Divergent and habitat-specific responses of benthic communities to environmental and climate change in a twenty-year time series (2004-2023) in the Northern Adriatic.

Temperature and precipitation source variability and glacial dynamics in the southwestern United States at Fish Lake, Utah, since late MIS 4

Abstract. To compensate for the high computational costs when modelling large-scale mountain glaciers, ice fields or ice sheets over multiple millennia, it is common practice to coarsen the spatial resolution of numerical models to 1 km or more, which is not sufficient to describe complex valley topographies. Here, we examine the influence of spatial resolution by modelling a growing and retreating exemplary ice field in the European Alps at resolutions ranging from 50 m to 2 km using the Instructed Glacier Model (IGM). We find that while ice-covered areas remain similar, ice volume increases substantially with coarser resolution. Compared to the reference run at 50 m spatial resolution, model results at a resolution of 300 m and finer are comparable and sufficiently accurate to simulate topographically constrained ice flow. However, at resolutions coarser than ∼800 m, topographic resampling artificially lowers slope angles and mountain peaks, providing a larger accumulation area at high altitudes, with thicker glaciers that are typically warm-based, while thinner glaciers at fine resolutions remain cold-based. Raised valley floors at coarse resolutions result in slower-flowing ice with increased thickness and glacial response times. The resulting hysteresis between climate forcing and glacial response at coarse resolutions is only partially decreased with slower temperature change. Seemingly stable model results at coarse resolutions may be misleading and accurate glacier geometries might arise from parameter choices that compensate for poorly resolved topography. Similar non-linear and altitudinal-dependent resolution effects are likely in mountain regions worldwide and emphasize the need for model advances to enable simulations at sufficiently high spatial resolutions to accurately resolve glacier dynamics.

Climate change intensifies the hydrological cycle, and reservoir responses to meteorological forces are vital for water sustainability, but long-term quantitative assessments remain scarce. In this study, we extracted the water surface area of Xinfengjiang Reservoir from 1990 to 2024 using multisensor Landsat/Sentinel imagery and a random forest classifier (RFC) on the Google Earth Engine (GEE) platform. Furthermore, we established an interpretable modeling framework that combined random forest regression with Shapley additive explanations (RFR-SHAP) to elucidate the nonlinear influences of meteorological drivers on multidecadal reservoir dynamics. The RFC model achieved high accuracy (OA = 96.90%, KC = 0.95) and efficiency (processing time = 1.17s per image), enabling the generation of a reliable 35-year time series dataset. Further analysis of the underlying mechanisms revealed that total precipitation (TP) was the most significant driving factor for the hydrological dynamics of reservoirs, with mean |SHAP| values for annual and monthly assessments recorded at 3.33 (16.15%) and 4.32 (20.36%), respectively. The synergistic and antagonistic effects of meteorological variables constituted the nonlinear adaptation mechanisms of reservoir systems to climate forcing. These findings quantify the hydrological dynamics and the driving mechanisms of meteorological variables on the water surface area of Xinfengjiang Reservoir, providing a basis for proactive reservoir operations based on precipitation forecasts.

Anthropogenic climate forcing is altering ocean circulation and water mass distribution across the Southern Ocean, reshaping the habitat of circumpolar marine predators such as threatened crested ( Eudyptes ) penguins. Understanding species vulnerability remains challenging due to substantial uncertainties in climate projections. Here, we integrate two state-of-the-art climate assessment tools—storylines and time of emergence—to evaluate the vulnerability of crested penguins to ocean warming while explicitly addressing projection uncertainties. Using this framework, we select a discrete set of projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) that capture qualitatively different global circulation responses and climate sensitivity. Uncertainty in global atmospheric circulation responses, particularly the degree of intensification of the Westerlies, strongly influences both the magnitude and spatial pattern of projected sea surface temperature (SST) warming within penguin foraging habitats. Storylines and climate sensitivity explain a greater proportion of overall projection uncertainty compared to conventional CMIP6 scenario ensembles. We identify two groups of SST sensitivity among crested penguins: (1) highly sensitive species, including Northern Rockhoppers ( E. moseleyi ) and Aotearoa/New Zealand endemic species, and (2) species with broader distributions, such as Southern and Eastern Rockhoppers ( E. chrysocome ) and Macaroni/Royal penguins ( E. chrysolophus/E. schlegeli ), which exhibit spatially heterogeneous exposure and sensitivity. Spatial variability in exposure among widely distributed species highlights opportunities for targeted monitoring to detect early climate change impacts. However, limited data on population dynamics, gene flow, and foraging ecology constrain vulnerability assessments, emphasizing the need for expanded ecological and tracking studies coupled with environmental monitoring. We advocate for interdisciplinary, uncertainty-aware approaches and transparent workflows, including open data and code sharing, to strengthen future climate vulnerability assessments for threatened species.

Climate extremes are intensifying worldwide, yet the mechanisms by which forest biomass responds to compound climatic and anthropogenic pressures remain poorly resolved. Here, we integrate multi-sensor remote sensing with explainable machine learning to quantify the interactions between multiple drivers and changes in aboveground biomass density (ΔAGBD) across a subtropical monsoon region of China during 2000–2019. Annual ΔAGBD maps were derived from Landsat and GEDI, and hotspots of climatic extremes were delineated using ETCCDI(Expert Team on Climate Change Detection and Indices). Across 92 predictors, attribution indicates that—even within areas exposed to extremes—AGBD is not driven by extreme events alone. Bioclimatic and extreme-climate variables dominate overall variability, whereas topography and human disturbance strongly modulate their effects. Gradient analyses further show that climatic influences intensify with elevation and heat load, whereas anthropogenic impacts remain pronounced at low population densities and in areas distant from impervious surfaces. Collectively, these findings demonstrate that biomass dynamics under climatic extremes do not arise from a single climatic forcing but from the mutual regulation of climate, topography, and human pressure. This nonlinear, compound-mechanism framework provides a transferable basis for assessing ecosystem vulnerability and designing adaptive, resilience-oriented management strategies under intensifying climate extremes.

Climate change poses an escalating threat to global water resources, with semi-arid regions such as Morocco being particularly vulnerable due to high climatic variability and limited adaptive capacity. In these regions, including the Tassaoute watershed in central Morocco, data scarcity and uncertainties related to data availability and quality frequently hinder robust assessments of climate change impacts. Recent advances in data science and remote sensing offer promising alternatives to overcome these limitations. This study investigates the potential of the PERSIANN-CDR satellite-derived precipitation product for assessing climate change impacts on water resources. The capability of PERSIANN-CDR to reproduce observed precipitation patterns and associated hydrological responses is evaluated through a comparative analysis using observed precipitation data. Results indicate that PERSIANN-CDR generally underestimates peak precipitation events and total rainfall amounts compared to in situ observations. Runoff is simulated using two hydrological models: GR2M (Génie Rural 2 parameters Mensuel) and the Thornthwaite water balance method, both driven by observed meteorological data and PERSIANN-CDR precipitation. The future water availability was assessed using 5 climate models, under two scenarios: RCP4.5 and RCP8.5 for the periods 2030–2060 and 2061–2090. Results show a marked temperature increase of 2–3 °C across all models, accompanied by a general decline in precipitation ranging from −30% to −60% under RCP4.5 and −20% to −80% under RCP8.5. These climatic changes translate into substantial reductions in runoff, with stronger decreases projected under the high-emission scenario and during the dry season. Monthly analyses reveal pronounced seasonal contrasts, highlighting the increased sensitivity of low-flow periods to climate forcing. Overall, runoff is projected to decrease by 50–90%, with model and data-source differences highlighting the importance of multi-model and satellite-derived approaches in data-sparse regions. These results emphasize the utility of satellite precipitation datasets in guiding climate-adaptive water management strategies.

Aerosol-cloud interactions remain one of the largest sources of uncertainty in estimates of anthropogenic climate forcing. Reducing aerosols to achieve air quality improvement and climate goals can cause unintended warming due to the weakening of aerosol cooling. Here we investigate the climate impacts of spatially optimized sulfur dioxide (SO2) emission reduction under a carbon-neutral pathway in China. We find that targeting reduction in highly polluted regions in China significantly suppresses the rise in effective radiative forcing due to aerosol-cloud interactions (ERFaci) during 2020-2060. Owing to the nonlinear aerosol-cloud interaction, the optimized emission reduction strategy limits the regional average increase in ERFaci of more than 0.89 W m-2 to less than 0.13 W m-2 in the short-term future during 2020-2040 and weakens the ERFaci increase by two-thirds in 2060 over China. These findings demonstrate the critical role of targeted emission control in mitigating short-term climate risks while pursuing air quality goals.

Bare black carbon (BC) can be coated by other nonabsorbing components, inducing light absorption enhancement (Eabs) via the so-called "lensing effect." The coating components/processes, however, are complex in terms of both chemical composition and microphysical properties; therefore, Eabs is highly uncertain and inconsistent among different observations. Particle heterogeneity in composition is critical to the accurate estimation of Eabs; here, we quantified the impacts of various coating components on (Eabs at 880 nm, thus no impact from light-absorbing organics) by using observations from three megacities in China (Beijing, Nanjing, and Shanghai). Under all three scenarios with contrastingly different atmospheric conditions investigated here, low-volatility/highly oxygenated secondary organic aerosol (SOA) species and sulfate were consistently important contributors to , while OA from both biomass burning and traffic appeared to be insignificant. The effects of semivolatile/less-oxygenated SOA species and nitrate varied largely, being highly dependent upon the meteorology and location. Specially, an industry-related OA resolved in Nanjing exhibited a notable contribution to thus its chemical and physical characteristics warrant future attention. Overall, our findings regarding the roles of specific sources in provide a direct and practically feasible guidance to effectively reduce BC pollution and its positive climate forcing.

Water quantity predictions have advanced rapidly, driven by ready-to-use benchmarks such as CAMELS (Catchment Attributes and Meteorology for Large-Sample Studies). In contrast, large-scale water quality predictions, especially for nutrients, lag behind due to a lack of comparable datasets. Existing water quality datasets face four major limitations: (1) underrepresentation of human-impacted systems, (2) absence of nutrient inputs, (3) incomplete watershed metadata, and (4) sparse monitoring coverage. To address these gaps, we developed IWAND-Nitrogen (Integrated Watershed Attributes and Nutrient Data for Nitrogen) for the contiguous United States. IWAND-Nitrogen integrates 574,767 nitrate records from 1,877 catchments (median 272 samples per gauge; IQR = 231-346) with at least 200 measurements each from 1980-2023, linked with 93 watershed attributes, eight nitrogen input forcings (both basin-averaged and gridded), and eleven climate forcings. Compared to existing benchmarks such as CAMELS-Chem, IWAND-Nitrogen complements prior efforts by extending spatial/temporal coverage and enhancing representation across anthropogenic gradients. IWAND-Nitrogen aims to serve as a nutrient community benchmark, advancing from model development to new insights from catchment to national scales.

Abstract Aerosol particles play a crucial role in the global climate by absorbing and scattering radiation and influencing cloud properties. This study explores the role of resolved convection on precipitation and subsequent removal by wet deposition of aerosol in the Community Earth System Model (CESM2.1.0) by comparing two configurations with distinct representations of precipitation characteristics. We contrast the conventionally parameterized configuration (CAM5‐ZM) with the multiscale modeling framework (CAM5‐MMF), which uses embedded 4 km cloud‐resolving models to explicitly simulate convection. We compare the results against observations from Integrated Multi‐satellite Retrievals for Global Precipitation Measurement, MODIS, MERRA2 Reanalysis, and simulations from GEOS‐Chem and Aerocom. The CAM5‐MMF configuration better captures the frequency and intensity of rainfall by reducing the overestimation of light precipitation frequency in CAM5‐ZM. Improved precipitation frequency is associated with aerosol lifetimes and removal rates that better match observations, leading to higher black carbon and primary organic matter burdens, with implications for future climate forcing and air quality changes.

North-south inhomogeneous variations of methane emissions observed by TROPOMI over China.

The COVID-19 pandemic and resulting reductions in worldwide emissions, associated primarily with the transport sector, provided an unprecedented opportunity to explore the response of atmospheric chemistry and composition to large anthropogenic emissions perturbations. While air quality generally improved in early 2020, this was tempered by increased formation of secondary pollutants (e.g., O3 and secondary particulate matter, PM) in many regions studied. Declines in NOx emissions were largely responsible for the changes in O3, driving decreases in O3 concentrations in remote regions and increases in urban regions due to both decreases in O3 titration by NOx and also nonlinear changes in O3 production. Lower NOx levels also increased the levels of other oxidants (e.g., OH and O3), leading to a general increase in atmospheric oxidation in polluted urban regions. This enhanced oxidation promoted additional PM formation in some regions but was generally outweighed by decreases in primary PM and other secondary precursors (SO2 and VOCs). The COVID-19 pandemic gave rise to large local perturbations in air quality but only modest reductions in the global abundance of short-lived climate forcers (including O3 and PM).

Midsommersø records the Holocene glacial history of Wandel Dal, Inutoqqat Nunaat (Peary Land) northern Greenland

While the impacts of permafrost degradation on Eurasian river discharge are well-documented, a systematic understanding of how these impacts vary across latitudes—critical for predicting continental water security and Arctic freshwater export—remains lacking. This study bridges this gap by analyzing latitudinal gradients in extreme and mean monthly discharges—lowest (LD), mean (MD), and highest (HD) monthly discharge—across 22 major Eurasian permafrost rivers, integrating snowmelt dynamics and winter river ice dynamics with watershed energy-water budgets. We find pronounced latitudinal gradients in hydrological responses. The most robust change is a pan-Eurasian increase in winter baseflow (LD, 5%–8% per decade), primarily driven by warming-induced river ice (24-d shorter freezing duration; 8.2% volume decline contributing 19.6% to LD rise). In contrast, high (HD) and mean (MD) discharge trends show distinct zonal divergence: significant increases in precipitation-driven low latitudes, a post-1990s reversal from decline to increase in mid-latitudes, and muted but more variable trends in high latitudes where precipitation increases are offset by evapotranspiration and storage changes. The late 1990s marked a critical shift, synchronizing abrupt hydrological changes with contemporaneous shifts in regional climate forcing and cryospheric processes. The identified latitudinal patterns and their underlying mechanisms provide a predictive framework for anticipating future hydrological extremes—from winter water scarcity to flood risks—in these vulnerable basins in a warming world.

Abstract Coastal sediments are globally significant sources and sinks of greenhouse gases (GHGs), yet their contributions to climate feedbacks of warming and ocean acidification remain uncertain, in part due to limited understanding of short‐term variability. Here, we use a fully factorial laboratory experiment to disentangle how diel light–dark and tidal inundation and exposure interact with warming and elevated p CO 2 to regulate benthic fluxes of CO 2 , CH 4 , and N 2 O in estuarine sediments, alongside concurrent changes in benthic oxygen exchange. While warming and p CO 2 exerted strong independent effects, their influence was shaped by diel and tidal fluctuations in redox conditions and oxygen availability, reflecting shifts in metabolic balance between primary production and respiration. Light consistently limited CO 2 , CH 4 , and N 2 O emissions through enhanced autotrophic uptake and oxygenation, while dark promoted anaerobic production pathways. N 2 O showed the greatest sensitivity to the combined effects of climate forcing and redox dynamics. Despite warming‐driven stimulation of benthic heterotrophy and the production of all GHGs, CO 2 remained the dominant greenhouse gas, with minimal CH 4 and N 2 O fluxes due to the limited organic matter availability within the sediment. This reflects the strong redox controls on CH 4 and N 2 O production, which relies on both oxygen depletion and organic substrate supply. Our findings emphasize that fine‐scale temporal variability can significantly shape both the magnitude and climate sensitivity of benthic GHG emissions. Capturing these fine‐scale controls is essential for accurately modeling the contributions of estuarine sediments to global GHG budgets and their feedbacks.

Abstract Quantifying the relative importance of El Niño–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) in shaping the spatial structure of the Indonesian Throughflow (ITF) is crucial for climate predictability. However, different climate modes often work together, and single data and conventional methods cannot uncover their relative effects. Using multiple high‐resolution reanalysis data sets and the Random Forest model, the spatiotemporal variability of ENSO and IOD influences across ITF inflow channels are systematically evaluated. Results show distinct dominance periods: ENSO prevails during 1993–2005 (median relative importance, MRI >60% in all channels), IOD dominates during 2002–2010 (Median Relative Importance >55% in the Sulawesi Sea and Maluku Sea), and both modes co‐dominate during 2014–2019 due to increased concurrent El Niño and positive IOD events. Mechanistically, sea level anomaly gradients driven by large‐scale climate forcing modulate upper‐layer transport, whereas wave‐induced thermocline variations affect lower‐layer dynamics. During single‐driver periods, anomalies in volume, heat, and freshwater transport are closely tied to concurrent flow field changes. These results demonstrate that ENSO and IOD differentially regulate the spatial structure of ITF variability through distinct oceanic channels.

Abstract. Contrails – thin ice clouds formed by aircraft – are a major contributor to aviation-induced climate forcing, yet their observational characterization remains limited. We present a manually labeled contrail dataset derived from observations of the Meteosat Second Generation (MSG) SEVIRI instrument over Europe and the North Atlantic, comprising 140 scenes of 256 × 256 pixels at 3 km nominal resolution. The dataset covers the time period in which Meteosat-10 was the operational satellite (from January 2013 through February 2018 and from March 2023 through March 2024) and scenes are distributed randomly over the whole SEVIRI disk. Each scene was independently annotated by three labelers, with ground truth established via majority consensus. To provide additional context, the dataset includes outputs from two satellite retrievals: CiPS (Cirrus Properties from SEVIRI) and ProPS (Probabilistic Cloud Top Phase retrieval), offering information on cloud cover and cloud top phase, cirrus probability, ice optical thickness, and ice cloud top height. These complementary variables enable detailed investigations, such as factors influencing contrail visibility. The dataset supports analyses of contrail detection, contrail characteristics, cloud-contrail interactions, and environmental conditions affecting detection. By providing high-quality labeled data with auxiliary cloud information, this resource facilitates the development and evaluation of contrail studies, contributes to improved understanding of aviation-related cloud effects, and informs strategies for climate impact mitigation. The full dataset is available under: https://doi.org/10.5281/zenodo.17669443 (Santos Gabriel et al., 2025) with version v2 presented in this study.

The Sundarbans, the world’s largest contiguous mangrove forest, represents a critical yet vulnerable ecosystem facing escalating climatic and anthropogenic pressures. This study provides a comprehensive decadal assessment (2013–2024) of the Bangladeshi Sundarbans by integrating remote sensing with a multi-stage statistical framework encompassing trend analysis, spatial pattern decomposition, causal inference, and forecasting. Contrary to narratives of widespread degradation, the analysis of vegetation health (NDVI) reveals a fundamentally stable ecosystem, where an apparent long-term greening trend is attributable solely to a strong seasonal cycle rather than sustained improvement. However, significant phenological restructuring was detected, characterized by asymmetric greening concentrated in the cooler transitional months (January, April, May, December), suggesting an extension of the productive season. Climatic driver analysis uncovered a complex relationship with rainfall, featuring a significant immediate suppressive effect likely from cloud cover, followed by delayed beneficial impacts at 3- and 5-month lags. Temperature exhibited no significant short-term influence on vegetation anomalies. Crucially, neither rainfall nor temperature demonstrated a predictive, Granger-causal relationship with future NDVI, underscoring the system’s stability. Empirical Orthogonal Function (EOF) analysis identified the dominant spatial structure of variability, revealing a hierarchical system comprising: (1) a dominant regional synchrony mode (33.3% of variance), reflecting uniform responses to large-scale climate forcing; (2) a north–south gradient mode, highlighting differential vulnerability along salinity and freshwater continua; and (3) a patch-scale heterogeneity mode, capturing localized disturbance and recovery. A highly accurate Seasonal ARIMA (SARIMA) model (MAPE: 3.99%) was developed and forecasts stable vegetation health through 2027, with all projected values remaining within the bounds of historical variability. The study concludes that the Sundarbans exhibits resilience through adaptive phenology and spatially organized responses rather than systematic degradation. This integrated spatiotemporal assessment provides a robust quantitative baseline and forecasting tool essential for effective, spatially targeted conservation, adaptive management, and climate adaptation planning.

Abstract Over the past two decades, the pan-Arctic has experienced rapid climatic change, accompanied by an unprecedented rise in extreme wildfire activity. However, a systematic and regionally comprehensive assessment of the recent extreme fire events in the pan-Arctic and the role played by human emissions is still pending. In this study, we apply a probability-based extreme event attribution framework to evaluate the role of anthropogenic forcings in enabling the extreme pan-Arctic wildfire seasons of 2019, 2020, and 2021. Using large ensemble simulations with the Community Earth System Model version 2, capable of isolating anthropogenic climate forcings, alongside remote sensing burned area products and ERA5 reanalysis, we assess both magnitude (burned area) and extreme fire risk (Canadian Forest Fire Weather Index, FWI). Our results demonstrate that anthropogenic forcings were a necessary condition for the occurrence of these extreme events: the fraction of attributable risk (FAR) exceeded 0.75 for burned area and reached virtual certainty (FAR <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:mrow> <mml:mo>&gt;</mml:mo> </mml:mrow> </mml:math> 0.99) for FWI in 2020 and 2021. However, the probability of sufficient causation remained low, highlighting that anthropogenic forcings alone are not enough to guarantee such extreme wildfire events. Risk ratios (RRs) indicate that recent extremes have become over 200 times more likely compared to a climate without anthropogenic influence (RR = 235 [5%–95% CI: 98–489], in 2021). By decomposing FWI, we show that temperature and humidity dominate the recent increase in fire weather risk, supported by a substantial elevation in vapor pressure deficit over the pan-Arctic region.