Atmospheric and Oceanic Sciences

INTRODUCTION: Climate change is a contemporary phenomenon of grave concern to global public health. Climate change events such as droughts, wildfires, tornadoes, heatwaves, floods, sea level rise, hurricanes, tropical cyclones, landslides, extreme rainfall, typhoons, dust storms, and desertification significantly affect local, regional, and global living conditions. In Sub-Saharan Africa, the most disturbing of these are desertification, droughts, and floods, which directly threaten water supplies, food security, and the livelihoods of millions of people. The climate crisis affects the health of older people, adults, children, and adolescents. However, climate-related events are gravely affecting the current and future health and well-being of children and adolescents. Although evidence exists, its integration is vital for policy and practice to protect children and adolescents in the ever-changing climate. Therefore, this review aims to map the existing reviews of the impact of climate change on the health and well-being of children and adolescents. METHOD: This review will be conducted according to Arksey and O'Malley's [36] recommendations and will be reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR). Scopus, JSTOR, Web of Science, PubMed, Embase and Cochrane Library will be searched to identify relevant records for inclusion in this review. Additional searches will be conducted in Google Scholar and Google for other relevant articles. The review protocol is registered at Open Science Framework: (https://doi.org/10.17605/OSF.IO/A7DEQ). ANALYSIS: Extracted data will be analysed using thematic content analysis, where data are summarised and qualitatively synthesized according to the recommendations of PRISMA-ScR and Tricco et al. [37]. The results and findings regarding the impacts of climate change on the health and safety of children and adolescents will be compiled, categorized, and presented using a qualitative narrative synthesis.

Multi-source remote sensing is transforming landslide susceptibility assessment from static terrain-based zonation toward observation-driven spatiotemporal inference and dynamic map updating. Satellite precipitation products, interferometric synthetic aperture radar (InSAR) deformation time series, optical image sequences, land-cover products, and multi-temporal terrain observations provide complementary evidence of hydrometeorological forcing, slope kinematics, land-system regulation, and geomorphic reorganization. However, these observation streams differ substantially in spatial support, temporal resolution, physical meaning, and uncertainty structure and therefore cannot be reliably integrated as generic predictors without process-aware interpretation. This review synthesizes recent progress in remote sensing-enabled dynamic landslide susceptibility assessment by linking four key components: dynamic factor construction from Earth observation data, spatiotemporal representation and learning, susceptibility map updating, and validation under temporal and spatial independence. The reviewed literature is organized around four process roles: rainfall- and soil moisture-related forcing, kinematic state and response captured by InSAR, land-system and ecological regulation derived from optical time series, and geomorphic memory represented by multi-temporal digital elevation models (DEMs). We further examine how these signals are encoded and integrated through temporal models, graph-based representations, attention mechanisms, and hybrid frameworks, with particular emphasis on consistency among process role, data structure, mapping unit, inference target, and validation design. Current progress remains constrained by temporally coarse landslide inventories, cross-scale incompatibility among remote sensing products, uneven and insufficiently process-aware multimodal fusion, and limited physical interpretability. Future advances require event-resolved inventories, uncertainty-aware multimodal fusion, process-consistent spatiotemporal learning, and validation designs that explicitly test whether susceptibility maps can be updated in a scientifically defensible manner as new Earth observation data become available.

Glacier boundary extraction on the Tibetan Plateau (TP) faces persistent challenges due to rugged terrain, seasonal snow, extensive debris cover, and topographic shadows. Traditional methods utilizing single-source or single-temporal data often yield limited accuracy. Thus, we propose an automated Double Random Forest (Double-RF) framework integrating single- and multi-temporal features from Sentinel-1 (SAR) and Sentinel-2 (Optical) data within the Google Earth Engine. We established a multidimensional feature space comprising spectral, textural, polarimetric, and topographic attributes. Feature optimization was performed using importance metrics and out-of-bag (OOB) error. A hierarchical classification strategy was employed: the first RF identifies clean glaciers and glaciers in shadow, while the second RF executes refined boundary extraction of debris-covered glaciers to mitigate spectral confusion. The results indicate that the Double-RF method significantly achieves an overall accuracy exceeding 0.84 across all sub-basins and reaching above 0.95 at best. The derived glacier inventory reveals a distinct spatial pattern: higher concentrations in the western and peripheral regions compared to the eastern and interior TP. Glaciers are predominantly distributed on shaded aspects with gentle-to-moderate slopes, highlighting the combined influence of climatic gradients and topographic controls. This multi-source, multi-temporal fusion strategy provides a robust methodological foundation for long-term glacier monitoring over the TP.

Geospatial foundation models are a new frontier in artificial intelligence, designed to understand and analyze spatial data at scale. Trained on huge sets of EO data, these models can support a wide range of applications—from monitoring natural disasters to guiding urban development and tracking climate change. To this aim, researchers and practitioners need to fine-tune the foundation models for specific tasks, utilizing a relatively small amount of additional data. As a result, geospatial foundation models are reshaping how we observe, manage, and protect our planet. Fine-tuning a geospatial foundation model requires carefully curated training datasets that reflect specific regions, time periods, or tasks—such as detecting deforestation or mapping urban growth. Yet preparing these datasets is often labor-intensive, involving steps like selecting relevant imagery, aligning spatial formats, and generating accurate labels. In practice, this means that the effectiveness of GFMs hinges on the availability of AI-ready data. This bottleneck limits the accessibility and scalability of GFMs for scientific and operational applications. In this work, we introduce a software library designed to automate these preparatory steps, streamlining the transformation of geospatial datasets into consistent, high-quality inputs for GFM fine-tuning. By reducing technical overhead and ensuring data readiness, the library enables faster, more reliable, and more inclusive adaptation of foundation models to local environmental challenges and specialized domain needs.

Abstract Urban areas are widely viewed as central to the global carbon challenge, yet estimates of the urban contribution to global CO2 emissions vary substantially because studies define “urban” and allocate emissions using different boundaries and accounting perspectives. We address this challenge by introducing a globally consistent, administrative-boundary framework that aligns emissions with governance units and distinguishes urban centres, peri-urban areas, and rural areas across all subnational administrative divisions worldwide. This governance-aligned classification provides a consistent basis for urban emissions typologies, enabling comparable urban emission quantification across regions and accounting perspectives. Combining high-resolution territorial emissions inventories (EDGAR, ODIAC, CEDS) with global consumption-based footprints (GGMCF), we estimate that in 2022 urban areas (urban centres + peri-urban areas) accounted for 72-76% of global territorial CO2 emissions, with urban centres contributing 30-37% and 39-42% from peri-urban areas. Using consumption-based data, we estimate that urban areas accounted for ~82% of CO2 emissions in 2015. We further show that the sectoral composition of urban territorial emissions has shifted from 1970 to 2022, with the energy sector increasing in prominence and relative contributions from buildings and industry declining. Finally, comparing territorial and consumption-based estimates across subnational units, we map consumption–production imbalances and find that 63% of regions are net embodied-emissions importers. These results provide a critical update to global assessments of urban emissions and demonstrate the value of pairing territorial and consumption-based accounting on governance-relevant boundaries for interpreting responsibility and identifying mitigation leverage points.

Drought events have become increasingly common in Central Asia, increasing the risk of vegetation degradation. In this study, the resilience of vegetation to drought and its drivers was investigated across different seasons. The findings revealed that western Central Asia faced a notably high incidence of spring droughts, characterized by longer durations and greater severity than droughts in other seasons. In contrast, the Aral Sea Basin experienced fewer droughts in summer and autumn, although these droughts were more severe and intense and had longer durations. Croplands, particularly those in northern Kazakhstan, generally demonstrated relatively low resistance but relatively strong resilience. In contrast, sparsely vegetated areas in regions such as southern Xinjiang and areas downstream of the Aral Sea Basin presented high drought resistance but relatively low resilience. Precipitation and vapour pressure deficit (VPD) had the most significant impact on drought resilience in Central Asia, with a combined contribution of 55.18%, particularly in the northern and eastern regions of the area. The vegetation in spring was characterized by the highest resistance and resilience levels (40.67% and 40.65%, respectively) in Central Asia, followed by those in summer. In terms of vegetation loss, vegetation in spring accounted for the greatest proportion (44.03%), followed by that in summer, at 31.07%. The main characteristics of drought (duration and intensity) were the major factors influencing the loss of vegetation, especially in grasslands and sparsely vegetated areas. During prolonged summer droughts (>40 months), grasslands and sparse vegetation suffered substantial declines in gross primary productivity (GPP). In contrast, forests exhibited more severe GPP reduction at a drought peak of around 2 and intensity above 1.5. Quantifying the resilience and loss of vegetation to drought across different seasons can aid in the formulation of effective strategies to prevent and manage vegetation degradation in Central Asia.

Flood-season lake spatiotemporal dynamics are vital for ecological security and socioeconomic development, requiring consistent high-resolution monitoring. However, precipitation fluctuations and sediment turbidity significantly alter water quality, while blurred boundaries between water and floodplain wetlands challenge precise monitoring. To address these issues, this study proposes a water body extraction method leveraging polarimetric Synthetic Aperture Radar data. utilizes the maximum between-class variance algorithm for initial segmentation, optimizes the threshold via a genetic algorithm, and employs dynamic morphological operations to refine boundary details. The method was validated using 2015–2025 Sentinel-1 flood-season time series of Dongting Lake on Google Earth Engine. The results demonstrate that the proposed method achieves stable and accurate water extraction across various years and seasons, with an overall accuracy surpassing 0.93, confirming its robustness and broad applicability. Furthermore, the spatiotemporal hydrodynamics and driving mechanisms of Dongting Lake were analyzed by integrating the extracted water areas with multi-source data, including water level, precipitation, discharge, temperature, and sunshine duration. Findings indicate that the flood-season water area exhibited a fluctuating trend, initially increasing and subsequently decreasing, peaking at 2202.26 km2 in 2020 and dropping to 614.04 km2 in 2025, a pattern primarily driven by extreme meteorological events such as heavy rainfall and prolonged droughts. Spatially, inundation patterns were characterized by deeper water in the north and shallower depths in the south, separated by a topographically higher central region. Regression analysis revealed a robust correlation between water area and water level with an R2 of 0.931, providing a quantitative reference for water level estimation in ungauged regions. Additionally, discharge and precipitation were positively correlated with water area, whereas temperature and sunshine duration exerted a negligible influence. This study supports flood regulation in the Dongting Lake basin and provides a robust framework for analyzing lake dynamics using long-term SAR data.

Simulated diving and decompression can impair endothelial function, but the upstream oxidant sources and their relationship with endothelial nitric oxide synthase (eNOS) coupling in the pulmonary circulation remain unclear. We investigated whether NADPH oxidase 2 (NOX2) is associated with oxidative stress, tetrahydrobiopterin (BH4) depletion, altered eNOS coupling, and pulmonary endothelial dysfunction after simulated air diving. Eighteen male Sprague-Dawley rats were assigned to three groups: control, decompression stress, and decompression stress with the NOX2 inhibitor GSK2795039 (100 mg/kg, intraperitoneal) administered before pressurization. Decompression stress was induced by hyperbaric exposure to 600 kPa for 1 h followed by decompression to ambient pressure; pulmonary arteries were collected 1 h after decompression. We evaluated NOX2 expression, oxidative stress indices, BH4 content, eNOS phosphorylation and dimer/monomer ratio, nitric oxide metabolites (nitrate plus nitrite), markers associated with endothelial activation, and vasoreactivity. Compared with controls, decompression stress increased NOX2 expression, reactive oxygen species and lipid peroxidation, decreased superoxide dismutase activity, reduced BH4 and nitric oxide metabolites. It also caused a shift in eNOS towards a lower dimer/monomer ratio, increased endothelin-1 and adhesion molecules, and impaired endothelium-dependent relaxation, though endothelium-independent relaxation remained intact. GSK2795039 pretreatment attenuated oxidative stress, improved BH4 availability, restored nitric oxide metabolites, and decreased markers of endothelial activation, partially improving endothelium-dependent relaxation. These findings suggest that NOX2-associated oxidative stress contributes to reduced BH4 availability and eNOS coupling imbalance, leading to pulmonary endothelial dysfunction after decompression.

Coastal water quality in the Bothnian Sea is shaped by interactions among local nutrient inputs, internal nutrient cycling, and basin-scale phosphorus enrichment, complicating the assessment and management of eutrophication. This study analyses long-term time series of nutrients (total phosphorus (TP), dissolved inorganic phosphorus (DIP), dissolved inorganic nitrogen (DIN), and total nitrogen (TN)) and phytoplankton indicators (chlorophyll a and biomass) from contrasting Finnish coastal systems off Uusikaupunki and Rauma. Despite higher external phosphorus loading in Rauma, nutrient concentrations and phytoplankton biomass remain lower than in the semi-enclosed Uusikaupunki coastal zone. In contrast, Uusikaupunki exhibits higher chlorophyll a concentrations and lower TP:Chl a ratios, suggesting greater phosphorus bioavailability. At the offshore station SR5, TP and DIP increase below the surface layer, while surface concentrations show no significant trends, indicating phosphorus accumulation in deeper waters. Declining DIN:DIP ratios indicate a shift toward nitrogen limitation, under which primary production increasingly depends on phosphorus-supported nitrogen fixation. Chlorophyll a increases across the coastal gradient, including the outer archipelago, indicating a spatial expansion of eutrophication. Together, these findings are consistent with a system-level shift toward phosphorus-driven production. The results demonstrate a dual-control system in which basin-scale phosphorus enrichment determines long-term background conditions, while local nutrient loading and legacy effects regulate spatial variability in ecosystem response. More broadly, the findings highlight the importance of cross-scale interactions between regional nutrient enrichment and local ecosystem processes for understanding and managing eutrophication in inland and semi-enclosed marine systems.

High Resolution Image Download MS PowerPoint Slide Exposure to ambient air pollution, including ozone and fine particulate matter (PM 2.5 ), is the world’s leading environmental health risk factor. Estimating how this burden may change in the future depends on projecting population growth and age structure as well as understanding how future meteorological changes may impact the production and removal of pollutants from the atmosphere. The net impact of these factors on a global scale has not been well-characterized. Here, we leverage recent meteorology, exposure, and mortality output from general circulation, atmospheric chemistry, and health impact models to isolate how changes in meteorology and populations will impact future global air-pollution-related mortality and the associated monetized impacts by the degree of global temperature change. In contrast to previous studies, we estimate that changes in meteorologically driven air pollution, in the absence of pollutant precursor emission changes, will result in 180 000 fewer deaths annually by 2100 relative to current levels, an annual monetized benefit of $7.3 trillion. Reductions are driven by decreases in PM 2.5 -attributable mortality in populated regions but are substantially offset by global increases in ozone-related mortality. We also highlight striking regional differences in the sign of net pollutant impacts by 2100, with net pollution decreases in the Northern Hemisphere driven by reductions in nitrate aerosol, while increases in both ozone and organic aerosol at higher temperatures lead to net increases in pollutant impacts in the Southern Hemisphere. Lastly, we assess sensitivities of these results to meteorological projections, health impact functions, and 10 000 future warming scenarios.

Concerns about mitigation deterrence have prompted calls for pathways that avoid multigigatonne reliance on carbon dioxide removal (CDR), yet such pathways can also discourage near-term investment in CDR, leaving the technologies technically and economically underprepared if large-scale deployment becomes necessary due to unfulfilled emission reduction pledges. Here we model a new pathway in which CDR and decarbonization “Co-Scale” aggressively in parallel without one undermining the other, with the intention that we can readily course-correct if effort in one domain does not materialize. We treat this scenario as a stylized upper bound of what is climatically achievable under ideal enabling conditions, rather than what is immediately feasible under current technical, economic, and environmental constraints. We compare this pathway with two conventional designs, i.e., CDR-Led and Decarb-Led. In CDR-Led, large-scale CDR can substitute for deep decarbonization, while in Decarb-Led, rapid emissions reductions are prioritized and CDR plays a limited complementary role. We find that compared to these two conventional scenarios, Co-Scale reaches net zero CO 2 seven years earlier, accumulates 4 times more net negative CO 2 by 2100, cuts the 1.5 °C overshoot duration by roughly half, and limits end-of-century warming to 1 °C rather than 1.37–1.39 °C. Relative to the recent focus on Decarb-Led pathways, Co-Scale’s main constraints is the feasibility of geological carbon storage, while food, water, and cost pressures are comparatively less restrictive.

Abstract. Marine stratocumulus clouds play a central role in Earth's climate system by reflecting incoming solar radiation and exerting a strong cooling effect. Their organization into open and closed mesoscale cellular morphologies can be thought of as an example of bistable dynamics driven by aerosol–cloud interactions and mesoscale processes. From the perspective of non-equilibrium thermodynamics, these structures are an example of a far-from-equilibrium open system that continuously produces and exports entropy. While entropy production has been studied in idealized deep convective systems, it has not yet been quantified for shallow clouds. Here, we compute and decompose the internal entropy production of open- and closed-cell stratocumulus using an ensemble of large-eddy simulations. We show that the overall entropy production of stratocumulus is low, reflecting the limited vertical extent and corresponding reduced ability to utilize the energy fluxes at the system's boundaries. Moist processes dominate the overall irreversibility, which, combined with their low entropy production, leads to a mechanical efficiency about an order of magnitude smaller than in deep convective systems. Although the dominant irreversible processes differ between open- and closed-cell regimes, the distributions of total entropy production largely overlap across the ensemble, limiting the ability to distinguish the dynamics of individual cases based solely on total entropy production.

Abstract To improve numerical prediction of mountain‐to‐plain (MTP) convection over the Beijing region, this study uses the MPAS–JEDI four‐dimensional ensemble‐variational (4DEnVar) data assimilation system on a global 3–60 km variable‐resolution mesh to evaluate assimilation strategies for radiances from the Advanced Microwave Sounding Unit‐A (AMSU‐A) and Microwave Humidity Sounder (MHS). Ten representative MTP cases were examined. The baseline experiment assimilates only conventional observations (Ctrl), while six additional configurations assimilate the same conventional observations together with AMSU‐A, MHS, or both, under either clear‐sky or all‐sky settings. A composite ranking based on multi‐threshold ETS, POD, and 1–FAR scores for radar reflectivity and hourly precipitation shows that configurations assimilating AMSU‐A generally rank above Ctrl, whereas MHS‐only configurations perform worse. The all‐sky configuration underperforms its clear‐sky counterpart, and the clear‐sky AMSU‐A + MHS configuration (Exp_clrM_A) significantly outperforms the all‐sky MHS‐only configuration (Exp_allM), consistent with observation‐minus‐background (OMB) and observation‐minus‐analysis (OMA) diagnostics. Clear‐sky AMSU‐A tropospheric temperature channels provide a strong and stable constraint, while MHS humidity channels retain larger residual spread. Adding MHS to AMSU‐A can slightly increase OMA bias/spread in some low‐frequency all‐sky channels compared to AMSU‐A‐only, whereas adding AMSU‐A to MHS‐only reduces MHS bias but can modestly enlarge humidity‐channel spread. Thermodynamic–dynamical diagnostics further show that Exp_clrM_A produces a stronger cold pool, enhanced low‐level convergence and ascent, and a more continuous band of organized convection with attached stratiform echoes. In contrast, Exp_allM yields a warmer, drier lower troposphere, a weaker cold pool, and rapid convective decay.

Study region. The Cagayan Valley Region (Region II) in northern Luzon, Philippines, includes the Cagayan River Basin, the country’s longest river system and largest catchment. The basin is a major rice- and corn-producing area, but recurrent tropical-cyclone and monsoon floods cause damage almost every year. Study focus. This study assesses future flood hazard, exposure, and private-sector damage by integrating climate, land-use, and population scenarios within a common time frame. Climate forcing came from three CMIP6 GCMs – ACCESS-CM2, CanESM5, and EC-Earth3-Veg-LR – under four SSPs, and bias-corrected rainfall drove the RRI model. Future land use was projected by combining Land Change Modeler susceptibility scores with population- and crop-area demand constraints, while population distribution was estimated from government projections and spatial covariates. By overlaying projected land use, population, and inundation, the study quantifies crop-specific exposure, separates climate- and land-use-driven contributions, and evaluates land-use regulation. New hydrological insights. This integrated assessment shows that simulated peak discharge increases across all future SSP scenarios because rainfall events exhibit higher short-duration intensity and sharper temporal peaks. The 100-year inundated area increases by more than 31% under higher-forcing scenarios. Under the medium population scenario, exposed population increases by about 100%, from approximately 1 million at present to 2 million by 2070. Climate change mainly increases frequent-flood exposure, whereas land-use expansion amplifies rare-flood exposure and damage. Risk-informed land-use regulation reduces future housing losses by up to 154 million PHP yr −1 .

Operational crop mapping requires classifiers capable of robust generalization across years. While feature importance is routinely used for model optimization, its temporal stability has rarely been systematically investigated, creating a critical gap in deploying reliable monitoring systems. This study moves beyond identifying “most important” features to systematically evaluate and quantify their inter-annual stability for enabling automated classification. Using six agricultural years (2018, 2019, 2020, 2023, 2024 and 2025) of Sentinel-1 and Sentinel-2 data over Morocco, we extracted 156 multi-sensor features across 12 monthly composites and analyzed their importance stability through statistical metrics, clustering, and novel composite indices: the Reliability Index (RI) and Automatic Selection Score (AuSS). This framework automates feature selection by ranking features with RI and AuSS and then applying Pareto optimization to identify a minimal stable feature set—without requiring annual retraining or expert intervention. Our analysis confirms a fundamental tension: the most discriminative features (e.g., NDVI, VH, VV) are also the most volatile, while stable features (e.g., NDRE, MSI, NDMI) offer modest predictive power. Hierarchical clustering revealed four behavioral typologies (Dominant Stable, Performant Volatile, Stable Minor, and Noise), guiding strategic feature management. Crucially, a Pareto analysis demonstrated that a refined portfolio of 6 indices (VH, VV, NDVI, NDRE, GCVI, RVI) captures 57.2% of cumulative predictive importance, filtering out inter-annual noise while preserving discriminative signal. The Voting Ensemble leveraging this Stable Portfolio maintained consistent high accuracy (87.4% accuracy, 87.2% F1-score) with minimal performance degradation during temporal transfer, while models based on volatile top features exhibited significant drops. Entropy analysis confirmed that all features in the Stable Portfolio provide consistent informational certainty, indicating that stability-driven selection does not increase model uncertainty. We conclude that feature stability is not merely a diagnostic metric but a foundational criterion for operational design. We propose a practical, metrics-driven framework for constructing automated crop classification systems that are more resilient to inter-annual climate variability.

River deltas exhibit a wide range of morphologies shaped by the balance between fluvial, wave, and tidal processes, yet existing classification frameworks often rely on qualitative descriptions or data-intensive environmental metrics that are not consistently available at global scales. To address these limitations, we present DeltaLatent, a deep-learning workflow that classifies delta morphotypes using only satellite imagery and latent-space representations. Landsat 8 images of 222 deltas were standardized using Google Earth Engine, converted to grayscale, edge-enhanced with a Prewitt filter, and manually rotated for orientation consistency. A convolutional autoencoder was trained to compress each image into a 256-dimensional embedding that captures key morphological patterns. Representative deltas from each morphotype were used to compute centroid embeddings, and the L1 distances between all embeddings and these centroids were converted into similarity scores plotted in a ternary morphospace. The resulting diagram successfully reproduces well-established fluvial, wave, and tidal end-members, while also capturing transitional and mixed systems consistent with geomorphic interpretations in the literature. DeltaLatent demonstrates scale flexibility, enabling meaningful comparison across deltas of very different sizes. This morphology-based, data-driven framework provides a reproducible alternative to traditional process-based classifications and offers a foundation for global morphodynamic analysis, monitoring, and hazard assessment.

The surface radiation budget, controlled by the surface albedo, is a key driver of the Earth’s energy balance. Snow-covered surfaces, with their high reflectivity, scatter a large fraction of the incoming shortwave radiation back to the atmosphere and thereby modulate both local and global energy exchange. In complex topography, multiple and anisotropic forward scattering redistributes solar radiation within valleys and across slopes. To investigate terrain-scattered radiation, we combined drone-based albedo measurements with simulations using the surface radiation model Groundeye in the Meierhofer Tälli near Davos, Switzerland. Albedo was measured at different heights above ground, which serves as a proxy for different footprints for remote sensing products or different grid resolutions in models. Our objective is to understand the impact of the complex terrain on albedo, and specifically how multiple reflections and anisotropic scattering affect local reflectivity at different heights above the ground. We discuss our results primarily in the context of the representation of albedo in complex terrain and the potential biases in energy balance models and remote sensing retrievals at different resolutions. We show that 1. The ground albedo at the chosen locations in this valley varies between 0.54 and 0.98, depending on local time, slope and aspect, and on the surrounding topography, if multiple reflections are considered in our model, and 2. That the changes in albedo with height are location-specific and can increase or decrease depending on the footprint of the upward radiation flux. The albedo decreases if more shaded areas enter the footprint and increases if more sunlit surfaces become visible. However, changes of albedo with height are not only a function of simplified terrain parameters, such as the skyview factor. These results highlight the complexity inherent in interpreting albedo measurements across mountainous terrain. Such a complex change in albedo with elevation above ground is demonstrated in connection with topographic effects and has implications for improving the parametrisation of surface reflectivity in models and the derivation of surface properties from remote sensing observations.

Abstract. Studies of the terrestrial carbon cycle commonly rely on geochemical proxies such as total organic carbon (TOC) and the organic carbon isotopic composition (δ13Corg). However, terrestrial TOC and δ13Corg data are widely dispersed across the literature and lack a unified compilation, limiting large-scale synthesis and cross-comparison. Here, we present a global, standardized dataset of TOC and δ13Corg measurements derived primarily from terrestrial sedimentary facies: Paleozoic–Mesozoic Terrestrial Total Organic Carbon and Organic Carbon Isotope Database (PM-TOCI). The dataset compiles 66 583 individual data points (49 014 TOC and 17 569 δ13Corg) from 619 publications, spanning the Devonian to Cretaceous (419–66 Ma). Each entry is accompanied by 37 standardized metadata fields, covering geographic information, stratigraphic age, lithology, and depositional facies, thereby enabling consistent filtering, comparison, and reuse across spatial and temporal scales. This dataset is intended to facilitate future data-driven studies of terrestrial organic carbon accumulation, paleoclimate variability, source rock assessment, and long-term carbon cycle dynamics, as well as the link between carbon cycle and biotic evolution. The dataset is openly accessible at https://doi.org/10.5281/zenodo.20486580 (Tian et al., 2026).

Accurate and precise estimation of forest volume and its changes are fundamental for sustainable forest management, resource assessment, and long-term inventory monitoring. Multi-temporal remotely sensed data provide spatial auxiliary information that can substantially increase the precision of estimates of volume and volume change obtained from probability-based field samples. However, methodological differences between indirect and direct change estimation approaches may lead to differences in variance estimation and uncertainty propagation. This study, therefore, compares forest volume and forest volume change estimation under design-based inference by integrating probability-sampled National Forest Inventory (NFI) data with multi-temporal Sentinel-2 auxiliary variables at 10 m and 20 m spatial resolutions, using the random forest (RF) prediction algorithm and both indirect and direct estimation approaches. Forest volume means are estimated for two inventory years, 2013 and 2023. For the indirect approach, simple expansion and model-assisted regression estimators are formulated separately for each year, and change is estimated as the difference between time-specific estimates. For the direct approach, plot-level change is used as the response variable, and model-assisted regression estimators are constructed using changes in auxiliary variables, with residual-based variance estimators. Random forest models using Sentinel-2 auxiliary data explained approximately 51–62% of the variation in forest volume, with RMSE values ranging from about 79 to 97 m3/ha. The 10 m resolution data produced slightly more precise predictions than the 20 m data, though the gain was small relative to the greater processing effort required. Model-assisted estimators using both 10 m and 20 m Sentinel-2 data produced substantially smaller standard errors than simple expansion estimators, with relative efficiency analysis indicating an approximately fivefold gain in efficiency. The direct and indirect model-assisted approaches produced similar estimates of volume change, although the direct approach resulted in smaller standard errors. Overall, remotely sensed auxiliary data primarily improved the precision of forest volume change estimates but not the magnitude of the estimated change.

Abstract. Triple oxygen isotopes are powerful tracers of hydrological processes, yet their variability in atmospheric water vapor and the underlying controls remain poorly understood. We present a one-year record of triple oxygen and hydrogen isotopes of atmospheric water vapor (δ18OV, d-excessV, 17O-excessV) measured below, within and above a downy oak forest canopy at the AnaEE platform O3HP in the French Mediterranean. This vapor dataset is complemented by isotopic data of precipitation (δ18OP, d-excessP, 17O-excessP) and groundwater, as well as monthly observations of stomatal conductance and transpiration. Seasonal variations in 17O-excessV and d-excessV likely reflect changing evaporative conditions at oceanic moisture sources. d-excessP and 17O-excessP show a similar seasonal pattern, enhanced by summer rain re-evaporation. However, no clear isotopic differences were observed in vapor or precipitation derived from different oceanic source regions and weather regimes, likely due to frequent mixing of multiple moisture sources. Diurnal variations in 17O-excessV and d-excessV reflect a combination of vegetation-related processes, including local evapotranspiration. However, the impact of evapotranspiration was not evident at daily timescales. Although precipitation often deviates from isotopic equilibrium with near-surface atmospheric water vapor at the event scale due to incomplete equilibration and rain re-evaporation, equilibrium water vapor reliably approximates the near-surface isotopic composition of atmospheric water vapor at annual scale. Our results highlight the potential of 17O-excess for understanding water exchange between the land and the atmosphere, regardless of climatic and vegetation conditions. They enhance the mechanistic interpretation of precipitation isotopes, which is essential for reliable paleoclimate reconstructions.

Cooling towers are an important part of the thermal system in industries, where they are used to remove unwanted heat and help maintain the proper performance of the machines. Four machine learning algorithms, namely random forest, support vector machine (SVM), decision tree, and AdaBoost are proposed in this paper for the performance forecasting of cooling towers. for the performance forecasting of cooling towers. These models were built in Python with the help of the following operational parameters: inlet water temperature (32-41°C), ambient air temperature (14-32°C), and relative humidity (35-92%). All the essential performance measures like outlet water temperature, water losses, the effectiveness, and the second law efficiency were predicted and assessed by statistical indicators such as coefficient of determination (R²), root mean square error (RMSE) and mean absolute percentage error (MAPE). The SVM algorithm had the best predictive accuracy and lowest prediction errors of all the tested models with a value of R2 of 0.985 and RMSE of 1.25 kg/s. Parametric analysis had indicated that the increase in relative humidity between 35% and 92% decreased the evaporation losses by about 55-70% and makeup water demand by about 58-68%. Thermodynamic analysis further revealed that the second-law efficiency improved by approximately 65-75% as the ambient temperature increased. The results indicate that predictive modeling with machine learning offers a useful method in the optimization of cooling tower operation and minimizing water use in industrial systems.

Study region The Middle Benue Trough, Nasarawa State, Nigeria. Study focus Groundwater quality deterioration is an increasing environmental and public health concern in this region; however, studies that integrate entropy-based water quality assessment, interpretable machine-learning modelling, explainable artificial intelligence, and health risk evaluation within a unified framework are limited. This study integrates hydrogeochemical analysis, entropy-weighted water quality indexing (EWQI), interpretable stacked-ensemble machine learning, and health risk assessment to evaluate seasonal groundwater quality dynamics across five Local Government Areas (LGAs). Twenty-four parameters from 600 groundwater samples collected during dry and rainy seasons were analysed. Hydrochemical facies comprised Na–Cl, mixed Ca-Mg-Cl, and Ca-Mg-HCO 3 water types, reflecting the combined influence of meteoritic recharge, carbonate and silicate weathering, cation exchange, and localised anthropogenic inputs. EWQI results identified Awe and Doma as contamination hotspots, with groundwater quality generally deteriorating during the rainy season and requiring management interventions. New hydrological insights for the region The stacked-ensemble model outperformed individual machine-learning algorithms for EWQI regression and classification, achieving excellent predictive performance (R 2 = 0.935; RMSE = 9.420) and classification accuracy (0.858). SHAP interpretation identifies As, Pb, Cd, and Mn as the main drivers of groundwater deterioration. Positive SHAP–HQ relationships demonstrated that contaminants exerting the greatest influence on groundwater quality deteriorations also contributed weak-to-moderate influence to health risks. The integrated framework provides a valuable tool to identify key contaminants, sensitive locations, and priority intervention targets in hydrogeologically complex areas.

Abstract High‐resolution atmospheric simulations are increasingly essential for applications such as urban air mobility, wind energy, and urban meteorology, where boundary layer turbulence plays a critical role. Large‐eddy simulation (LES) explicitly resolves turbulent motions, while mesoscale models provide the larger‐scale forcing that governs boundary layer evolution. In mesoscale‐to‐LES coupling, however, resolutions of the parent domain approaching the gray‐zone regime – where grid spacing becomes comparable to dominant turbulent scales – can influence the turbulence characteristics that develop in the nested LES domain. Understanding this sensitivity is important for improving multiscale coupling strategies. Using idealized convective boundary layer simulations within the WRF‐LES framework, this study examines the influence of gray‐zone parent domain resolution and the role of the cell perturbation (CP) method under different convective regimes. Simulations are benchmarked against stand‐alone LES to isolate the effects of mesoscale structures originating from the parent domain on turbulence representation in the nested LES. Results show that coarser resolutions of the parent domain enhance the penetration of mesoscale structures into the nested LES domain, which can suppress the development of resolved turbulence. The CP method facilitates earlier turbulence development by introducing buoyancy‐driven perturbations that accelerate eddy growth and shorten spin‐up time. In addition, CP exhibits a filtering effect on mesoscale structures imported from the parent domain, with its influence becoming more pronounced under weakly convective conditions and increasing grid refinement ratios. To systematically quantify these behaviors, the nesting ratio between the parent and child domains is used as the primary control parameter, enabling a sensitivity‐based assessment of gray‐zone‐induced mesoscale influence and CP‐mediated turbulence adjustment.

Abstract Future changes in North Atlantic sea surface temperatures (NASST) are uncertain. We assess how different future changes in NASST can affect the eddy‐driven jet and the atmospheric circulation over the North Atlantic and Western Europe. We use a set of atmosphere‐only simulations and show that NASST warming results in a negative North Atlantic Oscillation (NAO) phase, decreased European storm activity, and an equatorward shift of the eddy‐driven jet. The response to the NASST anomaly is asymmetric, with the warming of the NASST affecting the winter season while the cooling of the NASST only affects the late winter season (poleward shift of the eddy‐driven jet and positive phase of the NAO). We link the subseasonal asymmetry to troposphere–stratosphere interactions. The warming of the NASST strongly affects the troposphere, while the cooling of the NASST has a substantial impact on the stratosphere, with anomalies that are at the surface in late winter. In addition, we demonstrate that the change in subpolar gyre temperature affects the North Atlantic climate but contributes only moderately to the asymmetric response.

China is expanding hydropower capacity as a key climate change mitigation strategy, yet greenhouse gas (GHG) emissions from reservoirs can substantially offset this benefit. The influence of specific environmental drivers on these emissions remains poorly understood, and previous studies have rarely quantified their relative importance under multifactorial conditions. To fill this gap, this study quantifies CO2, CH4, and N2O emissions from 79 major hydroelectric reservoirs across China—representing over 60% of national hydropower generation—by integrating the G-res model and the IMAGE-DGNM model. We then employ a random forest (RF) model to evaluate the significance and marginal effects of 15 environmental drivers. Results show that reservoir-specific properties collectively explain 40.37% of the variance in total GHG emissions, and reservoir area emerges as the overwhelmingly dominant driver (MDI importance score = 1.41), far exceeding other key variables such as NH4+ concentration, dissolved oxygen, altitude, water temperature, catchment area, total phosphorus, and air temperature (all with MDI importance > 0.5). Partial dependence analysis further reveals that emissions rise sharply with expanding reservoir area, NH4+ concentrations above 0.15–0.2 mg/L, and catchment areas in the 360,000–680,000 km2 range, while elevated dissolved oxygen (6–9 mg/L) and higher altitude suppress emissions. This study moves beyond simple emission inventories by providing a national-scale, data-driven attribution of reservoir GHG emissions to interacting environmental factors, thereby offering actionable insights for sustainable hydropower planning.