Earth and Environmental Sciences

Urban parks, as essential urban green infrastructure, contribute significantly to public health, psychological restoration, and socially sustainable urban living. However, existing research has primarily emphasized landscape aesthetics while paying comparatively limited attention to routine landscape maintenance as an important component of sustainable urban park governance. Drawing on Stress Recovery Theory (SRT), this study examines how landscape maintenance quality influences perceived well-being through perceived safety and restorative experience. Survey data were collected from 278 urban park users in Wangjianglou Park, Chengdu, China, and analyzed using partial least squares structural equation modeling (PLS-SEM). The results indicate that maintenance quality positively affects perceived well-being both directly and indirectly through perceived safety and restorative experience, which serve as significant mediators. Multi-group analysis further reveals demographic differences, with female users demonstrating stronger safety-related responses and older users exhibiting stronger restorative and perceived well-being benefits associated with maintenance conditions. The findings highlight the importance of routine park maintenance in supporting perceived safety, psychological restoration, inclusiveness, and the long-term usability of urban public spaces. The study advances understanding of how maintenance practices shape psychological restoration and urban perceived well-being while providing empirical support for sustainable urban green space management and the achievement of Sustainable Development Goal 11.

Urban waste-governance programs rely on household source segregation, yet often assume that discards can be classified through stable technical categories. In culturally governed settings, post-use materials may also be classified through ritual status, propriety, edibility, and social obligation. This focused ethnography examined why source-based management of post-ritual offering materials, locally referred to as sisa upakara, remains difficult to sustain in urban Denpasar, Bali. Data were collected between January and March 2026 through 18 semi-structured interviews, four focus group discussions with 30 participants, six directed observation episodes totalling approximately 21 h, and document review across four anonymized urban sites. A hybrid deductive–inductive thematic analysis produced 2183 selectively coded segments. Five interdependent mechanisms explained practice formation and breakdown: post-ritual classification and legitimacy, domestic routinization, material-infrastructure fit, local-to-downstream verification, and system absorptive capacity. Management weakened when households could not distinguish edible remnants, ritually sensitive materials, and ordinary discards; when ceremonial peaks overloaded domestic routines; when fibrous, wet, bulky, or contaminated materials exceeded available infrastructure; and when downstream systems failed to preserve separated materials. The findings show that sisa upakara constitutes a hidden ritual-urban material sub-stream embedded within household waste. Culturally responsive waste governance requires alignment between classification guidance, household routines, material design, collection reliability, downstream verification, and decentralized processing capacity.

Infectious diseases transmitted through environmentally mediated faecal-oral pathways and climate-sensitive vector-borne routes remain major public health challenges in the Brazilian Amazon, where sanitation deficits interact with hydroclimatic variability. This study investigated how ENSO-related climate variability, sanitation conditions, and socioenvironmental factors jointly influence disease incidence across seven Amazonian states from 2010 to 2022. Epidemiological, climatic, and socioeconomic datasets were integrated using statistical models and explainable machine learning approaches. Disease incidence exhibited strong heterogeneity across transmission pathways. Climate was the dominant predictive domain for faecal-oral, contact-related, and arboviral diseases (mean SHAP up to 0.871), whereas spatial structure dominated protozoan vector-borne diseases and the combined model (up to 0.628). At the variable level, contributions were concentrated in a limited set of predictors, particularly state effects (up to 0.401) and key climatic variables, including temperature and lagged humidity and rainfall. Lagged climatic variables (t - 1) contributed substantially to model performance, indicating delayed responses to environmental variability. Generalized additive models showed high explanatory capacity, with explained deviance ranging from 68.2% to 94.9% across disease groups. Ensemble models outperformed conventional approaches, capturing nonlinear relationships among predictors. Overall, climate variability acted as a modulating factor within a broader system of structural vulnerability, rather than as an isolated driver. These findings support integrated strategies combining sanitation improvements, climate-informed early warning systems, and spatially targeted public health interventions.

Water table (WT) is a key indicator of peatland ecosystem functioning, but its spatiotemporal monitoring is challenging. Optical remote sensing has been used in peatland WT monitoring with varying success, but few studies have tested whether environmental variables—particularly topographic and tree stand structure variables derived from LiDAR—improve modelling performance. We tested whether environmental variables improve (1) uncrewed aerial vehicle-derived spatial WT models in two northern boreal, partly drained aapa mires and (2) satellite image-derived spatiotemporal WT models in a southern boreal drained peatland forest in Finland. We employed random forest regression and variable selection techniques to model WT, using optical remote sensing and environmental variables as predictors. Our results showed that environmental variables related to topography and tree stand structure improve modelling performance, with R 2 increasing by 0.01–0.19 compared to optical-only models. Our findings support the integration of optical and environmental data for spatial and spatiotemporal WT monitoring in boreal peatlands.

Urban trees play a crucial role in delivering ecosystem services (ES), such as reducing PM10 concentrations, and acting as carbon sinks to combat the climate crisis. While previous life cycle assessment (LCA) studies have highlighted how tree environmental performance largely depends on the species, transforming Urban Forests (UF) composition is a slow, long-term process. Therefore, this study relies on the lessons learnt from the Life Clivut project (LIFE18 GIC/IT/001217) in Mediterranean UF. Specifically, it aims to identify the processes within conventional urban tree management that exert the greatest environmental impact, enabling targeted intervention on pollution hotspots to achieve meaningful short-term reductions in overall environmental burdens. The LCA study, conducted using SimaPro 9.6.0 software, focuses on the city of Perugia (Italy) to assess the environmental benefits of implementing Clivut Best Practices. By analysing the most promising strategies tested in Clivut pilot cities, the study designs a "Future scenario" to further improve the municipal environmental performance by adopting electric tools in UF management. The study is based on ReCiPe 2016 Endpoint (H) and Midpoint (H) LCA impact assessment methods over a 70-year timeframe. Results indicate that the most significant improvements in environmental performance occur between the benchmark scenario and the implementation of Clivut Best Practices (21%), while shifting to the Future scenario determines only a 0.8% improvement. Notably, utilising chipped wood residues as mulch proves to be environmentally beneficial only on large volumes of pruning residues (>2554 Kg). However, the use of electric brush cutters and power saws would reduce the environmental impact of the tools by -2% and - 66%, respectively, while it would be detrimental in the case of car-driven green monitoring activities (+29%).

Abstract Climate change is intensifying compound flooding in coastal regions, caused by the co‐occurrence of coastal and fluvial floods. This trend increases the probability of disasters beyond historical precedent, posing growing threats to densely populated deltas. Existing studies, however, cannot fully characterize how a warming climate affects multiple flood drivers and their interactions, which may lead to biased projections of future compound extreme events. In this study, we develop a physics‐based method that dynamically integrates the impact of climate change on both tropical cyclone (TC) activities and rainfall‐runoff processes. We apply this method to project coastal‐fluvial compound flood risk in the Pearl River Delta (PRD). An ensemble of 100,000 storm events is generated to characterize possible compound extreme events under the historical (1974–2014) and future (2060–2100; SSP5‐8.5) climate scenarios. At a representative estuarine location, the projected results reveal a significant increase in the probability of coastal and fluvial extremes, with the return period decreasing from 55.5 to 15.5 years. These projections indicate that compound flood events unprecedented in the historical record are more likely to occur in the future. Our findings highlight the need for adaptive coastal hazard management strategies to address the escalating compound flood risk under climate change.

Abstract The global terrestrial water and carbon cycles have evolved rapidly during recent decades, particularly in regions with intensive anthropogenic interventions and vulnerable ecohydrological systems. However, current land surface models (LSMs) and global hydrological models (GHMs) often treat natural ecohydrological processes and human water use in isolation, leaving a critical gap in the holistic understanding of coupled human‐natural systems at global scale. To bridge this gap, we developed the Conjunctive Surface–Subsurface Process Model version 3 (CSSPv3), a global land surface‐ecology‐hydrology coupled model that innovatively integrates human activities including irrigation and reservoir operation, dynamic water‐carbon couplings, and glacier freeze–thaw cycles within a water–energy balanced framework. A global offline simulation for 1991–2020 using CSSPv3 with a spatial resolution of 0.25° is validated against extensive observations, demonstrating that CSSPv3 outperforms its predecessor, CSSPv2, particularly for simulations of streamflow over human and glacier influenced basins, and evapotranspiration over irrigation fields. Moreover, the CSSPv3‐generated global water product demonstrates advantages for representing irrigation amount, streamflow, surface soil moisture, evapotranspiration, and glacier mass change as compared with 26 global products. The CSSPv3‐generated global carbon product is consistent with global ensemble medians from other products. These results establish CSSPv3 as a robust and comprehensive platform for simulating coupled ecohydrological processes under both natural climate variability and human influence, providing a powerful tool for advancing land surface modeling in the Anthropocene.

Reliable drought prediction is crucial for early warning and decision-making regarding drought-related disasters. Flash droughts, characterized by their rapid onset and severe impacts, are challenging to predict and present critical challenges to water resource management. Specifically, streamflow flash droughts (SFDs) are significant as they directly influence water supply for irrigation, industry, and domestic use. Traditional hydrologic models and drought early warning systems are primarily designed to capture slowly evolving drought conditions and often fail to represent rapid transitions in streamflow that characterize the onset of SFDs. Despite increasing attention to the identification and characterization of SFDs, their prediction remains unexplored. This study evaluates the ability of different deep learning architectures, especially the Temporal Fusion Transformer (TFT), in comparison to the widespread baseline Long Short-Term Memory (LSTM) model and a two-source LSTM incorporating static catchment attributes, to predict SFDs based on streamflow percentiles. The model training and performance evaluation were conducted using hydroclimatic datasets from 671 Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) catchments in the contiguous United States. Performance was evaluated using standard accuracy and event detection metrics. Results show that the TFT achieved the highest overall performance, with a median KGE of 0.87, RMSE of 0.13, MAE of 0.10, and a correlation coefficient of 0.91, outperforming the LSTM with static features (median KGE = 0.81) and the baseline LSTM (KGE = 0.78). In terms of detecting SFDs, the TFT showed the highest median detection rate and lowest miss rate across all models. These improvements were consistent across most catchments and hydroclimatic regions, indicating robust performance gains relative to LSTM-based approaches, although some regions with complex hydrologic behavior remained challenging for all models. While model performance varied across hydroclimatic regions, particularly in data-sparse or hydrologically complex areas, the integration of static attributes and attention-based mechanisms consistently improved predictive skill. These findings demonstrate that Transformer-based models can improve prediction of rapid streamflow changes associated with the onset of SFDs; however, the results are based on deterministic predictions and do not explicitly quantify predictive uncertainty. These findings contribute new insights into the spatial patterns and physical drivers of model performance under non-stationary climate conditions.

Abstract. The climate in Europe is warming faster than the global average, raising concerns about how climate change will affect extreme fire events. In this study, we use ERA5-Land reanalysis data and an ensemble of 33 high-resolution regional climate models (RCMs) from the EURO-CORDEX framework to compute the Canadian Forest Fire Weather Index (FWI) and investigate both recent and projected changes in atmospheric conditions favorable for wildfires across Europe. Historical trends (1950–2023) based on ERA5-Land data reveal statistically significant increases in the frequency and intensity of extreme fire weather in regions such as the Iberian Peninsula, Central Europe, and parts of Eastern Europe. All RCM input fields were bias-adjusted prior to FWI calculation using Quantile Delta Mapping, resulting in improved FWI representation relative to unadjusted simulations. Projections based on the bias-adjusted EURO-CORDEX ensemble indicate that future extreme fire weather will become more frequent, more intense, and more widespread across Europe as global warming progresses. The strongest signals are projected for southern Europe, with a northward expansion of fire-prone conditions under higher global warming levels (GWLs). At 3 °C GWL, the spatial extent of robust changes in extreme fire weather metrics nearly doubles compared to 2 °C, with one metric increasing fivefold. Relative increases in frequency-based metrics generally exceed those in magnitude-based metrics. These changes coincide with rising vapor pressure deficit, suggesting that thermodynamic processes play a key role through atmospheric drying. The projected intensification of extreme fire weather in Europe highlights the growing need for coordinated climate action along with proactive mitigation strategies.

Abstract Permafrost regions store vast amounts of soil carbon, and thaw under global warming enhances microbial decomposition and CO 2 release, strengthening the permafrost carbon feedback. While this feedback has been extensively studied for continued warming, the processes controlling its magnitude and persistence remain uncertain. Using the Community Earth System Model version 2 (CESM2), we analyze permafrost carbon cycle dynamics under both warming and subsequent mitigation and quantify the drivers of net biome production (NBP). We show that soil moisture is a primary regulator of these dynamics. Thaw‐induced increases in liquid soil water stimulate plant photosynthesis but more strongly enhance microbial decomposition, shifting ecosystems toward net carbon loss. The sensitivity to soil moisture is highly heterogeneous, with the strongest response in central Siberia, where high litter carbon coincides with relatively dry background conditions, and weaker sensitivity in wetter North America. Elevated soil moisture also delays permafrost refreezing even after zero emission are reached, sustaining high heterotrophic respiration and prolonging net carbon loss. These results demonstrate that soil moisture amplifies and prolongs permafrost carbon losses from warming through mitigation, underscoring the need for improved representation of coupled hydrological and permafrost processes in carbon‐budget assessments.

Abstract Antarctic sea ice projected to have large reduction associated with increasing greenhouse gases, with potential impacts extending beyond the Southern Hemisphere. However, its influence on East Asian climate is not fully understood. Using Earth system model experiments and multi‐model simulations, we show that Antarctic sea‐ice loss by the end of the 21st century leads to a significant increase in summer precipitation over central and southern China. This precipitation response is further amplified with enhanced sea‐ice loss under quadrupled CO 2 forcing. Additional atmospheric model experiments indicate that atmospheric pathways alone cannot transmit the Antarctic signal to East Asia. Instead, Antarctic sea‐ice loss shapes a hemispherically asymmetric pattern of tropical sea surface warming through anomalous northward cross‐equatorial heat transport and southward of the intertropical convergence zone driven by interhemispheric energy imbalance, with the warming further amplified by positive low‐cloud shortwave feedbacks. The resulting warming in the tropical Atlantic and Indian Oceans induces anomalous northeasterly winds over eastern China and southwesterly flow over southern China through Rossby wave trains and modulation of the Hadley circulation. These circulation changes enhance upward motion and precipitation over central and southern China. Antarctic sea‐ice loss accounts for roughly 40% of the projected summer precipitation increase in this region under the high‐emission scenario, highlighting its potential role in shaping future East Asian climate.

This study aimed at (i) developing a multi-station deep learning rainfall–runoff–operation (DL-RRO) model for cascading dam systems and (ii) applying the model through the large ensemble dataset for Policy Decision-Making for Future Climate Change (d4PDF) to assess flood characteristic changes. The DL-RRO model was developed using a Long Short-Term Memory network that coupled rainfall–runoff processes and mimicked existing dam operation rules by learning operational logic from observed data. Dynamic water level–storage curves were generated to account for time-varying increases in sediment storage. The proposed method was applied to the upper Chikugo River Basin (CRB), where the Shimouke and Matsubara Dams play a critical role in flood control in Kyushu, Japan. The model showed high performance when trained, validated, and tested with 40 years (1986–2025) of hourly hydrological observations (NSE = 0.89, RMSE = 18.90 m 3 /s). Subsequently, d4PDF rainfall data for the future scenarios (2031–2090 and 2051–2110; 1,440 ensemble-year) were imposed to the DL-RRO model. Analysis of 8,725 simulated flood events revealed increased flood frequency and notable changes in hydrograph characteristics. Projected 100-year return flood may increase by +53 % at Shimouke and +165 % at Matsubara under future scenarios, exceeding existing spillway design capacities. Under the synergistic impacts of climate change and sediment dynamics, amplified flood characteristics may intensify flood-driven sediment deposition, leading to further reductions in flood control capacity of up to +2.9 % at the Shimouke and +4.1 % at the Matsubara. These findings highlighted the urgent need to upgrade dams, revise operations and sedimentation management strategies in the upper CRB.

Konservat-Lagerstätten provide exceptional records of past ecosystems, yet the environmental and climatic processes governing their formation in extreme climatic regimes remain a significant, unresolved conundrum. Here we present a novel, process-based mechanism for the Villaggio del Pescatore Konservat-Lagerstätte (northeastern Italy; Late Cretaceous, 80 Ma), which preserves a diverse terrestrial and aquatic biota. Integrating high resolution synchrotron micro-analyses, bulk geochemistry, detailed taphonomy and palaeoclimate simulations we identify a dynamic, monsoon-paced tidal flat ecosystem within a carbonate coastal margin. Field and core records comprise metrescale packages of laterally persistent light–dark laminae; microstratigraphic, spectral and statistical analyses indicate a semidiurnal tidal regime with neap–spring modulation. MicroXRF/XRD mapping reveals that individual microlaminae within couplets share the same elemental inventory refuting a seasonal varve origin. Laminae chronometry, tied to the tidal interpretation, constrains net accumulation of the fossil bearing rhythmites to decadal (~40 year) timescales. Geochemical proxies record alternating terrigenous rich, high TOC intervals and drier, better oxygenated intervals. Palaeoclimate model ensembles diagnose a vigorous, summer dominated monsoon domain at ~29° N under greenhouse boundary conditions. We document that episodic monsoon enhanced flood and tropical cyclone pulses delivered sediment, nutrients, carcasses and plant debris to a microbially active, carbonate tidal flat; microbial mats rapidly stabilised and promoted early cementation, enabling exceptional soft tissue preservation. We propose a genetic model in which monsoon–cyclone forcing, tidal trapping and microbial sealing combine to produce decadal, daily resolved vertebrate Lagerstätten in greenhouse settings, offering predictive targets for similar deposits and deep time analogues for modern coastal change.

Abstract Europe has recently experienced a series of hot and dry summers. Previous case studies indicate that, for a given atmospheric circulation, heatwave temperatures may increase by 1.5–3 K per degree of global warming, but the magnitude of this amplification varies substantially across regions and events, and its underlying mechanisms remain insufficiently constrained. Here, we investigate this variability using circulation-nudged regional climate storylines for the summers 2018–2022, dynamically downscaled over Europe and spanning climates from pre-industrial conditions to +4 K global warming. The present-day simulations realistically reproduce observed temperature, soil moisture, and surface fluxes. We find that warming amplification is spatially heterogeneous and seasonally dependent, with the strongest amplification over Central Europe occurring in August, indicating a disproportionate intensification of late-summer heatwaves. Using the evaporative fraction-soil moisture framework, we show that warming amplification is closely linked to changes in the evapotranspiration regime. The strongest amplification occurs when warming induces a transition from energy-limited to moisture-limited conditions, triggering rapid soil moisture depletion, a pronounced reduction in evaporative fraction, and enhanced sensible heating. In contrast, amplification is weaker when conditions are already moisture-limited in the pre-industrial climate, due to the reduced potential for further soil drying, and weakest when the regime remains energy-limited. These findings demonstrate that spatial and event-to-event differences in warming amplification can partially be attributed to the sensitivity of land-atmosphere coupling to soil moisture changes. Accounting for these feedbacks is essential for robust projections of future heat extremes and for identifying regions most vulnerable to disproportionate increases in heatwave intensity.

ABSTRACT Output from dynamically downscaled, convection‐permitting regional climate simulations is used to examine projected changes in the frequency, timing and spatial distribution of synthetic severe convective storm (SCS) detections based on peril proxies across three 15‐year epochs—1990–2005, 2040–2055 and 2085–2100—under intermediate and pessimistic emissions scenarios, including the first mid‐century projections of SCS activity, providing a rare assessment of how SCS climatology may evolve throughout the 21st century. An explicit multi‐hazard approach is employed to define severe convective perils using permutations of simulated upward vertical velocity and lowest model level reflectivity thresholds, allowing SCS activity to be assessed collectively for tornado, wind and hail perils. In response to enhanced greenhouse forcing and related changes in fundamental severe storm ingredients, projected synthetic SCS activity exhibits spatiotemporal changes relative to the historical baseline, including lengthening of the severe season, increases in the overall frequency of SCS days and an eastward shift of event frequency maxima. In addition to changes in mean frequency, results illustrate enhanced variability and increases in relatively high‐activity days across all future epochs, driven by more frequent spring and early summer occurrences. Regionally, increases in SCS days are projected for south‐central and Gulf Coast states, the southern Great Plains and the Midwest in the spring and the Midwest, Northeast and Southeast during the summer, while decreases are projected for the Great Plains during the summer. These changes are accompanied by a broadening geographic footprint of SCS activity, indicating a redistribution of severe storm risk under warming. Results provide physically grounded insight into the potential evolving severe storm landscape, which stakeholders, policymakers and the public may use to mitigate and build resilience against future events.

El Niño-Southern Oscillation (ENSO) is one of the key drivers of global climate anomaly, and its evolution during geological history is crucial for understanding the natural variability of the climate system. The Middle Miocene Climatic Optimum (MMCO, 16.9–14.7 Ma) was a period of relatively warm global climate. During this period, the contraction and closure of tropical seaways exerted profound impacts on global ocean circulation and climate. Using idealized sensitivity experiments with the FGOALS-g3 model, we artificially close the Indonesian Seaway to isolate its mechanistic influence on ENSO dynamics. Although this extreme scenario does not reflect the actual open-gateway conditions of the Miocene, it allows us to clearly identify the seaway’s role in modulating tropical climate variability. The results demonstrate that the closure of the Indonesian Seaway exerts a significant impact on ENSO, increasing ENSO amplitude by 140%, an impact far exceeding that of the Panama and Tethys Seaways. Diagnostics based on the mixed-layer heat budget equation reveal that the enhancement of ENSO amplitude is primarily driven by the strengthened zonal advection feedback. The closure of the Indonesian Seaway drives the formation of an “El Niño-like” mean state in the tropical Pacific, which facilitates the eastward propagation and amplification of sea surface temperature anomalies and wind stress anomalies. These findings underscore the critical role of the Indonesian Seaway in modulating ENSO variability during the MMCO and provides mechanistic insights into the potential amplifying effect of the weakening Indonesian Throughflow on ENSO under future global warming scenarios.

Under global warming, compound extreme events such as Drought–Flood Abrupt Alternation (DFAA) are becoming increasingly common, yet the structural asymmetry and spatial dynamic evolution between Drought to Flood (D–F) and Flood to Drought (F–D) processes remain under-researched. Using high-resolution daily precipitation data spanning 1961 to 2022 from nine major river basins in China, DFAA events were identified via the Standardized Weighted Average Precipitation (SWAP) index coupled with run theory, and their evolution was analyzed using multidimensional spatiotemporal metrics. Our results reveal a spatial frequency and severity mismatch, where southern basins exhibit high frequency occurrences dominated by slight to moderate events, whereas northern and inland basins experience lower overall frequency but a significantly higher proportion of severe events. Spatial polarity asymmetry is evident, with D–F events dominating nationwide and exceeding 74% in northern and inland basins, while southern humid basins exhibit a more balanced D–F/F–D structure. Temporally, D–F processes involve prolonged moisture accumulation, whereas F–D processes manifest as short-lived post-rainfall moisture deficits. Based on risk trajectories, basins were categorized into four impact patterns: highly oscillatory pattern, intensifying pattern, long-cycle accumulative pattern, and baseline pattern. Ultimately, regional DFAA risks are governed by polarity asymmetry and non-stationary evolution rather than absolute frequency alone, providing a critical scientific basis for basin-specific disaster mitigation strategies under climate change.

Abstract Arctic sea ice is disappearing rapidly, but the melt comes from two different pathways: direct surface warming and the movement of warm water into the Arctic. This study focuses on the latter, i.e., dynamic melting, defined specifically as sea-ice loss caused by circulation-driven ocean heat transport, not mechanical ice motion or thermodynamic surface heating. Using CESM2 and its slab ocean counterpart, which preserves surface warming but suppresses changes in ocean heat transport, we approximately separate these two pathways and examine how they respond to different climate forcing agents. We find that atmospheric circulation-driven ocean heat transport accounts for roughly 25.2% of the total sea-ice loss on the Pacific side of the Arctic. Aerosols strengthen a North Pacific anticyclone that pushes more warm Pacific water through the Bering Strait, accelerating sea-ice decline even though aerosols themselves cool the surface. Greenhouse gases, by contrast, generate atmospheric circulation patterns that partially offset their own thermodynamic warming. These results show that dynamic melting is strongly forcing-dependent and that models lacking realistic ocean-atmosphere circulation may systematically underestimate regional Arctic sea-ice vulnerability. Our framework separates a 12.2% thermodynamic sea-ice loss from a 16.3% coupled loss, implying that circulation-driven ocean heat transport explains about 25.2% of the total western Chukchi response. This dynamic contribution is forcing-dependent: aerosols enhance ocean-driven ice loss, offsetting 26.8% of their thermodynamic cooling effect, whereas greenhouse gases produce an opposite-signed circulation response that partly damps thermodynamic ice loss.

The Atlantic Meridional Overturning Circulation (AMOC) plays a critical role in regulating global climate patterns with its potential destabilization and collapse posing significant risks. In this study, we introduce two novel methods to assess AMOC stability, based on a Bayesian framework to estimate its sensitivity, and ensembles of parabolic approximations, respectively. They provide alternative indicators to detect early warning signals (EWSs) for a potential destabilization. By incorporating non-linear and physically motivated drivers, such as temperature and Greenland meltwater runoff, we obtain a more realistic representation of AMOC dynamics and address an important limitation of previous studies, which often rely on linear forcing assumptions. We detect significant EWS for a potential ongoing AMOC destabilization in our sensitivity-based indicator across many combinations of forcing scenarios and response models. However, it revealed large variations, dependent on the considered forcing scenario and methodological choices. While an AR(1) response to linear forcing, consistent with assumptions in previous EWS analyses of the AMOC, emerged as the “best” fit based on Bayes factor analysis, other evaluation criteria provided no clear support. Even though the second EWS indicator, obtained using parabolic approximations, revealed a recent significant peak under linear forcing, and appears to suggests that the AMOC might have passed a critical transition in the late 20th century assuming a temperature driver, we find no clear indication of a considerable destabilization in either case. Our findings highlight the sensitivity of AMOC stability assessments to assumptions about forcing scenarios, response models, and evaluation criteria, emphasizing the need for careful interpretation of EWSs for abrupt transitions in the Earth's climate. While our methods advance EWS analyses by incorporating non-linear forcing and alternative response functions to better represent AMOC dynamics, they also underscore the limitations of applying such tools to complex climate subsystems, represented by one-dimensional time series.

Abstract. Climate change is driving wildfires to higher elevations, yet the hazard cascades that follow the burning of pristine tropical mountain ecosystems remain largely unexplored. Here, we analyse the long-term cascade following a February 2012 wildfire that burned 31 km2 of forest and wetland in Uganda's Rwenzori Mountains National Park, including sections above 3800 m elevation with no major fire history in 12 000 years. Combining remote sensing, humanitarian records, field surveys and interviews, we document ten major floods since 2012, including two debris floods that required large-scale humanitarian responses. Post-fire increases in erosion and mass movement have widened the River Nyamwamba sevenfold since 2012, breaching copper-cobalt mine tailings and mobilising an estimated 744 000 t of waste into the river. Slow vegetation recovery at high altitudes and positive feedbacks between hazards have prolonged this high-risk state. These findings point to an urgent need to understand where emergent tropical mountain fires can occur, how their impacts cascade downstream, and where early interventions can reduce risk.

Abstract Diked marshes with decades of restricted tidal flow exhibit fundamentally altered hydrologic and ecologic dynamics that result in limited vertical accretion, land subsidence, and underdeveloped tidal channels. These changes complicate marsh restoration efforts and constrain the marsh's ability to build elevation in response to future sea levels. In this study, we developed an integrated hydrodynamic and marsh accretion model to conduct the first assessment of the restoration potential of a diked marsh in response to reintroduction of tidal flow and sea‐level rise (SLR). We applied our model to evaluate changes in biomass production in the Herring River estuary, Cape Cod, MA, USA—which has experienced significant ecological shifts due to more than a century of tidal restriction—in response to a range of SLR scenarios, tidal flow conditions, management interventions, and biomass production rates through 2100. We found that with restoration of tidal flow, the simulated marsh extent expanded in areas with efficient drainage, increasing biomass production by up to 4.4 times. However, when SLR was imposed, parts of the marsh gradually transitioned to open water, leading to a decline in marsh coverage from 19% to as low as 12%. Management interventions aimed at enhancing marsh elevation and improving the drainage capacity of tidal channels slowed marsh loss, supporting marsh area through 2100. Our findings provide insight into the key drivers of marsh resilience and loss, and our approach can be applied across tidally restricted and restored systems to develop targeted, adaptive restoration strategies that achieve desired ecological outcomes.

Abstract The incorporation of appropriate dynamic constraints in data assimilation (DA) is highly important for improving the forecasting of disastrous weather. It is often challenging to describe subgrid physical processes with strong nonlinearity and discontinuity in dynamic constraints because of difficulties in tangent linear and adjoint development. Based on the frictionless momentum equation weak constraint within the Weather Research and Forecasting ensemble‐three‐dimensional variational (En3DVar) DA, this study incorporates the subgrid planetary boundary layer (PBL) turbulent friction effects through a machine learning (ML) approach. The new constraint is aimed at improving the coupling between dynamic and thermodynamic variables. A deep neural network (DNN) is applied to emulate the model parameterized PBL horizontal momentum tendency. The tendency is introduced into the momentum equation as a turbulent friction term. The tangent linear and adjoint of the DNN are embedded into the variational framework to construct a ML‐improved DA scheme. Sensitivity experiments for tropical cyclones (TCs) reveal that including turbulent friction effectively compensates for the insufficient pressure adjustments during wind assimilation according to the original constraint framework. Three DA experiments that involve assimilating radar radial velocities are conducted using En3DVar (EVAR), En3DVar with the original constraint (EVAR–DC) and En3DVar with the ML‐improved constraint (EVAR–MLDC) for landfalling TCs Muifa (2022) and Doksuri (2023). In response to wind increments, compared with EVAR–DC and EVAR, EVAR–MLDC produces larger pressure adjustments around the eyewall. Overall, a better description of thermodynamic states is obtained by the new scheme, which plays a positive role in TC track and intensity forecasting.

Study Region Europe (pan-European domain), with particular focus on northern, central, southern, eastern, and Mediterranean Europe, covering the period 1902–2024. Study Focus Warming is well established to transform droughts from precipitation-driven to 'hotter' events dominated by evaporative demand. Building on this foundation, we provide a century-scale pan-European observational synthesis of the compound hot–dry hazard and explicitly separate shifts in joint hot–dry probabilities into contributions from marginal warming and from changes in heat–dryness dependence, a decomposition not previously applied at this spatio-temporal scale. We identify a marked seasonal and regional reorganization of hydroclimate, with cold-season wetting concentrated in northern Europe and warm-season drying across the broader central, southern, and eastern European domain. A direct comparison of the Standardized Precipitation Evapotranspiration Index (SPEI) and the Standardized Precipitation Index (SPI) trends shows that across central and southern Europe, the late-summer drying signal exceeds what precipitation changes alone can explain, identifying rising evaporative demand as a substantial and spatially coherent driver of recent drying. New Hydrological Insights for the Region Since the mid-1990s, compound hot–dry months have routinely affected more than 25% of Europe, and joint hot–dry probabilities have increased significantly. An explicit probability decomposition reveals that this increase is driven primarily by the rise in marginal warm-months frequency, while changes in heat–dryness dependence are spatially heterogeneous and act as a secondary, regionally specific amplifier in water-limited southern European climates. These findings highlight the need for drought monitoring, hazard assessment, and water-resources planning frameworks that explicitly incorporate evaporative demand and compound-event behavior, rather than relying solely on precipitation-based indicators.

Abstract Zooplankton play a key role in setting the particulate organic carbon (POC) distribution in the ocean, but this role remains poorly quantified on large scales due to the complexity of zooplankton ecosystems and the sparsity and variability of observations. We address this by applying boosted regression trees to a global in situ zooplankton image database from the Underwater Vision Profiler 5 and an Argo float and satellite‐derived POC product. We compute taxonomic, morphological, and trophic community metrics, then identify three metrics—abundance, mean gray level, taxonomic evenness—that explain almost half of the spatial variability in the vertical concentration gradients of small POC (<100 μm). Partial dependence analysis and the consistency of the identified relationships with known zooplankton‐mediated particle‐processing pathways suggest mechanistically interpretable ecological linkages. These results advance quantification of zooplankton communities' influence on ocean carbon cycling and indicate carbon‐cycle‐relevant zooplankton community properties for improving ocean biogeochemical models.

Abstract Global marine primary producers, phytoplankton, are the base of the marine food web and vary on short timescales, characterized by seasonal blooms. There is growing concern about the occurrence of short‐term extreme events in phytoplankton abundance, which may impact higher trophic levels and economically‐important species. Previous work has investigated the occurrence and impacts of extremes, but forecasting of large‐scale extremes has not been attempted. Here, we leverage the Community Earth System Model Seasonal‐to‐Multiyear Large Ensemble (CESM SMYLE) to assess the potential predictability of phytoplankton extremes. We find that low phytoplankton biomass extremes (LBX) are significantly predictable up to 6‐month in advance. LBX are closely related to enhanced upper ocean stratification, which impacts nutrient availability. We find that compound events (LBX with marine heatwave and low oxygen extremes) are also significantly predictable up to 6 months in advance. These results could inform future model development with impacts for marine resource managers.