Atmospheric and Oceanic Sciences

Abstract Mesoscale convective systems (MCSs) play a central role in producing extreme precipitation over the conterminous United States (CONUS), yet their simulation and future evolution remain elusive. Here we present a comprehensive, observation‐constrained evaluation of MCSs during 2001–2020 using multisource satellite and in situ observations, ERA5 reanalysis, high‐resolution Community Earth System Model (CESM‐HR), and a 4‐km convection‐permitting regional simulation (CONUS404). CESM‐HR faithfully simulates cold‐season MCS climatology, structure, and precipitation characteristics, with heavy precipitation dominated by resolved‐scale modeling processes. CONUS404 better captures warm‐season MCS precipitation intensity but tends to produce more compact systems. Despite differences in historical performance, both CESM‐HR and CONUS404 project intensified MCS‐related extreme precipitation, expanded spatial extent, increased frequency by the mid–twenty‐first century, indicating a robust response to warming. In contrast, projected future changes in MCSs geographic distributions and lifetime duration remain model dependent, highlighting the continued challenges in constraining the dynamical aspects of MCS evolution.

Abstract. This study describes a retrieval algorithm combining wavelength pairing and the multiplicative algebraic reconstruction technique (MART) to process Ozone Mapping and Profiler Suite (OMPS) limb observations for vertical ozone profiles. The retrieval algorithm employs scattered solar radiance measurements from the OMPS limb profiler, focusing on the visible spectral range, normalizes this radiance to that at the upper tangent height, and retrieves ozone concentrations between 12–40 km (∼ 1 km vertical resolution). Additionally, it enables the identification of cloud-contaminated measurements at specific altitudes within the instrument's field of view. The retrieval error in the upper troposphere attributed to the prior profile is estimated to be 10–25 %, while a 30 % uncertainty in the aerosol extinction coefficient causes ~ 5 % error at 15–25 km. OMPS data spanning the entire year of 2021 are processed, and the results are evaluated through comparisons with multiple independent datasets, including NASA official products, passive satellite observations, and in-situ measurements from balloon-borne ozonesondes. At 17–36 km, deviations from OMPS/LP v2.6 data are ≤5 %; at 18–35 km, consistency with Microwave Limb Sounder (MLS) v5.0 data ranges from 5–10 %; at 20–35 km, most deviations from OSIRIS v7.3 data are ≤5 % (except near 23 km). Comparisons with ozonesonde measurements reveal that differences in the 13–30 km range over northern mid-to-high latitudes are mostly <10 % (with 10–15% differences at 22–25 km in polar regions). Over southern mid-latitudes, the consistency within the same altitude range is 2–10%. Notably, deviations between the retrieved profiles and comparison products increase significantly in low-altitude tropical regions.

Abstract Nine years ago, the Department of Interior’s Bureau of Ocean Energy Management (BOEM), the Agency with Air Quality (AQ) jurisdiction over the Outer Continental Shelf (OCS) of the US Gulf Coast west of 87.5° W longitude, asked NASA to determine the feasibility of using satellite data to measure offshore emissions in a region of concentrated oil and natural gas (ONG) operations. To study this issue NASA and BOEM conducted the May 2019 Satellite Coastal and Oceanic Atmospheric Pollution Experiment (SCOAPE) cruise in the Gulf. SCOAPE addressed both technological and scientific issues related to measuring nitrogen dioxide (NO 2 , a common air pollutant), including contrasting near-shore and deepwater regimes. Given the April 2023 launch of the geostationary Tropospheric Emissions: Monitoring of Pollution (TEMPO) AQ satellite, a 2024 SCOAPE-II was conducted in the Gulf with both ship and aircraft measurements. We present an overview of the SCOAPE-II campaign, analysis and validation of satellite-observed NO 2 , and evaluate measurements of methane from ship, aircraft, and satellite near ONG platforms. Our SCOAPE-II results are as follows: 1) Satellite NO 2 measurements (∼13:30 local time) from the TROPOspheric Monitoring Instrument (TROPOMI) are more accurate than TEMPO’s hourly scans (8.6% vs. 23.6% mean absolute bias); a new version of TEMPO data is currently being processed; 2) ship and aircraft measurements captured dozens of NO 2 and methane plumes from ONG operations, showing that they are persistent emitters; 3) satellite measurements of methane failed to replicate ship and aircraft measurements, presenting ongoing challenges for operational emissions monitoring over the Gulf.

Abstract Precipitation over the Maritime Continent (MC) and Madden–Julian oscillation (MJO) activity have shifted eastward in recent decades, a change commonly attributed to Indo–Pacific warm-pool expansion. Here, we show that remote land surface warming, especially over the Congo basin, contributes to this shift. Using observations, a global reanalysis, and ensembles of Energy Exascale Earth System Model, version 3 (E3SMv3), simulations, we isolate a robust teleconnection where warm Congo conditions drive low-level heating that induces an equatorial Kelvin-like response with convergence over the western Indian Ocean and western Pacific and compensating divergence over the MC. This circulation anomaly reduces MC rainfall and enhances western Pacific rainfall, yielding an apparent eastward displacement of mean convection. Consistent with the mean shift, MJO events strengthen in the western Pacific during warm-Congo states. The combined evidence demonstrates that remote land surfaces modulate both MC hydroclimate and intraseasonal variability, partially counteracting the local influence of SSTs and reinforcing precipitation increases farther east. These findings point to a potential land-driven source of seasonal-to-interannual predictability for global precipitation. Significance Statement Rainfall variability over the Maritime Continent (MC) and Madden–Julian oscillation (MJO) have far-reaching socioeconomic influences on populations in the region and globally. This study identifies a new driver of that variability: year-to-year warming and cooling of the Congo basin. When the Congo is anomalously warm, it triggers an atmospheric response that promotes low-level divergence over the MC and convergence over the western Pacific and western Indian Ocean, shifting rainfall eastward and making MJO activity more likely to strengthen over the western Pacific. Multiple lines of evidence from observations, reanalysis, and realistic and idealized model simulations support this Congo effect. The results suggest that monitoring and better initializing African land surface conditions could improve seasonal-to-interannual predictions of tropical rainfall and MJO statistics, with practical benefits for water, energy, agriculture, and disaster preparedness across the Indo-Pacific and elsewhere around the world.

Abstract. While several studies have evaluated the impact of shallow foehn on air pollution, the effects of elevated foehn on O3 pollution remain poorly understood. Here, we investigate the role of elevated foehn in summer O3 pollution in Beijing through detailed case analysis and a long-term climatological evaluation. The case study reveals that elevated foehn exacerbates next-day O3 pollution through three primary mechanisms: first, by increasing boundary layer temperature, thereby enhancing photochemical O3 formation; second, by reducing the residual/boundary layer height, thereby inhibiting vertical diffusion of pollutants; and third, by slowing boundary layer winds, thereby suppressing horizontal dispersion. A ten-year climatological evaluation of 54 identified elevated foehn events strongly supports these mechanisms. On average, these events led to a post-foehn afternoon boundary layer temperature increase exceeding 3 °C an afternoon boundary layer height reduction of more than 100 m, and a decrease in afternoon boundary layer wind speed of more than 1.0 m s−1 compared to the pre-foehn days. Consequently, 87 % of elevated foehn events were associated with a worsening of O3 pollution. Post-foehn daily maximum 8 h average O3 concentrations frequently surpassed the national pollution threshold (160 µg m−3), with an average increase of 20 %–60 % (varying by site and higher in urban areas) compared to preceding days. These results demonstrate a robust and deterministic exacerbating effect of elevated foehn on surface O3 pollution, suggesting that elevated foehn can serve as a reliable meteorological precursor for O3 pollution warnings in summer Beijing.

This study provides a dynamical diagnostic analysis of the marine heatwaves that occurred in the southwest tropical Indian Ocean (SWTIO) during March and April 2023. These events are notable because they occurred following a La Niña winter, a phase typically associated with negative sea surface temperature (SST) anomalies in this region. Given that observations failed to show the deepening of the thermocline, the mechanism of downwelling Rossby waves deepening the thermocline—often used to explain SWTIO warming during El Niño decay phases—is inapplicable here. Mixed layer heat budget analysis indicates that the warming was primarily driven by the surface heat flux term, which reached its maximum value in 2023 over the 1993–2025 period. The critical driver was the dual thermodynamic effect induced by the unprecedentedly weak surface wind speeds, which resulted in extraordinary heat absorption via suppressed latent heat loss and a shallow mixed layer depth due to weakened vertical mixing. The synergy of the dual thermodynamic effect concentrated the anomalous surface heat flux within an exceptionally thin surface layer, ultimately triggering the unprecedented SST warming in the spring of 2023.

Abstract Heavy rainfall events (HREs) during the Meiyu season are a primary focus of disaster prevention and mitigation. Based on the observations and reanalysis data, the classification of flood and drought years was determined by cumulative Meiyu precipitation. Then the divergent pathways of the East-Asian subtropical westerly jet (EASWJ) impacting the HREs, along with the causes of the anomalous jet variations were analyzed. The flood-year HREs exhibit longer durations and higher frequency compared to drought-year events. The mid-high-latitude circulation exhibits a zonal pattern, characterized by by the EASWJ center being persistently positioned at 35–40°N, while precipitation locates northward. Jet intensity is enhanced and sustained by two factors: warm-region heating driven by low-latitude circulations and anomalous local Eliassen-Palm (EP) flux divergence. These processes provide essential dynamic conditions for HREs development and prolonged persistence. During the drought years, the mid-high-latitude circulation exhibits a meridional pattern, characterized by a southward shift of the jet and precipitation. The EASWJ migrates southward, accompany with the intrusion of cold air and intensifying the Meiyu front, thereby triggering HREs. Under the influence of cold air carried southward by mid-high-latitude vertical-meridional circulation and abnormal dispersion of disturbance energy, the jet strengthens. The southward migration of disturbance energy governs the shift of jet. However, the rapid southward advance of cold air reduces the temperature gradient across the jet’s northern and southern flanks. Concurrently, anomalous disturbance energy convergence weakens the EASWJ, leading to precipitation termination.

Precipitation is central to the global hydrological cycle, and its accurate monitoring is vital for preventing meteorological disasters. Traditional satellite retrieval methods fail to model nonlinear relationships and adapt to regional heterogeneity. Using China’s new-generation geostationary satellite FY-4B/AGRI, this study develops a two-step machine learning model—separating precipitation identification from intensity estimation—for the complex terrain of Anhui Province and further conducts experiments in the Huaibei Plain, Jianghuai Hills, and Jiangnan mountainous areas. This design separately addresses precipitation occurrence and rainfall intensity, which represent distinct classification and regression tasks. The model takes 37 features as input, including multispectral brightness temperatures, brightness temperature differences, spatiotemporal cloud-top temperature dynamics, secondary cloud parameters, and terrain. For identification, XGBoost at a 1:4 precipitation/non-precipitation ratio performed best, with POD, FAR, CSI, and ETS of 0.6961, 0.3676, 0.4956, and 0.4422, outperforming the FY-4B QPE product (0.5876, 0.5703, 0.3301, 0.2607). Subregional modeling further improved CSI to 0.4716, 0.5186, and 0.5210 for the three areas. For rainfall estimation, XGBoost trained with the original precipitation class ratio was optimal in all subregions, markedly surpassing the QPE product. Spatial aggregation of the three regional models yielded a correlation coefficient of 0.5304 and RMSE of 0.6274 mm, outperforming the unified model and QPE during the study period. This study provides a useful machine learning approach for precipitation retrieval, and the results demonstrate the efficacy of incorporating regional heterogeneity into machine-learning-based precipitation retrieval, leading to enhanced precipitation estimation during the June–July 2024 Meiyu period over Anhui Province.

Abstract Methane fluxes in brackish tidal wetlands are challenging to predict because common controls interact with temporally varying salinity. We measured ecosystem-scale methane fluxes during two hydrologically distinct years in a brackish marsh in Massachusetts during which methane fluxes collapsed and then recovered. The wetland experienced exceptionally high salinity levels during a drought in 2022 and consistently moderate levels in the subsequent year. Soil salinity did not return to the initial low values which was likely due to limited infiltration of fresh surface water in 2023. Methane fluxes averaged 0.120umolm-2s-1 before they were reduced to effectively zero fluxes when salinity increased rapidly. Fluxes recovered to 67% of original levels in August 2023. To understand the timescale and drivers of this ecosystem response as well as their interactions, we developed neural network models to predict methane fluxes for each year. We derived functional relationships by systematically varying each driver with the remaining drivers set constant. We found porewater specific conductivity, air temperature and to lesser degree, gross primary production to be the dominant drivers of methane fluxes. Our modeling determined strong interactions between specific conductivity and temperature controlling methane fluxes. We identified a threshold of about 15 mS cm−1 (8.7ppt) above which modelled methane fluxes decreased substantially, especially at higher temperatures. In 2023, the variation in measured specific conductivity was low and the neural network less predictive. At that time, our porewater measurements indicated variability of sulfate concentrations not captured by specific conductivity observations. Porewater methane concentrations were consistently detectable even during periods of flux suppression, indicating a role of methane oxidation in the prolonged flux suppression. Our findings demonstrate the value of applying machine learning approaches to flux analysis in dynamic wetland systems and suggest that drought-induced salinization can alter methane cycling in brackish tidal wetlands for prolonged periods of time.

Abstract We present the first comprehensive validation of the operational OCM-3 550 nm aerosol optical depth (AOD) product over land, retrieved using the SAC AErosol Retrieval (SAER) algorithm over South Asia. By using nearly 22 months of data (June 2023-March 2025) collocated with 13 AERONET stations, the study evaluates the product's accuracy across different land cover types and aerosol regimes. The overall agreement of R = 0.85, RMSE = 0.20, MAB = 0.15, and 70% of matchups within the expected error envelope (N-EE), based on N = 831 collocations, is found. Leveraging OCM-3's 1 km resolution, we assess performance over urban hotspots, agricultural plume regions, and other localized emission areas. Validation by land-cover type shows the highest accuracy over croplands (R = 0.91, RMSE = 0.19) and identifies a lower retrieval slope (0.67) in urban environments. Seasonal composites confirm the reliable capture of pre-monsoon peaks, sustained monsoon AOD under high humidity, post-monsoon resurgence, and winter accumulation. The high spatial resolution (1 km) enables mapping of finescale aerosol features, enhancing monitoring of urban pollution hotspots, crop residue burning regions, and localized sources. The results provide confidence in the operational use of OCM-3 AOD for high-resolution environmental monitoring, air quality applications and climate research.

Abstract. The development of extratropical cyclones (ETCs) is often significantly altered by diabatic processes, yet the representation of these processes in numerical weather prediction models has been shown to lead to significant forecast biases. To provide a systematic quantification of 12-h ETC forecast errors, this study uses a cyclone-centred composite framework for North Atlantic wintertime (DJF) ETCs using the ERA5 reanalysis for the period 1979 to 2022. Cyclones are categorised into strong and weak diabatic heating at the time of their maximum intensification based on the domain-averaged 70th and 30th percentiles of vertically integrated diabatic heating. While both groups exhibit a systematic underestimation of cyclone intensity, the error structures are markedly distinct. The weak heating group is characterised by an intensity underestimation near the cyclone core, whereas the strong heating group features a pronounced southwestward displacement bias together with a domain-wide intensity underestimation. After removing the displacement bias, the strong heating group exhibits distinct structural errors. In the warm sector, a clear underestimation of moisture transport and temperature, combined with an underdeveloped upper-level ridge, indicates a mis-representation of the intense moisture transport pathways and associated warm-sector moist processes. Conversely, in the cold sector, low-level winds are overestimated within the cold conveyor belt (CCB), sting jet (SJ), and dry intrusion (DI) regions. The wind field biases are accompanied by a pronounced overestimation of 850 hPa kinematic frontogenesis near the centre. The strong frontogenesis is associated with an enhanced secondary circulation and vertical velocity, yielding the overestimation of total column liquid water observed along the bent-back warm front. In contrast, cyclones in the weak heating group exhibit an underestimation of wind speed and moisture near the centre, consistent with the near-centre intensity underestimation. Overall, our findings demonstrate the critical impact of diabatic heating on structural forecast biases, highlighting that the representation of moist processes and the interaction with atmospheric dynamics through diabatic processes is a key area for future model developments.

Abstract. Reducing methane (CH4) emissions has become increasingly important in recent years due to its importance for radiative forcing. Fugitive emissions of CH4 from natural gas distribution infrastructure are of particular interest as a mitigation target within the oil and gas sector. Previous studies have shown the ability to detect these emissions by use of mobile surveys measuring CH4, with some studies using ratios to secondary co-emitted compounds as a means of predicting the source of emission. This study aims to adapt existing algorithm parameters by investigating the limitations of equipment within the platform used for mobile surveys. These changes suggest that previous methods may underpredict the number of Leak Indications (LIs) by 53.5 % with 27 LIs detected with the old methodology compared to 58 LIs detected with the new methodology. The majority of these LIs were found to be emitting in a leak rate category of 0–2 L min−1. Source determination was included as a core step within the algorithm, which was shown to reduce the misassignment of LIs, suggesting when not using this step, emissions from pyrogenics and biogenics are included within LI assignments.

Abstract Marine cold‐air outbreak (MCAO) clouds begin as shallow cloud streets near the ice edge and evolve into broken open‐cell convection downstream. Their rapid transitions in cloud structure and microphysics are closely linked to vertical motions, but this interplay has not previously been observed from space. The EarthCARE satellite, carrying the first spaceborne Doppler radar (Cloud Profiling Radar, CPR), now measures hydrometeor sedimentation velocity and vertical air motion. Using CPR observations over the Norwegian and Barents Seas (December 2024–May 2025), we analyze MCAO and non‐MCAO clouds. We find that strong updrafts in MCAO clouds coincide with larger supercooled liquid water (SLW) path (LWP). These conditions are accompanied by increases in sedimentation velocity and its vertical gradient, reflecting rapid riming‐driven ice growth. In contrast, non‐MCAO clouds show lower riming efficiency at similar LWP due to relatively weaker dynamical support. This emphasizes the importance of dynamics in shaping polar cloud structure and microphysics.

Abstract Recent studies have suggested that forecasts of intense mesoscale polar lows (PLs) are strongly sensitive to errors in their synoptic-scale environments, but the sources and case-to-case variability of these errors are not well understood. To address this, we investigate the initial-condition sensitivity of 705 PL-environment forecasts over the Nordic Seas at 5-day leadtimes using GraphCast, a deep-learning weather prediction model. On average, the PL-environment forecasts exhibit the strongest sensitivity over lower- to mid-tropospheric gradients of temperature and moisture along the east coast of North America. The sensitivity tilts upshear along these gradients, an orientation that is favorable for growth via baroclinic instability. The average sensitivities for the highest-error cases, in addition to being larger in magnitude, extend further upstream and exhibit a stronger upshear tilt than those for the lowest-error cases. The highest-error PL environments are also characterized by stronger baroclinity, whereas the lowest-error cases are associated with stronger marine cold-air outbreaks. These results suggest that rapidly growing moist baroclinic disturbances along the North Atlantic storm track are a critical source of forecast error for PL environments.

Abstract. In this work, we present a new, high-resolution earthquake catalog for the Kefalonia region, Greece, together with the distributed acoustic sensing (DAS) waveform dataset used for its construction (https://doi.org/10.60517/cv43p1601, Bocchini et al., 2025; https://doi.org/10.5281/zenodo.20558686, Bocchini, 2026). We build the catalog from DAS data recorded between 1 August 2024, 23:00 UTC and 15 August 2024, 23:00 UTC, combined with open-access seismic-station recordings from the Hellenic Unified Seismic Network (HUSN). The DAS dataset consists of continuous strain measurements acquired along a 15 km long telecommunications fiber-optic cable connecting northern Kefalonia and Ithaki. We use a semblance-based detector on the DAS waveforms to identify 5734 earthquakes within ∼50 km of the cable origin. We jointly locate 356 high-SNR (SNR >12 dB) events with DAS and seismic stations and calculate their local magnitudes from seismic records. We then apply waveform cross-correlation to match unlocated detections with the most similar template events and estimate relative magnitudes from amplitude ratios to enhance the newly constructed catalog. Enhancement adds 2515 earthquakes, resulting in 2871 events with assigned locations and magnitudes and represents a ∼32-fold increase in the number of earthquakes with respect to the official National Observatory of Athens (NOA) catalog. Most events (2790) cluster within a ∼5 km radius offshore northwest of Kefalonia, where seismicity rates reach >100 events per hour. We achieve a ∼38-fold increase in the number of earthquakes with respect to the official catalog from NOA in the region encompassing the earthquake cluster northwest of Kefalonia. Our dataset provides a detailed spatio-temporal view of seismicity in a region with limited station coverage and demonstrates the value of integrating DAS with conventional seismic networks to monitor intense earthquake sequences. The combination of high seismicity and open-access data from the HUSN makes this DAS dataset particularly valuable for the seismological community. We provide a 2-week-long catalog, the full detection list (local and distant events and false detections), and two weeks of continuous DAS recordings. Possible applications of the datasets include testing and benchmarking DAS processing algorithms for tectonic earthquakes, as well as studies of physical processes associated with complex seismic sequences.

Abstract. The Global Heat Flow Database is a comprehensive data compilation on published heat-flow measurements dating back to the 1950s. The International Heat Flow Commission first released the database in 1963. Recent activities within the World Heat Flow Database Project (funded by the DFG German Research Association) and the Task Force VIII of the International Lithosphere Program (ILP) have focused on (1) developing a new, modern digital data infrastructure with integrated quality control of the data, (2) creating a new dedicated metadata scheme for reporting heat-flow data, (3) conducting a comprehensive review of the original literature to supplement the original metadata according to the new scheme, and (4) thoroughly adding new measurements from the literature. As a result, the 2024 release presents a substantial update, with the number of heat flow observations increasing from 58,302 data points in 2012 to 91,182 in 2024, while the number of literature sources simultaneously increased from 572 to 1,586 documents. A key part of this process was the introduction of a new, comprehensive metadata scheme and the development of the GHFDB Data Template, which facilitates the structured and detailed reporting of heat flow observations in accordance with the new scheme. The GHFDB Data Template captures methodological details, uncertainty estimates, and contextual information, forming the basis for a newly implemented, multi-dimensional quality-assessment system. The improved data submission workflow, now supported by the option of obtaining digital object identifier (DOI), making the newly submitted data citable in literature, as is increasingly required by journals. This service encourages direct contributions from researchers and ensures transparency, attribution, and long-term data stewardship by the partner repository GFZ Data Services. The new heat flow database release marks a significant step towards establishing a global, quality-assured data infrastructure and lays the foundation for more reliable, reusable, and interoperable heat-flow datasets across scientific disciplines.

The study of wave climate is critical for understanding morphodynamics for shoreline management, safety, and marine engineering efficiency, especially in the context of climate change. However, obtaining continuous in situ surface gravity wave data remains a significant challenge globally, particularly along the northeastern Brazilian coast, where sparse measurements hinder a comprehensive understanding of its unique wave climate and its morphodynamic impacts. An alternative approach to obtain wave climate data is to use the amplitude of microseismic noise from inland seismographic stations as a proxy for the amplitude of surface gravity waves. Microseismic noise originates from the interaction between ocean waves and the seafloor, shoreline, or other bottom topography. The power amplitude spectra of microseismic noise typically exhibit two frequency ranges. One is between 0.05 and 0.1 Hz, called the primary microseism (PM), and the second is between 0.1 and 1 Hz, called the secondary microseism (SM). In this study, we investigated the interaction between meteorological systems and swells in northeastern Brazil, specifically in Recife, during the austral winter and spring of 2015. We used oceanographic, atmospheric, and seismological datasets to evaluate the relationship between significant wave height (Hs) and microseismic amplitudes. We observed a correlation between SM and Hs and determined region-specific seasonal transfer equations between these amplitudes. Thus, these results significantly advance ocean monitoring and climate science studies by providing the first regionally specific seasonal transfer equations for the northeastern Brazilian coast, which are essential for predicting and mitigating coastal hazards in this vulnerable region. This is a crucial contribution to an area that is critically underserved by continuous in situ buoy data, offering a robust alternative for long-term wave climate assessment.

BACKGROUND: Indoor residual spraying (IRS) and insecticide treated bed nets (ITNs) used either singly or in combination are the main mass mosquito vector control measures for malaria control. Despite their widespread use, malaria transmission rates remain high, the burden is unacceptably huge, and yet the disease is completely preventable. This study measured the impact of compounds used in different types and campaigns of IRS and ITNs, different permutations of IRS + ITNs on malaria test positivity rates in high disease transmission settings of Yumbe district and Gulu district. METHOD: The Ministry of Health's District Health Information System 2 (DHIS 2) records on distribution of ITNs, IRS schedules and malaria cases by gender, age and geographical location (Gulu and Yumbe districts) collected over a five-year period (DHIS 2 records accessed on 11th February 2025) were used in the final analysis. Data collection was done using a checklist developed in an Excel spreadsheet. Data on the following were extracted; socio-demographic characteristics, number of monthly malaria tests, monthly numbers of positive malaria tests, the type of Insecticide Treated Nets (ITNs) distributed, the type of Indoor Residual Spray (IRS) applied and the time points that the interventions were deployed. The data was exported into STATA ver 17.0,cleaned and additional variables generated prior to the interrupted time series and Difference in Difference analysis. The monthly malaria test positivity rate (TPR) was calculated while adjusting for variability in rainfall, temperature and relative humidity. A regression analysis and graphical plots using the Generalized Estimation Equation (GEE) population average models were performed. RESULTS: After controlling for monthly variation in rainfall, temperature, and relative humidity, the deployment of malaria vector control interventions in Yumbe district led to a much faster reduction in TPR of 0.006 units/0.56% per month; p < 0.00 (about 5 times faster than Gulu district). The 2020 distribution of Yorkool ITNs in Yumbe did not significantly change the long-term trend in TPR of the district relative to Gulu that maintained distribution of PermaNets ITN (trend change difference = 0.0111, beta = -0.0054, 95% CI:0.1145, 0.1038, p = 0.923). Fludora Fusion demonstrated a profound impact. The difference-in-differences interaction term was highly statistically significant, showing an absolute 21.2% drop in the Malaria Test Positivity Rate in Yumbe District relative to Gulu district (beta = -0.2123, 95% CI: -0.2801, -0.1444, p < 0.001). Following a co-deployment of Actellic 300 CS IRS+ Royal Guard ITNs in Yumbe and distribution of PermaNet ITNs in Gulu, the difference-in-differences interaction term was highly statistically significant, demonstrating an absolute 25.1% drop in the Malaria Test Positivity Rate in Yumbe District compared to Gulu district (beta = -0.2508, 95%CI:-0.3279, -0.1737,p < 0.001). CONCLUSION: In areas of high malaria transmission, deployment of either ITNs alone, IRS alone, or in combination can be an effective tool for malaria case reduction. However, a more sustained and significant reduction is achieved through the simultaneous deployment of IRS and ITNs. Crucially, the efficacy of these combined interventions is highly shaped by the specific classes of insecticides and active compounds utilized within the deployed ITN types and IRS campaigns.

Abstract. Here, we assess the amplification of near-surface warming in the Mediterranean (MED) resulting from global anthropogenic aerosol (AA) reductions, based on simulations from CMIP6 Earth system models (ESMs). The effective radiative forcing (ERF) and near-surface temperature (TAS) exhibit decreasing trends until around 1980 followed by increasing trends, driven by air pollution control policies. The annual mean ERF at the top-of-atmosphere over the MED changes by 2.37±1.06Wm-2 between the peak AA period (1970–1979) and the near-present period (2005–2014). During this interval, the annual mean TAS increases by 0.67±0.37 °C. Overall, the multi-model ensemble shows a robust amplification of warming over the MED on annual scale resulting from global AA reductions from 1970–1979 to 2005–2014, in good agreement with observational datasets over land. The model simulations indicate that AAs are responsible for 49 % (39 %) of the annual (summer) warming between the two periods. In the winter, ESMs produce an overestimated warming of 1.19 °C, with AAs contributing 60 % to this warming. Finally, we show that circulation changes caused by AA reductions can play an additional role in the redistribution of regional temperature changes apart from the radiative effects per se. Our results reveal a strong link between the recent acceleration of MED warming and global AA decreases, which unmask additional greenhouse gas-driven warming. This study highlights the sensitivity of the MED to recent global AA emission changes and the need for climate policies that couple air quality improvements with rapid greenhouse gas mitigation.

The urban heat island (UHI) effect represents a critical urban climate phenomenon arising from the combined pressures of rapid urbanization and climate warming. Although its association with carbon emissions has received increasing scholarly attention, the underlying behavior-mediated pathways and cross-regional spillover patterns remain insufficiently understood. Using multi-source geospatial data for the Yangtze River Delta urban agglomeration from 2014 to 2023, this study develops a multi-scale analytical framework integrating 1 km urban agglomeration exploratory analysis and 5 km spatial econometric modeling. Anthropogenic Energy Activity Intensity (AEAI) is constructed as a proxy for energy-related human activities, and a spatial Durbin model, combined with a spatial mediation approach, is employed to examine the spatial associations and statistically mediated pathways within the “heat-energy-carbon” nexus. The results indicate that: (1) carbon emissions exhibit significant positive spatial spillover effects, consistent with thermal diffusion processes and socioeconomic network interactions; (2) AEAI represents a substantial partial statistical mediation pathway in the association between UHI and carbon emissions, accounting for 44.63% of the total association. This suggests that the UHI–carbon emission linkage is partly embedded in spatial patterns of energy-intensive human activities rather than reflecting a purely direct thermal effect. These findings suggest that regional climate governance may need to move beyond single-city interventions and purely physical cooling strategies toward integrated approaches that combine cross-regional coordination with behavioral regulation. Promoting passive cooling-oriented urban planning and demand-side energy transitions may help reduce carbon lock-in risks and support the development of climate-resilient urban agglomerations.

Reliable quantification of crop evapotranspiration (ETc) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ETc maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R2 = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (Kc) derived from fused NDVI time series via the FAO-56 framework yielded ETc ranging from 0.99 mm day−1 (initial stage) to 6.40 mm day−1 (peak crop development). Seasonal precipitation–ETc deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ETc monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ETc validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions.

Introduction Against the background of global climate change and the increasing frequency of extreme weather events, the Guangdong–Hong Kong–Macao Greater Bay Area—as a typical coastal highdensity urban agglomeration—has long been exposed to compound natural disaster risks (typhoons, heavy rainfall, floods, storm surges, and geological disasters) under the combined influence of land–sea interactions, rapid urbanization, and multiple overlapping hazards. Regional disaster governance has therefore become a critical interdisciplinary issue bridging geoscience and public governance. To systematically evaluate the structural characteristics and design quality of disaster governance policies in this region, this study analyses 115 policy texts and constructs an integrated analytical framework. Methods We combine text mining, semantic network analysis, and the Policy Modeling Consistency (PMC) index. First, word segmentation statistics and keyword cooccurrence analysis are employed to identify policy themes and core semantic structures. Second, a PMC evaluation index system is built to quantitatively assess the policy texts. Finally, grade classification and PMC surface plots are used to compare structural differences among various policies. Results The results show that disaster governance policies in the Guangdong–Hong Kong–Macao Greater Bay Area have generally formed a relatively systematic institutional framework, with most sample policies rated as “excellent” or “good.” Excellent policies perform more evenly in terms of policy instruments, policy content, supporting measures, and target coverage, whereas average policies show deficiencies in the configuration of issuing bodies, temporal arrangements, crosslevel coordination, and evaluation and feedback mechanisms. Discussion Accordingly, this study suggests strengthening multiactor collaboration, improving closedloop policy management, and enhancing the balance and synergy of policy structures, so as to improve the governance capacity and regional resilience of the Greater Bay Area in response to compound natural disaster risks. These findings also provide a reference for disaster risk governance and regional disaster prevention and reduction policy optimization in the field of geoscience.

Abstract Environmental monitoring increasingly relies on wall-to-wall maps derived from remote sensing and machine learning for decisions, yet high map classification accuracy does not necessarily imply reliable knowledge across space. Here, we utilize a relatively simple classification scenario to examine how probabilistic ensemble models represent spatial uncertainty and whether these signals correspond to independent human interpretation. Using high-resolution imagery from the Bangladesh Sundarbans, we generated continuous mangrove probability maps by combining multiple base learners through stacked generalization. Rather than focusing solely on performance metrics, we quantified disagreement among models and compared stacked probabilities with scores assigned by three interpreters to pixels sampled from regions of agreement and ambiguity. Individual and stacking models achieved similarly high accuracy. Stacked probabilities served as a proxy for the epistemic uncertainty in the map, with disagreement among heterogeneous base learners reflecting where predictions are stable or contested. Extreme values corresponded to strong model consensus and higher interpreter confidence, whereas intermediate values coincided with greater variability in both model predictions and human judgment; the two signals proved complementary rather than interchangeable. These intermediate, most-contested probabilities covered only about 3.5% of the map, yet clustered along ecological transitions where conventional maps show false confidence. Propagated into a design-based area estimate, this uncertainty spans a 126 million to 1.26 billion US dollar range in REDD+ value across carbon-price tiers, so quantifying it lets projects target field verification where it most reduces financial risk.

ABSTRACT Compound climate extremes, such as concurrent precipitation and temperature extremes, cause significant impacts on socioeconomics and ecosystems. Recent studies have made substantial progress in the specific type of compound extremes; however, characteristics of different types of compound precipitation and temperature extremes across the globe and their driving mechanisms remain limited understood. This study investigated characteristics of compound extremes including dry‐warm (DW), wet‐warm (WW), dry‐cold (DC), and wet‐cold (WC) combinations in annual, JJA (June, July, and August), and DJF (December, January, and February) occurrences during 1901–2024 across global land areas and their relationships with climate variability modes. Results indicated that the spatial distribution of the frequency of DW (WW) showed a similar pattern to that of WC (DC). The overall frequency of compound warm‐related (cold‐related) extremes successively increased (increased and then decreased) from the period 1901–1941 to the period 1983–2024. The spatial extent of compound warm‐related (cold‐related) extremes presented an increasing (decreasing) trend in all the continents over the past 124 years. The areas with positive (negative) precipitation‐temperature correlation showed high frequency of WW and DC (DW and WC). The areas with positive (negative) dependence between precipitation and temperature extremes of DW and WC showed negative (positive) dependence between the two extremes of WW and DC, except for central Asia where the two extremes showed positive dependence in the four compound extremes. During 1901–2024, El Niño (La Niña) tended to induce annual high (low) DW and low (high) WC occurrences in northern South America, southern and central Africa, southern Asia, eastern Australia, and northwestern North America and high (low) WW and low (high) DC occurrences in South America, Africa, western and southern Asia, southern Europe, western North America, and western Australia. The positive (negative) Dipole Mode Index tended to induce high occurrences of compound warm‐related (cold‐related) extremes in most global land areas. The positive North Atlantic Oscillation (NAO) tended to induce high WW occurrences and low DC occurrences in central and northern Europe, northern and central‐eastern Asia, and eastern North America, especially during DJF. This study provides scientific insights into the spatiotemporal characteristics and driving mechanisms of different compound extremes across the globe under a changing climate.