Earths Future最新文献

Deep soil water (θ_d), defined here as past precipitation stored in deep unsaturated soils and not replenished by precipitation in a single growing season, plays a vital role in helping trees withstand prolonged droughts in deep-vadose-zone regions. However, its contribution to total water use across tree growth stages and the effects of limited θ_d access on tree transpiration and photosynthesis remain unclear. To address this, a process-based model was parameterized using in situ root-zone measurements (from the soil surface to the apparent maximum root depth) and then used to simulate root-zone soil moisture, canopy transpiration and photosynthesis for two common species, apple (Malus domestica fuji) and black locust (Robinia pseudoacacia), in northern China. For trees aged 3–22 years, θ_d below 200 cm (θ_d200) accounted for an average of 31.9% and 40.9% of total water use in apple and black locust trees, respectively. Restricted access to θ_d200 led to decreases in annual transpiration rates and daily photosynthetic rates by 19.7% and 17.4% in apple, and by 26.2% and 20.2% in black locust. On a monthly scale, precipitation and transpiration greatly influenced θ_d200 for both species, while tree age and diameter at breast height were key annual determinants. These findings highlight a trade-off between physiological stability achieved via deeper rooting and the associated carbon costs of accessing θ_d. The findings here provide insights into sustainability of planted trees in deep vadose-zone regions.

Terrestrial water storage (TWS) in China, with the world's largest irrigated expanse and extensive mid-low latitude glaciers, is essential for effective water resource management and socioeconomic risk adaptation. However, the responses of TWS to human intervention and climate change, both during historical periods and under future scenarios, remain inadequately quantified. We reconstruct and project long-term TWS using a data-driven framework that integrates remote sensing, Earth system model (ESM) and machine learning. Our reconstructed record reveals an amplified TWS decline in China's drylands and a moderate yet persistent TWS reduction in glacier regions during 1985–2015, accentuated since the 21st century with a 13% increase in affected areas. TWS changes in drylands are primarily attributed to human irrigation ($��\sim �$39%) and precipitation ($��\sim �$24%), with the impacts of irrigation magnified by 9%–12% during drought. Humid basins show a moderate TWS response to irrigation and precipitation, modulated by intricate but unexplored interactions between atmospheric drivers, glacier-snow dynamics and underlying hydrological processes. Such discrepant response highlights the necessity for region-specific water resource management strategies: northern drylands should prioritize optimized irrigation practices while southern humid basins would benefit from enhanced adaptation to climate variability. Projections from nine ESMs indicate a likely amplification of TWS decline (13%–43%) in drylands and glacial zones by mid-century if maintaining current human intervention levels. Our findings emphasize the need to reassess climate change-induced water scarcity and refine human management regulations, particularly as existing strategies may be overlooking broader sustainability challenges in a warming climate.

Ports provide critical infrastructure services, supporting global trade, economic growth and development. Owing to their exposed coastal locations, ports are expected to face increasing climate-related risks, such as sea-level rise (SLR) and changes in storminess. However, there is a gap in current literature evaluating how ports are addressing climate-related risks through implementation of adaptation actions. This study explores if, and how, some of the largest commercial ports in the UK are adapting to risk in practice. Evidence of implemented adaptation action is extracted from Adaptation Reporting Power (ARP) reports, as mandated under the UK Climate Change Act 2008. Evidence of incremental adaptation was identified, in response to an increasingly diverse range of perceived climate-related risks. However, uncertainty around future changes in some climate-related risks, and different risk perceptions, meant ports were also coming to different judgments on when and how they should adapt. A discord between short and longer-term planning was also identified. Consequently, there remains the need to shift thinking from business-as-usual toward a more systematic and integrated consideration of short- and longer-term climate risks, adaptation and wider benefits to support decision making. This would align with a more transformational adaptation approach. This could include exploiting the renewal and investment cycle so new port infrastructure is climate-proofed when constructed. The framework presented here, to identify, catalog and evaluate implemented adaptation actions in the UK, could be applied to other regions. This would provide a more comprehensive picture of how ports are implementing adaptation globally.

The Arctic has experienced the most rapid warming on Earth in recent decades. This affects Canada's landmass, which extends well into the Arctic. Nevertheless, limited spatial and temporal observational coverage, combined with large climate model uncertainties, pose challenges to understanding both past and future climate changes in these regions relative to preindustrial conditions. This is particularly challenging in a place like Canada that has insufficient historical data to determine preindustrial reference conditions. Emergent constraints can overcome this limitation by using historical observations for the modern post-industrial era to constrain estimates of both preindustrial reference levels and future warming. Here we apply a carefully tested Bayesian observational constraint method to simultaneously assess the externally forced historical and future warming in Canada. Testing indicates that the approach reduces bias and uncertainty in historical and future warming estimates, increasing confidence that it may also serve as a basis for developing a broader understanding of climate change in other high-latitude regions. We estimate that external forcing from human activity, has warmed Canada by 2.2 [1.3, 3.1]°C between the 1850–1900 pre-industrial period and the recent 2015–2024 decade. Applying these same observational constraints to future climate conditions indicates that Canada will warm to 5.1 [3.2, 7.0]°C above pre-industrial levels by the end-of-century under an intermediate emissions scenario SSP 2-4.5, and to 6.7 [4.6, 8.9]°C under a high-emissions scenario SSP 3-7.0, with the largest warming projected for Northern Canada, followed by Quebec.

Climate change calls for adaptive strategies to manage land system across governance levels, as differing multi-level policies distinctly shape land system and long-term ecosystem resilience. This study proposes an iterative approach for optimizing land-use pathways that balance competing policy objectives across national, provincial, and local levels without compromising ecosystem integrity in a changing climate. This approach was applied to the Huangshui River Basin on China's Qinghai-Tibet Plateau, a region facing significant challenges from climate change and human activities. We integrated the land-use change model CLUMondo with the dynamic vegetation model LPJ-GUESS to compare our sustainable development pathway against scenarios based on plans prioritizing national, provincial, and local governance objectives. The analysis revealed considerable mismatches in management goals across governance levels within the Huangshui River Basin, emphasizing the necessity of multi-scale coordination to align planning objectives for achieving desired goals. This study presents an optimization framework to quantitatively evaluate trade-offs and balance between sustainability objectives and ecosystem integrity in response to system feedbacks, offering critical insights into reconciling potentially conflicting sustainability goals across multiple scales within socio-ecological systems.

Climate change and land use/land cover (LULC) changes influence streamflow by altering precipitation patterns, evaporation, and hydrological processes. Disentangling their combined effects is critical for effective water management under increasing climatic and environmental pressures. This meta-analysis integrates quantitative and qualitative approaches to assess the impacts of precipitation, temperature, and LULC changes on streamflow using published data sets, multiple linear regression, and Random Forest models. Precipitation emerges as the dominant driver, showing significant variability and a direct linear correlation with streamflow. Temperature impacts are inconsistent, while LULC changes demonstrate nuanced effects. Conversions to agriculture generally increase streamflow, whereas transitions to forests reduce it. Multiple linear regression revealed that precipitation alone explains nearly half of the variance in streamflow, with LULC changes contributing an additional but smaller percentage. In contrast, temperature changes have minimal influence. Variability in LULC conversions correlates with residuals, underscoring diverse impacts across land use types. The Random Forest model, which allows the consideration of non-linear dependencies, achieved R² values of 0.7, confirming precipitation as the most critical predictor, followed by temperature and LULC changes. Including catchment area and climate zone added no significant improvement. These findings highlight the combined importance of precipitation, temperature and LULC changes in shaping streamflow dynamics. While comprehensive, the meta-analysis may overlook local factors such as micro-climate variations or land management practices. The variability in model predictive power underscores the challenge of modeling nonlinear relationships between climate, LULC changes, and streamflow. The results offer critical insights for sustainable water resource management and predictive hydrological modeling.

This study investigates the potential of Stratospheric Aerosol Injection (SAI), a solar climate intervention strategy, to mitigate climate extremes driven by greenhouse gas (GHG) emissions, comparing its effects to those of GHG-induced warming under the SSP5-8.5 scenario. Using the UKESM1 climate model and the GeoMIP G6controller scenario, we examine extreme temperature, precipitation, and fire risk indices in a risk-risk framework. The multi-latitude G6controller strategy, an improvement on the equatorial injection strategy G6sulfur, reduces global mean temperature from SSP5-8.5 to SSP2-4.5, significantly reducing temperature and precipitation extremes. Results show that G6controller effectively reduces temperature extremes relative to SSP5-8.5, especially in populated areas like Europe and South America, and reduces fire risk in high-risk areas, such as South America and southern Africa. While both scenarios project broad precipitation increases, G6controller moderates these without introducing new drying relative to SSP5-8.5, particularly in Southeast Asia. This study highlights G6controller's potential to lessen the magnitude of extreme climate events, offering insights into SAI's regional efficacy and highlighting the trade-offs between GHG warming with and without solar climate intervention.

Arctic Ocean albedo modification (AOAM) has been proposed as a potential means to mitigate some adverse climate impacts of amplified warming over the Arctic. Here we use an Earth system model to examine the response of physical climate and carbon cycle to a hypothetical AOAM implementation in which open seawater albedo in Arctic is set to the albedo of sea ice. Simulation results show that by the end of this century, relative to SSP5-8.5, AOAM would reduce Arctic mean warming by 1.6°C and delay the occurrence of 4°C Arctic warming by more than 20 years. Meanwhile, AOAM would prevent about 16% Arctic sea ice from melting. Although AOAM directly targets the Arctic Ocean, it has much larger impacts on the land carbon sink than that of the ocean. By 2100, AOAM would reduce 6% Arctic permafrost from thawing and prevent the release of permafrost carbon by 22 PgC compared to that of SSP5-8.5. On the other hand, AOAM would only decrease ocean carbon storage by 1 PgC. Regarding ocean acidification, AOAM would significantly postpone the onset of sea surface aragonite undersaturation over some Arctic Ocean areas by more than 10 years. Simulations show that a sudden termination of AOAM would cause rapid changes of climate and carbon cycle with a rate much larger than that under SSPs scenarios. Our study demonstrates the potential of AOAM to mitigate some impacts of Arctic warming, and illustrates modest effects of AOAM on the Arctic carbon cycle.

In high-mountain Third Pole (TP), increasing transboundary fluvial floods (TFFs) threaten nearly one billion people downstream. However, the changing roles of glacier and snow melt in the future TFFs and their socio-economic impacts remain unclear. Here, combining a high-resolution state-of-the-art cryosphere-hydrology model with observations and the latest climate projections, we project significant increases in annual-maximum TFFs across all TP basins from 1981 to 2100. By 2100, even under the intermediate-emission scenario (SSP245), the frequency of peak-over-threshold TFFs is projected to rise by 1.2 ± 1.1 to 13.5 ± 4.1 times compared to historical levels (1981–2020) in TP, with intensity increases ranging from 23% ± 30%–106% ± 39%. Although this increase is primarily driven by more frequent rainfall extremes, meltwater will still contribute over 20% to at least one peak-over-threshold TFF in 7 of 10 TP basins. For the Indus basin that has the largest glacier area, the number of peak-over-threshold TFFs with meltwater contribution exceeding 20% will increase by 3.2 ± 2.2 times by 2100 under SSP585; particularly in the years with heavy snowfall and less rainfall, the meltwater contribution could reach 80%. Moreover, the future ranges of meltwater contributions to the TP's TFFs become much wider than historical levels. By 2100, the exposed population and GDP to TP's TFFs are projected to increase by 6.2 ± 3.2 and 18.0 ± 12.6 times under SSP245, respectively, due to rising flood risk and socio-economic development. Further, increasing TFFs will threaten over 120,000 km² cropland downstream. These findings call for timely preparedness and close collaboration to cope with increasing TFFs among riparian Asian countries.

Interannual variability in snowmelt onset triggers dynamic responses in processes like surface energy exchange and the hydrological cycle through interactions between vegetation, soil and atmosphere, significantly affecting subsequent fire weather. However, it still remains unclear how shifts in snowmelt onset regulate fire weather and thereby wildfire dynamics during the following seasons. We analyze snowmelt onset dates and wildfire data for northern latitudes (>40°N) and find that interannual variability in snowmelt onset critically influences wildfire dynamics during post-snowmelt periods, with earlier snowmelt tending to increase wildfire incidence. This influencing role is primarily associated with the snowmelt onset-induced variations in vapor pressure deficit, which is geographically pronounced in 47.5% of the northern latitudes, contributing 57.1% of the total influencing role, approximately doubling the contribution from plant water deficit or fuel moisture content and fuel availability. Mechanistically, compared to late snowmelt onset, early vegetation spring green-up caused by an earlier occurrence in snowmelt leads to early moisture consumption. This, in turn, amplifies plant water deficits, which limit evaporative cooling and increase sensible heat fluxes, exacerbating atmospheric dryness and creating favorable conditions for wildfires during the post-snowmelt period. The reveal of these mechanisms has important implications for assessing wildfire risk, enhancing wildfire simulations and forecasting in regions vulnerable to ongoing and future climate change, and projecting carbon-climate feedback.