Atmospheric and Oceanic Sciences

BY Record-breaking heatwaves disrupt global water, energy and food systems, yet the co-occurrence of atmospheric and riverine events remains largely unexplored. Here we analyse 796 river basins in the USA and Central Europe to characterize such co-occurring atmospheric–riverine compound heatwaves. Combining water quality observations with a deep learning model, we find that compound heatwaves have increased by about 0.40 events per decade since the 1980s. This corresponds to a rise from roughly 0.76 events per year in the early period to about three times that frequency today, meaning their occurrence has effectively tripled over the past four decades. This trend coincides with the rapid intensification of riverine heatwaves, which have increased in frequency (114%), duration (148%) and intensity (95%) between 1981–1990 and 2010–2019, far outpacing changes in atmospheric heatwaves. Compound occurrences are primarily controlled by climatic (59.2%), topographic (22.4%) and hydrological (18.3%) factors, with amplified trends in high-elevation (>3,000 m) mountain rivers (+128% per decade). Compared with isolated riverine heatwaves, compound heatwaves drive an additional 16% rise in water temperature and a further 2.9% decline in dissolved oxygen. Under a high-emissions scenario, 98.5% of riverine heatwaves will coincide with atmospheric heatwaves by 2100. These findings highlight the escalating threats of compound heatwaves to freshwater ecosystems and the need to incorporate their dynamics into future water risk assessments.

Abstract Understanding how diabatic processes influence the dynamic evolution of weather systems and contribute to upscale error growth remains a central challenge for predicting extreme weather. This study examines a trans-Pacific high-impact weather sequence (20–25 September 2024) that produced record-breaking rainfall over Japan and catastrophic flooding in Alaska and British Columbia. Using the COAMPS adjoint model, we analyze the causal mechanisms and predictability drivers of this event. Adjoint sensitivity diagnostics show that uncertainties in initial moisture fields over the Western Pacific led to large-scale forecast errors downstream. A monsoon moisture surge interacting with a midlatitude trough triggered intense diabatic heating, producing over 250 mm of rain along Japan’s coast in six hours. Perturbation experiments reveal that the initial water vapor distribution and latent heat release were crucial to the event’s evolution. The intense diabatic heating enhanced upper-level divergence, amplifying a Rossby wave packet that propagated across the Pacific in three days, significantly faster than the associated moisture plume, ultimately amplifying a downstream trough and driving the Alaska and British Columbia flooding. The adjoint perturbations exhibited energy growth exceeding 300-fold, over 72 hours, driven by the efficient conversion of initial moist potential energy to kinetic energy, underscoring the role of diabatic processes in trans-Pacific dynamic linkages and forecast sensitivity. This analysis provides a dynamically consistent pathway demonstrating how upstream diabatic forcing can directly limit the predictability of remote, high-impact weather.

Abstract. Data drives our understanding of hydrological processes, supports model development, and enables anticipatory water management. This contribution introduces EARLS: European Aggregated Reconstructions for Large-sample Studies. EARLS offers daily streamflow reconstructions for more than 10,000 basins in Europe including uncertainty estimates, covering the period from 1953 to 2023. The reconstruction is derived from a single Long Short-Term Memory (LSTM) based rainfall–runoff model trained on more than 5,000 basins. LSTMs represent the state of the art in rainfall–runoff modeling and are well suited to provide predictions in ungauged basins. We evaluate the quality of the reconstruction through quantitative evaluation on two held-out sets of basins and by conducting a qualitative assessment that compares EARLS-based peak flows and flood timing to previous large-scale hydrological studies. EARLS represents a new generation of datasets that harness the capabilities of Deep Learning to obtain accurate and high-resolution data. EARLS is available at https://doi.org/10.5281/zenodo.13864843 (Klotz et al., 2024b)

Abstract. South and Southeast Asia is a major global hub for paddy rice cultivation, with the highest rice cropping intensity worldwide owing to its favorable hydrothermal conditions. The region has also experienced considerable spatiotemporal changes driven by climate change and anthropogenic activities. However, the absence of spatially explicit long-term datasets on paddy rice distribution and cropping intensity hinders effective agricultural and environmental management. This gap is particularly critical in the 21st century, as changes in climate, water resources, and food trade patterns increasingly reshape regional rice production systems. Using all available Landsat and Sentinel-2 archives, we refined a phenology-based algorithm to generate 30 m multi-year composite rice distribution and cropping-intensity products across South and Southeast Asia for four nominal reference years: 1995, 2005, 2015, and 2024. The algorithm addresses the challenge of detecting rice cropping intensity from long-term satellite time series and comprises three core steps: (1) identifying pixel-level rice phenological peaks using an enhanced peak detection method, thereby defining potential transplanting windows and minimizing monsoon-induced cloud and precipitation interference; (2) detecting paddy flooding signals and delineating rice cultivation areas based on phenological rules derived from the relationship between the Land Surface Water Index (LSWI) and Enhanced Vegetation Index (EVI); and (3) determining rice cropping intensity according to the number of valid crop peaks and associated flooding signals detected within the corresponding composite-period time series. The resulting maps were validated using 23 396 samples derived from a field photo library, visual interpretation of Sentinel-1/2 satellite imagery, and a sample migration algorithm. Across the four periods, the maps achieved overall accuracies ranging from 83.74 % to 87.60 %. In addition, the resulting products were compared with existing regional and period-specific rice datasets (e.g., NESEA-Rice10 and Open-SEA-Rice-10) for further evaluation. The comparisons demonstrated that the refined approach achieved higher accuracy and robustness in mapping both rice distribution and cropping intensity. When compared with official FAO statistics for South and mainland Southeast Asian countries, the derived maps yielded R2 values exceeding 0.9. These products hold great potential for applications such as methane emission estimation, water resource management, and crop yield monitoring, thereby supporting sustainable agricultural practices and policy development in the region. The dataset and source code are available at https://doi.org/10.5281/zenodo.21349862 (Zhao et al., 2026).

Abstract ENSO teleconnections are a key source of Antarctic sea‐ice predictability, particularly in the Antarctic Dipole (ADP) region, but whether this predictability remains stable under the recent transition toward Central Pacific (CP) El Niño is unclear. Here, using a Markov model, we reveal an 83% decrease in sea ice concentration (SIC) predictability, measured by the anomaly correlation coefficient (ACC), during austral winter and spring since 2002. We show that this decline is linked to a weakened ENSO–Antarctic teleconnection, in which the CP El Niño‐triggered Rossby wave train shifts northward away from the Antarctic sea‐ice zone and toward the open ocean. This northward shift reduces the wave train's influence on Antarctic sea ice, dampens sea‐ice persistence, lowers its signal‐to‐noise ratio, and consequently limits predictability. Our results suggest that ENSO–Antarctic teleconnections may provide a useful benchmark for evaluating and improving Antarctic sea‐ice prediction models.

Abstract. The Penman–Monteith–Leuning (PML) model is a widely recognized diagnostic framework for estimating coupled terrestrial evapotranspiration (ET) and gross primary production (GPP). To address the critical need for high-fidelity, long-term, and near-present eco-hydrological records, we developed the PML-V2.2 dataset, spanning from 1982 to 2025. Driven by observation-constrained Multi-Source Weighted-Ensemble Precipitation (MSWEP) and Multi-Source Weather (MSWX) meteorological variables, the dataset comprises three complementary products: (1) PML-V2.2a, an 8 d 500 m MODIS/VIIRS satellite-based product (2000–2024 and 2012–2025) optimized for near-present monitoring (updated annually); (2) PML-V2.2b, a half-month 0.1° AVHRR-based product (1982–2020) anchoring long-term climate attribution; and (3) PML-V2.2c, a consolidated half-month 0.1° record integrating the above products for seamless 44-year continuity (1982–2025). Our methodological framework features an expanded bottom-up calibration using 208 flux sites (∼ 1400 site-years) across various plant functional types (PFTs) and a refined parameterization that explicitly distinguishes between irrigated and rainfed croplands. This distinction effectively mitigated systematic biases in agricultural regions, reducing ET and GPP estimation errors by 8.7 % and 16.2 %, respectively. Performance evaluation reveals high accuracy across PFTs (cross-validation Nash-Sutcliffe Efficiency, NSE > 0.60, absolute bias < 5 %), while top-down water-balance validation across 56 large river basins during 1982–2016 and 152 basins during 2003–2020 confirms high reliability (NSE: 0.89–0.91) as compared with other products. The MODIS- and VIIRS-based PML-V2.2a datasets are internally consistent, and exhibit high agreement with PML-V2.2b during their overlapping period (NSE = 0.90 and 0.79 for annual ET and GPP anomalies), ensuring a seamless transition across satellite epochs. Based on the consolidated PML-V2.2c dataset, global terrestrial ET and GPP during 1982–2025 are estimated at 65.8 × 103 km3 yr−1 (with 58.2 % from transpiration) and 143.4 PgC yr−1, respectively. Long-term analysis reveals significant (p < 0.05) increasing trends in GPP (0.343 PgC yr−2) and ET (0.019 × 103 km3 yr−2) during 1982–2025, where vegetation greening impact on ET is partially offset by physiological water saving under rising atmospheric CO2, consequently enhancing water use efficiency. By bridging the gap between satellite epochs, PML-V2.2 provides an internally consistent long-term global dataset for hydrology, ecology, and other Earth science studies. The dataset is freely accessible, with the 500 m resolution PML-V2.2a product hosted on Google Earth Engine, and all 0.1° PML-V2.2a/b/c versions archived at the National Tibetan Plateau Data Center under https://doi.org/10.11888/Terre.tpdc.303314 (Xu et al., 2026).

Global drylands face compounding threats from climate change and rapid human expansion, yet satellite observations paradoxically show widespread vegetation greening. This study investigates the divergent ecohydrological impacts of agricultural and urban expansions across 8,507 global dryland basins from 2000 to 2022. By integrating cloud-based, multi-source Earth observation data with a Spatial Durbin Model, we decouple the local footprints and spatial spillover effects of human land-use on surface water loss. The results unveil a pervasive Greening Illusion associated with cropland expansion, where gains in vegetation productivity are strictly sustained by the severe depletion of surface water bodies, affecting 43.33 % of the studied basins. In contrast, urban growth consistently triggers simultaneous ecological and hydrological degradation. Crucially, the spatial econometric analysis (spatial interaction coefficient ρ = 0.616) demonstrates that the indirect spatial spillover effect of agricultural expansion on water loss is significantly positive (+0.0584) and vastly overpowers its non-significant direct local footprint (‒0.0051) under the baseline k = 6 specification. This massive spatial contagion is structurally robust, exhibiting a 16.0-fold magnitude over the local footprint under physical basin contiguity, which validates a beggar-thy-neighbor dynamic, indicating that localized water withdrawals propagate severe water scarcity to hydrologically connected ecosystems. Our global functional diagnosis identifies 6,679 desiccated hotspots globally that have experienced near-complete surface water desiccation. These findings challenge the assumption that vegetation expansion universally benefits drylands and highlight the urgent need for integrated basin-scale governance, strict regulation of land conversion based on finite hydrological carrying capacities, and transboundary water-sharing frameworks to mitigate systemic ecohydrological risks.

Abstract Urban areas are major sources of greenhouse gas (GHG) and air pollutant emissions. Given their substantial potential for mitigation, urban emissions need to be accurately monitored in a timely manner. Satellite observations have proven to be a practical approach -and an independent, objective verification support tool- for monitoring emissions from urban areas worldwide. In particular, the combined use of co-emitted GHG and air pollutant data, such as Carbon Dioxide (CO₂) and Carbon Monoxide (CO), can help characterize combustion type and efficiency across different countries and regions, as well as monitor changes in response to economic development and/or mitigation policy measures. This study examines the utility of space-based GHG and air quality (AQ) data collected by the Greenhouse Gases Observing Satellite-2 (GOSAT-2, 2018-present) for studying urban emissions. GOSAT-2 uniquely collects GHG and CO data simultaneously, enabling co-emitted gas analysis without requiring co-located satellite data. In particular, this study uses partial column CO₂ data and total column CO data retrieved for 2018–2021. We estimate concentration enhancements of CO₂ and CO over 69 urban areas worldwide (population > 1 million) and examine their relationships. We find that the CO₂-CO relationship is well represented by the Modified Environmental Kuznets Curve (MEKC), which describes the link between air quality and economic development. We also examine CO/CO₂ ratios calculated using GOSAT-2 data and the Emissions Database for Global Atmospheric Research (EDGAR) inventory. Urban areas in Southeast Asia and Africa regions show large discrepancies (>40%) between ratios from GOSAT-2 and EDGAR, which might be attributable to biofuel use that is poorly represented in inventories. These findings suggest that simultaneous CO₂ and CO observations could help monitor emissions in regions with less robust bottom-up estimates. Such observations also provide a top-down, independent assessment of emission inventories and progress toward sustainable development goals (SDGs).

Climate warming has driven widespread shrub and tree expansion across the Canadian boreal-tundra. This is widely expected to amplify surface warming. However, whether this expectation is supported by observations remains unresolved. Here we show, using satellite observations of vegetation change alongside climate and surface-energy data from 1986 to 2023, that about 70% of central and northern Canadian ecozones exhibit net surface cooling tendencies, driven by interactions between vegetation change and the environment that reduce the energy available to warm the land surface ( −0.003 to −0.009W m−2 yr−1). Cooling is strongest in landscapes transitioning toward mixedwood forests, broadleaf vegetation and treed wetlands, reducing warming by approximately 0.015–0.028°C yr−1 relative to shrub- and conifer-dominated areas. These findings challenge the view that vegetation expansion at high latitudes uniformly drives warming, showing that its effects depend on vegetation type, moisture and environmental context. They further suggest that the continued spread of mixed vegetation communities may moderate surface warming previously associated with conifer-dominated landscapes. Using four decades of satellite observations, this study shows that expansion of trees and shrubs across Canada’s boreal-tundra transition is associated with predominantly cooling-consistent surface-energy responses across most ecozones, highlighting important climate feedbacks of ongoing vegetation change.

Abstract. The vast majority of studies examining the impact of freshwater from ice sheet melting on the Atlantic Meridional Overturning Circulation (AMOC) use climate models that cannot resolve mesoscale ocean processes and do not include an accurate spatio-temporal distribution of the freshwater forcing. These two factors critically affect the nature of the AMOC response. Our study fills that gap with a set of three hosing experiments performed with the global configurations of the eddy-rich climate model EC-Earth3P-VHR. The model is forced for 21 years with a spatial and monthly distribution of Greenland meltwater fluxes derived from observations, equal to 0.04 Sv on an annual average. Within the first year, we observe a response of reduced salinity in the Greenland and Labrador currents. This is accompanied by an acceleration and a cooling along the currents that lead to a rapid weakening of the AMOC at subpolar latitudes. Around year 7, deep mixing in the Labrador Sea begins to weaken due to as freshwater anomalies accumulate through lateral exchanges with the boundary currents. This shallowing of the mixed layer further weakens the AMOC, resulting in a stronger reduction that reaches also the subtropical latitudes. By the end of the simulation, the AMOC has weakened by almost 3 Sv at subppolar latitudes (i.e. a decrease of around 20 %), with an average relative decrease of 10 % for the whole Northern Hemisphere. The reduction in the AMOC is strong enough for some global climate impacts to emerge, such as the “bipolar seesaw” temperature response.