Atmospheric and Oceanic Sciences

Increased snow accumulation on the Antarctic Ice Sheet mitigated global sea level rise by ∼ 11 mm during 1901–2000 according to ice core reconstructions. However, in the most recent 40 years of more intense observation and warming, the trend in the Antarctic-wide accumulation rate has been negligible. We attribute these trends by evaluating Earth system model experiments in comparison with dynamically consistent reconstructions of surface climate. Single-forcing experiments reveal that rising concentrations of greenhouse gases (GHGs) have been the underlying driver of increased accumulation, yet acting alone would have caused twice the observed accumulation-related sea level mitigation during 1901–2000. Aerosol-driven cooling partially compensates this overprediction, but the reconstructions provide evidence that poorly modeled processes can explain observation-model trend discrepancies. In particular, these data support a hypothesis that high-latitude winds have been working together with ice-shelf meltwater fluxes to dampen Southern Ocean surface warming and suppress the GHG-driven accumulation increase since the initiation of West Antarctic ice shelf thinning in the mid-20th Century. The wind pattern associated with strengthening of the Southern Hemisphere westerlies and deepening of the Amundsen Sea Low distributes accumulation unevenly across the continent in an orographic pattern that is consistent across models and the reconstructions. In reconstructions, these same wind and accumulation patterns are associated with muted surface warming across the eastern Pacific and Southern Ocean, a pattern not captured in climate projections including the all-forcings large ensemble studied here. However, the westerly wind history constrained by paleoclimate data assimilation largely reconciles differences between the model's ensemble-mean response and the observed world for both Antarctic-wide accumulation and large-scale warming patterns. Although the large ensemble simulates similar wind histories to the real one – driven by internal variability and anthropogenic forcing – its corresponding responses in SSTs and Antarctic-wide snow accumulation are decoupled from the wind. We discuss how this significant observation-model discrepancy, which has implications for projecting regional climate change, likely arises from omitted meltwater forcing and/or resolution limitations. As a component of the sea level budget and a gauge of the magnitude and spatial pattern of climate change, Antarctic snow accumulation is a critical target for models to replicate.

Outputs from complex Earth system models (ESMs) participating in the Coupled Model Intercomparison Project (CMIP) are a major source of information for policy-relevant assessments of climate change. The way we view and interpret the CMIP archive shapes our understanding of the real world, yet many assessments present analysis choices only implicitly. We describe a fully Bayesian approach and software package for analyzing CMIP data and present a series of interpretive models with gradually increasing complexity to illustrate the methodology. We also show how to update CMIP-derived posteriors using additional evidence, including observations, emergent constraints, and information about processes in the Earth system models themselves. Using these methods, we show that even apparently strong relationships between observable processes and equilibrium climate sensitivity (ECS) in ESMs do not necessarily tightly constrain ECS. We also show that estimates of β, the terrestrial carbon dioxide fertilization effect, are revised downward when considering the individual processes included in ESMs. Our results illustrate how these and other estimates may be updated as new information arrives.

Study Region The Yangtze River is a large regulated river system with pronounced variations in hydrology, sediment transport, and river–lake interactions. This study focuses on the mainstem from Yibin to Shanghai, particularly reaches influenced by the Three Gorges Dam (TGD), Dongting Lake, and Poyang Lake. Study Focus An integrated satellite-driven framework was developed to reconstruct turbidity dynamics during 2016–2024. Landsat-8/9 imagery was combined with ensemble machine learning, causal inference, and GeoDetector analysis to characterize turbidity variability and identify environmental controls. The Voting Regressor achieved the best performance (R² = 0.934, RMSE = 5.221 NTU, MAE = 3.874 NTU, MAPE = 18.618%). New Hydrological Insights for the Region Basin-wide turbidity declined by 32.2% during 2016–2024, reaching a minimum in 2022 followed by a slight rebound in 2023–2024. Turbidity was generally higher during the low-water period than during the high-water period. Spatially, turbidity generally increased downstream but exhibited pronounced heterogeneity. A marked reduction occurred downstream of the TGD, whereas turbidity increased farther downstream in reaches influenced by Dongting Lake. Near the Yangtze–Poyang Lake confluence, turbidity showed a positive correlation with runoff (r = 0.54), contrasting with upstream reaches. Hydrological factors exerted the strongest influence, while meteorological and land-use factors mainly contributed through their interactions with hydrological conditions. These findings underscore the importance of considering reservoir regulation, river–lake interactions, and watershed environmental factors in understanding turbidity dynamics in large regulated rivers.

Abstract The sixth assessment report (AR6) of the Intergovernmental Panel on Climate Change (IPCC) presented regional summaries of the scientific knowledge of changing weather extremes and the human influence upon this. The hexagon figures, found in the Summary for Policymakers (SPM), were a key part of this presentation. They provided clear visual overviews of this understanding on three hazard types (hot extremes, heavy precipitation and agricultural and ecological drought) for policymakers, scientists and communicators alike. We have shown that since 2021, when AR6 was published, there has been a rapid advancement in knowledge of changing extremes on the timescale of individual years, with a greater than doubling in the number of extreme event and trend attribution studies coupled with advances in methodologies, rising levels of anthropogenic forcing, and a growing sample of manifested extreme events. The coming seventh assessment report (AR7) is an opportunity to update and iterate these figures, expand to other hazard types and, we argue, to facilitate more regular updates in line with this rapidly advancing field of knowledge. To this end, we have updated the AR6 hexagons in several ways: we have added evidence levels to the figures to provide additional information without sacrificing visual clarity; we have built on the AR6 regional synthesis process with a step-by-step procedure and accompanying expert guidance and discussion; we have applied this to six illustrative regional case studies for heavy precipitation; we have updated the evidence tables and figures directly for hot extremes and heavy precipitation.

Accurate quantitative precipitation estimation (QPE) is critical for responding to severe weather events such as heavy rainfall. Deep learning (DL) methods, which can establish nonlinear mappings between radar observations and rain rate (R), have been widely applied to reduce QPE errors. This study evaluates point-based and spatial DL approaches for radar QPE across precipitation intensity ranges over Hainan Island using dual-polarization radar and rain gauge data. Four DL-based QPE models, including R-DNNNet, R-IncNet, R-Res-IncNet, and R-DenseNet, are trained using point-based or spatial grid-based datasets, and evaluated across retrospectively defined subsets based on the observed rain-gauge rain rate: low-intensity (R < 10 mm h−1), moderate-intensity (10 ≤ R ≤ 20 mm h−1) and high-intensity precipitation (R > 20 mm h−1). Results show that DL models generally outperform traditional empirical relationships. For overall precipitation, the point-based three-parameter (ZH-ZDR-KDP) R-DNNNet achieves the best performance (CC = 0.94, RMSE = 5.88 mm h−1), improving CC and RMSE by 4% and 23%, respectively, over the best traditional empirical method. R-DNNNet also performs best for low-intensity and moderate-intensity precipitation. For high-intensity precipitation, the spatial convolutional neural network (CNN) model R-Res-IncNet showed the best overall performance, with maximum improvements of 8%, 14%, and 19% in CC, RMSE, and MAE, respectively. R-Res-IncNet achieved an RMSE of 12.45 mm h−1 compared with 12.81 mm h−1 for R-DNNNet, and the paired-bootstrap 95% confidence interval for the model-to-model RMSE difference was 0.018–1.006 mm h−1, indicating a modest but statistically supported improvement. This result suggests that retaining neighborhood radar information may be beneficial under high-intensity conditions. These intensity-specific rankings are based on retrospective evaluation within subsets defined by observed rain-gauge rain rate and characterize conditional model performance across precipitation intensity ranges. Overall, the results show that the relative performance of the evaluated point-based and spatial-grid QPE configurations varies with rainfall intensity, while the three-variable polarimetric input provides consistent benefits across the evaluated datasets. In summary, the findings highlight the precipitation-intensity-dependent performance characteristics of point-based and spatial DL models and suggest their potential for improving radar QPE performance over Hainan Island, particularly for severe convective rainfall.

NASA's Tropospheric Emissions: Monitoring of Pollution (TEMPO) geostationary satellite sensor provides high temporal and spatial resolution measurements critical for monitoring air quality. During the Synergistic TEMPO Air Quality Science (STAQS) component of the 2023 AGES+ campaign, extensive surface, airborne, and remote-sensing observations were collected over the New York City/Long Island Sound region, enabling comprehensive investigation of ozone and its precursors, including nitrogen dioxide (NO 2 ) and formaldehyde (HCHO). Evaluating TEMPO (version 3) NO 2 and HCHO column retrievals against Pandora and GEO-CAPE Airborne Simulator (GCAS) observations, based on the limited number of coincident flight days available, shows TEMPO can capture urban-suburban pollution gradients and exhibits biases comparable to previous satellite validation studies with strong NO 2 column correlations ( R ≈0.79–0.81), though sharp transitions between high and low emission regions remain challenging. The high-resolution (1.33 km × 1.33 km) WRF-Chem simulation reproduces the major spatiotemporal patterns of surface ozone and NO 2 ( R ≈0.56–0.73), supporting its use to fill observational gaps. Integrating TEMPO, WRF-Chem, in situ measurements, and ozone and wind lidar observations, we characterize the spatiotemporal ozone dynamics under different pollution regimes. High-pollution days involve early urban precursor accumulation and sea-breeze-driven coastal recirculation of pollutant-rich air. Moderate days exhibit localized enhancements driven by transport, such as downwind plume transport, while low-pollution days show efficient dispersion and limited ozone formation. This multi-platform framework highlights the importance of resolving fine-scale variability in coastal and transition zones and illustrates TEMPO's potential for improving ozone forecasting and mitigation in complex environments.

The East Sea (Sea of Japan) is a semi-enclosed marginal sea with an energetic mesoscale eddy field along the Tsushima Warm Current and its subpolar front. Unlike prior point reconstructions of Dokdo sea level, we use 0.125° gridded satellite altimetry (DUACS, 1993–2024) to map the spatial structure of the basin’s rise, its variability, and its extremes. The basin-mean rise is 4.1 ± 0.5 mm yr−1, heterogeneous (1.6–6.6 mm yr−1) yet significant across essentially the entire basin, and fastest along the East Korea Warm Current and in the Ulleung Basin at Dokdo. With the trend removed, the leading mode of variability is basin-coherent, concentrated over the southern eddy-energy maximum, and only weakly related to remote climate modes or local wind stress (R2 ≤ 0.08). Extreme high sea-level days, counted against a fixed 1993–2002 95th-percentile threshold, rose roughly sevenfold (4.9 ± 1.0 days yr−1), an increase driven by the rising mean rather than by a broadening of the daily distribution, though the multiplication factor is sensitive to the threshold definition (three- to twenty-eight-fold) and reflects open-water exceedance rather than coastal flooding frequency. The altimetry agrees with open-water tide gauges (mean r = 0.83) and an in situ Dokdo bottom-pressure record (r = 0.88), and a steric/non-steric decomposition shows that Dokdo’s coherence with the basin is dynamic rather than thermal. These results reframe Dokdo sea level as an expression of regional warm-current and eddy dynamics relevant to coastal-hazard exposure.

Abstract Extreme wave events in the North Atlantic have been linked to the North Atlantic Oscillation (NAO), though the connection varies spatially and the related synoptic-scale mechanisms remain unclear. We quantify how the winter NAO affects both the occurrence and magnitude of significant wave height extremes, defined here as seasonal winter maxima, in the Nordic seas and the North Sea. We show that this relationship exhibits pronounced spatial variability, with more and higher wave extremes in the Norwegian Sea during positive NAO, a weaker influence in the western North Sea, and reduced wave extremes in the southern Barents Sea. By linking extreme wave events to synoptic-scale processes, we demonstrate that this spatial variability arises from differences in cyclone characteristics and wave growth mechanisms. We find that most wave extremes occur in connection with cyclones. However, wave extremes in the Norwegian Sea and the western North Sea exhibit substantial differences in cyclone pathways, intensity, translation speed, and development. Extreme wave heights in the Norwegian Sea are associated with running fetch conditions, i.e., cyclones moving close to the group velocity of the waves. In contrast, in the western North Sea and southern Barents Sea, wave growth is fetch-limited due to land interaction and sea ice, with extreme waves primarily generated by northerly winds in the rear of cyclones. These differences in cyclone characteristics help reconcile the regionally varying relationship between extreme waves and the NAO, demonstrating that the influence of large-scale atmospheric patterns on wave extremes depends critically on regional wave growth characteristics.

High wind and photovoltaic (PV) penetration reshapes intra-annual residual energy demand and changes hydropower generation space and operating patterns. Conventional medium- to long-term scheduling methods struggle to coordinate renewable-energy integration, supply reliability, and seasonal hydropower utilization. This study proposes a medium- to long-term cascade stored-energy dispatch-chart method under high-renewable penetration and derives the corresponding dispatch charts. A generation-space quantification model is developed for cascade hydropower, considering renewable-energy integration and thermal minimum-output constraints. The model describes how renewable expansion compresses hydropower generation space across planning years. Four key control points are then identified, including the end of drawdown, flood-season end, impoundment completion, and year-end. Stagewise recursion and closed-loop iteration are used to determine suitable stored-energy ranges at these points. Upper and lower envelopes of multi-scenario stored-energy trajectories are further used to construct a zoned dispatch chart. Generation-adjustment rules are then formulated according to the identified stored-energy zone. The method is validated using nine cascade hydropower stations in the Wujiang River Basin under a 2035 high-renewable scenario. Results show that hydropower generation-space contraction occurs mainly from the pre-flood period to the flood season. In April and May 2035, the upper bound of Wujiang cascade generation space falls to 19% to 24% of installed capacity. The end-of-drawdown control point should therefore be advanced from late April to late March. With the proposed dispatch chart, total cascade generation under the 2035 normal inflow increases from 27.80 to 28.45 TWh. Renewable-energy curtailment decreases from 2.43 to 1.22 TWh, a reduction of 49.8%. The proposed method converts complex operating boundaries into practical zonal stored-energy control rules. It supports medium- to long-term cascade hydropower operation under high-renewable penetration.

Large and moderate volcanic eruptions significantly impact the stratosphere by releasing sulfur dioxide, thereby affecting atmospheric dynamics. Mt. Pinatubo erupted in 1991 and induced a warming of 3 K in the stratosphere that led to prolonged easterly wind regimes of the Quasi-biennial oscillation (QBO). Using a three-member ensemble mean obtained from the ECHAM6-HAMMOZ model, we show the impact of eruptive volcanoes on the tropical stratosphere and the QBO from 2003 to 2013. Our simulations with volcanoes, when compared to simulations without volcanoes, show that volcanic sulfate aerosols enhanced the stratospheric aerosol optical depth (SAOD) 2 months after the eruptions of Rabaul (0.0024 ± 0.0018), Sarychev (0.0058 ± 0.0031), and Nabro (0.0075 ± 0.0053). The increase in mean tropical SAOD over the study period 2003–2013, driven by several volcanic eruptions, produced an average radiative forcing of −0.45 ± 0.21 W m -2 at the top of the atmosphere (TOA) and −0.47 ± 0.26 W m -2 at the surface in the tropical region. Volcanic aerosol precursors are injected into the tropical stratosphere through volcanic plumes, leading to an increase in sulfate aerosol concentrations of 59.6 ± 11.1 ng m -3 and heating rates of 0.05 ± 0.02 × 10 −2 K d -1 in the tropical lower stratosphere. The model simulations show that stratospheric heating caused by the volcanoes disrupts the QBO phases, resulting in the reversal of the easterly to westerly phase and vice versa. Our study shows that moderate and large volcanoes modulate the QBO.

This study assesses the impact of assimilating remote sensing observations of significant wave height (SWH) and 10 m wind speed (WSPD) into the analysis using a weakly coupled data assimilation (WCDA) framework to improve ocean surface monitoring offshore of the Contiguous US (CONUS). We assimilated SWH and WSPD data from 10 altimeters and six scatterometers into the Local Ensemble Transform Kalman Filter (LETKF) data assimilation (DA) scheme over 6 months, from 1 January to 1 July 2024, to update the SWH and WSPD variables of the 30-member Global Ensemble Forecast System wave component (GEFS-wave), which provided the background fields. We validated the SWH and WSPD analyses against assimilated remote sensing observations and found that assimilating these observations corrected the systematic background bias in WSPD values above 15 m/s to near zero, with uncertainty as low as 0.4 m/s. For SWH, the background overestimation averaged 30 cm for observed values between 6.2 and 6.4 m and was reduced to 6 cm in the analysis, with a 30 cm RMSE. These results were consistent with cross-validation experiments that assimilated data from six altimeters for the SWH analysis and three scatterometers for WSPD analysis updates, respectively, and were evaluated against independent remote sensing observations. We show that the diurnal cycle in the spatial coverage and temporal sampling of state-of-the-art altimeter and scatterometer constellations over 6 months in maritime CONUS identified regions where remote sensing data had the greatest impact on the analysis and where they were scarce and had limited impact when assimilated using a reduced 1-h window. A case study showed that assimilating altimeter data during a Pacific storm that developed on 1 February 2024 allowed the LETKF to adjust the SWH analysis spatially.

Renewable energy production is strongly influenced by climate variability and change, making the energy sector sensitive to fluctuations on decadal timescales. Decadal climate predictions, which aim to forecast climate variability over the next few years, therefore offer potential value for anticipating near-term changes in wind and solar resources and supporting climate-informed energy planning. However, the predictive skill of decadal forecasts for energy-relevant indicators remains poorly quantified, which is crucial to know the potential usability of any forecast product. This study evaluates the skill of decadal climate predictions over Europe for forecast years 1–3 using a multi-model ensemble from the Coupled Model Intercomparison Project Phase 6 (CMIP6) Decadal Climate Prediction Project (DCPP). We assess three energy-relevant indicators: photovoltaic potential (PVpot), wind capacity factor (WCF), and a compound indicator describing the number of energy drought days (NED), defined as days with inefficient production from both wind and solar resources. The skill is evaluated against the ERA5 reanalysis, and the added value of the model initialization is estimated by comparing the decadal predictions against the non-initialized historical forcing simulations. PVpot exhibits the highest and most spatially homogeneous skill for annual, spring and summer aggregations, closely reflecting the high predictability of surface solar radiation. WCF shows low and spatially heterogeneous skill, consistent with the high intrinsic variability of wind. The compound NED indicator displays strong seasonal dependence: its predictability is largely controlled by solar conditions in high-radiation seasons and by wind in winter and autumn. Model initialization generally provides added value where historical simulations already show some skill, especially for PVpot, while its impact is lower for WCF. This work shows the specific seasons, regions and energy indicators for which decadal predictions can provide actionable climate information to support renewable energy applications.

A variety of chemical and dynamical processes in the troposphere and stratosphere affect tropical total column ozone (TCO), the net effect of which may cause changes in surface UV radiation and impact human and ecosystem health. We use dynamical linear modeling to estimate changes in tropical TCO and partial column ozone (PCO) in the troposphere and three stratospheric layers to assess agreement between satellite composites and chemistry-climate model simulations from two multi-model experiments (CCMI-1 and CCMI-2022). While both model experiments show tropical TCO increases over 2000–2021, multimodel-mean CCMI-2022 changes (+2.5 DU) agree slightly better with observations (+3.3 DU) than CCMI-1 (+1.6 DU). However, this overall agreement obscures multiple systematic differences in PCO changes between the models and observations across atmospheric layers. For example, since 2000 tropical tropospheric PCO increased significantly in CCMI-2022 (+1.5 DU) but not in CCMI-1 (+0.3 DU), largely explaining the difference in TCO changes. Also, despite nearly identical stratospheric PCO changes, CCMI-2022 changes are slightly more negative than CCMI-1 in the lower stratosphere (by ∼ 0.5 DU) and more positive in the middle/upper stratosphere. Crucially, substantial differences exist between observational PCO changes, particularly in the troposphere and middle/upper stratosphere, and these disagreements limit the ability to evaluate CCM fidelity for past changes. While early and late century trends are correlated across models, suggesting a potential emergent constraint on ozone, the spread in observational trends means they are unable to provide guidance on the credibility of model projections for a given emissions scenario.

Stratosphere-troposphere exchange (STE) plays a fundamental role in the global atmospheric budget of chemical constituents. The troposphere-to-stratosphere transport (TST), as a part of STE, can inject anthropogenic pollutants from the Earth’s surface into the stratosphere, changing its chemical composition and influencing radiative processes. On record, TST is a multi-scale process with various contributing mechanisms, often not fully qualified nor quantified. In the tropics, typhoons and the corresponding overshooting convection and updrafts have recently been highlighted as one of the TST mechanisms, contributing for instance to the moistening of the lower stratosphere. Expanding on this, our study proposes a novel mechanism for TST connected with the interaction of typhoons with orography, including modulation of typhoon updrafts and convection, orographic lifting, and orographic gravity waves. Combining a Lagrangian modeling tool with a high-resolution simulation of the landfall of typhoon Molave (2020) in the Philippines, our results show that the presence of orography enhances the transport of air from the planetary boundary layer to the upper troposphere–lower stratosphere (UTLS) region. The presented findings advance our understanding of tropical cyclones impacts on STE and may have significant implications for the long-range atmospheric transport of pollutants originating from tropics.

Abstract The Solotvyno salt mine in West Ukraine presents significant environmental and infrastructural risks due to uncontrolled salt dissolution following mine flooding, causing ground subsidence, sinkhole formation, and transboundary contamination of the Tisza River. This study employs an innovative multidisciplinary approach to characterize subsurface conditions and contamination pathways. The UX-1Neo autonomous underwater robotic platform conducted 14 dives in two flooded mine shafts, revealing intact main shaft structures but worrying blockages in horizontal passages, distinct haloclines at different depths (60 m in ventilation shaft 9, 140 m in shaft 10), and evidence of ongoing salt crystallization. Ground geophysical surveys such as electrical resistivity tomography, very low frequency radio-magnetotellurics and horizontal loop electromagnetics identified compromised protective clay layers (“pallag”) and potential air-filled voids, explaining accelerated dissolution and surface deformation. Hydrodynamic and contaminant transport modeling quantified salt fluxes (~ 2000 m 3 /day through “pallag” zones, ~ 13–15 m 3 /day from direct mine leakage) and confirmed dissolution rates of approximately 5.0–5.5 m 3 /day of rock salt, driven by both anthropogenic influences and natural gradients toward the Tisza River. NETPATH mixing models revealed variable contamination throughout the system, with the Black Moor area showing highest vulnerability (up to 27.4% mine water contribution) while the Tisza River maintains relatively good water quality despite measurable contamination (1.97% mine water). The suggested approach provides critical insights into risk assessment and remediation planning, representing a significant advancement in monitoring and managing complex environmental hazards associated with abandoned salt mines.

Foundation models have shown remarkable generalization ability for few-shot learning (FSL). However, their potential for remote sensing scene classification has not been fully explored. Existing methods mainly adapt a single vision–language model and seldom exploit the complementary strengths of different foundation models. Moreover, the inconsistency between visual and textual representations limits the effectiveness of cross-modal learning under limited supervision. To address these issues, we propose a framework for few-shot remote sensing scene classification leveraging collaboration of foundation models. Specifically, the proposed framework integrates a large language model, a text-to-image diffusion model, and a vision–language model to enrich class semantics, synthesize category-related training samples, and learn transferable visual-textual representations. Furthermore, a Deep Cross-modal Alignment (DCA) module is developed to improve feature consistency across modalities. The DCA module incorporates multi-scale visual features, lightweight adapters, and a contrastive learning objective to obtain more discriminative task-adaptive representations. Extensive experiments on 12 remote sensing scene classification datasets under various few-shot settings demonstrate that the proposed framework achieves competitive performance compared with existing prompt learning and efficient parameter tuning methods, with consistent improvements observed on average across the evaluated datasets. Recent multimodal large language models evaluated in the zero-shot setting are additionally reported as references, while the 2-shot results of our method illustrate the performance when limited labeled data are available. This comparison provides a broader view of the trade-offs between label availability, classification accuracy, and inference cost across the two paradigms.

Extreme weather hazards are increasing and stakeholders need rapid, transparent information during unfolding events. We present RHITA (Real-time Hazard Identification and Tracking Algorithm), an open-source framework and web tool for near real-time detection and tracking of weather-related hazards over Europe. RHITA identifies grid cells exceeding local quantile thresholds, groups them into spatial clusters, and links clusters through time to reconstruct three-dimensional events in longitude, latitude, and time. For each event, RHITA provides intensity, extent and duration metrics and estimates rarity through return periods derived from a long historical record. RHITA is operated with ECMWF open forecasts for daily monitoring and ERA5 reanalysis for a consistent historical archive from 1950 to 2024. We target four hazards: heatwaves, cold spells, heavy precipitation and strong winds. Key spatial and temporal parameters are optimized against EM-DAT disaster records (2000 to 2023). Applying RHITA to ERA5 yields a European climatology of hazard events and reveals robust increases in heatwave frequency, intensity and affected area, a decline in cold spell frequency, and more heterogeneous signals for heavy precipitation and strong winds at the continental scale. RHITA provides open access data and an interactive interface to support rapid hazard characterization, event contextualization and downstream risk analysis.

Urban environmental monitoring increasingly requires methods that translate Earth observation (EO) data into spatial units relevant to ecosystem-service assessment and planning. This study develops a landscape-unit framework for smart monitoring of urban cooling in two Polish Functional Urban Areas (FUAs): Zielona Góra and Gorzów Wielkopolski. Landsat-derived land surface temperature and surface urban heat island (SUHI) intensity were integrated with vegetation, water, imperviousness and NDVI indicators, together with the characteristics of directly adjacent landscape units. Spearman correlations, ordinary least squares and Spatial Error Models (SEMs) were used to diagnose the spatial structure of thermal conditions. The SEM variants removed residual spatial autocorrelation and showed the strongest full-sample fit, with RMSE values of approximately 0.85 SUHI units and MAE values of approximately 0.66; these statistics are used as model-fit diagnostics rather than as measures of out-of-sample predictive performance. The results were translated into a continuous weighted HeatCool Index and seven Jenks natural-break classes that represent the relative capacity of landscape units and their neighbourhoods to support cooling or reinforce warming. The classification is interpreted as an ecosystem-service screening product rather than as an independent validation of its component variables. Independently modelled annual and seasonal NO2 indicators were then aggregated to the same landscape units as a complementary pressure layer. After EDM correction, the NO2 model achieved R2 = 0.77, RMSE = 3.98 µg m−3 and MAE = 2.36 µg m−3 on the held-out validation subset. Across the HCI gradient, Classes 1–2 showed the lowest and least variable annual NO2 conditions, whereas Classes 6–7 exhibited the strongest winter deterioration and Class 7 contained the highest annual NO2 concentrations. Because NO2 was not used to construct the HCI, these gradients represent an independent comparison of co-occurring thermal and air-pollution pressures rather than evidence of a causal relationship. The proposed framework extends pixel-based thermal assessment by integrating landscape-unit classification, neighbourhood context and independently modelled air-pollution pressure within a common planning-oriented spatial framework.

Accurate estimation of clear-sky surface longwave downward radiation (LWDR) is fundamental to surface energy budget studies, yet widely used parameterizations and conventional lookup-table (LUT) searches often suffer from regional biases and limited transferability. This study develops a scene-mapped LUT framework that operates in brightness-temperature (BT) space and uses surface temperature (ST) and total column water vapor (TCWV) as physical descriptors to define scene-specific subsets and constrain spectral matching to locally consistent surface–atmosphere states. Two implementations are proposed within the scene-mapped framework: S-Poly (polynomial regression) and S-LUT (sub-LUT distance search). Validation against multi-network ground measurements using MODIS inputs yielded RMSEs of 26.36 and 26.89 W m −2 and biases of −0.86 and −2.75 W m −2 for S-LUT and S-Poly, respectively. The corresponding RMSEs were lower than those of the global-LUT benchmark (29.26 W m −2 ) and the evaluated general clear-sky parameterization benchmark (30.03 W m −2 ), representing reductions of 12.2% and 10.5% relative to the latter. Both methods generally maintained stable accuracy across diverse surface types and climatic regions. S-LUT was more consistent across the evaluated configurations and achieved an RMSE of 22.48 W m −2 under the mixed MODIS–ERA5 configuration. The flexible scene-mapping framework may also provide a useful methodological concept for adapting radiation retrieval to different sensors and exploring future extensions toward all-sky conditions.

Textual accounts establish essential context for port disruptions but can bias large language model synthesis before independent observations are examined. We present Port Agent, a remote-sensing agent that aligns Automatic Identification System (AIS) trajectories, Sentinel-2 vessel detections, and documents while retaining acquisition time, footprint, processing, and uncertainty. The evaluation follows the evidence chain from physical observation to agent synthesis across 12 views of 11 U.S. event-or-control groups, 24 quality-screened scenes, and 145,570,911 AIS positions. AIS first resolved distinct operational responses: cargo/tanker occupancy fell by 58.8% during the New York/New Jersey strike, 41.5% during the Baltimore channel restriction, and 44.1% around Hurricane Beryl at Houston, whereas the Seattle cyber case showed no comparable portwide contraction. Sentinel-2 then added spatially explicit evidence through a validated detector, georeferenced AIS–image correspondence, and observations during near-synchronous AIS gaps. Finally, a fixed-backend experiment tested how this evidence affected synthesis. Adding the aligned physical record improved concise review, and applying the evidence gate to that same record produced a further gain, particularly for misleading reports. The complete configuration achieved the highest scores under both report conditions. Relative to concise text-only review, it improved the common-outcome score by 19.24 and 22.16 points and reduced misleading-claim false acceptance from 100.0% to 33.3%. Supporting analyses found no detectable model-interface difference, little change in mean performance across temperatures, and no benefit from a longer prompt. Although the system comparison remains exploratory, the results support an auditable evidence chain in which AIS and satellite observations constrain text-led synthesis.

ABSTRACT Based on the Global Precipitation Climatology Centre (GPCC) monthly precipitation data, the Extended Reconstructed Sea Surface Temperature (SST) version 5 (ERSSTv5) dataset derived from the National Oceanic and Atmospheric Administration (NOAA), and the Fifth‐generation European Centre for Medium‐Range Weather Forecasts (ECMWF) atmospheric reanalysis (ERA5) monthly dataset, the synergistic influences of the SST anomalies (SSTAs) in the North Atlantic (NA) and the Indian Ocean (IO) on summer precipitation in Central Asia (CA) are investigated. Results show that SST in both the NA and the IO positively correlates with summer precipitation in southern CA. The meridional movement of the subtropical westerly jet (SWJ) plays an important role in linking the SSTAs in the NA and the IO and summer precipitation in southern CA. The positive SSTAs in the NA can excite an eastward‐propagating wave train across Eurasia and cause an anomalous cyclone over CA, which is responsible for the southward movement of the SWJ. The positive SSTAs in the IO can induce an anomalous cyclone in the upper troposphere over the northwestern part of the South Asian summer monsoon region, according to the Gill‐type atmospheric response, and the SWJ over CA also moves southward. The southward movement of the SWJ over CA responds to the simultaneous SST warming in the NA and the IO. Furthermore, it can induce an anomalous cyclone in the middle troposphere over CA and strengthen the transport of water vapour from the Arabian Sea. Therefore, more summer precipitation can be observed in southern CA. In the process of synergistic influences of SSTAs in the NA and the IO on summer precipitation in CA, the former plays a more important role.

Abstract Saline irrigation represents a pivotal alternative strategy for enhancing crop production under freshwater scarcity. However, previous assessments have mainly quantified average yield responses, while how interacting environmental and management conditions shape region-specific irrigation-water salinity thresholds and associated spatial risks remain elusive across global major breadbaskets. This study combined a meta-analysis of 551 field observations with machine learning to quantify global wheat yield responses to saline irrigation, identify optimal region-specific thresholds, and evaluate spatial risks in four major wheat-producing areas: the North China Plain (NCP), Indo-Gangetic Plain (IGP), Middle East (ME), and Central California Valley (CCV). Meta-analysis revealed that saline irrigation led to significant average yield reductions ranging from 12.4% in the NCP to 31.4% in the CCV. Machine learning further showed that irrigation water salinity, growing-season temperature, and irrigation amount emerged as the dominant drivers, collectively explaining more than 54% of yield variation. An optimum irrigation amount of 500 mm was optimal for maximizing yield under saline conditions. Region-specific best practices included border irrigation at flowering and grain-filling stages for the NCP, drip irrigation at these stages for the IGP, and drip irrigation at the flowering stage for the ME and CCV. Spatial risk assessment revealed a mismatch between salinity tolerance thresholds and actual exposure. Under a conservative 5% yield-loss scenario, the IGP exhibited the highest model-derived salinity threshold of 4.14 dS m-1 but the largest risk area proportion due to elevated background salinity, whereas the ME faced the lowest risk, with a risk area of 7.7%, indicating high potential for saline water utilization. These findings highlight the limitations of universal water quality guidelines and demonstrate the necessity of localized management. By establishing a data-driven framework, this study provides actionable insights to safeguard global wheat supply, build climate resilience, and ensure agricultural sustainability in salt-affected environments.

Urbanization has increased impervious surfaces in cities, raising surface temperatures and consequently enhancing sensible heat fluxes and longwave emission while reducing latent heat exchange. These surfaces influence runoff temperature, reduce infiltration and increase runoff volumes, thereby affecting urban thermal environments and human thermal comfort. Existing models tend to inadequately represent coupled thermal–hydrological processes. Data-driven approaches often omit fine-scale spatial heterogeneity, while physically based water temperature models are mainly designed for rivers and lakes and only partially address heat exchange for urban pavements. Although recent advances have improved this understanding, these findings are rarely incorporated into spatially distributed hydrodynamic frameworks resolving flow depth, velocity, and routing in urban environments. This study develops a spatially distributed and temporally resolved modeling framework for urban environments that explicitly resolves runoff dynamics and water temperature evolution. The framework extends a two-dimensional hydrodynamic model based on the full shallow-water equations by incorporating a water temperature module with heat transfer driven by prescribed surface temperatures. Model performance is evaluated using in-situ measurements from a controlled experiment on a concrete surface that lasted approximately 6 min, during which water temperatures rose by more than 7 °C before reaching steady state. Including time-dependent surface temperature forcing, in addition to meteorological forcing, substantially improves agreement between simulated and observed water temperatures, reducing the mean absolute error from 3.4 °C to 0.2 °C. The application to the Seestadt Aspern city quarter demonstrates the framework’s potential for spatially resolving runoff temperature dynamics of heavy rain events in a realistic urban setting.

Background Wastewater surveillance has emerged as a cost-effective and powerful approach for monitoring infectious diseases at the community level, as demonstrated during the COVID-19 pandemic. However, translating wastewater viral signals into reliable epidemic forecasts remains challenging because measurements are noisy, environmental conditions vary over time, and disease transmission is inherently non-linear. Mechanistic models such as the Susceptible-Exposed-Infectious-Recovered-Virus (SEIR-V) framework can bridge wastewater signals with infection dynamics, but their performance depends heavily on accurate parameter estimation, and conventional least-squares optimization is prone to convergence to local optima. Methods We developed a Genetic Algorithm-optimized SEIR-V (GA-SEIR-V) model that applies evolutionary optimization for robust multi-parameter estimation. To improve biological realism, the model incorporates a temperature-dependent viral decay rate represented by a sinusoidal function fitted to regional climate data, enabling dynamic simulation of seasonal viral persistence in wastewater. The framework was calibrated and evaluated using wastewater viral load, clinical case, and temperature data from California (September 2020-November 2022), Greater Boston (September 2020-May 2021), and Switzerland (January 2022-December 2022). Results Using a unified evaluation protocol with 30 independent genetic algorithm runs, we explicitly separated in-sample calibration from out-of-sample prediction. During calibration, GA-SEIR-V reproduced the reported model fit for California and consistently outperformed a carefully re-tuned least-squares baseline on large, heterogeneous datasets, demonstrating greater robustness and more reliable parameter estimation where gradient-based optimization became trapped in local optima. In contrast, long-term prediction beyond the calibration period remained difficult for all methods, particularly across multiple epidemic waves, resulting in limited out-of-sample forecasting accuracy. Conclusion GA-SEIR-V combines evolutionary optimization with temperature-aware viral decay modeling to improve the robustness and identifiability of SEIR-V parameter estimation, particularly for complex wastewater datasets. By explicitly distinguishing calibration from prediction, the study shows that excellent in-sample fitting does not necessarily translate into reliable long-term forecasting. The proposed framework is therefore most suitable for robust parameter estimation and short-term epidemic tracking, while providing a foundation for future improvements toward more accurate long-range prediction.

Abstract In NWP, the assimilation of various observations contributes to improving forecast accuracy. The contribution of each observation can be estimated by existing methods. Empirically, it is well known that only a fraction of assimilated observations are diagnosed as beneficial, meaning that they improve forecast accuracy. Previous studies have indicated that the beneficial observation rate depends on model imperfections. Building on this sensitivity, we propose a new method that uses the beneficial observation rate to trigger adaptive model parameter estimation for mitigating model errors and bias. Specifically, the method activates model parameter estimation when the beneficial observation rate exceeds a prescribed threshold. Using the Lorenz96 40-variable system, we demonstrate that the new approach successfully detects model bias and improves analysis accuracy, even when the true model parameter varies in time. Furthermore, we find that the beneficial observation rate is useful for detecting model bias even when the model and observations have similar biases, in which case the time-averaged observation-minus-background does not provide a clear signal of the bias. Nevertheless, because the beneficial observation rate is sensitive to both observation bias and the observation network, it should ideally be calculated using unbiased anchor observations that are distributed as uniformly as possible.