Earth and Environmental Sciences

Freshwater contamination by phosphate and pathogenic bacteria requires low-cost multifunctional treatment materials. Unlike previous studies that use a single biogenic agent to synthesize a single nanoparticle type, this work uses one fixed Aloe vera extraction protocol to generate three chemically distinct nanoparticles (Ag, CuO, Fe3O4) on the same waste-derived carbon support, enabling a direct, extract-controlled comparison of nanoparticle identity on water-treatment performance. Activated carbon (AC) was prepared from waste wattle bark (Acacia mearnsii) by steam activation at 700 °C and functionalized with biogenically synthesized Ag, CuO, and Fe3O4 nanoparticles (NPs) using Aloe vera extract as a reducing and stabilizing agent. Average nanoparticle sizes were 43 nm for Ag, 59 nm for CuO, and 13 nm for Fe3O4. FTIR, PXRD, SEM-EDS, TEM, DLS, TGA, and BET analysis characterized the materials. Among the composites, Fe3O4NPs/AC showed the best phosphate removal performance, achieving 93% removal and a maximum adsorption capacity of 9.3 mg/g under acidic conditions, compared with 3.3 mg/g for pristine AC. Equilibrium data were better described by the Freundlich model (R2 = 0.999), indicating adsorption on a heterogeneous surface. Ag NPs/AC exhibited complete inactivation of both Escherichia coli and Staphylococcus aureus within 2 h, while CuO NPs/AC (a more economical alternative) achieved near-complete inactivation of both bacteria within 6 h. AC from spent wattle bark and functionalized with green-synthesized nanoparticles is thus a promising platform for combined phosphate removal and antibacterial water treatment. Consistent with their respective roles, Fe3O4 NPs/AC was evaluated exclusively for phosphate adsorption, while Ag NPs/AC and CuO NPs/AC were evaluated exclusively for antibacterial activity; no single composite was tested for both functions.

Abstract. This study investigates how the dominant predictors of Normalized Difference Vegetation Index (NDVI) based vegetation stability metrics vary across gradients of hydroclimatic extremity. While previous studies have documented the impacts of droughts and heavy rainfall on ecosystem functioning and resilience inferred from stochastic fluctuations, less attention has been given to whether the relative importance of climatic, biodiversity-related, and landscape predictors changes systematically under different levels of climatic stress. To address this question, we quantified vegetation resistance and resilience responses and compared the contributions of meteorological variables, modeled anthropogenic species-change proxies and topographic factors across a global range of hydroclimatic conditions. We find that under normal to moderately dry conditions, vegetation stability metrics are primarily associated with meteorological variables, particularly temperature and precipitation, consistent with earlier global assessments. Under severe and extreme drought conditions, resistance decreases markedly across most regions, whereas resilience responses exhibit weaker and more spatially heterogeneous changes. Importantly, in sparsely vegetated ecosystems such as grasslands and open shrublands, the relative importance of leading predictors shifts from climatic to non-climatic variables under intensified drought stress, suggesting context-dependent associations with vegetation stability. Deciduous needle-leaf forests show consistently low resistance and resilience values across climatic regimes, suggesting elevated sensitivity to hydroclimatic variability. Overall, our findings suggest that vegetation stability under climatic extremes cannot be fully explained by meteorological forcing alone and that these modeled proxies and landscape heterogeneity provide additional predictive information under intensifying climate variability.

Abstract. Accurately quantifying drought impacts on terrestrial carbon cycling is essential for advancing predictions of climate-carbon feedbacks. However, current biogeochemical models exhibit limited capability in simulating drought-induced transformations of soil organic carbon (SOC), particularly regarding microbial processes. Here, we conducted a systematic comparative evaluation of three prevailing SOC modeling structures, including conventional three-pool partitioning scheme (SM1), mineral and particulate- associated carbon partitioning scheme (SM2) and Michaelis-Menten regulated carbon-stabilization scheme (SM3), to elucidate their capacity in simulating soil carbon dynamics under decadal drought scenarios in a subtropical forest. We found divergent effects of drought in soil C input (SM1, 66%; SM2, 10%; SM3, -4%) and mean residence time (MRT; SM1, -31%; SM2, -14%; SM3, 65%), which lead to the predicted SOC substantial accumulation for both SM1 and SM3 (+39.5% and +56.9%, respectively) and moderate depletion (-6.1%) for SM2. The different C input directly affect the passive SOC (SM1) and mineral-associated organic carbon (SM2 and SM3). In comparison, the drought effects on passive SOC (SM1), microbe biomass (SM2) and MAOC (SM2 and SM3), lead to notable spread in MRT. These findings highlight critical model structural dependencies in simulating drought-affected soil carbon dynamics and emphasize the necessity for models to integrate microbial-physicochemical interactions for improved climate-carbon coupling projections.

Abstract. Relative to research efforts in higher latitiudes, the impact of climate shifts in the tropical treeline remains understudied. Little is known about the tree growth dynamics and climate response at this treeline over the past few centuries, and at present under a rapidly changing environment. Here we provide information on recent changes in tree-ring patterns of Polylepis pepei BB.Simpson, a tropical tree species that grows in a monospecific forest at the elevational treeline in the Andes-Amazon ecotone of Bolivia and identify factors that limit its radial growth. We first developed a ring width (RW) chronology spanning 1867–2018 C.E. using dendrochronological methods and independently verified annual periodicity with radiocarbon dating. The RW chronology indicates a significant (p < 0.01) radial growth decline in P. pepei since 1997, a trend that mirrors a decrease reported in other Polylepis species from the drier central Andes of South America. P. pepei tree-ring width (RW) was mostly limited by mean, minimum, and maximum temperature and precipitation during austral summer (November–January). Over the instrumental period (1981–2019) prior-year temperatures negatively affected current-year tree growth (p < 0.05), while prior-year wet conditions were associated with higher growth (p < 0.05). Gridded temperature records (1901–2019) showed a significant increase in minimum temperatures and a decline in the diurnal temperature range since 1967, which may reduce orographic convection and water availability at higher elevations where our forest is located. In situ daily measurements from dataloggers in the forest recorded higher temperatures and lower relative humidity values when data was available. Our results suggest less moisture availability associated with warming conditions was related to the observed tree-growth decline. If temperature continues to rise at current rates, one of the highest-elevation tree species on the globe, P. pepei, could face severe consequences. This work provides insights into the past and historical trends of a tropical Andean treeline, which shows a recent decline also observed in other high-elevation forests (4657–4800 m.a.s.l.) of tropical South America (>17° S).

Soil salinization, referring to the excessive accumulation of soluble salts in soils, adversely influences nutrient cycling, biodiversity, soil structure, crop production, soil health, and ecosystem functioning. Accurately assessing soil salinity via electrical conductivity (EC) is key to mitigating its impacts. Thus, developing predictive tools for soil EC at regional and continental scales is essential for sustainable soil management. Here, we apply machine learning models to predict soil EC in the European Union (EU) and United Kingdom (UK) soils using different environmental factors like soil, climate, topography, and satellite data as predictors. The model is trained by ≈40,000 soil EC data points from the 2015 and 2018 Land Use/Cover Area Frame Survey data (LUCAS) surveys, complemented by the EC observations from World Soil Information Services (WoSIS) dataset. To improve the model performance, a forward feature selection technique was used resulting in selection of 17 covariates out of initially 34 predictors. The final selected XGBoost model achieved R 2 values of 0.68, 0.6, and 0.63 for the training, internal testing, and independent validation datasets, respectively. For the year 2018, we estimate ≈21.7 Mha of EU + UK land exceeds an EC of 0.6 dS/m (at a 1:5 soil to water ratio, the so-called EC 1:5 ). This estimate should be interpreted as elevated predicted EC 1:5, rather than a direct estimate of soils meeting protosalic diagnostic criteria. The output of the predictive model consists of a gridded dataset that illustrates the spatial distribution of EC 1:5 throughout the study area for the year 2018, along with an associated uncertainty map with a spatial resolution of 1 km.

Fine particulate matter (PM 2.5 ) from industrial sources remains a major health concern. However, current emission controls and source apportionment models ignore condensable particulate matter (CPM). Here, we provide the first nationwide quantification of industrial CPM contributions to atmospheric PM 2.5 in China through an integrated framework of field measurements, receptor modeling, and chemical transport simulations. Industrial CPM exhibits a chemical profile dominated by sulfate and ammonium, distinct from filterable PM, which creates a risk that its contribution may be misattributed to secondary aerosols in traditional source apportionment. Across industrial sources, CPM mass proportions vary significantly ( p < 0.01) for NH 4 +, NO 3 –, and Cl – . Based on receptor modeling, CPM contributes a median of 3.6% (95% CI: 0.9%–14.5%) to urban PM 2.5 across all four seasons in 2023, in the industrialized cities examined (Shanghai, Xi’an, Hefei, Shijiazhuang). The contribution peaks in winter (9.5%, 95% Monte Carlo-derived intervals: 2.4%–23.1%) due to enhanced gas-to-particle condensation at low temperature in these cities. From 2014 to 2023, the iron and steel sector emerged as the dominant CPM source, with its contribution increasing by up to 31.0% in key regions during winter. This overlooked source appears to substantially compress the apportioned contribution of secondary inorganics (by an estimated 5.6%–16.6%), suggesting that omission of CPM may lead to overestimation of secondary inorganic aerosol in traditional source apportionment. The results underscore the priority control of CPM emissions from industrial sources.

Abstract. This paper presents the development, validation, and preliminary application of a sub-national scale crop yield emulator to be integrated into the compact Earth system model OSCAR. The emulator simulates yields for four major food crops: maize, rice (two growing seasons), soybean, and wheat (spring and winter varieties), in alignment with the Agricultural Model Intercomparison and Improvement Project (AgMIP) and the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework. Key drivers include atmospheric CO2 concentration (represented as C), growing season temperature (T), water availability (W), and nitrogen fertilization (N). The emulator is trained on an ensemble of process-based crop model simulations from AgMIP’s Global Gridded Crop Model Intercomparison Projects (GGCMI), which is based on the ISIMIP Phase 3 protocol. The crop models used bias-corrected historical and future climate scenarios under fixed socioeconomic conditions, to estimate yield responses under various scenarios until the end of this century. Evaluation of the emulator against the crop model outputs demonstrates the emulator's ability to replicate complex model behavior with high fidelity. Additionally, the emulator-derived yield sensitivities to CO2 and temperature are consistent with those observed in field experiments, reinforcing its empirical robustness. Historical simulations incorporating time-varying nitrogen inputs show significantly improved agreement with FAO yield statistics, underscoring the emulator’s reliability over the historical period and its potential for future impact assessments. This study provides a computationally efficient yet empirically grounded tool for representing crop yield responses, bridging the gap between complex crop models and statistic models. The developed crop emulator facilitates probabilistic projections across large ensembles of climatic and socio-economic scenarios at policy-relevant, sub-national scales. Potential applications include integrated assessments of future food security under climate and land-use change, as well as evaluations of bioenergy with carbon capture and storage (BECCS) potential from crop residues.

The rapid intensification (RI) of tropical cyclones (TCs) remains most persistent challenges in operational forecasting, particularly in western North Pacific (WNP), where RI events are most frequent and intense globally. Although numerical weather prediction continues to advance, adaptive tools are needed to resolve the multi-scale processes driving sudden intensity changes. Existing studies often rely on static binary thresholds for RI occurrence. However, the physical mechanisms favoring RI emerge at varying stages during the intensification process across different cases, governed by concurrent environmental conditions and internal vortex dynamics rather than any fixed RI definition. To address these limitations, this study establishes a diagnostic system for TC intensification in the WNP by integrating vortex-scale reanalysis data for capturing structural drivers of RI, a new continuous intensification rate (IR) index that moves beyond traditional binary RI classifications, and machine learning techniques. Following systematic hyperparameter optimization, the Random Forest, Support Vector Regression, and Artificial Neural Network models all demonstrated consistent and reliable performance in linking higher IR index values to increased probabilities of TC intensification. By integrating physically meaningful vortex-scale features with a continuous IR metric, this framework offers a versatile approach to advance the understanding and forecasting of TC intensification in the WNP.

Abstract Drought events are becoming more frequent and severe under global climate change, threatening food security and ecosystem stability. As a key indicator of atmospheric water cycling and precipitation formation, precipitable water vapor (PWV) is closely linked to drought occurrence and evolution. However, existing PWV-based drought monitoring approaches are mostly limited to regional applications, show weak performance at short time scales, and have rarely been extended to future forecasting. To address these limitations, a novel multi-scale drought monitoring and forecasting framework based on PWV and precipitation is proposed. First, a multi-scale precipitation conversion index (PCI) is calculated, and an XGBoost-based standardized PCI, termed SPCI-XG, is developed. Subsequently, a convolutional long short-term memory (ConvLSTM)-driven spatiotemporal forecasting model is developed for drought forecasting. Results indicate that global SPCI-XG agrees well with the standardized precipitation evapotranspiration index (SPEI) across time scales from 1 to 24 months, with a correlation coefficient (R) of 0.95 at the 12-month scale. It also captures the spatiotemporal evolution of drought like SPEI in semi-arid, tropical, and desert regions. Moreover, the spatiotemporal forecasting model achieves a root mean square error (RMSE) and an R value of 0.29 and 0.95 for single-step forecast (i.e., one-month-ahead prediction), and maintains relatively reliable predictive skill within the 1–5 month forecasting range for multi-step forecasts (i.e., forecasts for future 1–10 months) across six continents. These findings highlight the potential of PWV-precipitation coupling for globally scalable drought monitoring and forecasting, providing a new pathway for early drought warnings and climate risk assessments.

This study presents the first systematic assessment of CO 2 storage potential in the Cretaceous Abu Roash-A sandstone reservoir of the Beni Suef Field in Egypt’s Western Desert. An integrated, multidisciplinary workflow was employed, combining two-dimensional (2D) seismic interpretation, petrophysical evaluation, and 3D static reservoir modelling to characterise the structural framework, assess reservoir quality, and quantify the anticipated CO 2 storage volume. Structural interpretation reveals a fault-bounded anticlinal trap with three-way dip closures against NE-SW and NW-SE trending normal faults, suggesting geological conditions favourable for lateral containment. The reservoir shows a well-defined oil-water contact at 4,200 ft and a hydrocarbon column of 300 ft. Stratigraphic interpretation indicates a thick, regionally extensive caprock system (Khoman and Apollonia formations) overlying the sandstone reservoir. Petrophysical analysis of the Abu Roash-A sandstone indicates low to moderate shale content (Vsh up to 0.25 in northeastern zones, lower in southwestern areas), favourable porosity (15%–24%), and moderate permeability (mean ∼12.5 mD, reaching up to 150 mD) across the structural crest, identifying the elevated block between wells BS-5X and BS-11 as the optimal injection interval. Probabilistic storage calculations yield an estimated CO 2 capacity ranging from 20 Mt (low scenario) to 140 Mt (high scenario), with a base case of approximately 60 Mt, and the central and southwestern sectors showing the highest potential. The results provide a transferable framework for CO 2 storage projects in mature oil reservoirs across North Africa and deliver critical scientific insights to advance Egypt’s decarbonisation and climate-action goals under the National Climate Change Strategy 2050.

Abstract Wintertime explosive cyclones (ECs) pose severe wind-related hazards in the midlatitudes, yet the variability of their wind destructive potential (WDP) over the North Pacific and the dynamics driving this variability remain elusive. This study investigates the leading modes in WDP variability of wintertime North Pacific ECs and their formation processes. Two leading modes are identified by empirical orthogonal function analysis: EOF1 exhibits a monopole over the central North Pacific, whereas EOF2 displays a southwest–northeast dipole. These modes are dynamically linked to the Pacific/North American (PNA; EOF1) and East Pacific/North Pacific (EP/NP; EOF2) teleconnection patterns. The associated large-scale regimes—featuring a zonally extended upper-level jet in EOF1 and a northward-curving jet exit in EOF2—modulate baroclinicity and moisture convergence. Diagnostics of high-frequency available potential energy (APE H ) and kinetic energy (KE H ) during the EC lifecycle show that WDP anomalies form through two pathways. One is a local pathway, in which lower-tropospheric KE H is supplied by local mid-tropospheric baroclinic conversion of APE H generated through baroclinic and diabatic processes. The other is a downstream-development pathway, in which upper-tropospheric KE H dispersed downstream along the anomalous jet contributes to lower-tropospheric KE H . For positive WDP anomalies in both modes, the two pathways operate during cyclogenesis and rapid intensification, whereas the local pathway dominates at the lifetime maximum wind stage when the anomalies reach the poleward flank of the anomalous upper-level jet. In contrast, the negative WDP anomaly over the northwestern Pacific in EOF2 arises primarily from reduced diabatic APE H generation and weaker local baroclinic conversion.

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 &lt;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 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.

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.

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.

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.