Atmospheric and Oceanic Sciences

Abstract This study investigates spatiotemporal changes in socioeconomic exposure to tropical cyclone-induced precipitation (TCP) extremes across China using high-resolution rainfall and gridded population datasets. Results show that extreme TCP exhibits a significant increasing trend over central–southern China, while decreasing trends are observed in parts of the Yangtze River Delta. These contrasting patterns are linked to changes in TC characteristics, including intensified precipitation rates and reduced translation speeds over coastal regions, which enhance rainfall persistence and accumulation and thereby contribute to increasing extreme TCP events inland. Regions with high socioeconomic exposure are mainly concentrated in southeastern and central–eastern China. Based on a modified exposure index that integrates physical hazard and socioeconomic factors, we find that although exposure is jointly determined by these components, its recent increase is primarily driven by intensified TCP intensity and duration, with population growth and economic development further amplifying the impacts. Population and Gross Domestic Product exposure to extreme TCP events (>200 mm) increase by approximately 27% and 17%, respectively, with pronounced spatial heterogeneity. Rapid urbanization and economic expansion in recent decades have further elevated vulnerability to TC-induced precipitation extremes. This multiscale framework provides actionable insights for prioritizing mitigation and adaptation strategies, enhancing climate resilience in high-risk zones.

Abstract. Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by years, preventing a timely understanding of CH4 sources, sinks, and trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates using a machine-learning emulator to reconstruct spatially explicit monthly emission fields at 1° × 1° resolution. We apply this framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record that covers the 2000–2020 emissions through 2025. In the test data (∼ 30 % of the total dataset), the emulator achieved a global R2 of 0.65 ± 0.003 (mean ± 95 % CI, hereafter) and an RMSE of 5.49±0.12×10-3 Tg CH4 yr−1. The emulator is trained on 35 GMB model estimates, including 22 process-based models and 13 atmospheric inversions, paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. Our results show that the global mean predicted wetland CH4 emissions for 2021–2025 (157.8 ± 2.4 Tg CH4 yr−1) are not significantly higher (∼ 0.05 Tg CH4 yr−1) than the 2000–2020 baseline. However, this stability masks a significant hemispheric redistribution of emissions. We detect an increase in Northern Hemisphere (NH) emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 and 0.35 ± 0.03 Tg CH4 yr−1, respectively, while the tropics and Southern Hemisphere (SH) extratropics show offsetting negative trends (−0.95 ± 0.19 and -0.11±0.02 Tg CH4 yr−1, respectively). The predicted emissions are able to capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Furthermore, we identify a distinct seasonal amplification of global emission trends that peaks in late boreal summer. This new modeled dataset and operational framework bridge the gap between the latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).

Abstract Weather radar is an essential observation tool that facilitates real-time situational awareness of the atmosphere providing forecasters with critical information to issue watches and warnings. In the continental United States, the WSR-88D and TDWR networks have been operating for several decades and have provided a significant improvement in the detection of severe weather including, but not limited to, tornadoes, hail, severe wind, and flash flooding with continuing research to operations. Despite the impressive capabilities of the radars, there are many areas within the CONUS where radar coverage is inadequate for proper low-level analyses of the atmosphere. In addition, with changing weather and climate patterns, the need for coverage in previously underserved areas has increased. The National Weather Service has announced plans for a future radar network that will replace the existing aging network and could consist of a hybrid network of radars, transmitting at different wavelengths and consisting of a mix of federal and non-federal radar networks. Climavision, a U.S. based weather and climate start-up, is in the process of establishing a supplemental radar network throughout the United States that will consist of upwards of 200 polarimetric X-band radars. This paper provides an overview of the supplemental radar network and presents preliminary results from the fleet.

Abstract Climate conditions worldwide are influenced by the mean and variability of the tropical Pacific zonal sea surface temperature (SST) gradient. How this gradient responds to greenhouse gas forcing is therefore critical for accurate future climate projections. The nature of the response, however, remains debated: historical model simulations favor a weakening trend, whereas observational records from the same period are characterized by a strengthening trend. To explain this model–observation discrepancy, some attribute the observed trend to internal variability or observational uncertainties, while others suggest that models may inaccurately simulate the radiatively forced response. Past studies have analyzed different trend intervals and observational datasets, potentially contributing to conflicting conclusions about whether observations reflect the forced response. We present a comprehensive analysis of observed zonal SST gradient trends and their statistical significance. We estimate observed trends over all 20-year or longer intervals within the 1870–2024 period and subsequently evaluate these trends against a series of null hypotheses using bootstrapped ensembles of various statistical, conceptual, and geophysical models. Our analysis reveals that both strengthening and weakening trends are observed, depending on the analyzed intervals; however, intervals extending into the 21st century, particularly those since 1950 or those over a century or longer, exhibit statistically significant strengthening trends, suggesting that such trends are unlikely to have emerged from internal variability alone. This finding has implications for the historical and probable near-term transient responses, indicating they are likely radiatively forced. We confirm these findings with multiple observational datasets, demonstrating that data uncertainties minimally influence our conclusions.

Abstract Oceanic currents redistribute nutrients, phytoplankton, and other biogenic materials, fundamentally shaping marine biodiversity and ecosystem functioning. Yet, the topology of fine‐scale material transport remains poorly resolved due to limitations in high‐resolution flow observations. Here, by constructing Lagrangian flow networks from the Surface Water and Ocean Topography (SWOT) observations, we analyze surface fine‐scale transport features in the South China Sea in 2023. Compared with networks derived from conventional altimetry products, SWOT‐derived networks identify more sinks, sources, and transport gateways at 2–10‐day timescales and spatial scales below ∼60 km (90 km) in summer (winter). As such, SWOT resolves hydrodynamic provinces that remain invisible to conventional altimetry, revealing previously undetected corridors and barriers of surface exchange. This advantage also provides better dynamic explanations for complex phytoplankton community structures and evolution. Our results highlight SWOT's transformative capacity to improve the diagnosis and prediction of ocean material transport, opening new avenues for interdisciplinary oceanographic and ecological applications.

Accurate forecasting of fine particulate matter (PM2.5) remains a global challenge due to spatial gaps, data imbalance, and limited representation of extreme events. This study presents an enhanced Deep Imbalanced Regression (DIR) framework that integrates NASA’s GEOS-FP forecasts with global ground-based PM2.5 observations using a Temporal Convolutional Network (TCN) and a Residual Mixture-of-Experts (ResMoE) architecture. The model was trained on 378,000 samples (2021–2025) from U.S. Embassy AirNow sites and OpenAQ sensors, increasing geographic diversity. To address the imbalance, Label Distribution Smoothing (LDS) and weighted loss were applied, while ResMoE adaptively routed samples to specialized experts across meteorological-aerosol regimes. This configuration achieved strong performance (R² = 0.88, MSE = 23.4, MAE = 2.86 µg/m³) and generalized well across polluted and clean regions, including unseen sites. During the May 2025 Minnesota wildfire, the model captured both temporal evolution and peak magnitude missed by the TCN baseline, demonstrating improved responsiveness to extreme events. Uncertainty quantification and sensitivity analysis confirm model consistency. Beyond forecasting, the framework enables spatiotemporally consistent PM2.5 reconstruction for exposure assessment and policy analysis in data-scarce regions. This study provides a scalable and interpretable pathway for next-generation global air-quality forecasting.

Mesoscale Convective Systems (MCSs) are the primary drivers of extreme rainfall and flood hazards. Understanding their response to global warming is therefore important for improving climate projections and disaster risk reduction. In East China, however, evidence remains largely limited to case studies, and long-term projections of MCSs are scarce due to their poor representation in coarse-resolution climate models. Using convection-permitting model simulations with a pseudo–global warming approach, this study presents 22-summer projections of future MCSs over East China under the SSP5-8.5 scenario. Results show that future MCSs exhibit higher precipitation intensity and wider convective areas. Convective precipitation intensifies more than stratiform precipitation, indicating a transition toward more convection-dominated systems, accompanied by enhanced mid-level updrafts and super–Clausius–Clapeyron scaling of extreme rainfall. Increases in convective available potential energy (CAPE) and total column water vapor (TCWV) largely explain the intensification of maximum convective precipitation, with vertical zonal wind shear acting as a key dynamic modulator. The product of TCWV and vertical velocity is identified as an effective predictor of MCS peak precipitation. These findings imply heightened risks of intense rainfall from MCSs in East China and highlight the combined roles of thermodynamic and dynamic processes in shaping future MCSs.

Abstract Metasomatized lithospheric mantle plays a critical role in the petrogenesis of CO 2 -rich magmas, which are important hosts of rare-earth element deposits. However, the relationship between the structure of the lithosphere and the global distribution of CO 2 -rich magmas remains poorly quantified. Here we analyse the locations of young (<200 million years ago) continental intraplate CO 2 -rich silicate magmas and magmatic carbonatites in conjunction with upper-mantle shear-wave velocity anomalies and lithospheric thickness estimates. Our results document systematic increases in lithospheric thickness with estimated magma CO 2 content from basanites (<5 wt% CO 2 ), which erupt through seismically slow and thin non-cratonic lithosphere, to nephelinites, melilitites and ultramafic lamprophyres, which occur within progressively faster, thicker lithosphere and, finally, to lamproites and kimberlites (<20 wt% CO 2 ), which are emplaced on thick cratonic lithosphere. Carbonatites are associated with lithospheric thicknesses similar to those of nephelinites, melilitites and ultramafic lamprophyres, implying the derivation of carbonatites from these mafic CO 2 -rich silicate magmas via liquid immiscibility and/or fractional crystallization. We illustrate our lithospheric thickness–magma type relationship using Cretaceous–Pleistocene alkaline magmatism across western North America, ultimately demonstrating how lithospheric thickness controls the global occurrence of CO 2 -rich magmas and, consequently, their associated rare-earth element deposits.

Abstract Satellite and reanalysis rainfall datasets are crucial for meteorological research, hydrological applications, and validating numerical weather prediction models. However, their reliability must be carefully assessed before operational use. The Mumbai MESONET, a high-density network of automatic rain gauges providing minute-level temporal resolution, offers a unique and robust ground-based reference for such validation efforts. This study uses MESONET observations to evaluate the accuracy and consistency of various satellite-based precipitation estimates (MPEs) and reanalysis products across daily and sub-daily timescales. Our findings indicate that the IMERG-F and ERA5 reanalysis datasets have the best performance on daily scales, successfully capturing the overall variability of rainfall, albeit with some discrepancies in magnitude. While satellite and reanalysis products can capture diurnal rainfall cycles, they often differ in their representation of rainfall peaks, particularly during heavy precipitation events. Bias-corrected products, such as GSMaP-MVK and IMERG-F, demonstrate improved accuracy but continue to underestimate extreme rainfall events. Although satellite-based products show limitations in detecting light rainfall at shorter timescales, they perform reasonably well for moderate to heavy rainfall events. Statistical indices show that the performance of the target datasets considered for evaluation improves significantly after a 12-hour accumulation period. Based on the normalized composite score derived from all evaluation metrics, IMERG-E emerges as the most suitable product for real-time applications, such as flood forecasting and hydrological modeling. IMDAA outperforms INSAT-based products (i.e., INSAT-HE and IMSRA) in terms of overall accuracy over the study region.

Abstract. Accurate diagnosis of ozone (O3) formation sensitivity (OFS) is essential for designing effective precursor-control strategies, yet long-term, observation-based, and interpretable national-scale assessments remain limited. Here, we combined OMI satellite observations of tropospheric nitrogen dioxide (NO2) and formaldehyde (HCHO) with ground-based O3 measurements to derive region-specific FNR (HCHO / NO2) threshold ranges from the relationship between FNR and O3 exceedance probability. Based on these thresholds, we characterized the spatiotemporal evolution of OFS across China during the warm season (April–September) from 2005 to 2023 and coupled Random Forest (RF) with SHapley Additive exPlanations (SHAP) to quantify contributions from emission-related and meteorological factors. The results reveal a clear phase reversal in OFS over China. From 2005 to 2012, the national area fraction of nitrogen oxides (NOx)-limited regimes decreased by 7.9 %, while transitional and volatile organic compound (VOC)-limited regimes increased by 5.6 % and 2.3 %, respectively. After 2013, this pattern reversed, and by 2023 NOx-limited regimes expanded to 76.5 % of the polluted area, whereas VOC-limited regimes accounted for only 2.8 %. Regionally, after 2013, both Beijing–Tianjin–Hebei (BTH) and Fenwei Plain (FWP) exhibited transitions between transitional and NOx-limited regimes, while the Yangtze River Delta (YRD) showed a shift toward NOx-limited regimes. Sichuan Basin (SCB) remained predominantly transitional, and Pearl River Delta (PRD) also shifted toward NOx-limited regimes. SHAP analysis shows emission-related variables contributed 50.1 %–69.4 % of total importance, exceeding meteorological factors, which increased after 2013. Overall, OFS evolution is primarily associated with emission changes but increasingly modulated by meteorological conditions under cleaner conditions.

The Gravity Recovery and Climate Experiment (GRACE) mascon data relies on minor gravitational field variations to map terrestrial water storage anomaly (TWSA). However, the coarse spatial resolution of three degrees by three degrees restricts their application for evaluating small-scale changes in water storage. To address this challenge, in this study, GRACE and GRACE Follow-On (GRACE-FO) data from 2003 to 2023 were downscaled to 800-m resolution across the Coastal Lowland Aquifer System (CLAS) in Texas, Louisiana, Mississippi, Alabama, and Florida. This downscaling used machine learning (ML) models, including Random Forest (RF), Artificial Neural Network (ANN), and Deep Neural Network (DNN). These models incorporated variables such as anomalies in total precipitation (APT), mean temperature (ATM), normalized difference vegetation index (ANDVI), evapotranspiration (AET) from 2003 to 2023, Shuttle Radar Topography Mission DEM, slope angle, soil type, and lithology to generate monthly 800-m TWSA maps. The ANN model showed strong predictive performance (R2 = 0.869–0.989 with low RMSE), although the DNN achieved slightly better statistical accuracy and spatial evaluation metrics; however, ANN was selected for its more realistic and spatially consistent outputs regionally. Building on this improved spatial resolution, analysis of the downscaled TWSA data from 2003 to 2023 identified an overall declining trend in water storage. Trend analysis using linear regression shows that the western CLAS—particularly the Gulf Coast aquifer in Texas and western Louisiana—experiences the strongest depletion, with rates of −0.30 and −0.17 cm/year in Zones 1 and 2, respectively, with Zone 1 being statistically significant. In contrast, the eastern CLAS shows relatively stable conditions, with weak, non-significant increases (+0.05 to +0.18 cm/year), likely reflecting natural variability rather than sustained long-term gain. Therefore, ML-based downscaling of GRACE data enables high-resolution TWS assessment and provides a framework for future extraction of groundwater storage anomalies (GWSA), supporting improved groundwater management.

Abstract. Carbonyl oxide (CH2OO) is paramount in atmospheric oxidation chemistry, yet quantitative kinetics data for its bimolecular reactions are very limited and even unknown. Here we establish a computational framework to obtain quantitative kinetics from small to large reaction systems. For CH2OO + HCHO, we develop electronic structure methods to reach CCSDTQ/CBS accuracy for its activation enthalpies at 0 K. For CH2OO + aldehydes (RCHO; R= CH3–C5H11, CH2F, CHF2, CF3), we introduce two strategies that recover CCSDTQ/CBS-quality activation enthalpies at 0 K. A dual-level strategy has been used to calculate their kinetics. The calculated rate constants show excellent agreement with available experimental data for CH2OO + RCHO (R = CH3–C3H7), which validates the designed computational framework. We find that fluorination leads to exceptional rate enhancement, with reactions of CHF2CHO and CF3CHO exceeding 10−10 cm3 molecule−1 s−1 over 200–320 K, approaching the collision limit. We also find that fluorination-driven reactivity enhancement originates predominantly from lower-level electronic effects than that of post-CCSD(T). Incorporation of the kinetics into a global chemical transport model uncovers previously unrecognized atmospheric impacts, with CH2OO + HCHO reducing nighttime CH2OO and gas-phase sulfate concentrations by 25.3 % in Antarctica and 12.2 % over Canada, respectively. The present findings address a long-term challenge in how to obtain quantitative kinetics for large molecular systems, where post-CCSD(T) calculations are prohibitive and provide new insights into the chemical transformation of CH2OO and fluorinated aldehydes in the atmosphere.

Abstract. A new series of mascons are made from GRACE and GRACE-FO data, specifically designed for use by oceanographers interested in studying variations in ocean mass transport and circulation. This series has pre-removed those changes in ocean mass distribution caused by barystatic gravity, rotation, and deformation (GRD) signals, as well as the non-oceanographic signals caused by four major oceanic earthquakes, neither of which impact circulation. Subtle changes in the processing and regularization schemes also help reduce the visibility of instrument/orbital errors in the ocean signal, particularly in the Arctic and near the sites of the removed earthquakes. The primary benefit of this data set is increased ease of use for researchers interested in ocean dynamics, as the product is designed to be used “off the shelf” with no additional corrections required, even by those less familiar with GRACE data usage. The complete dataset is available at https://doi.org/10.18738/T8/3VUPEW (Pie et al., 2025).

Abstract. Tropospheric ozone pollution in South Asia is mainly blamed on anthropogenic emissions. However, based on ERA5 reanalysis data, this study highlights the contribution of stratospheric ozone intrusions into the Upper Troposphere and Lower Stratosphere (UTLS) associated with Sudden Stratospheric Warming (SSW) events in enhancing upper tropospheric ozone over the South Asian region. We report an enhancement in ozone in the UTLS by more than 80 % for 2018 and ∼ 30 % within ±6 d of the onset during SSW events concurrent with the westerly phase of Quasi-biennial oscillation (WQBO-SSW) compared to non-SSW years. The equatorward shift (south of 30° N) of the subtropical jet during WQBO-SSW causes lowering of the tropopause and more Rossby-wave breaking in the upper troposphere. This results in higher stratospheric ozone intrusions over the South Asian region. The ozone enhancement during WQBO-SSW events produces an instantaneous radiative forcing at the top of the atmosphere of 0.09±0.05 W m−2 due to UTLS ozone changes and 0.17±0.05 W m−2 from total-column ozone changes over South Asia.

ABSTRACT Northwest China (NWC), as a typical arid and semi‐arid region, has exhibited a pronounced warming and wetting trend since the 1980s. However, changes in the precipitation structure in the NWC remain poorly understood. Based on observational data and CMIP6 multi‐model simulations from 1961 to 2014, this study analyses variations in precipitation structure over the NWC and conducts the attribution analysis using the optimal fingerprinting method to quantify the relative contribution from different external factors. The projection of changes in precipitation structure is also studied. The results demonstrate a transition in precipitation structure characterised by reducing light precipitation frequency and increasing amount and frequency of heavy precipitation. Attribution analysis identifies greenhouse gases (GHG) forcing—primarily driven by anthropogenic emissions—as the dominant driver of these structural changes. In contrast, aerosols (AER) forcing and natural (NAT) forcing contributions remain statistically insignificant. The characteristics of changes in the precipitation structure in the NWC are projected to continue in the future, with a marked increase in both the frequency and amount of heavy precipitation events. Compared with the raw projection, the constrained projections exhibit a substantially stronger magnitude in precipitation structure changes. These findings underscore the urgent need to mitigate greenhouse gas emissions and to implement adaptive strategies to address heavy precipitation in the region.

The atmospheric boundary layer (ABL) has strong diurnal variability, but routine radiosonde launches at 00:00 and 12:00 UTC cannot fully resolve its daily evolution. This study develops and evaluates a 13-year (2007–2019) hourly ABL profile dataset using Aircraft Meteorological Data Relay (AMDAR) observations from 42 selected European airports, and applies it to characterize airport-scale diurnal, seasonal, and regional variations in ABL structure. AMDAR-derived temperature and wind profiles were validated against collocated radiosonde observations by season, pressure layer, and airport–radiosonde distance. Errors decrease for shorter separation distances and lower-tropospheric layers. For separations < 50 km and pressures > 850 hPa, spring, summer, autumn, and winter RMSEs are 0.9/1.0/1.4/1.2 K for temperature, 1.7/2.0/1.9/1.9 m/s for zonal wind, and 1.4/1.6/1.9/1.6 m/s for meridional wind. Hourly AMDAR profiles reveal distinct diurnal ABL evolution at airport scale. Seasonal ABL height (ABLH) composites are mainly 250–900 m, with available nighttime and early-morning values of about 300–450 m and spring–summer afternoon maxima of 800–900 m at far-inland airports. Coastal airports show weaker daytime growth, mostly below 600–650 m. These results demonstrate AMDAR’s value as a supplementary profile dataset for characterizing European airport-scale ABL structure and diurnal variability.