Earth and Environmental Sciences

Abstract. Although heterotrophic prokaryotes (HP) play a crucial role in biogeochemical carbon cycles, microbial oceanographic studies associated with heavy precipitation-induced large-scale freshwater runoff are understudied in the East China Sea (ECS), the largest continental shelf in the northwest Pacific. To elucidate the impact of Yangtze River diluted water (YRDW) on HP production (HPP) and growth-limiting resources, we conducted comprehensive microbial oceanographic measurements in combination with analysis of satellite images and optical property analyses of dissolved organic carbon (DOC) over three consecutive years in the northern ECS. Our results revealed that the HPP and chlorophyll a were consistently highest in summer due to the supply of excess DOC and nutrients via YRDW, which is intriguing considering the enhanced HPP coupled with spring phytoplankton bloom in middle latitudes in general. However, the exceptionally great YRDW runoff induced by heavy rainfall resulted in excessive supply of terrestrial-origin recalcitrant DOC and nutrients imbalance with high N : P ratio (34), which was responsible for the limited DOC bioavailability and phosphorus-limitation for the HPP. Accordingly, the enhanced HPP-to-primary production ratio (>0.5) in summer may suggest enhanced carbon flow via microbial food web, potentially altering food-web structure and energy transfer efficiency. Our results, demonstrating that YRDW can either stimulate or suppress HPP, provide new insights into microbial responses to large-scale freshwater discharge, which may be relevant to systems influenced by substantial freshwater inputs (e.g., Amazon River and Arctic Ocean).

Abstract. The biological carbon pump (BCP), involving photosynthesis at the surface and remineralisation at depth, maintains a significant vertical gradient in dissolved inorganic carbon (DIC), thereby promoting the ocean's ability to absorb atmospheric CO2. Remineralised DIC is a good indicator of the strength of the BCP. It can be estimated from apparent oxygen utilisation (AOU), which measures the deficit of oxygen relative to saturation. AOU is projected to increase under climate change due to changes in remineralisation rates and ventilation. However, the amplitude of the change remains uncertain. Here, we identify linear relationships between trends in AOU and ideal-age in the deep ocean, based on simulations of the contemporary (1982–2013) and future (2015–2099) periods from five Earth system models (ESMs). Our analysis underscores the substantial role of ventilation slowdown in increasing remineralised DIC. Furthermore, the study highlights considerable inter-model variability in their sensitivity of AOU to age changes, with this sensitivity remaining relatively stable over time. With more observational data, refined estimates of age changes from ocean tracers and a larger model ensemble, constraining this variability will become feasible. These insights emphasise both the challenges and opportunities for constraining future BCP projections arising from uncertainties in ventilation.

A reconnaissance survey was conducted at a province-wide scale to document the occurrence of pesticides in drinking water from Québec, Canada. Twenty-four target pesticides were investigated in tap water from 340 municipalities across 17 administrative regions. Of 425 samples, 398 (94%) had detectable levels of at least one target pesticide (maximum: 16 pesticides). The most frequently detected compounds were atrazine (detection rate = 72%; min-max ≤0.01-51 ng/L; 95th percentile = 33 ng/L) and its degradation products desethylatrazine (52%; <0.20-59 ng/L; 95th percentile = 31 ng/L) and hydroxyatrazine (78%; <0.01-28 ng/L; 95th percentile = 13 ng/L). Neonicotinoids were rarely detected in finished tap water (e.g., detection rates of imidacloprid and thiamethoxam: 9-11%). Most tap water samples had low pesticide levels, with profiles typically dominated by triazines. However, a multivariate analysis revealed watershed-specific trends that correlated with the water source and known regional uses. For instance, tap water samples produced from the St. Lawrence River (Great Lakes watershed) had greater levels and prevalence of triazines. Hexazinone was more frequently detected in tap water from the Saguenay-Lac-Saint-Jean, likely related to its use in blueberry fields. Hot spots with relatively high levels of insecticide residues included 3 sites from Portneuf's Regional County Municipality (ΣNeonicotinoids up to 250 ng/L) and 2 sites from Montérégie (chlorantraniliprole up to 66 ng/L), all sourced from groundwater. Tap water samples were all compliant with Québec and Canadian drinking water guidelines and almost all compliant (99% of samples) with the European Union guideline of 100 ng/L for individual pesticides, indicating the good quality of drinking water with respect to pesticide exposure.

Balancing the global mean sea level (GMSL) budget is essential for understanding sea level changes. Large uncertainty after 1960 is reduced by accounting for recent observational advances. Budget closure occurs within 0.18 millimeters per year for all periods analyzed (1960-2023, 1993-2023, and 2005-2023). Trends for these three periods are 2.06, 3.41, and 3.94 millimeters per year, revealing an increase in the rate. The annual residual between observed GMSL and the sum of contributions is only between -13 and 10 millimeters since 1960 and ±5 millimeters after 2005. Further, the GMSL acceleration budget is now closed. The principal drivers for the GMSL trend (acceleration) since 1960 are 43% (41%) from thermosteric ocean expansion, 27% (9%) from glacier melting, 15% (16%) from Greenland, 12% (13%) from Antarctic, and 3% (21%) from land water storage. Results highlight the importance of data processing and bias correction techniques in tracking GMSL and its contributions.

Abstract El Niño‐Southern Oscillation (ENSO) asymmetry, characterized by stronger warm anomalies during El Niño than cold anomalies during La Niña, remains underestimated in climate models. Although nonlinear atmospheric and oceanic feedbacks are known to contribute to this asymmetry, the small‐scale processes modulating their strength remain unclear. Here, we show that interannual variations in the diurnal amplitude (DA) of sea surface temperature (SST) act to enhance ENSO asymmetry. Analyses of 35 Coupled Model Intercomparison Project Phase 6 (CMIP6) models reveal that those with larger SST DAs capture this asymmetry more realistically. In targeted model experiments, enhanced DA strengthens asymmetric zonal SST anomalies across the equatorial Pacific during ENSO events, reinforcing nonlinear air‐sea feedback. The resulting positive SSTA modification, caused by overall wind speed weakening, improves ENSO asymmetry by 38.5% in the Niño 3 region. Our finding indicates large DA as an important role in shaping ENSO nonlinearity and its simulation in climate models.

Abstract Drier and warmer climates have allowed fires to increasingly burn carbon-dense peatland ecosystems. Here we document a 2025 Scottish megafire in the UK, which spread rapidly and burned severely across peatlands in Scotland with anomalously low soil moisture, emitting 38,600 MgC (25,200–119,000 MgC). Peat combustion contributed nearly 85% of total emissions, suggesting drier climates increase fire emissions from peat, which can require decades to centuries to recover.

Seawater intrusion poses a major threat to freshwater resources in coastal aquifers worldwide. In China alone, these types of aquifers provide a vital groundwater source for nearly 400 million people. Whereas modern seawater intrusion is often attributed to human activities and climate change, the role of paleo–salt water remains poorly quantified. This continental-scale investigation of seawater intrusion in China reveals that paleo–salt water, not modern seawater, dominates salinity patterns in large sedimentary basins. Analysis of over 2100 water samples shows a strong correlation between intrusion and tectonic subsidence zones, with paleo–salt water affecting 66% of the observed salinized areas. In northern coastal plains, paleo–salt water trapped in confined aquifers is characterized by salinity levels exceeding 144.6 grams per liter, whereas modern seawater intrusion (34%) occurs in hilly regions with thinner sediments. Isotopic signatures (δ 18 O) trace paleo–salt water intrusion to Quaternary sea level changes. These findings improve seawater intrusion source identification and inform global salinity management.

Abstract Global warming is driving changes in atmospheric moisture seasonality and an increase in the frequency of prolonged precipitation anomalies. These anomalies are often assumed to be characterized by moisture sourced from oceanic evaporation, rather than being moderated by recycled terrestrial evapotranspiration. However, current indexes used to evaluate hydroclimatic anomalies, which exclude particle tracking, do not account for different precipitation moisture sources. Here, stable isotopes of hydrogen and oxygen are used to differentiate precipitation moisture sources, enabling a novel approach to tracking anomalous dry periods through an isotope‐based Evaporation and Moisture Recycling Index (iEMI). iEMI correlated evaporation‐sourced precipitation with prolonged dry periods for European droughts (2011–2013), the Cape Town South Africa “Day Zero drought” (2015–2018) and the Australian Millennium drought (1997–2010). iEMI aligned best to anomalous precipitation events linked to the strength and phase of the El Niño Southern Oscillation across all sites and could be used for drought management interventions.

Abstract Winter Arctic sea ice concentration (SIC) variability reflects both anthropogenic warming and internal climate variability, but separating their Arctic signatures remains difficult. Here, we apply Canonical Correlation Analysis to October–March global sea surface temperature and Arctic SIC, 2-m air temperature (T2M), and sea level pressure fields over 1950–2024, to isolate coupled modes associated with anthropogenic influence, multidecadal and interannual internal variability. The leading pair represents the winter Arctic response to anthropogenic warming, explaining 35% of SIC and 43% of T2M variance, and is characterized by basin-wide SIC loss coupled with increased T2M. The second pair captures a similar but weaker in magnitude multidecadal signal, with the strongest SIC impacts in the Barents–Kara Seas and Baffin Bay. Two additional pairs reflect interannual variability and show dipolar SIC–T2M structures. Causality and physical diagnostics support these attributions, showing mainly thermodynamic coupling for anthropogenic and multidecadal drivers and circulation-driven advection for interannual pairs. Since 1980, Arctic winter SIC decline has been dominated across most sectors by the anthropogenic impact, with additional regional modulation from multidecadal Atlantic variability.

Abstract Drained and cultivated grasslands on peat soils behave as a significant source of greenhouse gasses by oxidation. However, the lack of empirical estimates of carbon losses from peatlands with adequate spatial and temporal resolution has forced researchers to rely on process‐based model approximations to make quantitative, regional‐ or national‐scale estimations. Here we use satellite‐based synthetic aperture radar interferometry to estimate the land motion per parcel with a daily resolution, discriminate a reversible and an irreversible component, and convert this to an upper bound of ‐equivalent emissions over the western part of the Netherlands. We find an upper bound of 21.5 ‐eq/ha/yr, corresponding to a total regional output of 2.3 ‐eq/yr, or approximately 1.3% of the entire greenhouse gas emissions of the Netherlands in 2019. The method also allows us to provide estimates for future emissions as well as evaluate the efficacy of installed subsidence mitigation measures.

Abstract. The operational online version of the Regional Air Quality Deterministic Prediction System (RAQDPS) is a chemical weather forecast system that has been employed by Environment and Climate Change Canada (ECCC) since 2009. It is run twice daily to produce 72 h forecasts of hourly 10 km abundance fields of three key predictands, NO2, O3, and PM2.5 total mass, as well as other gas-phase chemical species, PM2.5 chemical components, and dry and wet deposition for Canada, the contiguous US and Alaska, and northern Mexico. Version 023 of the RAQDPS (RAQDPS023) went into service at ECCC in December 2021 and was replaced by the RAQDPS025 in June 2024. A companion paper by Moran et al. (2026) describes the RAQDPS023 in detail. In this paper we present the results of a five-year performance evaluation of prospective and retrospective annual air quality (AQ) simulations made with the RAQDPS023. The annual simulations considered were the first year of operational RAQDPS023 forecasts in 2021/2022 and four years of retrospective annual simulations for the 2013–2016 period that used historical, year-specific emissions. This version of the RAQDPS023, which did not include biomass burning (BB) emissions, is referred to in the text as the RAQDPS-OP023. Forecasts made by the RAQDPS-FW023, a duplicate operational system to the RAQDPS-OP023 except for the addition of time-dependent BB emissions, were also evaluated for the 2021/2022 period. A near-real-time measurement data set consisting of hourly NO2, O3, and PM2.5 surface measurements for Canada and the US was used for the 2021/2022 evaluation, whereas a much more extensive set of air-chemistry and precipitation-chemistry measurements was used for the 2013–2016 RAQDPS-OP023 evaluations. Some evaluation results were also compared with results for the 2010–2019 period for forecasts made by earlier operational versions of the RAQDPS and with evaluation results for several peer AQ forecast models. In addition to looking at a number of highly aggregated “headline” scores, many stratified analyses were also performed, including evaluations by network, season, month, hour of day, region, and land-use type. Consideration of simulations for multiple years with the same model but year-specific input emissions helped to identify systematic model errors by reducing the influence of year-to-year variations in meteorology and emissions, and a comprehensive evaluation for many additional chemical species for 2013–2016 supported by stratified analyses provided diagnostic insights that allowed the scientific basis for the RAQDPS-OP023 forecasts to be assessed (e.g., were the right answers obtained for the right reasons?). Although one confounding factor for this study was the sizable reduction in the emissions of some pollutants in North America that occurred from 2013 to 2021, it was found that the trends in AQ observations over this period agreed with the year-specific description of emissions used for the five annual simulations from a rank-ordered perspective. While RAQDPS-OP023 evaluation scores for hourly NO2 and O3 volume mixing ratio forecasts were found to be competitive with peer models and often met suggested performance benchmarks for the five simulation years, another key finding was that the RAQDPS-OP023 forecasts consistently underpredicted hourly PM2.5 total mass concentrations for all months in 2021/2022 and for the majority of months in 2013–2016. The largest underpredictions occurred in summer and at rural stations, whereas overpredictions often occurred in the cold season at urban stations. The model also missed the observed bimodality in monthly PM2.5 concentrations and exaggerated the observed diurnal variations in hourly PM2.5 concentrations. Additional evaluations with daily PM2.5 chemical composition measurements and daily gravimetric PM2.5 total mass measurements from the US. PM2.5 mass monitoring network were also examined to better understand the hourly PM2.5 underpredictions. Consistent overpredictions of elemental carbon and sea salt concentrations and underpredictions of sulfate concentration were identified, but scores for predictions of daily gravimetric PM2.5 total mass were better than those for hourly PM2.5 total mass, directing attention to differences in measurement methods. SO2 and HNO3 levels were also found to be overpredicted in general while NH3 levels were underpredicted: these three gas-phase species are all PM2.5 precursors, which raises concerns about some process representations in the model such as those for sulfur oxidation and gas-phase dry deposition. As well, springtime O3 levels were underpredicted while isoprene levels were consistently overpredicted in all seasons. The impact of BB emissions on predictions of NO2, O3, and PM2.5 was also characterized in detail by comparing evaluation results for the 2021/2022 RAQDPS-OP023 and RAQDPS-FW023 forecasts. Negligible impact was found for monthly NO2 forecasts when BB emissions were included, but monthly O3 forecast scores for the RAQDPS-FW023 were modestly improved and monthly PM2.5 forecast scores were markedly improved from July to September 2021, as well as summer and annual scores. Taken together, the results of this comprehensive multi-year evaluation point to a number of RAQDPS023 system components where improvements are desirable. These results also provide a strong benchmark against which to compare the performance of future versions of the RAQDPS.

Abstract Rock glaciers are ice-debris landforms commonly found in high mountain environments. Shaped by long-term creep of ice-rich permafrost, they provide critical information for permafrost studies, mountain hydrology, and hazard assessment. Although the characteristics and controlling factors of rock glacier velocities across various temporal scales have been studied at individual sites, their environmental drivers in the spatial domain over large regions remain poorly understood. In this study, we employ four machine learning methods, i.e., Support Vector Machine, Extreme Gradient Boost, Random Forest, and Back Propagation Neural Network, to model the relationship between rock glacier velocities and environmental variables for 5,163 rock glaciers in the Pamir-Karakoram-Kunlun region. Subsequently, we use SHapley Additive exPlanations to quantify variable importance. Results show that the upslope connection to a glacier is a critical factor controlling rock glacier velocities. In our study area, glacier-connected rock glaciers exhibit on average faster movement (median velocity = 38 cm/yr) than talus-connected ones (median velocity = 28 cm/yr). We also find that geomorphological properties exert stronger controls on the spatial variability of rock glacier velocities than regional climate variability. Rock glacier area and slope are identified as the second and third most important variables, with larger areas and steeper slopes associated with higher velocities. Snow cover duration ranks fourth, followed by precipitation, while air temperature shows minimal influence on velocity. Overall, these findings bridge a critical knowledge gap regarding the environmental controls on rock glacier dynamics at the regional scale, extending our understanding of rock glacier kinematics beyond site-specific investigations.

The Karun-Karkheh-Marzi-e-Gharb river basin complex, a strategically vital but data-scarce and human-regulated region in Western Iran characterized by semi-arid conditions and complex topography. This research evaluates future hydroclimatic shifts by integrating the Community Water Model (CWatM) with a dual-benchmark calibration framework. The study utilizes a computationally efficient strategy—reducing runtime by 55%—to enable a 30-member ensemble projection (General Circulation Models) without high-performance computing. To address anthropogenic non-stationarity and data constraints, the model was validated against both a naturalized benchmark (KGE=0.71) and regulated local observations (KGE=0.60). Furthermore, SHAP-based sensitivity analysis, validated by high-accuracy surrogate models, including XGBoost (R2=0.98, RMSE=0.04) and Linear Regression (R2 =0.95, RMSE=0.1), was employed to quantify the influence of physical and anthropogenic drivers on the basin's hydrological response. Projections reveal a consistent hydrograph transformation driven by thermal forcing across all scenarios. Warmer winters are projected to reduce snow storage, advancing peak flow timing toward late winter. Crucially, intensified evapotranspiration is found to outweigh precipitation variability, shifting the basin from a storage-controlled to an evaporation-dominated system. This leads to declining annual water yields and escalating late-century drought extremes. These findings provide a refined, physically-based projection that potentially extends beyond traditional national climatic reports, suggesting that regional water security should prioritize demand-side management and a transition from stationary allocation rules toward adaptive, climate-resilient strategies. • Dual-benchmarks quantify human-induced non-stationarity in data-scarce basins. • Early runoff shifts peak flow timing, impacting long-term reservoir recharge. • Thermal forcing outweighs precipitation in driving late-century drought extremes. • SHAP values reveal a shift from storage-controlled to evaporation-dominated. • Automated pipelines reduced CWatM simulation runtimes by 55%.

Abstract Since the 1970s, Pine Island Glacier has exhibited thinning, acceleration, and retreat. During the last decade, the ice shelf has undergone major geometric changes, whilst the quantity and temperature of modified Circumpolar Deep Water on the Amundsen Sea continental shelf fluctuated significantly. Untangling how these factors modulate ice‐shelf basal melt rates is critical, as ocean‐driven melt may be mitigated through emission reductions, whereas geometry‐driven retreat may be irreversible. We use ocean model experiments to partition the relative importance of ice geometry and ocean temperature changes in driving melt variability between 2011 and 2021. Simulations use observed ice‐shelf geometries from CryoSat‐2 and ocean boundary conditions from moorings in Pine Island Bay. Temporal variability of melt and implied ice loss during this period was largely controlled by ocean conditions, while geometric evolution primarily controlled the spatial distribution of melt through cavity circulation reconfiguration, with a non‐negligible impact on buttressing.

The El Niño-Southern Oscillation (ENSO) exhibits a pronounced decline in predictability during boreal spring, referred to as spring predictability barrier (SPB). While tropical basin interactions among the Indian, Atlantic, and Pacific Oceans potentially enhance ENSO predictability, their roles in mitigating SPB within deep learning (DL) frameworks remain underutilized. Here, we introduce GL-Geoformer, a DL model for global tropical ocean-atmosphere prediction. GL-Geoformer captures spatiotemporal evolutions of wind and three-dimensional temperature anomalies across the tropical basins. Our modeling demonstrates that incorporating tropical basin interactions substantially reduces SPB, enabling GL-Geoformer to achieve skillful ENSO predictions up to 16 months in advance when initiated in spring. Pacemaker experiments are performed to quantify individual and synergistic contributing nonlinearities of Indian Ocean Dipole and Atlantic Niño via subsurface heat transport and Walker circulation mechanisms, respectively. This study provides a data-driven framework to represent tropical basin interactions and reduce SPB, thereby deepening understanding of ENSO predictability.

Abstract. Cold spells in the Northern Hemisphere mid-latitudes have been linked to Rossby waves. Yet the mechanisms by which these large-scale waves impact cold-spell formation remain unclear. Here we develop novel metrics to separately determine the amplitude and speed of large-scale ridges and troughs, derived from the first five zonal Fourier decompositions of the geopotential height field. This approach allows us to examine the behavior of large-scale ridges and troughs during winter cold spells. These ridges and troughs mainly represent climatological features, which can be regarded as wobbling around their climatological positions due to interactions with background flow. Our findings indicate that while ridges and troughs across the entire mid-latitudes experience significant changes during cold spells, the local ridge and trough near the cold spell's location play a major role in the development of these events. The nearest upstream ridge and downstream trough of the cold-spell region are located in a way that facilitates development of the extreme cold anomaly. This ridge and trough amplify and slow down, enhancing and prolonging southward advection of cold air from the Arctic into the cold-spell region. The slow and amplified upstream ridge and downstream trough occur several days before the region’s minimum temperature, suggesting these local wave anomalies induce cold-spell formation.

Abstract The Arctic has warmed at more than twice the global mean rate, with winter anomalies over the Barents‐Kara Seas (BKS) linked to remote tropical forcing. While El Niño Southern Oscillation is often emphasized, the role of the Atlantic Niño remains poorly understood. Using reanalysis data and CESM2 large‐ensemble simulations, we show that summer Atlantic Niño sea surface temperature anomalies persist into autumn and induce cross‐basin atmospheric circulation adjustments that extend their influence to the Arctic. These changes trigger wave activity that propagates into the stratosphere, where the signal is stored and released downward in winter, producing a negative NAO‐like circulation. This response reinforces a dipole in Arctic surface air temperature, with cooling over the BKS and Eurasia and warming over Greenland and northeastern Canada. These results identify the Atlantic Niño as a predictable driver of Arctic variability, with implications for seasonal prediction.

Interannual variations in terrestrial carbon uptake (IVTCU) strongly regulate anthropogenic CO2 growth rate dynamics, yet their controlling mechanisms remain uncertain. Previous studies disagree on whether precipitation (P) or temperature (T) predominates and whether soil moisture (SM) or vapor pressure deficit (VPD) is the primary driver. Using ground-based meteorological observations and remotely sensed gross carbon uptake data (1982–2016), we reveal that the global IVTCU is controlled by T and VPD, whereas the influences of water availability (P or SM) are relatively weak. Regionally, the VPD exerts dominant control in arid areas and common drought-adapted ecosystems, whereas T predominates in other regions, especially cold areas. In contrast to the prevailing view that water availability constrains carbon uptake in arid systems, our analysis reveals that although P yields greater influences in these regions than elsewhere, the VPD remains the main factor limiting the IVTCU. This highlights the notable inhibitory effect of atmospheric dryness on arid ecosystem carbon uptake. Globally, the sensitivity of the IVTCU to T is greatest between 7 °C and 16 °C and decreases rapidly at higher T, where the VPD gradually becomes the dominant factor. The IVTCU responds less strongly to the VPD than to T, but the VPD maintains a substantial influence, especially below 10 hPa. Both the sensitivity and threshold vary across land cover/climate zones. These findings refine our understanding of the mechanisms driving carbon flux variability, challenge conventional assumptions regarding arid-region controls, and provide critical insights for improving land–atmosphere coupling models under a changing climate.

Abstract Arctic ozone is projected to recover over the 21st century in compliance with the Montreal Protocol. Chemistry‐climate models show a large spread in recovery rates and future changes in key dynamical features such as the polar vortex and the residual circulation. Here, we quantify the spread in Arctic ozone recovery explained by uncertainties in these dynamical changes, using simulations from the Chemistry‐Climate Model Initiative, phases 1 and 2022. About 60% of the spread in projected ozone recovery is attributed to differences in polar vortex trends and about one‐third to the intermodel spread in residual circulation trends. Ozone‐depleting substances and greenhouse gas‐driven global warming contribute similarly to the spread in ozone recovery, although uncertainties in dynamical trends primarily affect the warming component. While dynamical changes clearly separate the simulated ozone recovery among different models, the large variability in the Arctic stratosphere precludes establishing an observational constraint on future ozone recovery.

Abstract Previous machine learning approaches for surface ozone (O 3 ) forecasting are often limited to site‐specific applications and struggle to capture regional pollution dynamics. Here, we introduce an inverted Transformer (iTransformer), a time‐series model that integrates hourly satellite‐derived surface O 3 with key meteorological and environmental drivers. The model is applied to eastern China and demonstrates strong skill in 72‐hr surface O 3 forecasting, achieving an overall correlation of 0.86 (mean bias = 0.29 μg/m 3 ). It effectively captures spatial patterns, diurnal variability, and high‐ozone episodes, maintaining stable performance over time. During pollution events, the model reproduces both localized and large‐scale ozone enhancements with high fidelity. Forecast biases remain low, particularly at shorter lead times, indicating robust predictive skill. Overall, the results highlight the ability of advanced deep learning architectures to bridge satellite observations and air quality forecasting, offering a scalable framework for near‐real‐time ozone prediction and supporting timely environmental and public health decision‐making.

Abstract Climate change is a major factor contributing to biodiversity loss globally and in Australia. Addressing this conservation challenge requires information about present and future species distributions. To improve assessment of future species distributions, we have developed 19 bioclimatic indices calculated from dynamically and statistically downscaled Coupled Model Intercomparison Project 6 (CMIP6) Global Climate Models (GCMs) over Australia at a 5 km resolution through 1975 – 2099 for three emissions scenarios. These data are available on EcoCommons, an ecological modelling web platform used by conservation researchers and practitioners. Here, we demonstrate the use of this dataset in species distribution modelling (SDM) using a case study of the southern and central populations of the greater glider (Petauroides volans, referred to as P.volans hereafter). By the end of the century, projections for bioclimatic indices which measure temperature have increased by 2.9 – 4.0°C for the high emission scenario (SSP3-70; Shared Socioeconomic Pathway 3 with 7.0 W/m2 radiative forcing). Indices related to temperature range tend to decrease in northern Australia and increase in southern Australia, while isothermality increases along the northern coast of Australia. Precipitation based indices show a high amount of variability, due to the model uncertainty in precipitation projections for Australia, though there is a tendency for increases in precipitation during the warm and wet seasons, and decreases during the cooler and drier seasons. Our case study of the greater glider shows that by the end of the century under the high emission scenario (SSP3-7.0), its potential climatic range will be reduced by about 30% - 50% across Australia, and in Queensland by over 90%. This study illustrates the utility of the 5km statistically downscaled data and the use of the EcoCommons platform for analysing projected climate change impacts on species distributions in Australia.

• A multi-component WSDIs framework is proposed to quantify the spatiotemporal evolution of drought based on GRACE data. • WSDI-TWSA exhibits the highest synchronicity with scPDSI, effectively capturing complex drought dynamics in the Weihe River Basin. • Cascade propagation chain of Meteorological→Agricultural→ Hydrological→Groundwater drought is clearly delineated. • Groundwater storage shows a significant response lag and a “decoupling” trend from meteorological drivers due to human activities. • WSDI component indices identify potential groundwater risks, providing a more comprehensive perspective for regional drought management. Drought is a complex phenomenon characterized by the spatiotemporal propagation of water deficits. Conventional surface-based drought indices often fail to fully capture the intricate feedback mechanisms within terrestrial water storage components. This study develops a multidimensional standardized Water Storage Deficit Index framework (WSDIs, including WSDI-TWSA, WSDI-GWSA, WSDI-SMSA, WSDI-CWSA, and WSDI-SWEA) based on GRACE satellite data, using the Wei River Basin on the Chinese Loess Plateau as a representative study area. The synchronicity between WSDIs and the traditional surface drought index (e.g., scPDSI, SPEI, SRI) is investigated to track the evolutionary trajectories of major historical drought events. The results indicate that (1) the WSDI comprehensive index WSDI-TWSA exhibits the highest gray relational grade with scPDSI, demonstrating its superiority in characterizing integrated drought conditions; (2) the WSDI component indexes effectively reveal the cascade propagation from meteorological → agricultural → surface hydrological → groundwater drought; and (3) a significant lag and “decoupling” phenomenon between GWSA and meteorological drivers are identified, exacerbated by anthropogenic interventions (e.g., irrigation). The WSDIs framework provides a robust tool for capturing the spatiotemporal patterns and internal mechanisms of drought, offering critical data support for risk mitigation and water resource allocation in water-scarce regions.

Abstract The European Alps and their surroundings (hereby referred to as the Extended European Alpine Region, EEAR ) are known to be a hot-spot of climate change as they are experiencing a faster warming rate than other regions in the world. However, the complex nature of the Alpine terrain makes it more difficult to understand how climatic changes are distributed over space, and in particular with elevation. In this study, we present a comprehensive analysis of how air temperature, precipitation and a broad set of extreme indices have changed over the EEAR during the period 1961–2020, based on a newly developed daily observational dataset with unprecedented spatial density. The analysis relies on robust trend estimation using the non-parametric Sen’s slope method, with statistical significance assessed via the Mann–Kendall test. In addition, elevation-dependent climate change is investigated through a twofold approach that accounts for both linear and non-linear patterns. The analysis of the trends of air temperature and precipitation highlights the enhanced warming in the Alpine region, which amounts to about $$+2^{\circ}\textrm{C}$$ on average during the 1961–2020 period. In terms of temperature extremes, the same period is characterized by a significant increase in warm spells duration index (WSDI), +10.1 days, and in both minimum and maximum temperature indices, respectively +48 warm nights (TN90p) and +49 warm days (TX90p). While mean precipitation does not show a significant change in time, the frequency of extreme rainfall events (R95p index) significantly increased by about +13 days since 1961. Moreover, an enhanced warming with elevation is observed for mean and minimum temperature from February to May, while increasing precipitation trends with elevation are found, mainly in summer.

Individual environmental exposures have been linked to later-life cognition, but their combined effects, particularly on cognitive decline, remain unclear. Using data from 18,462 participants in the Nurses' Health Study cognitive substudy, we assessed the independent and joint associations of long-term exposure to multiple environmental factors with cognitive function and decline, using generalized estimating equations (GEE) and quantile g-computation models, allowing exposures to operate in both protective and harmful directions. Cumulative average exposures to air pollution, seasonal temperature, noise, greenness, light at night (LAN), and neighborhood socioeconomic status (nSES) from 1988 onward were assigned to updated residential addresses. In multiexposure GEE models, each interquartile-range increase in greenness and nSES was associated with 0.026 (95% CI: 0.013, 0.040) and 0.017 (0.005, 0.029) standard-unit increases in cognitive function, respectively. Higher summer temperature was associated with a 0.011 (-0.023, 0.000) standard-unit decrease. Jointly, each quartile increase across all exposures corresponded to a 0.013 (0.001, 0.024) standard-unit higher cognitive score. Higher summer maximum temperature and LAN were associated with faster cognitive decline only in single-exposure models, while no significant joint association with decline was observed. These findings suggest that enhancing protective exposures (greenness, nSES) may help offset the adverse effects of harmful exposures (high temperature, LAN) and support healthier cognitive aging.

Abstract. Culverts play a critical role in conveying surface runoff during flash flood events, yet their failure due to blockages can significantly alter local flood dynamics, particularly in small, topographically complex catchments. Despite this, culvert blockages are often neglected in flood modeling. To address such gap, this study presents a comprehensive analysis of culvert blockages using the open-source hydrodynamic model TELEMAC-2D, applied to a flash-flood-prone catchment in central Germany. First, the study assesses recent flood events and evaluates the completeness and accuracy of official culvert datasets, identifying missing culverts. A dynamic culvert blockage module is then implemented, simulating varying degrees and timings of blockage based on water level thresholds at culvert inlets. Through a series of flood scenarios, the study identifies culverts whose blockages have a greater impact on local flood hydrographs and inundation extents, whereas their impact at the catchment scale remain small. Results highlight the importance of accurate culvert representation and present a scenario-based blockage modeling framework that can support the identification of critical infrastructure. This enables the development of targeted mitigation strategies, such as prioritized maintenance or emergency protection, ultimately reducing flood risks. The findings underscore the need to integrate culvert blockages into flash flood modeling and risk assessments and support future research into blockage formation mechanisms and improved field data acquisition.