Earths Future最新文献

Knowledge of coastal hydrogeology and hazards as groundwater responds to sea-level rise (SLR) can be improved through installation of shallow groundwater monitoring piezometers and continuous observations. Interpolation of site data enables mapping of the present-day state of groundwater elevation, depth to groundwater (DTW), their temporal statistical variation, and differing spatial responses to tides and rainfall. Future DTW and its variability can be projected under increments of SLR, with assumptions and caveats, to show where and when episodic and/or permanent inundation can be expected. This methodology is outlined in a case study of Dunedin, New Zealand, which enabled comparison of rising groundwater's contribution to pluvial flooding and groundwater emergence with coastal inundation. Changes in relative land exposure with SLR shows evolution in flood hazard from current pluvial-dominated events, into “flooding from below” and groundwater emergence, in advance of any overland coastal inundation. Dunedin exemplifies how groundwater transfers effects of SLR surprisingly far inland, but the lowest-lying or shoreline-proximal suburbs are not necessarily the most vulnerable. Unlike coastal inundation, rising groundwater is unconstrained by protective topography and presents as a creeping hazard, or contributor to hazards such as pluvial flooding, which can be widespread, occurring already and difficult to defend against. The empirical models contain assumptions and uncertainties important to the veracity of results and application. While conservative (“risk averse”) and a compromise from computationally expensive numerical solutions, their value is in providing the spatial and temporal precision needed for multi-source hazard assessment and holistic adaptive planning.

As the severity of climate change and its associated impacts continue to worsen, schemes for artificially cooling surface temperatures via planetary albedo modification are being studied. The method with the most attention in the literature is stratospheric sulfate aerosol intervention (SAI). Placing reflective aerosols in the stratosphere would have profound impacts on the entire Earth system, with potentially far-reaching societal impacts. How global crop productivity would be affected by such an intervention strategy is still uncertain, and existing evidence is based on theoretical experiments or isolated modeling studies that use crop models missing key processes associated with SAI that affect plant growth, development, and ultimately yield. Here, we utilize three global gridded process-based crop models to better understand the potential impacts of one SAI scenario on global maize productivity. Two of the crop models that simulate diffuse radiation fertilization show similar, yet small increases in global maize productivity from increased diffuse radiation. Three crop models show diverse responses to the same climate perturbation from SAI relative to the reference future climate change scenario. We find that future SAI implementation relative to a climate change scenario benefits global maize productivity ranging between 0% and 11% depending on the crop model. These production increases are attributed to reduced surface temperatures and higher fractions of diffuse radiation. The range across model outcomes highlights the need for more systematic multi-model ensemble assessments using multiple climate model forcings under different SAI scenarios.

In recent decades, terrestrial water storage anomaly (TWSA) has experienced systematic shifts. Despite these observations, debates continue regarding the hotspots where terrestrial water storage changes dramatically and their causes. This study aims to address these controversies. Utilizing four TWSA products, this research analyzes TWSA's changing patterns and identifies hotspots of significant shifts from 1982 to 2019. The study employed the Bayesian Three-Cornered Hat method to synthesize the best-quality TWSA from original four TWSA products and the trends consistent method to identify regions with highly consistent trends. Subsequently, the elasticity coefficient method was used to reveal the causes of TWSA's dramatic changes in hotspots. Results show that TWSA has a declining trend over 66.1% global terrestrial areas during 1982–2019, with an average rate of −0.5 mm/y. The study identified six regions where marked changes in TWSA occurred, including Northern China, Southern Canada, Northern India, Central-Southern Europe, Southwestern Africa, and Northeastern South America. Attribution analysis reveals that the leaf area index is the predominant factor affecting TWSA changes, dominating in 40.3% of global regions. Potential evapotranspiration (PET) follows closely, dominating in 39.8% of global regions. Meanwhile, only 13.1% and 6.8% of global regions are primarily influenced by precipitation and cropland density respectively. The dominant factor varies in different latitudes. Vegetation greening primarily controls TWSA changes in the high-latitude regions of the Northern Hemisphere. This study identified hotspots of TWSA changes and investigated the causes of these variations. Those results will offer direction for prioritizing areas in future water resource management.

Forest soils store about one-fifth of the global terrestrial biosphere carbon stock. However, our understanding of how soil geochemical, plant and microbial factors regulate forest soil organic carbon (SOC) storage, stability, and saturation levels remains limited. Here, we conducted a sampling campaign across a 5000-km natural forest transect in China, measuring climate, geochemical factors and SOC fractions with varying stability. Additionally, we compiled a global data set of SOC fractions in major forest biomes. Our field survey and global synthesis consistently demonstrate that warmer climates not only reduce the content of labile particle organic matter (POM), but also decrease the typically stable mineral-associated organic matter (MAOM), leading to a significant decline in total soil carbon storage. Additionally, warmer climates promote the crystallization of Fe/Al oxides, which decreases the formation efficiency of Fe/Al oxide associated organic complexes. Consequently, the mineralogical carbon saturation level declines from boreal forests (37%) to tropical forests (25%). Our findings underscore that, beyond the well-established climate impacts, soil geochemical properties play a pivotal role in shaping forest SOC composition and saturation levels across latitudes. This highlights that colder regions harbor larger and more stable carbon pools, and that ongoing climate warming and associated soil geochemical properties shift could potentially lead to a decline in soil carbon storage and its capacity to mitigate climate change.

Gaining insights into current and future urban water demand patterns and their determinants is paramount for water utilities and policymakers to formulate water demand management strategies targeted to high water-using groups and infrastructure planning strategies. In this paper, we explore the complex web of causality between climatic and socio-demographic determinants, and urban water demand patterns across the Contiguous United States (CONUS). We develop a causal discovery framework based on a Neural Granger Causal (NGC) model, a machine learning approach that identifies nonlinear causal relationships between determinants and water demand, enabling comprehensive water demand determinants discovery and water demand forecasting across the CONUS. We train our convolutional NGC model using large-scale open water demand data collected with a monthly resolution from 2010 to 2017 for 86 cities across the CONUS and three Köppen climate regions—Arid, Temperate, and Continental—utilizing this globally recognized climate classification system to ensure a robust analysis across varied environmental conditions. We discover that city-scale urban water demand is primarily driven by short-term memory effects. Climatic variables, particularly vapor pressure deficit and temperature, also stand out as primary determinants across all regions, and more evidently in Arid regions as they capture aridity and drought conditions. Our model achieves an average $��R^2�$ higher than 0.8 for one-month-ahead prediction of water demand across various cities, leveraging the Granger causal relationships in different spatial contexts. Finally, the exploration of temporal dynamics among determinants and water demand amplifies the interpretability of the model results. This enhanced interpretability facilitates discovery of urban water demand determinants and generalization of water demand forecasting.

Climate change threatens the sustainable use of groundwater resources worldwide by affecting future recharge rates. However, assessments of global warming's impact on groundwater recharge at local scales are lacking. This study provides a continental-scale assessment of groundwater recharge changes in Europe, past, present, and future, at a $��\left(��5�\times �5��\right)�$ $��{\text{km}}^2�$ resolution under different global warming levels (1.5, 2.0, and 3.0 K). Utilizing multi-model ensemble simulations from four hydrologic and land-surface models (HMs), our analysis incorporates E-OBS observational forcing data (1970–2015) and five bias-corrected and downscale climate model (GCMs) data sets covering the near-past to future climate conditions (1970–2100). Results reveal a north-south polarization in projected groundwater recharge change: declines over 25%–50% in the Mediterranean and increases over 25% in North Scandinavia at high warming levels (2.0–3.0 K). Central Europe shows minimal changes ($��\pm �$5%) with larger uncertainty at lower warming levels. The southeastern Balkan and Mediterranean region exhibited high sensitivity to warming, with changes nearly doubling between 1.5 and 3.0 K. We identify greater uncertainty from differences among GCMs, though significant uncertainties due to HMs exist in regions like the Mediterranean, Nordic, and Balkan areas. The findings highlight the importance of using multi-model ensembles to assess future groundwater recharge changes in Europe and emphasize the need to mitigate impacts in higher warming scenarios.

Globalization has led to an increasing geographical separation of primary input, consumption and production, and consequently to a substantial transboundary transfer of air pollution and associated health burdens through international trade. Here, we develop an integrated framework to determine the consumption- and income-based global atmospheric emissions, and quantify the drivers of associated health impacts from 2000 to 2015, and evaluate the impacts of international trade on PM_2.5-related deaths by hypothetical scenarios. Results show that consumption transferred more primary PM_2.5 emissions (2.2 Mt, 23.5%) and caused more additional mortality (241,000 deaths) through international trade than primary input (emission: 1.1 Mt, 12.3%, mortality: 167,000 deaths) in 2015. Top three key sectors contributed to more than half of emission flow driven by consumption (commercial, construction, electrical and machinery) and primary inputs (commercial, petroleum, and mining). Health benefits of reduced emissions intensity, which avoided 1.4 million deaths, were largely offset by not only increases in consumption and primary input levels but also population vulnerability, resulting in the increase in mortality (0.8 million) from 2000 to 2015. Changes in primary input (1.2 million deaths) contributed more to the rise in health burdens than changes in consumption (1.0 million deaths). Hypothetical scenarios show that the participation of Western Europe in international trade contributed to the reduction in global health burden, while the USA gained health benefits from international trade. Accordingly, our findings provide profound suggestions for future policy decisions from different perspectives and demonstrate that optimizing global supply chain through cooperation would mitigate the PM_2.5-related health impacts.

Extratropical cyclones (ETCs) significantly impact mid-latitude weather patterns and are crucial for understanding the societal implications of regional climate variability, climate change, and associated extreme weather. In this study, we examine the projected future changes in winter-time ETCs over South Africa (SA) using simulations from CORDEX-CORE Africa. We utilized three regional climate models, each driven by three different global climate models that simulate both the current climate and a future climate experiencing strong human-induced warming. From these, we assess changes in ETC frequency, track density, intensity, storm severity, and associated rainfall. The results indicate a significant reduction in the aggregate ETC frequency and track density, although track density is projected to increase prominently along the western coastal regions. Models show mixed trends in cyclone intensity projections, but overall results indicate weaker future cyclones, with reduced peak relative vorticity and increased minimum sea level pressure. Examining the Meteorological Storm Severity Index reveals notable regional variations in future storm severity. Average rainfall associated with ETCs is projected to decrease across SA, especially around Cape Town, highlighting a potential shift in the spatial distribution of rainfall with substantial consequences for water supply. We further investigated extreme ETCs (EETCs) and found that the trends for EETCs are generally similar to those for ETCs, with a notable decrease in frequency and regional variations in storm severity. These findings underscore the importance of developing targeted adaptation strategies to address the projected impacts of future ETCs on SA's climate and communities.

Recent efforts to assess coastal compound surge and rainfall-driven flooding hazard from tropical (TCs) and extratropical cyclones (ETCs) in a warming climate have intensified. However, challenges persist in gaining actionable insights into the changing magnitude and spatial variability of these hazards. We employ a physics-based hydrodynamic framework to numerically simulate compound flooding from TCs and ETCs in both current and future climates, focusing on the western side of Buzzards Bay in Massachusetts. Our approach leverages hydrodynamic models driven by extensive sets of synthetic TCs downscaled from CMIP6 climate models. We also perform a far less extensive analysis of ETCs using a previously produced event set, dynamically downscaled using the WRF model driven by a single CMIP5 model. This methodology quantifies how climate change may reshape the compound flooding hazard landscape in the study area. Our findings reveal a significant increase in TC-induced compound flooding hazard due to evolving climatology and sea level rise (SLR). Although compound flooding induced by ETCs increases mostly in coastal areas due to SLR, inland areas exhibit almost no change, and some even show a decline in rainfall-driven flooding from high-frequency ETC events toward the end of the century compared to the current climate. Our methodology is transferable to vulnerable coastal regions, serving as a tool for adaptive measures in populated areas. It equips decision-makers and stakeholders with the means to mitigate the destructive impacts of compound flooding arising from both current and future TCs, and shows how the same methodology might be applied to ETCs.

Coastal subsidence, the gradual sinking of coastal land, considerably exacerbates the impacts of climate change-driven sea-level rise (SLR). While global sea levels rise, land subsidence often increases relative SLR locally. Thiéblemont et al. (2024, https://doi.org/10.1029/2024ef004523) reached a remarkable milestone by providing a continental-scale estimate of vertical land motion (VLM) across European coastal zones by utilizing European Ground Motion Service (EGMS) data, obtained from Interferometric Synthetic Aperture Radar (InSAR) data from Sentinel-1 satellites. Their findings reveal widespread coastal subsidence, with nearly half of the coastal floodplains, including major cities and ports, subsiding at rates exceeding 1 mm/yr, thereby exacerbating relative SLR. The study emphasizes the critical role of InSAR-data calibration, indicating that the EGMS geodetic reference frame significantly influences VLM estimates. This study highlights the need for a robust InSAR-data processing framework to accurately interpret VLM and its relationship to relative SLR. The processing pipeline should ensure internal consistency of SAR data and rigorously assess output accuracy, considering also post-processing effects. Correct interpretation of results is essential as InSAR satellites measure reflector movement, which may not always align with land surface movement, particularly in urban areas. Ignoring these discrepancies can lead to underestimation of subsidence rates. While InSAR data offers valuable research opportunities, it poses risks of oversimplification and misinterpretation, especially when linked to sea-level change. We call for standardized processing workflows and cross-disciplinary collaboration, essential for accurate VLM interpretations, particularly in coastal cities and river deltas, to ultimately enhance the reliability of relative SLR projections and inform effective coastal management strategies.