Atmospheric and Oceanic Sciences

Abstract Compound events (CEs), defined as the co-occurrence of multiple climate extremes, often amplify societal and environmental impacts beyond those of individual extremes. While carbon dioxide removal (CDR) is increasingly considered essential for achieving global climate targets, the response of CEs to CDR pathways remains poorly understood. We investigate changes in three types of CEs—consecutive day and night heatwave event (CDNHE), compound drought and heatwave event (CDHE), and compound precipitation and wind event (CPWE)—under a CO2 ramp-up and ramp-down experiment using CESM1 with 28 ensemble members. Although atmospheric CO2 returns to its initial concentration, the global mean of temperature, precipitation, and wind speed, as well as the frequencies of all three CEs, have not returned to their initial levels by 2300, indicating incomplete recovery. Over the final 30 years of the ramp-down period, a recovery deficit of approximately 15–25% remains. CDNHE increases across low-latitude regions and the Southern Hemisphere in association with persistent SST warming, enhanced surface heating and increased atmospheric moisture, while decreases over Greenland and northern and western Europe coincide with cooling associated with the delayed recovery of Atlantic Meridional Overturning Circulation. CDHE increases in tropical and mid-latitude dry regions through land–atmosphere feedbacks involving soil moisture depletion, increased vapor pressure deficit, and enhanced sensible heat flux. CPWE exhibits a more spatially heterogeneous and temporally delayed response, with its spatial changes more closely associated with precipitation than with wind speed. Regional increases in CEs can exceed those of both individual components by 20–35 percentage points, revealing changes that may be overlooked when individual extremes are considered separately. Our results highlight the importance of accounting for compound climate hazards and their delayed recovery when evaluating CO2 removal pathways.

DESERTAS (Desert Emission Sensing & Energetic Rock-Tectonic Analysis System) introduces the first mathematically integrated, AI-driven geophysical framework for the systematic quantification of geogenic gas emissions from rock fissures in hyperarid environments — the Desert Rock-Gas Intelligence Score (DRGIS). Built on eight physically orthogonal parameters (ΔΦ_th, Ψ_crack, Rn_pulse, Ω_arid, Γ_geo, He_ratio, β_dust, S_yield), DESERTAS transforms the continuous geochemical breath of desert rock fractures into a quantitative diagnostic tool for pre-seismic hazard assessment. Validated against 2,491 Desert Rock-Gas Units (DRGUs) spanning 36 monitoring stations across 7 arid craton systems (Saharan, Arabian Shield, Kaapvaal, Yilgarn, Atacama-Pampean, Tarim Basin, Scandinavian Shield) over 22 years (2004–2026). Key results: - DRGIS Classification Accuracy: 90.6% - Pre-seismic Radon Detection Rate: 93.1% · False Alert Rate: 5.4% - Mean Pre-Seismic Lead Time: 58 days before M ≥ 4.0 events - Maximum Lead Time: 134 days (Saharan Shield, 2019) - Rn_pulse/DRGIS Correlation: r = +0.904 (p < 0.001, n = 2,491) - He_ratio Source Discrimination: 99.1% · depth ±800 m - β_dust Particulate Transport: detectable at 340 km downwind - AI Ensemble vs. single-parameter: +18.2% improvement Submitted to Nature Geoscience, March 2026. Code: https://gitlab.com/gitdeeper07/desertas Dashboard: https://desert-as.netlify.app PyPI: https://pypi.org/project/desertas/1.0.0/

Abstract Flash flooding from intense rainfall causes major damage and loss of life across Africa, particularly in the Sahel, where rainfall is dominated by mesoscale convective systems. Convective cores, which represent regions of intense convective activity, evolve rapidly, limiting short‐term predictability, especially in data‐sparse regions where numerical weather prediction models face substantial uncertainty in convective initiation, evolution, and organisation. This study presents an object‐based deep‐learning framework for probabilistic convective‐core nowcasting using geostationary satellite data. Convective cores are identified from Meteosat Second Generation infrared imagery using a two‐dimensional wavelet transform applied at mesoscale spatial scales. The framework is first demonstrated at a single location, where nearby core attributes are used to predict local core occurrence up to six hours ahead. Explainable artificial intelligence analysis indicates that predictions are driven by physically meaningful factors related to core proximity, size, and intensity. The approach is then extended to the western Sahel through Nowcasting with a Core‐Aware Spatio‐temporal Transformer (NCAST), a spatio‐temporal transformer architecture that models the evolution and interactions of convective‐core populations and produces gridded probabilistic nowcasts. Ablation experiments reveal that self‐attention contributes most strongly to forecast skill, while temporal information becomes increasingly important at longer lead times. Evaluation against persistence and an operational conditional‐climatology benchmark demonstrates skilful forecasts at one‐, three‐, and six‐hour lead times using multiple probabilistic and spatial verification metrics. These results highlight the potential of object‐based learning to provide reliable short‐range nowcasts in data‐sparse environments.

Abstract Organosulfur compounds are important constituents of atmospheric aerosols and have been extensively investigated through field observations, laboratory experiments, and atmospheric modeling. However, the mechanisms underlying their formation and atmospheric abundance remain incompletely understood. Ubiquitous in the atmosphere, micrometer-sized droplets provide unique interfacial reaction environments that may facilitate organosulfur formation. Here, we show that sulfite-derived inorganic sulfur species react with oxygenated volatile organic compounds in microdroplets to generate organosulfur compounds under catalyst-free conditions without external energy input. Theoretical calculations suggest that interfacial electric fields facilitate organosulfur formation by lowering reaction activation barriers. Furthermore, several organosulfur species identified in laboratory experiments were also detected in ambient aerosols collected from an urban site and a high-altitude mountain station, supporting the potential atmospheric relevance of this pathway. Our findings provide insights into the role of microdroplet interface in organosulfur formation and their potential contribution to atmospheric aerosol chemistry.

Abstract. Copernicus Land Monitoring Service (CLMS) produces biogeophysical maps of the global land surface. The CLMS portfolio so far did not include actual evapotranspiration (ETa), despite it being a direct link between the energy, water and carbon cycles and its importance for global food security, efficient water resources management and weather forecasting. However, a global CLMS ETa product is currently under development and will enter operational production by the end of 2025. It will have a spatial resolution of 300 m, dekadal (10-daily) temporal resolution, will consist of evaporation and transpiration sub-products and (like all other CLMS products) will be distributed under free and open data policy. It will be based mainly on Copernicus input data with primary satellite imagery coming from the observations of OLCI and SLSTR sensors on board of Sentinel-3 satellites. Such product will fill a gap in currently existing global and operational ETa products, thus satisfying a wide range on potential users' needs. In this paper, we describe the various design choices taken during the development of the ETa product, ranging from cloud masking and gap-filling, through derivation of biophysical traits, radiation components and weather forcings to spatial sharpening of the land surface temperature observations. Those data were then used to drive two evapotranspiration models: TSEB-PT and ETLook. A prototype implementation of the ETa processing chain was used to produce ETa data across a globally representative range of climatic zones and plant functional types, which was validated against measurements from 104 Eddy Covariance flux tower sites. The resulting overall best root mean squared error (RMSE) of 0.80 mm/day (relative RMSE of 47 %), bias of -0.12 mm/day (relative bias of 7 %) and coefficient of determination of 0.84 compare well with a similar global ETa dataset and are encouraging for the upcoming operational production of ETa maps.

Abstract The Surface Water and Ocean Topography (SWOT) satellite mission offers unprecedented estimates of global river discharge. This study provides a first continental‐scale assessment of SWOT Consensus discharge performance across South America and compares it with a large‐scale hydrodynamic model and in situ observations. Consensus generally exhibits higher skill with a median Kling‐Gupta Efficiency (0.43) across the 569 benchmarked reaches. Performance varies systematically with physiographic settings. Random Forest and Spearman correlation analyses identify slope and distance to outlet as the top‐ranked controls among the variables tested, though no single variable explains performance, reflecting its multi‐dimensional nature. Consensus discharge inherits systematic bias from its machine learning prior, and substantial unexplained variability highlights that further work is needed to fully diagnose low‐skill reaches.

Black carbon (BC) is a highly toxic air pollutant and potent short-lived climate pollutant. China’s Dual Carbon strategy—peaking carbon emissions before 2030 and achieving carbon neutrality before 2060—has driven rapid BC emission reductions. However, global inventories still misrepresent BC emissions in China, biasing estimates of its health and climate impacts. Using a localized BC inventory and revised health risk function, we estimate that over 540,000 premature deaths were attributable to BC in China in 2015, more than twice previous reports. Conversely, the Coupled Model Intercomparison Project Phase 6 (CMIP6) inventory overestimates BC radiative forcing by 45%. Future projections show that demographic aging may offset health benefits from emission reductions. Our findings reveal that simulation based on CMIP6 inventory systematically underestimate health burden while exaggerating climate mitigation potential of BC. Accurate, region-specific assessments are urgently needed to guide integrated air quality and climate policies under the Dual Carbon goals. China-specific estimates suggest over 540,000 premature deaths were related to black carbon in 2015, with future health gains from emission reductions partly offset by ageing and climate effects lower than global datasets imply.