Earths Future最新文献

We analyze projected tropical sea surface salinity (SSS) changes in 32 CMIP6 models' historical and SSP5-8.5 scenario simulations, examining both the multi-model mean (MMM) and inter-model diversity. By 2100, MMM inter-basin contrasts strengthen, with freshening in the tropical Indian Ocean (TIO) and equatorial-northern Pacific (ENPO), and saltening in the southern Pacific (SPO) and tropical Atlantic (TAO). Basin-scale future SSS changes are primarily driven by surface freshwater fluxes, with lateral advection redistributing anomalies within each basin. Precipitation dominates the freshwater flux changes, except in the tropical Atlantic where evaporation plays a key role. Two uncorrelated indices, contrasting SPO versus TIO and TAO versus ENPO, explain 76% of the variance across models. Physically, stronger relative warming of the Northern Hemisphere enhances rainfall over the TIO monsoon region (freshening) while suppressing rainfall along the South Pacific Convergence Zone (saltening). The increasing TAO–ENPO contrast arises from two distinct mechanisms: in the Pacific, an enhanced El Niño–like warming pattern reduces atmospheric stability, intensifying rainfall and freshening ENPO; in the Atlantic, saltening reflects stronger evaporation under warmer conditions, though at a weaker rate than predicted by Clausius–Clapeyron scaling ($��\sim �$4.2 vs. $��\sim �$7% $��K^{�-�1�}�$). Previous studies linked strengthening of inter-basin salinity gradients to a thermodynamically intensified hydrological cycle. Our analysis highlights a more nuanced picture: Atlantic saltening reflects this thermodynamic control, while SSS changes elsewhere are mainly driven by atmospheric circulation and rainfall changes tied to uneven SST warming. The CMIP6 statistical analyses highlight dynamical mechanisms that motivate further testing through targeted ocean simulations.

Climate and anthropogenic perturbations, including warming temperatures, shifting precipitation patterns, and changes in contamination, can alter geochemical reactions that shape stream chemistry. To date, our understanding of how climate and humans are altering water-rock interactions and geogenic stream chemistry is limited, partly because of the difficulty in mapping geochemical processes in the subsurface. Here, we leverage historic climatic, hydrologic, and geochemical data from 40 United States watersheds to evaluate changes in geogenic stream chemistry from 2000 to 2024. We analyze temporal trends in geogenic solutes in the context of changing precipitation, discharge, and water-rock interactions. We show that changes in stream chemistry are variable across the U.S. and are driven by multiple drivers that impact subsurface processes over long-timescales, including warming temperatures, changing groundwater storage, road-salt application, and acid-rain recovery. To fully map how climate and anthropogenic perturbations are impacting water quality, we explore the spatial and temporal heterogeneity in subsurface geochemical reactions and the propagation of the signals of those reactions into surface waters, in particular, how long-transit times and intermediate geochemical reactions may delay in-stream responses to perturbation.

Recent projections indicate that the range of the annual sea-level cycle (ASLC) may increase, affecting flood risk, groundwater, and ecosystems. However, existing projections have several limitations, such as their exclusion of the inverse-barometer effect, their uncertainty due to internal variability, and/or their regional focus. Furthermore, the historical ASLC in the climate models used for these projections was not extensively evaluated. Here, we address these limitations using a large ensemble of simulations from the Coupled Model Intercomparison Project 6. Compared to observations, we find that four climate models perform particularly poorly. Excluding these, our multi-model median projections for 2071–2100 indicate an average increase in the ASLC range at tide-gauge locations of 7.9% under a high emissions scenario. Changes under lower scenarios are similar spatially but have a smaller magnitude. Regions with above-average changes include Northwestern Europe, the Mediterranean, Asia, and the North Pacific. In several regions, the inverse-barometer effect substantially contributes to the total changes. The median projected shifts in the peak month of the ASLC are mostly small, but like for the projected range changes, larger changes cannot be excluded given the substantial uncertainties. Finally, we find that the ASLC changes translate to differences between seasonal and annual mean sea-level changes of up to 5.4 cm under the highest emissions scenario. Consequently, sea-level projections that exclude seasonal changes under- or overestimate total sea-level change in specific seasons, sometimes by more than 8%. This will influence projections of hazards and impacts tied to specific seasons.

Recently, unprecedented compound drought and heatwave (CDHW) events have severely damaged terrestrial ecosystems, but their dynamics, formation mechanisms, and threats are still insufficiently understood. Here, using simulations from nine-member ensemble under three future scenarios, we project that the expected annual probability of record-shattering CDHW events (i.e., events exceeding historical severity records by more than two standard deviations) will double between 2015 and 2040 and continue to rise throughout the 21st century under the SSP5-8.5 scenario (0.05% per year). The mean temperature, relative humidity, shortwave radiation, and precipitation are critical drivers of CDHW severity. Record-shattering CDHW events severely impact ecosystems, particularly in regions such as southern North America, northern South America, and southern Europe. Observational data from 1951 to 2022 indicate that compared to nonrecord-shattering CDHW events, record-shattering CDHW events are correlated with weaker water vapor transport, reduced convective activity, and greater ecosystem damage, resulting in an additional global mean gross primary productivity (GPP) loss of −2.76 to −3.96 g C m⁻² month⁻¹. Between 2080 and 2099, the global average GPP anomalies caused by these events are projected to range from 46% to 119% of the warm season's monthly average ecosystem carbon sink. Our study underscores the urgent need for adaptive measures to mitigate the ecological threats from record-shattering CDHW events to ensure ecosystem sustainability.

Wildfires have increasingly affected human and natural systems across the western United States (WUS) in recent decades. Given that the majority of ignitions are human-caused and potentially preventable, improving the ability to predict fire occurrence is critical for effective wildfire prevention and risk mitigation. We used over 500,000 wildfire ignition records from 2000 to 2020 to develop machine learning models that predict daily ignition probability across the WUS and incorporate a wide range of physical, biological, social, and administrative variables. A key innovation of this work is development of novel sampling techniques for representing ignition absence. Unlike traditional purely random sampling or hyper-sampling, which does not account for temporally autocorrelated factors (such as droughts, insect outbreaks, and heatwaves) and spatially autocorrelated factors (such as proximity to human settlements, infrastructure presence, and fuel type), we introduce spatially and temporally stratified sampling of ignition absence. By drawing absence samples near the location and time of historical ignitions, we better captured the complex environmental and anthropogenic conditions associated with fire occurrence or lack thereof. Models trained without stratified sampling produced ignition probability maps that consistently overestimated fire risk during high fire danger periods, whereas models incorporating stratified fire absence samples more accurately captured the spatial and temporal variability of fire potential and achieved predictive accuracies exceeding 95%. In addition to operational utility for fire prevention and resource allocation, our approach offers insights into the drivers of wildfire ignitions and highlights the value of incorporating spatial and temporal structure in absence sampling for wildfire modeling.

Plants modify their functional traits in response to changing environmental conditions under climate change. However, it remains unclear whether tree planting alters patterns and acclimation of hydraulic traits across spatial scales. Here, we compiled a site-level data set of hydraulic traits in natural (NF) and planted forests (PF) to examine trait patterns and relationships, quantified environmental and ecological drivers on ecosystem-scale hydraulic traits of PF and NF across China, and computationally projected future trait acclimation using the space-for-time approach. We identified distinct differences in hydraulic traits between NF and PF, with PF exhibiting higher hydraulic safety but lower hydraulic efficiency than NF at the species level. NF demonstrated a trade-off between hydraulic efficiency and safety, whereas PF exhibited a contrasting positive correlation between these traits. We confirmed that both environmental and ecological factors influence ecosystem-scale hydraulic traits in NF and PF, although dominant drivers vary among specific traits. Projections under future climate scenarios suggest that, despite persistent differences in trait acclimation between NF and PF, both forest types tend to exhibit increased water-use efficiency and enhanced drought resistance in response to rising precipitation and air dryness. These findings provide a valuable benchmark for estimating potential changes in hydraulic traits under climate change, supporting improved simulations of carbon and water fluxes in response to climate and anthropogenic influences.

While the impacts of subsurface fluxes on mediating hydrologic response to droughts are often ignored, several studies indicate that baseflow can sustain rivers during droughts and decrease the vulnerability of water supplies. Therefore, given the increasing impacts of droughts on economic and environmental issues, understanding the baseflow drought (BFD) evolution and its drivers are critical. In this study, we quantify and analyze the long-term evolution of BFD characteristics across the Contiguous United States. We use long-term daily baseflow values of DeepBase data set and explainable machine learning models to identify and rank the climatic and physical drivers of BFD. Our analysis reveals notable regional disparities in BFDs, with western regions, particularly the Southwest, experiencing increased frequency and prolonged durations, while much of the eastern areas show declining trends. During the past decade, BFD frequency has been governed mainly by anomalies in the atmospheric water balance and by soil properties. Its duration has been primarily influenced by hydrogeologic attributes, and its intensity has been modulated most strongly by topographic setting. Highlighting the non-stationary and complex nature of BFD mechanisms, our results have practical implications for water resource management and drought adaptation strategies.

This study employs a novel application of Impact Chains combined with tailored metrics to conduct a cross-country comparative analysis of the same hazard event. This is a research approach currently absent from the literature, yet essential for developing transferable Disaster Risk Management (DRM) lessons. Given the increased frequency of hydrometeorological hazards under climate change, the importance of such lessons cannot be overstated. Focusing on the devastating 2021 floods in Europe, this study investigates (1) how did the interplay of impacts, vulnerabilities, and adaptation options produce divergent disaster outcomes in two contrasting European contexts, and (2) what transferable lessons for DRM can be elicited. The selected case studies focus on the areas hardest impacted by the 2021 flood events in Romania (Alba County) and the Netherlands (Limburg Province). The three-tiered analysis performed at the level of impacts, vulnerabilities, and adaptation options shows that response in the two countries was well-directed, while vulnerability mitigation remains a critical problem, differing between the two case studies. Another highlight is that development in flood-prone areas and flood protection standards below (flood) hazard return periods ranked as the most influential vulnerabilities in both case studies. This research provides a new methodology for comparative disaster analysis, offering critical insights for scientists and practitioners in the face of increasingly frequent and impactful hydrometeorological hazards.

Less than 6% of US farmlands are cover cropped, an on-farm management practice with potential to sequester carbon and provide environmental co-benefits (e.g., improved soil health and water quality). Despite their promise as a nature-based climate solution that can enhance soil carbon storage, cover crops remain underutilized, in part due to farmer perceptions that benefits are not assured and management risk is high. This scoping review synthesizes research from multiple disciplines to identify persistent knowledge gaps that limit both the effectiveness and sustained adoption of cover cropping. We focus on Midwestern US agroecosystems, where scientists generally agree that cover cropping has the potential to increase soil organic carbon and contribute to long-term carbon sequestration. Our synthesis reveals critical challenges across domains: carbon outcomes are highly variable across space and time; water and nutrient dynamics exhibit tradeoffs and context dependence; economic returns remain difficult to quantify; and adoption patterns are shaped by feedbacks between perceived risk, observed outcomes, system constraints, and social factors such as norms and identity. We use Ostrom's social-ecological systems framework to structure our analysis across biophysical, economic, and social domains, linking scientific uncertainty to real-world implementation barriers. The review culminates in a set of research priorities designed to advance transdisciplinary work on cover cropping, clarify its climate mitigation potential, improve the design of private and public interventions, and support adaptive management.

Groundwater irrigation plays a crucial role in sustaining India's food security and rural livelihood. However, the carbon footprint of its energy (72% coal-based) requirement is also equally alarming. Under such scenarios, the Farm Power Subsidy (FPS) policy for groundwater irrigation, one of India's costliest public support systems, needs a relook, keeping in view the Sustainable Development Goals (SDGs). Although aimed at bridging social disparities, it is observed that FPS encourages simplification and intensification of the food production system, leading to inter-state and district scale inequality in income. States implementing FPS, even if experiencing physical water scarcity, are growing water guzzling crops for economic benefits. Conversely, without adequate access to energy, states reeling from economic water scarcity are witnessing low farm incomes. India consumes about a quarter of the world's groundwater for irrigation, and also leads the energy use. Notably, since the mid-2000s large-scale implementation of the FPS policy, the CO₂ emission (equivalent) has been rising by 5.77 million tons annually, now surpassing 100 million tons. These outcomes bear testimony to the globally accepted view that those least responsible for causing greenhouse gas emissions are the hardest hit. We suggest repurposing of agricultural policies, without compromising farmers' income, for efficient management of energy and water.