Earths Future最新文献

Temperate rainforests are rare ecosystems globally; restricted to cool, moist conditions that are sensitive to a changing climate. Despite their crucial conservation importance, a global assessment of how temperate rainforests will be impacted by climate change is lacking. We calculated historical (1970–2000) climate conditions for the temperate rainforest biome using ERA5 reanalysis data for three key bioclimatic variables: warmest quarter temperature, annual precipitation and proportion of rainfall during warmest quarter. We used high-spatial resolution climate projections for these variables to identify regions likely to become unsuitable for temperate rainforests under four future shared socioeconomic pathway (SSP) scenarios. We predict unmitigated climate change (SSP 5–8.5) would lead to a 68.3 (95% confidence interval (95 CI): 53.4–81.3)% loss in the existing temperate rainforest biome by 2100 at a global scale with some national-level reductions exceeding 90%. Restricting global warming to <2°C (consistent with SSP 1–2.6), limits loss of global temperate rainforest biome to 9.7 (95 CI: 7.8–13.3)% by 2100 and is crucial to ensuring temperate rainforest persistence. Deforestation has resulted in loss of up to 43% of the current temperate rainforest biome with only 37% of primary forest remaining, and some regions like Europe with virtually none. Protection and restoration of the temperate rainforest biome, along with emissions reductions, are vital to its climate future.

The state of Colorado's West Slope Basins are critical headwaters of the Colorado River and play a vital role in supporting Colorado's local economy and natural environment. However, balancing the multi-sectoral water demands in the West Slope Basins while maintaining crucial downstream deliveries to Lake Powell is an increasing challenge for water managers. Internal variability of the hydroclimatic system and climate change complicate future vulnerability assessments. This work contributes a detailed accounting of multi-sectoral drought vulnerability in the West Slope Basins and the impacts of drought on downstream deliveries. We first introduce a novel multi-site Hidden Markov Model (HMM)-based synthetic streamflow generator to create an ensemble of streamflows for all West Slope basins that better characterizes the region's drought extremes. We capture the effects of climate change by perturbing the HMM to generate an ensemble of streamflows reflecting plausible changes in climate. We then route both ensembles through StateMod, Colorado's water allocation model, to evaluate spatially compounding drought impacts across the West Slope Basins. Our results illustrate how drought events emerging from the system's stationary internal variability in the absence of climate change can significantly impact local water uses and deliveries to Lake Powell, exceeding extreme conditions in the historical record. Further, we find that even modest climate change can cause a regime shift where historically low downstream delivery volumes and extreme drought impacts become routine. These results can inform future Colorado River planning efforts, and our methodology can be expanded to other snow-dominated regions that face persistent droughts.

Cities are concentrators of complex, multi-sectoral interactions. As keystones in the interconnected human-Earth system, cities have an outsized impact on the Earth system. We describe a multi-lens framework for organizing our understanding of the complexity of urban systems and scientific research on urban systems, which may be useful for natural system scientists exploring the ways their work can be made more actionable. We then describe four critical dimensions along which improvements are needed to advance the urban research that addresses urgent climate challenges: (a) solutions-oriented research, (b) equity-centered assessments which rely on fine-scale human and ecological data, (c) co-production of knowledge, and (d) better integration of human and natural systems occurring through theory, observation, and modeling.

Land reclamations influence the morphodynamic evolution of estuaries and tidal basins, because an altered planform changes tidal dynamics and associated residual sediment transport. The morphodynamic response time to land reclamation is long, impacting the system for decades to centuries. Other human interventions (e.g., deepening of fairways or port construction) will add more morphodynamic adaptation timescales. Our understanding of the cumulative effects of anthropogenic interference with estuaries is limited because observations usually do not cover the complete morphological adaptation period. We aim to assess the impact of land reclamation works and other human interventions on an estuarine system by means of digital reconstructions of historical morphologies of the Ems Estuary over the past 500 years. Our analysis demonstrates that the intertidal-subtidal area ratio altered due to land reclamation works and that the ratio partly restored after land reclamation ended. The land reclamation works have led to the degeneration of an ebb and flood channel system, transitioning the estuary from a multichannel to a single channel system. We infer that the 20th-century intensification of channel dredging and re-alignment works accelerated rather than caused this development. The centennial-scale observations show that the Ems estuary evolution corresponds to a land reclamation response following tidal asymmetry-based stability theory as it moves toward a new equilibrium configuration with modified tidal flats and channels. Considering the long history of land reclamation in the Ems Estuary, it provides an analogy for expected developments in comparable tidal systems where land reclamations were recently carried out.

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.