maintained at approximately 3 km. Dust aerosols can act as effective ice nuclei, which catalyze the formation of ice clouds and inhibit the occurrence of liquid precipitation. The continuous evolution of ice clouds was well studied with multiple meteorological data, which improves the understanding of dust-cloud-atmosphere interactions in the desert hydrological cycle."
    },
    {
        "DOI": "10.3390/RS18071097",
        "Title": "A Physically Driven Interpretable Machine Learning Framework for Early Forecasting of Summer Hypoxia in the Semi-Enclosed Bohai Sea Using Remote Sensing Data",
        "Year": 2026,
        "Abstract": "Hypoxia has become increasingly frequent in the semi-enclosed Bohai Sea since the early 2000s, posing significant risks to marine ecosystems. To address the limitations of existing dissolved oxygen modelsparticularly their poor predictive ability and lack of interpretabilitywe developed a two-month lead probabilistic forecasting framework for summer hypoxia using only multi-source remote sensing and reanalysis data, supplemented by in situ observations for validation. Environmental conditions in June were used to predict hypoxia probability in August via machine learning; among the seven algorithms tested, the optimized Random Forest model achieved the best performance (F1 = 0.76 and AUC = 0.92 on the independent test set). The model successfully reproduced observed hypoxia patterns in 2019 (validated against numerical simulations) and 2022 (validated against field measurements), capturing an increase in hypoxic area from 8229 km2 to 13,866 km2, which is consistent with intensifying thermal stratification under climate warming. SHAP-based interpretability analysis identified reduced wind speed and enhanced thermal stratification as the dominant physical drivers, highlighting the critical role of suppressed vertical mixing in limiting bottom-water oxygen supply. This study demonstrates that a physics-informed, interpretable machine learning approach based solely on satellite and reanalysis data can deliver reliable, early, and physically consistent hypoxia forecasts, offering a scalable solution for environmental monitoring of data-limited coastal seas."
    },
    {
        "DOI": "10.5194/GMD-19-2437-2026",
        "Title": "Deep learning representation of the aerosol size distribution",
        "Year": 2026,
        "Abstract": "Abstract. Aerosols influence Earth's radiative balance via the scattering and absorbing of solar radiation, affect cloud formation, and play important roles on precipitation, ocean seeding and human health. Accurate modeling of these effects requires knowledge of the chemical composition and size distribution of aerosol particles present in the atmosphere. Computationally intensive applications like remote sensing and weather forecasting commonly use simplified representations of aerosol microphysics, prescribing the aerosol size distribution (ASD), introducing uncertainty in climate predictions and aerosol retrievals. In this work, we develop a neural network model, MAMnet, to predict the ASD and mixing state for seven lognormal modes based on the bulk aerosol mass and the meteorological state. MAMnet is designed to operate with outputs from single-moment, mass-based aerosol schemes, making it compatible with existing models. We demonstrate that MAMnet can accurately reproduce the output of a two-moment modal aerosol scheme, and also agrees well with field measurements when driven by reanalysis data. Our model paves the way to improve the representation of aerosols in atmospheric models while maintaining the versatility and efficiency required in large scale applications."
    },
    {
        "DOI": "10.5194/OS-22-629-2026",
        "Title": "Subpolar Atlantic meridional heat transports from OSNAP and ocean reanalyses  a comparison",
        "Year": 2026,
        "Abstract": "Abstract. Ocean reanalyses are potentially useful tools to study ocean heat transport (OHT) and its role in climate variability, but their ability to accurately reproduce observed transports remains uncertain, particularly in dynamically complex regions like the subpolar North Atlantic. Here, we evaluate currents, temperatures, and resulting OHT at the OSNAP (Overturning in the Subpolar North Atlantic Program) section by comparing OSNAP observations with outputs from a suite of global ocean reanalyses. While the reanalyses broadly reproduce the spatial structure of currents and heat transport across OSNAP West and East, systematic regional biases persist, especially in the representation of key boundary currents and inflow pathways. Temporal variability is well captured at OSNAP West, but none of the reanalyses reproduce the observed OHT variability at OSNAP East, especially a pronounced peak in 2015. This discrepancy in 2015 is traced to the glider region over the eastern Iceland Basin and Hatton Bank, where OSNAP data show a strong, localized inflow anomaly associated with the North Atlantic Current (NAC). This signal is absent from all reanalyses as well as from independent, indirect heat transport estimates based on surface heat fluxes and heat content. Investigation of sea level anomalies and implied geostrophic currents further confirm that this mismatch is mainly driven by differences in flow structure rather than temperature anomalies alone. Our results highlight both the value and limitations of reanalyses in capturing subpolar heat transport variability. While higher-resolution products such as GLORYS12V1 better represent circulation features, significant mismatches remain, especially in regions with sparse observational coverage. The findings underscore the need for improved observational networks and higher-resolution modeling to more accurately constrain subpolar OHT."
    },
    {
        "DOI": "10.1007/S00703-025-01106-7",
        "Title": "Nighttime thunderstorms over southwestern Amazon basin: characterization and preconditioning large-local scales ingredients",
        "Year": 2026,
        "Abstract": "This study investigates severe nighttime thunderstorms (NT) over the southwestern Amazon Basin from 1998-2013. NT events were identified using the Tropical Rainfall Measuring Mission Precipitation Features database, based on vertical depth, horizontal extent, rainfall volume, and lightning criteria. A total of 97 NT events were selected, typically starting between 18:00 and 00:00 local time and lasting 7.5 to 22.5 hours. Most occurred near the Andes foothills, highlighting orographic influence, with a seasonal peak in OctoberDecember. Events were classified by lightning rate: 40% were intense (>32 flashes/min) and 25% extreme (>47 flashes/min). Two low-level synoptic patterns were identified: one linked to the South American Low-Level Jet (55 cases), and another showing a northwestsoutheast low-level confluence, typically organized by frontal systems to the south (42 cases). Large-scale composites show both patterns form at least 12 hours before NT onset. Environmental composites centered on NT cores (66 boxes) were analyzed for intense and non-intense cases. Intense NTs featured low-level confluence, warmer environments, increasing convective available potential energy, decreasing convective inhibition before convection, and a strong horizontal and vertical moisture contrast, especially a dry layer at 500 hPa over a moist lower troposphere. These results suggest the vertical moisture structure, notably the dry air layer at 500 hPa, plays a key role in NT severity, serving as a preconditioning factor for forecasting. The vertical humidity gradient, variables at 850 hPa, and conditions near 18:00 UTC are critical indicators for next-day NT forecasting in the southwestern Amazon."
    },
    {
        "DOI": "10.5194/AMT-19-549-2026",
        "Title": "Forward modeling of spaceborne radar observations",
        "Year": 2026,
        "Abstract": "Abstract. Accurate forward models, particularly radiative transfer models, are essential for the assimilation of both passive and active satellite observations in modern data assimilation frameworks. The Community Radiative Transfer Model (CRTM), widely used in the assimilation of satellite observations within numerical weather prediction systems, especially in the United States, has recently been expanded to include a radar module. This study assesses the new module across multiple radar frequencies using observations from the Earth Clouds, Aerosols and Radiation Explorer Cloud Profiling Radar (EarthCARE CPR), the CloudSat CPR, and the Global Precipitation Measurement Dual-Frequency Precipitation Radar (GPM DPR). Simulated radar reflectivities were compared with the spaceborne measurements to evaluate the impacts of hydrometeor profiles, particle size distributions (PSDs), and frozen hydrometeor habits. The results indicate that both PSD selection and particle shape largely influence the simulated reflectivities, with snow particle habits introducing differences of up to 4 dBZ in W-band comparisons. For the GPM DPR, reflectivities simulated using the Thompson PSD showed closer agreement with the observations than those using the Abel PSD; this agreement should be interpreted in the context of the limited independence between the observations and the retrievals used as input to the CRTM, which themselves rely on PSD-related assumptions. The sensitivity of forward radar simulations to microphysical assumptions, underscores their importance in the assimilation of radar observations in numerical weather forecast models."
    },
    {
        "DOI": "10.1038/S41597-026-06691-9",
        "Title": "Machine learning estimates for G20 subnational urban GHG emissions from 20002020",
        "Year": 2026,
        "Abstract": "Reliable, comparable greenhouse gas (GHG) emissions data at the subnational level remain scarce, despite growing expectations for cities and regions to lead on climate action. Inconsistent reporting, methodological variation, limited coverage of self-reported inventories hinder efforts track progress guide mitigation opportunities. To address these challenges, we develop a machine learning (ML) framework estimate annual Scope 1 2 CO2-equivalent jurisdictions in G20 countries from 2000 2020. Our approach integrates publicly available geospatial, socioeconomic, environmental with where available, aligns predictions administrative boundaries. Compared traditional downscaling or proxy-based approaches, our model improves spatial relevance predictive performance while capturing locally specific emission drivers. This globally consistent, administratively-aligned dataset can serve as baseline assessing progress, especially data-poor inconsistent reporting contexts, supports more targeted, data-informed policy decisions urban regional decarbonization."
    },
    {
        "DOI": "10.5194/AMT-19-1385-2026",
        "Title": "Volcanic plume height during the 2021 Tajogaite eruption (La Palma) from two complementary monitoring methods  implications for satellite-based products",
        "Year": 2026,
        "Abstract": "Abstract. Volcanic emissions from the Tajogaite volcano, located on the Cumbre Vieja edifice on the island of La Palma (Canary Islands, Spain), caused significant public health and aviation disruptions throughout the eruption (19 September13 December 2021, officially declared over on 25 December). Nonetheless, it is considered the most significant volcanic event in Europe over the past 75 years due to the substantial amount of SO2 released into the atmosphere. The Instituto Geografico Nacional (IGN), the authority responsible for volcano surveillance in Spain, implemented extensive operational monitoring to track volcanic activity and to provide a robust estimation of the volcanic plume height using a video-surveillance network. In parallel, the State Meteorological Agency of Spain (AEMET), in partnership with other Spanish ACTRIS (Aerosol, Clouds, and Trace Gases Research Infrastructure) members and collaborating institutions, conducted an unprecedented instrumental deployment to evaluate the impacts of this volcanic event on atmospheric composition. This effort included a network of aerosol profilers surrounding the volcano. A total of four profiling instruments were installed on La Palma: one MPL-4B lidar and three ceilometers. Additionally, a pre-existing Raman lidar on the island contributed valuable data to this study. These efforts are undertaken due to the importance of monitoring volcanic plume height in terms of air quality (necessary for the implementation of effective civil protection policies), volcanic activity surveillance (for tracking and forecasting eruptive behaviour), and, from a scientific perspective, for improving our understanding of the climatic and radiative impacts of this type of aerosol. In this study, the eruptive process was characterised in terms of the altitude of the dispersive volcanic plume (hd), measured by both IGN and AEMET-ACTRIS, and the altitude of the eruptive column (hec), measured by IGN. Modulating factors such as seismicity and meteorological conditions were also analysed. The consistency between the two independent and complementary datasets (hd,IGN and hd,AEMET) was assessed throughout the eruption (mean difference of 258.6 m). Our results confirmed the existence of three distinct eruptive phases, encompassing a range of styles from Strombolian explosive to effusive activity. While these phases have been characterised in previous studies, the results of the present work provide complementary information and novel insights from an alternative observational approach, which may be of use in future volcanic crises and will be applied to operational surveillance during such events. A subsequent comparison of hd,AEMET with the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol layer height product (ALHCALIOP) revealed a systematic underestimation by the satellite product, with a mean difference of 392.2 m. Finally, the impact of using hec in estimating SO2 emissions from the NASA MSVOLSO2L4 satellite-based product was evaluated. When a fixed (standard) plume altitude of 8 km was used instead of the observed hec, the total SO2 emission was significantly underestimated by an average of 56.2 %, and by up to 84.7 %. These findings underscore the importance of accurately determining the volcanic plume height when deriving SO2 emissions from satellite data."
    },
    {
        "DOI": "10.1175/JHM-D-25-0032.1",
        "Title": "Multiday Precipitation Extremes are Projected to Become Less Likely in Southern Pakistan",
        "Year": 2026,
        "Abstract": "Abstract South Asia is highly susceptible to the impacts of hydroclimatic extremes, with unusual monsoon precipitation heightening the risk of severe flooding in arid coastal regions. This study investigates the rainfall characteristics of the unprecedented multiday wet spell of 2022 in southern Pakistan, particularly in the Sindh and Balochistan Provinces. Additionally, the study examines how future climate scenarios may alter the frequency of multiday extreme monsoon precipitation in this data-scarce region. To investigate the changing risk of 2022-like accumulated rainfall from the historical context of monsoon rainfall [JuneAugust (JJA)], this study applies generalized extreme value (GEV) models to the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement (GPM) (IMERG; 200023) and a bias-corrected and statistically downscaled 30-member ensemble of the Seamless System for Prediction and Earth System Research (SPEAR) historical (19902014) and future simulations (20702100) under two emission scenarios [Shared Socioeconomic Pathways (SSPs) 2-4.5 and SSP5-8.5]. The 2022 maximum 5-day accumulated rainfall was unusually intense in southwestern Balochistan, with a standardized precipitation index (SPI) above +2. SPEAR ensemble projections suggest a reduced future risk relative to the historical baseline. Regionally, the probability of a 2022-like event may decline to 22% of the historical likelihood under the highest emission scenario SSP5-8.5. While large member single climate model simulations show consistency in trends, a multimodel analysis is essential to better support adaptation and mitigation measures and to understand the regions highly variable monsoon rainfall."
    },
    {
        "DOI": "10.1175/JHM-D-24-0171.1",
        "Title": "Understanding LandAtmosphere Interactions during Coupling Whiplash Events",
        "Year": 2026,
        "Abstract": "Abstract Whiplash events, defined by abrupt transitions between dry and wet conditions, have severe environmental and societal impacts, disrupting agriculture, water resources, and infrastructure. This study examines the role of landatmosphere (LA) interactions in driving these events by utilizing the convective triggering potentialhumidity index (CTPHI) framework to analyze dry-to-wet (DW) and wet-to-dry (WD) transitions across different regions and seasons. The results highlight that moisture availability in the lower atmosphere (HI) is a critical driver of whiplash intensity. At the same time, convective potential (CTP) is more responsive to seasonal and thermal variability, particularly during warmer periods. A global assessment identifies key high-risk hotspots in North America and Europe and localized areas across southern Africa, southern Asia, and South America, where transitions are both frequent and intense. These high-risk regions exhibit dual vulnerability: rapid moisture loss during WD events due to positive feedback and limited recovery during DW events, driven by suppressed evaporation. In contrast, low-risk regions demonstrate stronger recovery capacity and more moderated transitions. By revealing distinct LA feedback mechanisms and transition patterns, this study improves our understanding of whiplash dynamics and highlights the global regions most susceptible to extreme shifts. The insights gained offer a valuable basis for enhancing predictive models and guiding mitigation strategies in areas facing the most tremendous hydrometeorological stress. Significance Statement This study advances our understanding of whiplash eventsabrupt transitions between dry and wet conditionsby utilizing the convective triggering potentialhumidity index (CTPHI) framework to examine their global dynamics across seasons. The findings reveal that moisture availability in the lower atmosphere is a critical driver of transition intensity. High-risk areas exhibit clustered whiplash activity across multiple continents, characterized by frequent and intense transitions associated with rapid drying and limited recovery. By revealing the role of feedback dynamics and landatmosphere (LA) interactions, this work offers key insights to enhance climate models through a process-based understanding of regional differences in abrupt dry and wet transitions."
    },
    {
        "DOI": "10.1175/JHM-D-24-0060.1",
        "Title": "Quantifying the Effect of a Parallax-Correcting Algorithm for Passive Microwave Satellite Precipitation Retrievals across the Conterminous United States",
        "Year": 2026,
        "Abstract": "Abstract Satellite precipitation retrieval algorithms whose measurement instruments are tilted to the zenith line are subject to a spatial mismatch between the theoretical ground coordinates and the coordinate pair corresponding to the cloud layers sending spectral signals to the satellite. This is the case of the precipitation retrievals of the Global Precipitation Measurement (GPM) Microwave Imager (GMI) on board the core satellite of the GPM that uses the Goddard profiling algorithm (GPROF). Currently, no geometrical correction is applied to GMI retrievals of surface precipitation, creating a horizontal displacement (or parallax mismatching) between the reported surface and the corrected coordinates corresponding to the cloud structures intersecting the field of view. GPROF precipitation retrievals over the conterminous United States are analyzed using the ground-validated Multi-Radar Multi-Sensor (GV-MRMS) system data and the fifth generation European Centre for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) temperature profiles. Results applying this parallax correction scheme show improvements in the overall retrieval accuracy of GPROF, mainly during the summer months, for every precipitation type, when the freezing level (FL) is relatively high. The development of this new parallax correction algorithm for passive microwave radiometers will significantly improve the accuracy of remote sensing data by minimizing spatial distortions in atmospheric measurements, leading to more precise weather forecasting, climate monitoring, and environmental assessments. Significance Statement Our study addresses a critical issue in satellite precipitation retrieval algorithms regarding the spatial mismatch from tilted measurement instruments. By examining the precipitation retrievals of the Global Precipitation Measurement Microwave Imager (GMI) instrument, we highlight the consequential parallax mismatching between reported surface precipitation from observed cloud structures as well as the resulting error from this discrepancy. Through a novel geometrical correction scheme applied to GMI retrievals over the conterminous United States, we demonstrate significant enhancements in retrieval accuracy, particularly during summer months that are characterized by high-altitude freezing levels. These findings advance the reliability of satellite-based precipitation estimation, which is crucial for meteorological research and operational forecasting."
    },
    {
        "DOI": "10.1175/MWR-D-25-0062.1",
        "Title": "Improved Analysis and Prediction of Tropical Storm Hermine (2022) over the Eastern Tropical Atlantic Using CPEX-CV Observations",
        "Year": 2026,
        "Abstract": "Abstract In September 2022, NASA conducted the Convective Processes Experiment-Cabo Verde (CPEX-CV) campaign over the data-sparse eastern Atlantic. Over this region, CPEX-CV collected a suite of dense observations to aid in the study of convective systems. Tropical Storm (TS) Hermine formed in late September and had an unusual northward trajectory. Hermine was sampled by two consecutive research flights prior to becoming a TS, which provided an opportunity to improve Hermines forecast via data assimilation and verify model forecasts. With an improved forecast after assimilating CPEX-CV observations, model data is used to study the processes controlling Hermines evolution more accurately. Two experiments were conducted. One experiment assimilated CPEX-CV observations (WCPEX) while the other did not (WoCPEX). Compared to WoCPEX, the assimilation of CPEX-CV observations in the WCPEX analysis produced a stronger Saharan air layer, more intense dry-air intrusion, more easterly wind bias corrections, and a stronger mid-level circulation within pre-Hermine. Forecasts show that the strengthening of pre-Hermine into a TS in WoCPEX was delayed by 12 hours due to the large vertical tilt of the vortex and weak mid-level vorticity; it also had a westward track bias. Compared to WoCPEX, in WCPEX, while convection near pre-Hermine was weaker at early forecast times due to a more intense dry-air intrusion, stronger, more organized mid-level vorticity and better vertical alignment of the vortex improved the intensity and track forecast. Additional sensitivity tests revealed that assimilating only CPEX-CV remote sensing observations improved Hermines forecast nearly as much as assimilating all CPEX-CV observations."
    },
    {
        "DOI": "10.1007/S42865-026-00121-9",
        "Title": "Seasonal dynamics of Sulfur dioxide and Sulfate aerosols over India: Insights from Sentinel-5P and MERRA-2 datasets",
        "Year": 2026,
        "Abstract": "This study examines the spatiotemporal variability of Sulfur dioxide (SO2) and Sulfate (SO4) over India (20192022) using the TROPOMI (Sentinel-5P) satellite for SO2 and Modern-Era Retrospective analysis for Research and Applications Version 2 (MERRA-2) reanalysis for SO4. The study finds both SO2 and SO4 exhibit maxima in Winter and Pre-monsoon and distinct minima during the Southwest Monsoon and regains in Post-monsoon. This Monsoon (JuneAugust). decline is due to heavy rains and high humidity in JuneAugust cause strong wet scavenging of SO2 and SO4. Spatially, SO2 is highest over the Indo-Gangetic Plain (IGP) and Central-Eastern India which is known for its dense clusters of coal-fired power plants, refineries, steel mills and cement factories. These regions also show elevated SO4, consistent with abundant SO2 precursors and the humid conditions that favour SO2SO4 conversion. Year-to-year variations reported in the study shows that meteorology and human activity like anomalous rainfall patterns, shifts in coal and steel output, and the COVID lockdown have influenced sulfur levels. Wind analyses show that the southwest monsoon circulation carries SO2 east-to-north across the subcontinent, linking source regions to downwind areas. These findings provide an improved understanding of sulfur pollution dynamics and offer essential context for emission control strategies and regional climate assessments. Importantly, limited prior work have seldom used MERRA-2 SO4 over India; the current work fills this gap by contextualizing MERRA-2 SO4 fields with surface hotspots and precipitation patterns."
    },
    {
        "DOI": "10.1073/PNAS.2529774123",
        "Title": "Global stratospheric methane loss from satellite observations",
        "Year": 2026,
        "Abstract": "Stratospheric CH 4 oxidation represents both an important sink in the global CH 4 budget and a major source of stratospheric water vapor and hydrogen radicals, exerting strong influences on global climate and ozone chemistry. Yet, the magnitude of stratospheric CH 4 chemical loss remains highly uncertain, with previous estimates largely relying on chemistry-climate models (CCMs). Here, we present an observationally based estimate of stratospheric CH 4 loss ( L STR ), derived from the CH 4 diabatic flux across the isentropic surface fitted to the tropical tropopause, using satellite measurements of CH 4 concentration, temperature, and radiative heating rates for 20072010. We obtain an L STR of 49.8 7.8 Tg/y, compared with 38.1 Tg/y estimated from reanalysis, and 25.7 Tg/y (range: 19.6 to 35.9 Tg/y) derived from CCMs, indicating that both reanalysis and CCMs systematically underestimate stratospheric CH 4 loss. We show that discrepancies in global CH 4 diabatic fluxes from the reanalysis and CCMs, when compared with observations, are mainly driven by biases in CH 4 concentrations and further enhanced by errors in temperature and radiative heating. Substituting our observational estimate for the model-based stratospheric loss in the bottom-up global CH 4 budget reduces the reported imbalance for the 2000s from 23 to 3 Tg/y, bringing it into close agreement with the 5 Tg/y (range: 4 to 13 Tg/y) imbalance inferred from top-down estimates. These findings highlight the critical role of observational constraints on L STR in reconciling the global CH 4 budget. They also carry important implications for understanding stratospheric water vapor and ozone chemistry."
    },
    {
        "DOI": "10.1109/IGARSS55030.2025.11242673",
        "Title": "Monitoring drought over the Gavkhouni Lake using Terrestrial Water Storage data",
        "Year": 2025,
        "Abstract": "Drought is one of the major challenges in water resources and environmental management. Every year, this phenomenon has numerous detrimental effects on various sectors countries, including environment, economy, social conditions, agriculture. Effective drought management can significantly reduce damage caused by these impacts. Satellite data plays a crucial role predicting monitoring drought. In study, trend Gavkhouni Wetland region Isfahan Province Iran been monitored evaluated from 2000 to 2022. This paper uses Severity Index based Terrestrial Water Storage (TWS) derived NASA Global Land Data Assimilation System (GLDAS). index reflects actual conditions reduces impact unnatural factors estimation. The obtained results have compared with Standardized Precipitation (SPI) over different time periods. indicate significant correlation between indices exhibit high accuracy determining severity its trends."
    },
    {
        "DOI": "10.1038/S41467-026-68655-2",
        "Title": "Substantially lower estimates in Chinas offshore wind potential using farm-scale spatial modeling and wake effects",
        "Year": 2026,
        "Abstract": "Abstract Renewable energy is critical for addressing global climate change, and accurate assessments of its potential are key for decision making and planning. This study provides a detailed, farm-level evaluation of offshore wind power potential in China, incorporating realistic turbine layouts derived from remote sensing data, wake loss modeling, and future climate scenarios. Our findings show that accounting for the farm-level details results in a Chinas offshore wind potential of 2.54.2 PWh yr 1 which is significantly lower than previous estimates, which often exceeded 5.6 PWh yr 1 . Through modeling the wake loss effects within wind farms, the study reveals that wake losses are higher than previously assumed in earlier research. Additionally, the study highlights substantial economic and technical disparities between nearshore bottom-fixed and deep-water floating wind farms, with the latter offering higher potential density but at greater costs. Our results provide a more realistic foundation for setting energy targets, optimizing regional strategies, and promoting floating wind technologies to harness deep-water resources, thereby supporting Chinas transition to a sustainable energy future."
    },
    {
        "DOI": "10.1016/J.EJRH.2026.103335",
        "Title": "Trend shifts and hydroclimatic drivers of Caspian Sea Level (20032024) revealed by GRACE and multi-source observations",
        "Year": 2026,
        "Abstract": "Caspian Sea This study estimated level (CSL) variations during 20032024 using multi-source datasets, including observations from the Gravity Recovery and Climate Experiment (GRACE), accounting for leakage correction steric effect, which significantly improved consistency with satellite altimetry. Through wavelet decomposition, we reconstructed distinct periodic components underlying trend signal. Change-point detection of revealed four linear phases CSL 20032024: rapid rise (6.66 cm/yr, 2003/012006/02), sustained decline (-9.99 2006/022016/08), slowed (-3.32 2016/082019/06), accelerated drop (-23.18 2019/062024/12). Subsequently, difference integral curves coherence analysis were applied to investigate primary driving mechanisms hydroclimatic factorsprecipitation, evaporation, runoffon variations. Results showed that at annual scale, runoff nearly in phase; while interannual different factors dominated periods. From 20032016, trends closely matched Between 2016 2019, despite decreased precipitation intensified increased, resulting a brief period slow decline. During 20192024, sharp precipitation, reduced jointly contributed If current conditions persist, will continue rapidly, posing serious challenges regional ecosystems resource management. GRACE-based effects, better agreement Wavelet change-point analyses identified 2003 2024, after 2019. Integral how evapotranspiration, drive changes."
    },
    {
        "DOI": "10.5194/ANGEO-44-195-2026",
        "Title": "Variability and trend analysis of temperature and height in the upper troposphere and stratosphere region over the tropics (Reunion), by combining balloon-sonde and satellite measurements",
        "Year": 2026,
        "Abstract": "Abstract. Tropopause height and temperature play a crucial role in atmospheric chemistry and radiative forcing and serve as key indicators of anthropogenic climate change. However, accurately determining this parameter requires advanced remote sensing techniques. This study compares tropopause height and temperature estimated from in-situ and remote sensing instruments (SHADOZ and COSMIC-1) with reanalysis data from MERRA-2 over Reunion from 2006 to 2020. The results reveal strong agreement between vertical temperature profiles obtained from SHADOZ and COSMIC-1, demonstrating that both can reliably estimate tropopause height using the Cold Point Temperature (CPT) and/or Lapse Rate Temperature (LRT) methods. Conversely, while MERRA-2 assimilates data from these sources, its fixed vertical resolution limits its ability to capture tropopause height variations accurately. Given the consistency between SHADOZ and COSMIC-1, their data were combined to construct a more refined dataset, which was then used to assess temperature trends. The analysis indicates a high influence of annual and semi-annual oscillations in Tropopause height dynamics, as well as, a decreasing trend in CPT and a slight increase in the Lapse Rate Tropopause (LRT) height."
    },
    {
        "DOI": "10.1088/2752-5309/AE4770",
        "Title": "Temporal trends in short-term PM2.5 exposure and adverse birth outcomes in Japan: nationwide modeling study across four decades",
        "Year": 2026,
        "Abstract": "Abstract Maternal exposure to PM 2.5 during pregnancy is associated with elevated risks of adverse birth outcomes, including preterm birth (PTB), low birth weight (LBW), and small-for-gestational-age (SGA). Nonetheless, comprehensive investigations into temporal trends in this association remain scarce, primarily because long-term datasets integrating detailed exposure information with extensive birth records have been lacking. This study investigated temporal trends in the associations between short-term PM 2.5 exposure and adverse birth outcomes (PTB, LBW, and SGA) in Japan over a 44 year period, and assessed subgroup-specific vulnerabilities. We analyzed nationwide vital statistics of singleton live births in Japan from 1980 to 2023, linked with daily municipality-level PM 2.5 estimates from the Modern-Era Retrospective Analysis for Research and Applications, Version 2. A two-stage analysis with 5 year stratification was conducted to estimate relative risks per 10 g m 3 increase in the 3 d moving average of PM 2.5 concentrations. In the first stage, municipality-specific associations were assessed using a time-stratified case-crossover design. In the second stage, municipality-specific estimates were pooled using a random-effects meta-analysis. Temporal trends of the association were assessed using linear trend analysis. Stratification analyses by subgroups were performed to explore potential vulnerabilities. The analysis included PTB ( n = 2143 950), LBW ( n = 3362 124), and SGA ( n = 3429 611) across 1747 municipalities. Short-term PM 2.5 exposure was associated with elevated risks of all outcomes in early study periods, but risks for LBW and SGA declined over time ( p -trend < 0.001 and p = 0.033, respectively). There was no evidence of temporal change for PTB. Subgroup analyses indicated stronger associations in winter for PTB and in spring for LBW and SGA, with higher risks observed in northern Japan. Over the past four decades, short-term PM 2.5 exposure-related LBW and SGA risks have decreased in Japan, whereas PTB risk has remained stable. The findings highlight the need for geographically and seasonally tailored interventions to efficiently protect vulnerable populations."
    },
    {
        "DOI": "10.37828/EM.2026.95.1",
        "Title": "Abiotic Conditions for the Formation of Pelagic Phytoplankton Community in the Volga River Delta",
        "Year": 2026,
        "Abstract": "This paper presents the results of an expedition conducted from May 28 to 30, 2022, in the avandelta of the Volga River and within the Astrakhan State Biosphere Reserve. The study encompassed various biotopes of the avandelta, including river channels, Gryaznukha Bay, the Gandurinsky and Babushkin Channels (intrazonal section of the avandelta), and the Chistaya Banka area in the shallow waters of the Northern Caspian Sea near Zhemchuzhny Island. Measurements in the aquatic study area included key nutrients (mineral nitrogen species, phosphates, silica) and oxygen; Apparent Oxygen Utilization (AOU) was calculated, and samples were collected for analysis of phytoplankton communities. Two microalgae groups, conditionally marine and conditionally riverine, were identified for phytoplankton with a high level of probability (Global R = 0.873, p = 0.1%). Cyanoprokaryotes dominated both assemblages. The riverine group was dominated by Kovacikia cf. muscicola and Cyanodictyon cf. filiforme, while the marine group was dominated by Aphanothece clathrata, Kovacikia cf. muscicola, and Aphanothece floccosa. Biotopic conditions characterizing the riverine group exhibited higher nutrient concentrations compared to those of the marine group. The phytoplankton communities demonstrated significant differences in the composition of dominant complexes, structure, and life forms from the communities identified in the same area during the comparable seasonal period of the previous year (May 18-20, 2021), which were dominated by diatoms."
    },
    {
        "DOI": "10.1002/QJ.70110",
        "Title": "Moisture inversions in the central Arctic: Product assessment and longwave radiative effect",
        "Year": 2026,
        "Abstract": "Abstract Water vapour is an important greenhouse gas that plays a key role in the thermalinfrared (longwave) radiation balance at the surface. In the Arctic, previous studies have identified uncertainties regarding water vapour trends and integrated water vapour (IWV) estimates in certain seasons and regions, mostly due to missing highquality observations from ground stations and uncertainties in satellite remote sensing. Therefore, accurate field measurements of water vapour are crucial for providing reference observations in the datasparse central Arctic to evaluate satellite products, reanalyses, and weather forecast models. In this study, we use observations from groundbased microwave radiometers and radiosondes from the yearlong Multidisciplinary Drifting Observatory for the Study of Arctic Climate expedition to analyse the quality of two global reanalyses, two weather forecast models, and two satellite products. Specifically, we evaluate the IWV and specifichumidity profiles. Our analyses revealed dry biases regarding specifichumidity profiles and IWV in dry (moist) conditions for the reanalyses and one of the weather forecast models (satellite products). We found a strong correlation between specifichumidity profile deviations to the reference (radiosondes) and the representation of specific humidity inversions. We therefore also statistically analysed the representation of humidity inversions in each dataset and determined which inversions can be detected. The presence of surfacebased inversions (found in of the radiosonde profiles) is generally well captured by all datasets except the satellite products, whereas elevated and weak inversions are often missed. To assess the importance of humidity inversions, we quantified their impact on downward longwave radiation (DLR) using radiative transfer simulations of original and modified humidity profiles in clearsky conditions. The presence of humidity inversions increases the DLR in clearsky conditions by up to 16 Wm, whereas misrepresentations thereof can cause DLR differences of 5 Wm."
    },
    {
        "DOI": "10.1007/978-3-032-12579-8_21",
        "Title": "Analysing the Effect of Climate Change on Photovoltaic Solar Plants in South Africa",
        "Year": 2026,
        "Abstract": ""
    },
    {
        "DOI": "10.5194/ACP-26-1565-2026",
        "Title": "Shortening of the Arctic cold air outbreak season detected by a phenomenological machine learning approach",
        "Year": 2026,
        "Abstract": "Abstract. Marine cold air outbreaks (CAOs) frequently occur in the Arctic when cold air moves over the relatively warm ocean, resulting in large turbulent fluxes, instability and cloud formation. Given the high frequency of CAOs during the Arctic winter, the associated clouds have a large impact on the region's radiative balance. Due to Arctic warming, the prevalence of CAOs and their clouds may change, impacting the Arctic radiative balance and potentially amplifying or mitigating local and global warming. To better understand how CAO clouds respond to Arctic warming, this study has developed a phenomenological CAO cloud classification tool that utilizes machine learning methods to identify closed and open cell clouds in CAOs from MODIS satellite imagery. This new approach achieves better performance in identifying CAO clouds compared to the marine cold air outbreak index calculated using MERRA-2 reanalysis, with accuracies of 85.4 % and 78.0 %, respectively. The new approach has revealed frequent CAO cloud formation in regions of high sea surface temperatures, with occurrence maxima along the Norwegian coast and the Northern Atlantic region south of Iceland. Furthermore, the approach reveals trends in CAO cloud cover that suggest a shortening of the CAO season, characterized by an approximate 10 %, increase in cloud coverage during winter and a nearly 20 % decrease during the shoulder months over the past 25 years. These trends suggest a positive radiative feedback during winter in response to climate change, underscoring the importance of further investigating these clouds to understand the trajectory of future Arctic climate."
    },
    {
        "DOI": "10.1007/978-3-031-84736-3_8",
        "Title": "Physical Dynamics of the Lake: Is it Dying?",
        "Year": 2026,
        "Abstract": ""
    },
    {
        "DOI": "10.5194/AMT-19-1675-2026",
        "Title": "Aerosol Composition and Extinction of the 2022 Hunga Plume Using CALIOP",
        "Year": 2026,
        "Abstract": "Abstract. We use the CALIOP (Cloud-Aerosol Lidar with Orthogonal Polarization) instrument to determine the microphysical properties of the stratospheric aerosol plume after the Hunga eruption in 2022, the largest so far after the Pinatubo in 1991. In the early stages, low depolarization (<2 %) is found everywhere except in patches of high depolarization (up to 35 %) detected within the plumes of sulfur compounds up to 3 d after the eruption. As standard CALIOP L2 products are not operational in the case of the Hunga aerosol plume, we implement an iterative method of successive approximations to retrieve extinction profiles, by estimating the aerosol optical depth (AOD) and then the lidar ratio (LR). The AOD of the plume at 532 nm is between 0.5 and 1.25 on the first four days, then decreases rapidly and stabilizes at 0.047 0.011 for March 2022. The LR is initially between 60 and 80 sr, consistent with the early growth of sulfate aerosol particles, and then decreases to 48 6 sr between late January and late March 2022. Results are compared and validated with the solar occultation instrument SAGE III (Stratospheric Aerosol and Gas Experiment) on board the International Space Station (ISS) and Mie calculations. A comparison with limb-viewing instruments highlights significant quantitative disagreements in extinction and AOD estimates, which we attribute, in part, to the unusual size distribution of the aerosols within the Hunga plume."
    },
    {
        "DOI": "10.1007/S11869-026-01889-7",
        "Title": "Impact of forest fire on surface black carbon concentration over Mizoram, North-east India",
        "Year": 2026,
        "Abstract": "In order to understand the influence of forest fires on one of the critical pollutants impacting air quality over a hilly station, this study was undertaken utilizing integrated observation from in-situ station, satellites and reanalysis data. Black Carbon (BC) surface mass concentration was measured over Mizoram, the southernmost state of North east India (NEI) for the first time during 4th March 3rd April 2022 under a campaign mode. Mean BC during the study period was 6.18 3.1 g/m3 that varied between a daily mean of 2.43 0.41 g/m3 to 12.87 2.12 g/m3. Reanalysis data is found as not sufficiently representative for this region as MERRA-2 reanalysis significantly overestimated BC (~ 4 times than the observed mean) over Mizoram during the fire active period while underestimated during other times. The possibilities of long range transport increasing the BC load over Mizoram was found limited during the study period. An average absorption Angstrom exponent (AAE) of 1.22 0.1 suggests dominant influence of biomass burning, consistent with flaming combustion of local and regional wood sources. Higher forest fire activity within 250 km of the station coincided with increased BC levels and implicated forest fire as dominant BC emission source during this season. CALIPSO Lidar observations detected an elevated smoke layer at 4.55 km AMSL, indicating fire induced convective activities, which was gradually replaced by dust and polluted dust as the season advanced. This study highlights the importance of long-term in-situ measurements of aerosols over fragile ecosystems like the NEI."
    },
    {
        "DOI": "10.1175/BAMS-D-25-0131.1",
        "Title": "A Survey of Applications of the NASA GEOS-CF Global Atmospheric Composition Forecasts: Case Studies for NASA Open Data and Earth Science to Action",
        "Year": 2026,
        "Abstract": "Abstract The Goddard Earth Observing System Composition Forecast (GEOS-CF) is a global atmospheric constituent forecasting system created and operated by the Global Modeling and Assimilation Office at the National Aeronautics and Space Administration (NASA) Goddard Space Flight Center. In alignment with the NASA Earth Science to Action Strategy, GEOS-CF forecasts support a variety of research and practical applications, including NASA satellite missions, aircraft campaigns and instrument teams, local and regional air quality forecasters, and public and occupational health experts investigating air pollutant exposure. In alignment with the NASA Open-Source Science Initiative, GEOS-CF supports several data access methods, thereby serving a range of users with varying technical capabilities. This paper surveys several case studies of applications making use of GEOS-CF, investigating how the data were accessed and examining the perceived benefits and limitations of GEOS-CF in each case. The global coverage and data availability of multiconstituent information at a relatively low data latency was broadly perceived as the strength of GEOS-CF, while cited weaknesses included the relatively coarse spatial resolution for local-scale applications, lack of outputs of interest to specific applications, and uncertain data quality for regions and outputs with limited validation observations. Our goal is to present these examples and lessons learned to the broader community both as illustrations of NASAs Earth Science to Action Strategy and Open-Source Science Initiative and to inform future developments of GEOS-CF and similar systems, with the aim of improving the free and open provision of actionable Earth science information for societal benefit. Significance Statement The National Aeronautics and Space Administration (NASA) Goddard Earth Observing System Composition Forecast (GEOS-CF) is a global atmospheric constituent forecasting system, each day producing 5-day forecasts of airborne trace gases and particulate matter. These forecasts support a variety of users, including NASA missions and science teams, regional and local air quality forecasters, and public health and occupational exposure experts. This paper surveys several use cases, identifying how GEOS-CF is accessed and used, the benefits and limitations of GEOS-CF for each case, and what future improvements could improve its application. We share these insights with the community to provide examples of successes, lessons learned, and important points to consider for others developing similar products in alignment with open science principles for providing actionable Earth science data."
    },
    {
        "DOI": "10.59188/EDUVEST.V6I2.52788",
        "Title": "The Impact of NO2 and SO2 Pollutants on Respiratory Diseases: A Case Study of Indonesia",
        "Year": 2026,
        "Abstract": "Air pollution remains a significant public health challenge in Indonesia, driven by rapid industrialization and urbanization. Key pollutants such as NO2 and SO2 are strongly linked to respiratory conditions, yet comprehensive national evidence on their age-specific impacts remains limited. This study aims to examine the causal effects of NO2 and SO2 exposure on the incidence of acute respiratory infections (ARI), pneumonia, and asthma in Indonesia, with a particular focus on differences across age groups. This study employs a quasi-experimental design using district-level pollution data and individual health data from Riskesdas 2018. Analysis was conducted via multiple linear regression, coefficient stability testing, and IPW to estimate robust causal associations. Age-stratified analysis was performed across five groups: 04, 517, 1849, 5074, and 75+ years. Results show that NO2 exhibits a strong positive association with ARI incidence, particularly among children aged 04 and 517 years, and is linked to asthma in adults aged 1849. SO2 shows significant positive effects on ARI among older adults (5074 years) and on asthma in those aged 75 and above. However, unexpected negative or non-significant relationships were found between NO2 and pneumonia/asthma, and between SO2 and certain outcomes, likely reflecting data constraints and unobserved confounders. In conclusion, this study reveals age-specific pollutanthealth relationships and underscores the need for targeted air quality interventions. Recommendations include strengthening monitoring systems, implementing pollutant-specific warnings, and integrating environmentalhealth data to support evidence-based policies and protect vulnerable groups."
    },
    {
        "DOI": "10.1007/S00382-026-08083-6",
        "Title": "Reanalysis comparisons in tropical upper troposphere and stratosphere",
        "Year": 2026,
        "Abstract": "Tropical stratospheric variability is crucial for global climate and atmospheric circulation. Understanding variations in key variablestemperature, water vapor, and zonal windsis essential for climate trend assessment and model predictions. Reanalysis datasets are vital for such studies, although their reliability varies across datasets and time periods. This study compares tropical stratospheric temperature, water vapor, and zonal winds across seven reanalysis datasets (NCEP 1/2, ERA5, MERRA2, JRA55, JRA55-3Q, NOAA-20CRv3) from 1940 to 2022, validating their performance against observational benchmarks such as MLS/Aura, SWOOSH, and OBS. For temperature, ERA5, MERRA2, and JRA55-3Q most accurately capture long-term trends and climate events, closely aligning with MLS/Aura for the period of 20042022. In contrast, NCEP-1, NCEP-2, and NOAA-20CRv3 exhibit significant biases, especially in the upper stratosphere, with NOAA-20CRv3 performing weakest due to limited upper-air data assimilation. For water vapor, ERA5, MERRA2, and JRA55-3Q effectively capture long-term trends and the tropical tape recorder signal, though JRA55 overestimates moisture transport at 50100 hPa. For zonal winds, ERA5 and JRA55-3Q accurately capture the Quasi-Biennial Oscillation (QBO) periodicity and amplitude after 1960, with significant improvements post-2004 due to satellite wind observations and advancements in data assimilation. In contrast, NOAA-20CRv3 fails to capture a coherent QBO cycle due to its lack of upper-stratospheric wind assimilation. These findings provide insights into dataset selection for climate studies and emphasize the importance of evaluating reanalysis datasets for improving stratospheric process simulations."
    },
    {
        "DOI": "10.1007/S41748-026-01043-4",
        "Title": "Modeling and Analysis of Long-Range Dust Transport from the Sahara and Arabian Deserts To West Asia Using WRF-Chem and Remote Sensing: Insights from the Spring 2015 Dust Event",
        "Year": 2026,
        "Abstract": "This study investigates the transboundary dust transport from the Sahara and Arabian Deserts to West Asia during a major event in spring 2015. The analysis integrates satellite observations, ground-based PM10 measurements, and simulations from the WRF-Chem model configured at a 10 10 km grid resolution. The model was coupled with the Global Ozone Chemistry Aerosol Radiation and Transport (GOCART) dust emission scheme and employed the Four-Dimensional Data Assimilation (FDDA) technique. Model outputs were evaluated against in-situ PM10 observations and satellite-derived aerosol data. Results indicate two distinct dust intrusion episodes: the first, originating from the Arabian Desert, affected northern Iraq and southeastern Turkiye on May 23-24; the second, associated with the Sahara Desert dust, arrived on May 27, merging with the earlier plume by May 28. This convergence led to elevated PM10 concentrations, exceeding a daily mean of 500 g/m across parts of West Asia. Aerosol observations confirmed substantial contributions from both desert sources, with Aerosol Optical Depth (AOD) values frequently reaching or exceeding 1.0 in several regions. These events were driven by strong Sahara-Arabian heating, which enhanced pressure gradients and generated Shamal winds and easterly surges, promoting dust uplift and long-range transport. Significant spatial variability was observed, from severe air pollution in some areas to minimal impacts elsewhere. During dust events, modeled-observed PM10 correlations ranged 0.14-0.81 with systematic underestimation. These results highlight the need for improved dust emission parameterizations, early warning systems, and high-resolution regional models to better support air quality management and public health planning."
    },
    {
        "DOI": "10.5194/AMT-19-1837-2026",
        "Title": "Methods for validation of random uncertainty estimates and their applications to ozone profiles from limb-viewing satellite instruments",
        "Year": 2026,
        "Abstract": "Abstract. For satellite measurements of atmospheric composition, the random uncertainty estimates provided by retrieval algorithms might be imperfect due to various approximations used in the retrievals or the presence of unknown error sources. This paper presents an overview of the methods used for the validation of random uncertainty estimates. All methods discussed in this study are categorized, and assumptions and limitations of each method are discussed. This overview evaluates these methods in application to ozone profile measurements from limb and occultation satellite instruments and provides practical illustrations of random uncertainty validation."
    },
    {
        "DOI": "10.1029/2025GL119494",
        "Title": "Northern Hemisphere Warm Fronts Are Less Efficient at Precipitating Ice Than Their Southern Hemisphere Counterparts",
        "Year": 2026,
        "Abstract": "Abstract Using satellite observations, ice water path (IWP), liquid water path (LWP), and surface precipitation across warm frontal regions are examined in the Northern (NH) and Southern (SH) Hemispheres, accounting for the life stages and characteristics of extratropical cyclones (ETCs). Focusing only on oceanic ETCs over a 4year period, composite transects of the observations reveal that most hemispheric differences in warm frontal IWP, LWP, and precipitation align with variations in precipitable water, cyclone strength, and storm maturity. However, for similar cyclone strength and environmental moisture, NH warm fronts during early development contain more ice but are less efficient at precipitating than those in the SH. Higher dust concentrations in NH might explain the greater ice amounts, while higher seasalt concentrations in SH might explain the greater precipitation efficiency in their respective warm frontal regions. , Plain Language Summary This study investigates cloud and precipitation properties in warm frontal regions of extratropical cyclones (ETCs), focusing on how they evolve through different stages of the cyclone life cycle. Using global satellite observations of oceanic ETCs, we examine transects across warm fronts of vertically integrated ice and liquid amounts, along with surface precipitation, and compare these variables between Northern and Southern hemispheres. The observations reveal that ice, liquid and precipitation amounts depend strongly on the environmental moisture and cyclone strength, regardless of cyclone age. However, when selecting the Northern and Southern Hemisphere cyclones to have similar environmental factors such as environmental moisture, storm intensity, and maturity, Northern Hemisphere (NH) warm fronts in the early stages of cyclone development contain more ice but produce less precipitation than their Southern Hemisphere (SH) counterparts. One hypothesis is that higher dust concentrations in the NH storms may suppress ice fallout, contributing to the observed precipitation inefficiency. In contrast, SH cyclones may have more efficient precipitation due to sea salt enhancing hydrometeor removal. , Key Points Northern hemisphere warm fronts contain more ice and liquid than their southern hemisphere counterparts due to larger available moisture Developing warm fronts contain more ice and precipitate less frequently in the Northern than Southern hemisphere While liquid amounts and rain rates are well controlled by cyclone characteristics, ice amounts in developing cyclones are not"
    },
    {
        "DOI": "10.1109/LAGIRS68367.2025.11414763",
        "Title": "Hydrological Analysis of the Middle and Lower Paraguay Basin Based on Water Balance and Storage Using Imerg, GLDAS, and Grace Products (2003-2023)",
        "Year": 2025,
        "Abstract": "This study examines the hydrological behavior of Middle and Lower Paraguay Basin using satellite data in situ measurements. It analyzes trends precipitation, evapotranspiration (ET), groundwater storage (GWS), streamflow over a 20 -year period, emphasizing basins sensitivity to climate variability, particularly El NinoSouthern Oscillation (ENSO) events. The use GRACE combined with cross-correlation proves effective revealing delayed response. results highlight dynamics interannual variability. Evapotranspiration remained relatively stable, while GWS exhibited high reflecting seasonal recharge depletion patterns. analysis monthly mean from 1931 2023 revealed extreme events-both floods droughts-correlated ENSO phases. Notably, 2015 2016 were identified as persistently wet years, whereas 2020 2021 marked widespread stress across all variables. Standardized anomaly analyses synchronicity between surface subsurface components. calculation water balance anomalies allowed for spatial identification vulnerable zones. A classification scheme based on Groundwater Index (SGI) was applied map dry (SGI $\\lt-1$), intermediate ($-1 \\leqslant S G I 1$), ($\\mathrm{SGI}\\gt1$) Crosscorrelation residual ($\\mathbf{P}- \\mathrm{ET}-\\mathrm{Q})$ $\\triangle \\mathrm{GWS}$ indicated that responds one-month lag relative fluxes. finding highlights buffering capacity supports an indicator integrated Overall, emphasizes need continuous monitoring control river tributaries, well their interaction climate-driven processes region."
    },
    {
        "DOI": "10.5194/ACP-26-523-2026",
        "Title": "Constraining a Radiative Transfer Model with Satellite Retrievals: Contrasts between cirrus formed via homogeneous and heterogeneous freezing and their implications for cirrus cloud thinning",
        "Year": 2026,
        "Abstract": "Abstract. The efficacy of the climate intervention method known as cirrus cloud thinning (CCT) is difficult to evaluate in climate models, largely due to uncertainties governing the relative contributions of homogeneous and heterogeneous ice nucleation. Here we take a different approach by employing recent satellite retrievals from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) which provide estimates of the fraction of cirrus clouds dominated by homogeneous and heterogeneous ice nucleation and their associated physical properties. We employ a radiative transfer model (RTM) to quantify the cloud radiative effect for homogeneous and heterogeneous cirrus clouds at the top of atmosphere (TOA), Earth's surface, and within the atmosphere. The RTM experiments are initialized using cirrus microphysical profiles derived from CALIPSO retrievals for cirrus clouds dominated by homogeneous and heterogeneous ice nucleation across different regions (Arctic, Antarctic, and midlatitude) and surface types (ocean and land). We define two bounds: the lower bound assumes a full microphysical transition from the observed composition of homogeneous- and heterogeneous-dominated cirrus to only heterogeneous cirrus and production of new cirrus. The upper bound assumes production of new cirrus and that the atmospheric dynamics enables homogeneous freezing nucleation to occur regardless of the concentration of ice nucleating particles. Based on these bounds, we estimate an instantaneous surface effect ranging from 0.5 to +0.6 W m2 and a TOA effect from 0.9 to +1.1 W m2, respectively, showing the possibility of both cooling and warming. Recommendations are provided to improve the treatment of cirrus clouds in climate models."
    },
    {
        "DOI": "10.63225/NRCP.RJ.2025.0019",
        "Title": "Simulated Changes in the Phytoplankton Community Structure at the Subsurface Chlorophyll Maximum in the Philippine sea: Sensitivity Analysis and Possible Temperature Scenarios",
        "Year": 2025,
        "Abstract": "Our study simulated a size-structured phytoplankton community in the Philippine Sea to determine the factors that regulate the vertical phytoplankton distribution using a one-dimensional coupled physical-biological individual-based model in the Virtual Ecosystem Workbench (VEW) software. Three phytoplankton groups (pico-, nanoand microphytoplankton) were governed by specific metabolic and reproductive rates and simulated to be grazed on by copepods, which in turn were controlled by carnivorous zooplankton. Sensitivity analysis using three salinity scenarios (33, 34 and 36 Practical Salinity Units [PSU]) showed that nutrient availability drives the phytoplankton communities towards the end of the simulations, wherein only the 34 PSU simulation was able to recreate the Subsurface Chlorophyll Maximum (SCM) profile similar to the 2011 in-situ observation. Three temperature scenarios (+ 1.0 oC,+ 2.0 oC,+ 10.0 oC) were then used to predict phytoplankton responses to changing temperature regimes. The scenarios predicted the SCM would develop deeper than the original simulation and a significant increase in the abundance of the dominant phytoplankton at the SCM, possibly affecting the higher trophic web or increasing the deep carbon export to deeper waters. Although the VEW software has been useful for investigations on plankton dynamics of global and specific regions, our study finds that the physical dynamics of the software is not attuned to simulate the highly variable Philippine Sea setting, limiting the model runs only to the drier months of the year. We suggest caution in the use of the version of the software as it needs restructuring to be more useful in such areas."
    }
]