Atmospheric and Oceanic Sciences

Abstract Earth’s albedo is fundamental to the planetary energy budget 1 . The Northern Hemisphere (NH) and Southern Hemisphere (SH) contribute essentially equally to the planetary albedo—a remarkable yet puzzling phenomenon known as hemispheric albedo symmetry 1–6 . Although such symmetry is rare, it is not unique 7 . Nevertheless, other symmetry pairs have remained unexplored, despite their potential to illuminate possible causes of albedo symmetries and implications for the planetary energy budget. Using a 25-year satellite record, here we show that Earth also exhibits a unique and persistent east–west (E–W) albedo symmetry: the 27° E meridian divides the planet into an Eastern Hemisphere (EH) and a Western Hemisphere (WH) that reflect nearly identical amounts of sunlight. In contrast to the NH–SH symmetry, the EH–WH symmetry encapsulates a distinctive ‘triple symmetry’ in which clear-sky albedo, cloud radiative effect and open-ocean fraction all exhibit hemispheric symmetry around this meridian. This EH–WH symmetry arises from greater high-cloud reflection in the EH balancing greater low-cloud reflection in the WH. Furthermore, interannual variability in the EH–WH symmetry tracks the phase of the El Niño–Southern Oscillation (ENSO), indicating a potential connection to general circulation. This discovery of the EH–WH albedo symmetry and its emergence as a triple symmetry provides a reduced degree-of-freedom constraint for Earth system models (ESMs) and stresses the critical nature of continued Earth radiation budget observations under a rapidly changing climate.

Abstract Extreme precipitation (EP) exerts an important impact on Antarctic mass changes, and consequent global sea level changes. However, its drivers and remote influences remain insufficiently understood. Validation against in situ observations shows that ERA5 reanalysis robustly captures Antarctic EP events. Using ERA5, we examine the spatiotemporal variability in annual and seasonal Antarctic EP from 1979 to 2022, and explore the underlying mechanisms involving atmospheric rivers (ARs) and large-scale circulation modes. Antarctic ice sheet (AIS)-averaged EP intensity and frequency have increased significantly since 1979, with strong seasonal and regional contrasts linked to the Southern Annular Mode (SAM) and the two Pacific South American (PSA1 and PSA2) patterns. AR landfalls contribute 53.4% of annual EP totals and explain 72% of interannual EP variability. Notably, large-scale EP events affecting >20% of the ice sheet are preceded by enhanced deep convection over the tropical western Pacific (TWP). This tropical heating anomaly generates upper-level divergence that excites stationary Rossby wave trains. The resulting wave responses project onto zonal wave 3 (ZW3) and zonal wave 4 (ZW4) circulation patterns over the Southern Ocean, establishing multiple meridional moisture corridors toward Antarctica, thereby intensifying AR activity and promoting large-scale EP occurrence. These findings underscore ARs and tropical-polar teleconnections as critical drivers of Antarctic hydroclimatic variability, with implications for ice-sheet mass balance projections.

Abstract California's Sierra Nevada region plays a key role in the state's water supply, which is often stressed due to swings between dry and wet conditions characteristic of the region's climate. Here, we refer to these dramatic interannual swings in streamflow as hydrologic whiplash. We produce the first robust projections of hydrologic whiplash by analyzing a hybrid‐downscaled large ensemble data set of streamflow in Sierra Nevada watersheds. Due to warming‐induced water losses, we project statistically significant future increases in the frequency and intensity of extreme dry streamflow years (≤5th percentile), along with insignificant changes in the frequency of extreme wet streamflow years (≥95th percentile). Consequently, increases in hydrologic drought drive a three‐fold increase in the frequency of hydrologic whiplash events. The hydrologic changes identified in this paper are not evident from analysis of precipitation changes alone and demonstrate remarkable statistical agreement, providing greater clarity for California water managers as they adapt to climate change. Altogether, these findings signal a shift toward dryer and more volatile future hydrologic conditions that could challenge California's ability to replenish its water supply between increasingly frequent and severe dry events.

Abstract. Oceanic oxygen levels, crucial for marine ecosystems and biogeochemical cycles, have declined significantly over the past few decades due to climate change, posing severe environmental risks. However, historical dissolved oxygen (DO) measurements, especially below 2000 m, remain sparse, limiting comprehensive annual and seasonal analyses. Here, we introduce the BLENDR framework (Bayesian-optimized Learning and ENsemble modeling for Data Reconstruction), a Bayesian-optimized ensemble of six machine-learning models (Random Forest, XGBoost, LightGBM, CatBoost, Extremely Randomized Trees and Histogram-based Gradient Boosting) fused via a spatially coherent dynamic weighting scheme, to reconstruct global monthly DO distributions at a 1° × 1° resolution from the surface to 5902 m from 1960 to 2023. Validation against an independent dataset demonstrated that BLENDR achieves better performance than any individual model, with an R2 of 0.968. Our dataset captures depth-dependent deoxygenation, with the most pronounced decline occurring between 150 and 200 m at approximately −0.12 µmol kg−1 yr−1, and shows severely accelerated oxygen loss in the Arctic Ocean and North Atlantic over the past decade. This work provides a long-term, global monthly DO product from the ocean surface to 5902 m. The bathypelagic DO data provided in this work are a significant contribution to deep ocean oxygen dynamics and global biogeochemical cycles. The data product is publicly accessible at https://doi.org/10.5281/zenodo.19705526 (Han and Zhou, 2026).

Abstract Polar amplification (PA) is a robust feature of climate change in coupled atmosphere–ocean general circulation models (AOGCMs), yet its magnitude varies substantially across models. Prior work showed that PA and its drivers, primarily positive feedbacks, are strongly linked to the degree of sea-ice loss. Here, we assess to what extent this inter-model spread narrows when sea ice and sea-surface temperatures (SSTs) are prescribed in a coordinated set of atmosphere-only GCM (AGCM) simulations. Three AGCMs are forced with SST and sea-ice fields from the SSP5-8.5 projection of a single reference model. Comparisons between AGCM and AOGCM ensembles reveal that prescribing sea-surface boundary conditions substantially reduces the spread in PA and its associated amplifying feedbacks. This indicates that much of the divergence in coupled projections arises from feedbacks operating on different warming and sea-ice melt patterns. Cloud feedbacks, in particular, exhibit strong sensitivity to local SST patterns in regions that contribute prominently to inter-model differences in climate sensitivity, especially the Southern mid-latitudes. Remaining spread in AGCMs isolates intrinsic atmospheric model differences. The Arctic cloud feedback emerges as a major residual uncertainty, reflecting its state dependence on cloud properties such as liquid water path. Decomposing the AGCM response further shows that roughly three-quarters of Arctic amplification is driven by sea-ice-related processes. Nevertheless, a weakened Arctic amplification persists without evolving sea ice via temperature feedbacks. In contrast, Antarctic amplification remains more uncertain and is closely linked to historical sea-ice amount, which partly determines the sea ice trajectory in future climates.

Small-scale oceanic eddies and filaments mediate the vertical exchange of heat and carbon within the global ocean. The Surface Water and Ocean Topography (SWOT) mission resolves these features through wide-swath interferometry, but internal tides often mask these observations. Non-phase-locked internal tides present special difficulty because they vary with the evolving ocean background. We show that this chaotic variability can be predicted. We use a data-assimilative ocean forecast model to resolve the mesoscale environment and separate tidal signals from the broader circulation. The model captures the organized structure of these incoherent waves in the independent SWOT measurements. Correcting for the total (phase-locked plus non-phase-locked) internal tide signal reduces the error by 59% relative to current empirical methods. These findings show that non-phase-locked internal tides become predictable when the evolving ocean state is explicitly modeled. Our framework allows wide-swath altimetry to more accurately map climatically important fine-scale dynamics in the ocean.

Abstract Uncompensable heat stress (UHS), characterized by the loss of homeostasis due to excessive environmental thermal loading, causes substantial heat‐related health risks in India. However, the spatial and seasonal heterogeneity, as well as temporal changes of UHS in India remain poorly understood. Using observations, reanalysis data, and climate model projections, we highlight the surge of UHS during the monsoon season (July–October) as the climate warms. In the observed period (1979–2021), the frequency and area affected by UHS have increased significantly across India. The observed UHS is more prevalent in summer (March–June) and affects 8% of India, whereas only 1% of the country is affected in the monsoon season. The summer UHS is also more strongly associated with annual heat‐related mortality ( R 2 = 0.38). However, the monsoon season (July‐October) UHS, predominantly characterized by hot‐humid conditions, is projected to increase rapidly with climate warming and affect nearly equivalent areas of the country as the summer season (60% in summer and 53% in the monsoon season) under 2°C warming relative to the preindustrial period. This will create long‐lasting UHS across both seasons, posing critical challenges to public health, labor productivity, and climate resilience in densely populated and vulnerable regions.

Hydrological modeling is essential for water resources management but often requires specialized expertise, creating a significant barrier to its broad application. To address this challenge, we developed HydroCraft, a web-based platform designed to democratize the modeling process. HydroCraft provides a comprehensive and integrated workflow for basin extraction, driving data generation, parameter calibration, and result visualization, enabling rapid modeling for any watershed globally. Key features of the platform include: an adaptive watershed delineation method that optimizes the distribution of computational units based on user-defined sub-basin area thresholds; the integration of three hydrological models (THREW, Xin’anjiang, and Miyun Hydrological Model) suitable for diverse climatic and geological conditions; and the incorporation of a Large Language Model (LLM) agent, which serves as a robust assistant to provide guidance and automated task execution. Users only need a device with a web browser and an internet connection to perform advanced hydrologic simulations. Two case studies (user-driven vs. LLM-agent-assisted) demonstrated the platform’s efficiency and intelligence. The platform advances hydrological modeling toward greater intelligence and accessibility, providing support for applications such as water resource management and flood forecasting

Abstract. Biogenic volatile organic compound (BVOC) emissions from vegetation represent a major source of volatile compounds globally and play an important role as precursors for tropospheric ozone. Understanding their emissions is therefore crucial for quantifying the impact of ozone on air quality. We present two datasets of biogenic volatile organic compound emissions that cover the European modelling domain of the Copernicus Atmospheric Monitoring Service at a resolution of 0.1° × 0.1° to support the study of European scale air quality. The compounds included in the dataset follow the VOCs included in the regional atmospheric chemistry model mechanism (RACM). The datasets were produced within the framework of the EU's SEEDS project. We produced each dataset by coupling modelling output variables from the SURFEX land surface model with the MEGAN3.0 BVOC emission model. In one instance, the SURFEX model was run in free-running mode, which we term the open-loop (OL) and in the other case we assimilated satellite observations of leaf area index (LAI), which we term the analysis. The OL and analysis land surface model outputs form the basis for each emission dataset that are called SURFEX-MEGAN3.0 OL (https://doi.org/10.7910/DVN/LAUVTU, Hamer et al., 2025a) and SURFEX-MEGAN3.0 analysis (https://doi.org/10.7910/DVN/69G1FX, Hamer et al., 2025b), respectively. The OL dataset is available over a five-year period from 2018–2022 and the analysis dataset is available over the three-year period 2018–2020. SURFEX was run for both the OL and analysis simulations in a configuration that allowed simulated vegetation to respond to variations in meteorology over time to more realistically track vegetation phenology. Evaluation of the land surface model output LAI and root-zone soil moisture (RZSM) showed that the OL and analysis simulations had good skill at tracking temporal changes in both variables, with the analysis performing better in each instance. We perform a variety of evaluations on the isoprene emissions specifically given the importance of this compound for atmospheric chemistry. We evaluated the temporal variability of isoprene emissions in both datasets and found that the majority of the interannual and monthly variability was linked to variability in LAI that in specific cases, like the summer of 2019, could be linked to drought impacts on vegetation growth simulated by SURFEX. We evaluated the daily temporal variability of the OL and analysis isoprene emission datasets against in-situ online observations of isoprene concentrations at 8 sites in western Europe and found moderate to strong correlation between the emissions and observations in almost all location-year pairings. We also evaluated the OL and analysis emission datasets against other published bottom-up isoprene emission datasets over the same European domain used in this study. We found that the SURFEX-MEGAN3.0 OL and analysis isoprene emission datasets lie between the minimum (CAMS-GLOB-BIOv3.1) and maximum (MEGAN-MACC) published emission datasets based on bottom-up approaches. Furthermore, we were able to attribute differences in seasonality between SURFEX-MEGAN3.0 and other emission inventories to differences in the temporal variability of the underlying LAI dataset used to compile them. Overall, our findings show the importance of variability in LAI in controlling isoprene emissions on monthly to annual timescales. Combining this with the demonstrated skill of the emissions in evaluation with independent data, this points towards the value of an Earth-system approach to BVOC emission modelling.

Abstract The Caspian Sea, the Earth's largest inland water body, faces water level decline, drawing comparisons to the collapse of the Aral Sea. Unlike the Aral Sea, the relative roles of climatic variability, hydrological changes, and anthropogenic pressures on the Caspian Sea remain poorly understood. Here, we integrate satellite observations, in situ hydrological records and reanalysis data to examine recent drivers of the Caspian water loss. We show that total river inflow to the Caspian Sea has declined significantly, primarily due to reduced discharge from the Volga River. At the same time, precipitation over the basin has remained broadly stable, while evaporation over the sea has shown a modest upward trend. These findings point to compound anthropogenic and climatic influences on the regional water balance. We also detect a long‐term increase in chlorophyll‐ a concentrations in the shallow Northern Caspian, signaling growing ecological stress associated with ongoing hydrological change. Avoiding further ecological disruption requires coordinated international action and policies to mitigate shrinkage by optimizing water allocation and environmental releases, as well as prioritizing long‐term ecosystem resilience. Without urgent intervention, the Caspian Sea risks following the trajectory of other desiccating inland water bodies, with long‐lasting ecological and socioeconomic consequences.