Earths Future最新文献

We examine daily surface air temperatures (SAT) in the Arctic under global warming, synthesizing changes in mean temperature, variability, seasonality, and extremes based on five Earth system model large ensembles from the Coupled Model Intercomparison Project Phase 6. Our analysis shows that the distribution of daily Arctic SAT changes substantially, with Arctic mean temperatures being distinguishable from pre-industrial levels on 84% and 97% of days at 1.5 and 2°C of global warming, respectively, and on virtually every day at 3°C of global warming. This shift is primarily due to the rapid rise in average temperature resulting from Arctic amplification and is exacerbated by a decrease in the variability of daily Arctic SAT of approximately 8.5% per degree of global warming. The changes in mean temperature and variability are more pronounced in the cold seasons than in summer, resulting in a weakened and shifted seasonal cycle of Arctic SAT. Moreover, the intensity and frequency of warm and cold extreme events change to varying degrees. The hottest days warm slightly more, while the coldest days warm 4–5 times more than the global average temperature, making extreme cold events rare. Changes in local SAT vary regionally across the Arctic and are most significant in areas of sea-ice loss. Our findings underscore the Arctic's amplified sensitivity to global warming and emphasize the urgent need to limit global warming to mitigate impacts on human and natural systems.

Many water markets in the western United States (U.S.) have the ability to reallocate water temporarily during drought, often as short-term water rights leases from lower value irrigated activities to higher value urban uses. Regulatory approval of water transfers, however, typically takes time and involves high transaction costs that arise from technical and legal analyses, discouraging short-term leasing. This leads municipalities to protect against drought-related shortfalls by purchasing large volumes of infrequently used permanent water rights. High transaction costs also result in municipal water rights rarely being leased back to irrigators in wet or normal years, reducing agricultural productivity. This research explores the development of a multi-year two-way option (TWO) contract that facilitates leasing from agricultural-to-urban users during drought and leasing from urban-to agricultural users during wet periods. The modeling framework developed to assess performance of the TWO contracts includes consideration of the hydrologic, engineered, and institutional systems governing the South Platte River Basin in Colorado where there is growing competition for water between municipalities (e.g., the city of Boulder) and irrigators. The modeling framework is built around StateMod, a network-based water allocation model used by state regulators to evaluate water rights allocations and potential rights transfers. Results suggest that the TWO contracts could allow municipalities to maintain supply reliability with significantly reduced rights holdings at lower cost, while increasing agricultural productivity in wet and normal years. Additionally, the TWO contracts provide irrigators with additional revenues via net payments of option fees from municipalities.

Tree phenology, the timing of periodic biological events in trees, is highly sensitive to climate change. Previous studies have indicated that forest greening can impact the local climate by modifying the seasonal surface energy budget. However, the understanding of tree phenological responses to forest greening at large spatial scales remains limited. Utilizing satellite-derived phenological and leaf area index data spanning from 2001 to 2021, herein we show that forest greening led to earlier spring and autumn phenology in both temperate and boreal forests. Our findings demonstrated that forest greening during winter and spring contributed to a reduction in surface albedo, resulting in biophysical warming and consequently advancing spring leaf phenology. Conversely, forest greening in summer and autumn induced biophysical cooling through increased evapotranspiration, leading to an earlier onset of autumn leaf phenology. Our findings highlight the significant impact of forest greening-induced local seasonal climate changes on shaping tree phenology in temperate and boreal forests. It is crucial to consider these greening-induced alterations in microclimate conditions when modeling changes in tree phenology under future climate warming scenarios.

Global spatial patterns of vascular plant diversity have been mapped at coarse grain based on climate-dominated environment–diversity relationships and, where possible, at finer grain using remote sensing. However, for grasslands with their small plant sizes, the limited availability of vegetation plot data has caused large uncertainties in fine-grained mapping of species diversity. Here we used vegetation survey data from 1,609 field sites (>4,000 plots of 1 m²), remotely sensed data (ecosystem productivity and phenology, habitat heterogeneity, functional traits and spectral diversity), and abiotic data (water- and energy-related, characterizing climate-dominated environment) together with machine learning and spatial autoregressive models to predict and map grassland species richness per 100 m² across the Mongolian Plateau at 500 m resolution. Combining all variables yielded a predictive accuracy of 69% compared with 64% using remotely sensed variables or 65% using abiotic variables alone. Among remotely sensed variables, functional traits showed the highest predictive power (55%) in species richness estimation, followed by productivity and phenology (48%), spectral diversity (48%) and habitat heterogeneity (48%). When considering spatial autocorrelation, remotely sensed variables explained 52% and abiotic variables explained 41%. Moreover, Remotely sensed variables provided better prediction at smaller grain size (<∼1,000 km), while water- and energy-dominated macro-environment variables were the most important drivers and dominated the effects of remotely sensed variables on diversity patterns at macro-scale (>∼1,000 km). These findings indicate that while remotely sensed vegetation characteristics and climate-dominated macro-environment provide similar predictions for mapping grassland plant species richness, they offer complementary explanations across broad spatial scales.

Water scarcity is a critical threat in arid regions in China due to dry climate and rising human water demand. The sustainability of a recent wetter trend and its impact on future water security remain uncertain. This case study focuses on a hotspot region, the North Slope of the Tianshan Mountains (NSTM), to assess water scarcity in the coming decades (2030–2050) under two climate scenarios. To this end, we developed an integrated agro-hydrological model to simulate historical and future hydrological processes and crop water dynamics in arid regions. Our results indicate nonsignificant increases in precipitation (around 3%) and evident rising temperatures (0.9–1.5°C) in the NSTM compared to the present-day (2011–2020) climate. This translates to a projected increase in water availability (5.6%–11.2%) during 2030–2050, with slightly larger increases (6.3%–14%) in glacier runoff. However, the spatial mismatch between precipitation increases and water demand makes this potential gain largely offset by rising irrigation water demand (over 7%) if cropland remains constant from 2020 onwards. As a result, the current annual water deficit (3.3 km³) is likely to increase by 5%–11%, with 32% of NSTM basins facing persistent water scarcity. Most croplands are at high risk of groundwater depletion and 17%–34% of basins will experience intensified water scarcity. These findings highlight the urgent need for comprehensive water management strategies, including improved irrigation efficiency and exploration of alternative water sources, to ensure water security and sustainable development in arid China facing a changing climate.

Wetland methane (CH₄) emissions have a significant impact on the global climate system. However, the current estimation of wetland CH₄ emissions at the global scale still has large uncertainties. Here we developed six distinct bottom-up machine learning (ML) models using in situ CH₄ fluxes from both chamber measurements and the Fluxnet-CH₄ network. To reduce uncertainties, we adopted a multi-model ensemble (MME) approach to estimate CH₄ emissions. Precipitation, air temperature, soil properties, wetland types, and climate types are considered in developing the models. The MME is then extrapolated to the global scale to estimate CH₄ emissions from 1979 to 2099. We found that the annual wetland CH₄ emissions are 146.6 ± 12.2 Tg CH₄ yr⁻¹ (1 Tg = 10¹² g) from 1979 to 2022. Future emissions will reach 165.8 ± 11.6, 185.6 ± 15.0, and 193.6 ± 17.2 Tg CH₄ yr⁻¹ in the last two decades of the 21st century under SSP126, SSP370, and SSP585 scenarios, respectively. Northern Europe and near-equatorial areas are the current emission hotspots. To further constrain the quantification uncertainty, research priorities should be directed to comprehensive CH₄ measurements and better characterization of spatial dynamics of wetland areas. Our data-driven ML-based global wetland CH₄ emission products for both the contemporary and the 21st century shall facilitate future global CH₄ cycle studies.

In the context of increasingly frequent and severe climate extremes, an understanding of the impacts of these events on gross primary production (GPP) and thus on land carbon uptake is crucial. However, research utilizing new model outputs to assess the future trends, characteristics, and driving factors of GPP reduction associated with extreme events remains limited. Here, we use model outputs from Phase Six of the Coupled Model Intercomparison Project (CMIP6) to investigate the spatiotemporal patterns of negative GPP extreme events during the 21st century. We find a notable increase in negative GPP extremes globally under the SSP5-8.5 scenario. They are characterized by longer durations and larger sizes, despite the smaller number of events. Under the SSP1-2.6 scenario, while the total negative GPP extremes remain relatively stable, hotspots, including tropical forests, southern China, and boreal forest zones, still experience increases in negative extremes. By attributing these GPP extremes to climate conditions, we identified compound hot and dry conditions, which contributed to over 40% of the negative GPP extremes under both scenarios, as the dominant driver, followed by single-driver dry conditions. Under SSP5-8.5, the increasing contribution of compound hot and dry conditions leads to greater GPP reductions through prolonged and intensified negative extreme events. Compared with CMIP5 models, CMIP6 models project an asymmetry of negative and positive GPP extreme events that favors more negative extremes across most regions. Our findings highlight the escalating damage from climate extremes on future ecosystem productivity, emphasizing the urgent need for effective mitigation and adaptation actions.

Salt marshes are intertidal coastal ecosystems shaped by complex feedbacks between hydrodynamic, morphological, and biological processes. These crucial yet endangered environments provide a diverse range of ecosystem services but are globally subjected to high anthropogenic pressures, while being severely exposed to climate change impacts. The importance of salt marshes as “blue carbon” sinks, deriving from their primary production coupled with rapid surface accretion, has been increasingly recognized within the framework of climate mitigation strategies. However, large uncertainties remain in salt marsh carbon stock and sequestration estimation. In order to provide further knowledge in salt marsh carbon assessment and investigate marsh carbon pool response to management actions, we analyzed organic matter content in salt marsh soils of the Venice Lagoon (Italy) from 60 sediment cores to the depth of 1 m and estimated organic carbon stock and accumulation rates in different areas. Organic carbon stocks and accumulation rates were highly variable in different marshes, being affected by organic and inorganic inputs and preservation conditions. Our estimates suggest that the studied marshes store 17,108 ± 5,757 tons of carbon per square kilometer in top 1-m of soil and can accumulate 85 ± 25 tons of carbon per square kilometer per year. However, flood regulation may reduce the annual marsh CO₂ sequestration potential by more than 30%. Our results contribute valuable information for regional carbon assessments, reinforcing the need for integrated coastal management policies to preserve the ecosystem services of coastal environments, and underscore the importance of considering local variability and methodological variations.