Atmospheric and Oceanic Sciences

High-resolution regional forecasts support disaster mitigation and sectoral decisions, yet numerical regional models remain costly. Here we introduce AERO-ODE, a physics-guided framework that combines global dynamical information, parameterization increments, and regional terrain constraints. From global initial conditions, AERO-ODE generates 72 h regional forecasts at hourly intervals and 3 km resolution in approximately 21 s, covering pressure-level Z , T , S , U and V and near-surface MSLP, U 10 , V 10 and T 2 m , without separate global surface lateral-boundary inputs. At 48 h, the median reductions in RMSE across the 29 outputs are 29–32% and 28–33% compared with WRF-ARW and YingLong-WRF, respectively; all reductions relative to WRF-ARW are significant in both regions ( p < 0.05). Median anomaly correlation is 0.04–0.05 higher than WRF-ARW, while extreme-event CSI and ETS improve for most variables. AERO-ODE generally shows lower errors than interpolated NeuralGCM, IFS and PanGu-Weather fields. These results demonstrate rapid, high-resolution regional forecasting from global dynamical information and regional constraints.

Abstract The Antarctic Ice Sheet is the largest source of uncertainty in sea-level rise projections, with uncertainties propagating through emissions scenarios, atmosphere–ocean general circulation models, ice-sheet dynamics and sea-level physics. In ice-sheet model intercomparison exercises, these uncertainties—typically attributed to intermodel differences—stem from modelling choices that may bias ensembles towards commonly adopted approaches regardless of observational consistency. Here we quantify how each individual physical assumption cascades into projection uncertainty using a machine-learning emulation framework, which also enables Bayesian calibration against satellite observations to reduce projection bias. Our results suggest it is very likely (≥0.92 probability) that the Antarctic Ice Sheet is committed to twenty-first-century mass loss, even under aggressive emissions-reduction scenarios. Higher emissions drive greater Antarctic mass loss by 2100 (≥0.89 probability), directly elevating near-term coastal risks. Under very high-emissions scenarios, we identify cascading mechanisms that could produce up to 25.4 cm of sea-level rise by 2100 (95th percentile; median = 15.7 cm) while remaining consistent with satellite observations. Effective management of these risks to densely populated coastal communities requires rapid emissions reductions and improved constraints on climate model selection, sliding laws and ice-shelf melt parameterizations.

Abstract Flood frequency analysis (FFA) forms the basis of many hydrological assessments and practical design calculations. Traditionally, FFA is based on the assumptions of stationary distributions and homogeneous samples. These assumptions are questionable in light of climate change and the wide variety of hydrological processes that lead to flooding. Furthermore, these assumptions can obscure important insights, including process‐specific changes. In this study, we develop a process‐aware, non‐stationary mixture model that combines type‐specific distributions with time‐varying parameters. An application to more than 600 catchments across Europe shows that the various flood types change differently over time. Heavy rainfall floods at many locations in Europe (e.g., the Alps and UK) are increasing significantly, whereas snowmelt floods are decreasing. In conventional models, these findings are obscured by the combined analysis of opposing trends, and trends are often not recognised. This causes design quantiles, for example, for a 100‐year return period, to be underestimated by about 15% on average in many regions of Europe when non‐stationary, type‐specific distributions are not used. Our results highlight the importance of incorporating process understanding into FFA to improve design‐flood estimation and our understanding of changing flood‐generating processes.

Severe convection produces localized hazards that often require warnings before radar echoes fully reveal storm development. Convective initiation and the maintenance of intense convection remain challenging for radar-only nowcasting because pre-convective signals may be absent from recent radar observations, and strong echoes often decay rapidly in forecasts. Here we present FuXi-Nowcast, an environment-conditioned deep learning system that combines high-resolution observations with three-dimensional atmospheric forecasts to predict composite reflectivity, precipitation, wind gusts, and surface variables up to 12 h ahead. In April–July 2024 evaluations over East China, FuXi-Nowcast outperforms operational numerical weather prediction, persistence, and extrapolation baselines for reflectivity and precipitation. Case studies, diagnostics, and ablation experiments suggest that atmospheric moisture information and explicit preservation of strong convective signals contribute to forecasts of convective initiation and maintenance. These results show that environmental conditioning can mitigate important failure modes of radar-only nowcasting for high-impact convective weather.

Understanding the Earth’s deep interior remains challenging, as traditional geophysical methods face ambiguities in linking seismic observations to temperature, composition, or mass density variations. Atmospheric neutrinos, produced by the constant flux of cosmic rays colliding with the upper atmosphere, offer a complementary probe: with energies of a few GeV, they traverse the Earth and experience flavor oscillations influenced by the planet’s electron density distribution, which depends on both its mass density and composition. Combining seismic observations with neutrino measurements in a joint inversion framework could provide complementary constraints on gross spatial and compositional variations in the deep Earth. We consider the next-generation neutrino detectors KM3NeT/ORCA, Hyper-Kamiokande, and DUNE, as well as an idealized hypothetical detector representing the intrinsic sensitivity of the method. The idealized detector is most sensitive to perturbations in the core, whereas realistic detector performance shifts the sensitivity toward the mantle, including the mantle transition zone, where hydrogen enrichment could produce detectable variations in electron density. These results establish the sensitivity of neutrino oscillations to the Earth’s electron density profile and provide a basis for future joint neutrino–seismic tomography.

ABSTRACT Drought is a complicated and recurrent natural hazard that creates substantial challenges to sustainable water management and climate adaptation. To address these challenges, Multi‐Model Ensembles (MMEs) of GCM simulations are extensively used for assessing future drought conditions. However, to attain correct and precise characterization of drought there is a need to enhance the development of MMEs to reduce uncertainties and augment spatial consistency. Since the geospatial performance of individual GCMs varies considerably across different regions, incorporating these spatial characteristics into the ensemble formation process is essential for developing an efficient and regionally robust ensemble framework. The study proposes a new framework of drought assessment under GCMs simulations based MMEs to enhance the reliability of future drought projections called—Precipitation‐Concentration Kriging Ensemble Drought Assessment Framework (PCK‐EDAF). The construction of PCK‐EDAF comprises two sequential phases. The first phase, termed Geostatistical Homogeneity Analysis and Ensemble Weight Optimization (GHA‐EWO), derives optimal weights based on the spatial and temporal consistency of GCM outputs with observed precipitation data. The second phase, called the Kriging‐Weighted Ensemble Standardized Drought Index (KWESDI), applies these optimized weights to future ensemble simulations and standardizes the resulting drought estimates. Application of the PCK‐EDAF is based on the 103 grid points across Pakistan using simulations from 22 GCMs under multiple Shared Socioeconomic Pathways (SSP1–2.6, SSP2–4.5, and SSP5–8.5). Under PCK‐EDAF, the performance of the GHA‐EWO is evaluated as compared to conventional Equal Weighted Ensemble (EWE) and Mutual Information based ensemble in terms of Root Mean Square Error, Mean Average Error and correlation measures. Findings indicate that the GHA‐EWO is always the most effective, with lower values of RMSE and MAE and higher correlation coefficients, which prove that the model is more accurate and more consistent with the observed precipitation patterns. To determine the trend in drought under KWESDI we applied Mann‐Kendall test and the slope estimator of Sen. Results related to trend analysis show a significant increasing tendency in drought, particularly under SSP1–2.6 and SSP5–8.5 scenarios at longer time scales. SSP2–4.5 shows a weaker drought signal with statistically insignificant trends due to moderate emissions forcing. These results also suggest an amplification of drought and wetness cycles, which indicate increased climate variability in the future. Overall, the findings reveal that integrating kriging‐derived spatial weights within PCK‐EDAF significantly enhances ensemble reliability. Moreover, the developed PCK‐EDAF framework is modular and generalizable, allowing its application to other regions and datasets. This integration further enables a more realistic and spatially consistent assessment of future drought conditions under changing climate scenarios.

Atmospheric evaporative demand is increasing with climate warming and can intensify drought, yet how strongly long-term atmospheric drying propagates into realized river-flow deficits remains poorly quantified. Here using streamflow observations from approximately 19,000 catchments worldwide, we provide a global observational assessment of hydrological drought and compare its evolution with atmospheric drought diagnosed from precipitation and evaporative demand. We show that atmospheric drought trends exhibit a broad spatial predominance towards drying over the past four decades, whereas streamflow drought trends are substantially more heterogeneous and show highly spatially variable change at the global scale. This heterogeneity is consistent with nonlinear drought propagation that can attenuate, delay, reshape or amplify atmospheric drought signals before they emerge in streamflow. Our results provide global observational evidence that long-term atmospheric drying is not transmitted uniformly or proportionally into river flow but can be weakened in many catchments and reinforced in others, highlighting the need to account explicitly for hydrological propagation when assessing water-security risks under climate warming. Atmospheric drought has tended towards drying in recent decades, but this signal is expressed unevenly in river flow. Observations from approximately 19,000 catchments show that hydrological drought responses can be weakened, reshaped or amplified as atmospheric drought propagates through catchments, underscoring the need for drought assessments tailored to the water-related impacts of interest.

Quantifying and understanding methane emissions of cities are of great importance given their role in current and future mitigation efforts to reduce climate-forcing emissions. However, it remains challenging to routinely and accurately characterize and verify city-scale methane emission inventories. Although previous studies of urban methane emissions have employed a range of emission quantification methods, a simple, efficient, and widely applicable framework for quantifying urban-scale emissions remains lacking. In this study, we describe our development of an advanced mass balance emissions accounting using TROPOspheric Monitoring Instrument (TROPOMI) satellite observations to estimate the orbit-level net bulk (city-level) methane emissions and corresponding emissions uncertainties due to the method. We have tested and demonstrated that the novel integration of hourly-resolved wind data under the planetary boundary layer (PBL) enables a more conceptually-accurate assessment of methane emissions from urban areas than methods that do not consider thermodynamic variability. In addition, we have introduced an upwind-based approach for background determination, in which background methane concentrations were defined using city-adjacent regions along the PBL-pressure-weighted-mean upwind direction. Initial assessments with this approach were tested for three megacities (London, Los Angeles and New York) between 2021 and 2023. Results indicate that existing emission inventories generally underestimate urban methane emissions across all three cities, but with significant inter-annual and inter-city variability. Satellite-derived emissions from 2021 to 2023 range from 5.99 to 11.90 t h −1 in London, 26.21 to 62.77 t h −1 in Los Angeles, and 30.85 to 44.77 t h −1 in New York, corresponding to factors of approximately 1.5–3.0, 1.3–3.1, and 7.0–10.2 times the inventory estimates, respectively. Compared with previous top-down urban studies for the same cities, our results are generally consistent with the reported emission estimates, with most estimates falling within our uncertainty bounds. These results demonstrate that satellite observations can facilitate ongoing city-scale emission quantification, support inventory reconciliation and reporting, offer the potential for long-term monitoring globally, and further aid efforts to assess whether stated methane emission targets are being met.

Mount Qomolangma (MQ) serves as a natural laboratory for investigating aerosol–cloud–precipitation interactions over the Tibetan Plateau (TP). Using satellite and comprehensive ground-based observations, we identify pronounced ice-cloud activation associated with transported exogenous aerosols. Under different large-scale atmospheric circulation regimes, ice-phase cloud activated over MQ can be efficiently transported downstream through distinct pathways, exerting a pronounced ice crystal seeding effect on cloud–precipitation conversion. The spatial patterns of these downstream pathways are highly consistent with regions of enhanced ice-phase occurrence, precipitation, and upper-tropospheric latent heat release. This study provides new insight into the downstream impacts of aerosol transport through ice seeding for cloud precipitation. The findings highlight the important role of aerosol-induced ice-phase processes in modulating cloud and precipitation systems over the “Third Pole” and its downstream regions, with significant implications for understanding downstream extreme precipitation and environment change under South Asian increasing anthropogenic influences.

Nitrogen (N) and Phosphorus (P) are essential nutrients for sustaining life on Earth and regulating ecosystem productivity and Earth system biogeochemistry, and have been increasingly applied in global agriculture to meet the growing demand for food production. Quantifying the spatial and temporal dynamics of nutrient inputs to the terrestrial biosphere is crucial for understanding global biogeochemical cycles, analyzing nutrient flows in crop-livestock systems, managing nutrient resources sustainably, and mitigating nutrient-related environmental impacts. Here, built upon our previous work mapping global nitrogen inputs (History of anthropogenic Nitrogen inputs, HaNi), this study presents the History of anthropogenic P inputs (HaPi) dataset, a comprehensive quantification of human-driven P fluxes to terrestrial ecosystems. HaPi covers the period from 1860 to 2020 and has a spatial resolution of 5 arcmin (about 10 km at the equator) with an annual time-step. This harmonized dataset integrates seven components, including P fertilizer application on croplands and pastures, manure P application on croplands and pastures, manure P deposition on pastures and rangelands, and atmospheric P deposition. The results reveal that global total P input increased more than tenfold, from 3.8 Tg yr −1 in the 1860s to 41.0 Tg yr −1 in the 2010s, with mineral fertilizer and livestock manure contributing equally to the increase. Regional patterns have shifted significantly over the study period, with China, South Asia, and Brazil surpassing Europe and the USA as the regions with the highest P inputs in recent decades. Furthermore, mineral fertilizers dominate P inputs in most industrialized countries in the Northern Hemisphere, whereas manure P remains the primary source in many countries of the Southern Hemisphere. The HaPi dataset improves P mass budget calculations and provides essential forcing data for empirical or mechanistic models, supporting critical research in Earth system biogeochemistry, agricultural nutrient management, water quality control, and assessments of the coupled human-Earth system. The dataset is available at https://doi.org/10.6084/m9.figshare.29930279 (Bian et al., 2026).