Earths Future最新文献

Increasing the awareness of society about climate change by using a simplified way for the explanation of its impacts might be one of the key elements to adaptation and mitigation of its possible effects. This study investigates climate analogs, which allow the possibility to find, today, a place on land where climatic conditions are similar to those that a specific area will face in the future. The grid-based calculation of analogs over the selected European domain was carried out using a newly proposed distance between multivariate distributions, the Wasserstein distance, that has never been used so far for climate analog calculations. By working on the whole multivariate distributions, the Wasserstein distance allows us to account for dependencies between the variables of interest and for the shape of their distribution. Its features are compared with the Euclidean and the Mahalanobis distances, which are the most used methods up to now. Multi-model climate analogs analysis is achieved between the reference period 1981–2010 and three future periods 2011–2040, 2041–2070, and 2071–2100, for seasonal temperatures (mean, min, and max) and precipitation, from five different climate models and three different socio-economic scenarios. The agreement between climate models in the location and degree of similarity of the best analogs decreases as warming intensifies and/or as time approaches the end of the century. As the climate warms, the similarity between future and current climatic conditions gradually decreases and the spatial (geographical) distance between a location and its best analog increases.

Climate change poses significant threats to global agriculture, impacting food quantity, quality, and safety. The world is far from meeting crucial climate targets, prompting the exploration of alternative strategies such as stratospheric aerosol intervention (SAI) to reduce the impacts. This study investigates the potential impacts of SAI on rice and wheat production in India, a nation highly vulnerable to climate change given its substantial dependence on agriculture. We compare the results from the Assessing Responses and Impacts of Solar climate intervention on the Earth system with Stratospheric Aerosol Injection-1.5°C (ARISE-SAI-1.5) experiment, which aims to keep global average surface air temperatures at 1.5°C above preindustrial in the Shared Socioeconomic Pathway 2-4.5 (SSP2-4.5) global warming scenario. Yield results show ARISE-SAI-1.5 leads to higher production for rainfed rice and wheat. We use 10 agroclimatic indices during the vegetative, reproductive, and ripening stages to evaluate these yield changes. ARISE-SAI-1.5 benefits rainfed wheat yields the most, compared to rice, due to its ability to prevent rising winter and spring temperatures while increasing wheat season precipitation. For rice, SSP2-4.5 leads to many more warm extremes than the control period during all three growth stages and may cause a delay in the monsoon. ARISE-SAI-1.5 largely preserves monsoon rainfall, improving yields for rainfed rice in most regions. Even without the use of SAI, adaptation strategies such as adjusting planting dates could offer partial relief under SSP2-4.5 if it is feasible to adjust established rice-wheat cropping systems.

Urban overheating presents significant challenges to public health and energy sustainability. Conventional radiative cooling strategies, such as cool roofs with high albedo, lead to undesired winter cooling and increased space heating demand for cities with cold winters, a phenomenon known as heating energy penalty. A novel roof coating with high albedo and temperature-adaptive emissivity (TAE)—low emissivity during cold conditions and high emissivity during hot conditions—has the potential to mitigate winter heating energy penalty. In this study, we implement this roof coating in a global climate model to evaluate its impact on air temperature and building energy demand for space heating and cooling in global cities. Adopting roofs with TAE increases global urban air temperature by up to +0.54°C in the winter (99th percentile; mean change +0.16°C) but has negligible effects on summer urban air temperature (mean change +0.05°C). Combining TAE with high albedo effectively provides summer cooling and does not increase building energy demand in the winter, particularly for mid-latitude cities. Sensitivities of air temperature to changes in emissivity and albedo are associated with local “apparent” net longwave radiation and incoming solar radiation, respectively. We propose a simple parameterization of air temperature responses to emissivity and albedo to facilitate the development of city-specific radiative mitigation strategies. This study emphasizes the necessity of developing mitigation approaches specific to local cloudiness.

Effective wildfire prevention includes actions to deliberately target different wildfire causes. However, the cause of an increasing number of wildfires is unknown, hindering targeted prevention efforts. We developed a machine learning model of wildfire ignition cause across the western United States on the basis of physical, biological, social, and management attributes associated with wildfires. Trained on wildfires from 1992 to 2020 with 12 known causes, the overall accuracy of our model exceeded 70% when applied to out-of-sample test data. Our model more accurately separated wildfires ignited by natural versus human causes (93% accuracy), and discriminated among the 11 classes of human-ignited wildfires with 55% accuracy. Our model attributed the greatest percentage of 150,247 wildfires from 1992 to 2020 for which the ignition source was unknown to equipment and vehicle use (21%), lightning (20%), and arson and incendiarism (18%).

Ecological restoration efforts in less developed regions confront a sustainability challenge due to the undervaluation of their substantive benefits. Soil conservation, as a crucial ecosystem service supporting both ecological and socioeconomic systems in less developed regions, is often overlooked in estimating the benefits of restoration efforts. We introduce a framework that integrates the multi-model approach and scenario analysis on cloud computing platforms to capture the significance of soil conservation benefits by assessing the world's largest restoration programs from China. Our analysis reveals that these restoration programs, with a total investment of $133 billion, have prevented 7.29 ± 1.01 Pg of soil erosion, valued at $243.0 ± 25.9 billion from 2000 to 2019. Notably, two critical programs that synergize forest conservation, cropland conversion, and human well-being in China's less developed regions account for approximately 85% of the soil conservation benefits. Our findings underscore that soil conservation benefits significantly enhance the substantive benefits and prioritization of restoration efforts in less developed regions, reinforcing the potential for global restoration efforts to contribute to a sustainable future.

Increases in the frequency, intensity, and duration of extreme climate events (ECEs) are already impacting ecosystems, with many of the strongest effects associated with high-elevation areas. Most research on the ecological impacts of climate change has focused on climatic averages, which might differ from ECEs. Rhododendron, a diverse genus of alpine and subalpine woody plant, plays a crucial role in ecosystem stability and biodiversity in the biodiversity hotspots of the Himalayas and Hengduan Mountains. Here, we compared the predicted impacts of average climate with those including ECEs on 189 Rhododendron species in China for the historical period (1981–2010) and the future period (2071–2100) under two emissions scenarios (SSP2-4.5 and SSP5-8.5). We analyzed changes in suitable habitat and patterns of species richness, weighted endemism, and phylogenetic diversity, identifying areas of coinciding high-risk as priority conservation areas. Inclusion of ECEs altered the projected areas of suitable habitat across all species from an increase of over 3% to a decrease exceeding 10%, with the distribution of most Rhododendron species strongly influenced by extremes of drought and high temperatures. We found fewer than 18% of high-risk areas of diversity loss were currently protected, with priority conservation areas mainly located in the Daxue, Daliang, Wumeng, and Jade Dragon Snow Mountains, as well as in the Nyingchi. We suggest inclusion of ECEs is critical when projecting changes in alpine and subalpine species distributions for effective conservation planning for climate change.

Boreal and temperate forests are undergoing structural, compositional and functional changes in response to increasing temperatures, changes in precipitation, and rising CO₂, but the extent of the changes in forests will also depend on current and future forest management. This study utilized the dynamic vegetation model LPJ-GUESS enabled with forest management (version 4.1.2, rev11016) to simulate changes in forest ecosystem functioning and supply of ecosystem services in Sweden. We compared three alternative forest policy scenarios: Business As Usual, with no change in the proportion of forest types within landscapes; Adaptation and Resistance, with an increased area of mixed stands; and EU-Policy, with a focus on conservation and reduced management intensity. LPJ-GUESS was forced with climate data derived from an ensemble of three earth system models to study long-term implications of a low (SSP1-2.6), a high (SSP3-7.0), and a very high (SSP5-8.5) emissions scenario. Increases in net primary production varied between 4% and 8% in SSP1-2.6, 21%–25% in SSP3-7.0 and 25%–29% in SSP5-8.5 across all three forest policy scenarios, when comparing 2081–2100 to 2001–2020. Increased net primary production was mediated by a higher soil nitrogen availability and increased water use efficiency in the higher emission scenarios SSP3-7.0 and SSP5-8.5. Soil carbon storage showed small but significant decreases in SSP3-7.0 and in SSP5-8.5. Our results highlight differences in the predisposition to storm damage among forest policy scenarios, which were most pronounced in southern Sweden, with increases of 61%–76% in Business-As-Usual, 4%–11% in Adaptation and Resistance, and decreases of 7%–12% in EU-Policy when comparing 2081–2100 to 2001–2020.

Urban land expansion is a major driver of many environmental and societal changes that challenge human well-being and sustainable development, but its evolutionary process and dynamics are neither clear nor well-integrated into urban science quantitatively. We analyzed the global urban extent data based on nighttime lights to examine the statistical distribution of urban land area at the global scale, and in 13 regions and countries over 29 years. The results reveal a converging temporal trend in urban land expansion from subnational to global scales, characterized by a coherent shift of urban area distribution from an initial power law toward an exponential distribution. This trend is well captured by a unified mathematical model based on the shifted power law distribution function and is reflected in the gradual predominance of medium-size cities over small-size cities in the configuration of urban systems across the world. The shift of urban area distributions bears the consequence of reduced urban system stability and resilience, and can be linked to increasing exposure of urban populations to extreme heat events and air pollution. These changes are likely to be driven by the increasing influence of external economies of scale associated with globalization. The findings challenge the status quo of land urbanization practices and emphasize the importance of medium-size cities in urban planning.

Prediction of the rapid intensification (RI) of tropical cyclones (TCs) is crucial for improving disaster preparedness against storm hazards. These events can cause extensive damage to coastal areas if occurring close to landfall. Available models struggle to provide accurate RI estimates due to the complexity of underlying physical mechanisms. This study provides new insights into the prediction of a subset of rapidly intensifying TCs influenced by prolonged ocean warming events known as marine heatwaves (MHWs). MHWs could provide sufficient energy to supercharge TCs. Preconditioning by MHW led to RI of recent destructive TCs, Otis (2023), Doksuri (2023), and Ian (2022), with economic losses exceeding $150 billion. Here, we analyze the TC best track and sea surface temperature data from 1981 to 2023 to identify hotspot regions for compound events, where MHWs and RI of tropical cyclones occur concurrently or in succession. Building upon this, we propose an ensemble machine learning model for RI forecasting based on storm and MHW characteristics. This approach is particularly valuable as RI forecast errors are typically largest in favorable environments, such as those created by MHWs. Our study offers insight into predicting MHW TCs, which have been shown to be stronger TCs with potentially higher destructive power. Here, we show that using MHW predictors instead of the conventional method of using sea surface temperature reduces the false alarm rate by 30%. Overall, our findings contribute to coastal hazard risk awareness amidst unprecedented climate warming causing more frequent MHWs.

There is an urgent need for models that can robustly detect past and project future ecosystem changes and risks to the services that they provide to people. The Fisheries and Marine Ecosystem Model Intercomparison Project (FishMIP) was established to develop model ensembles for projecting long-term impacts of climate change on fisheries and marine ecosystems while informing policy at spatio-temporal scales relevant to the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) framework. While contributing FishMIP models have improved over time, large uncertainties in projections remain, particularly in coastal and shelf seas where most of the world's fisheries occur. Furthermore, previous FishMIP climate impact projections have been limited by a lack of global standardized historical fishing data, low resolution of coastal processes, and uneven capabilities across the FishMIP community to dynamically model fisheries. These features are needed to evaluate how reliably the FishMIP ensemble captures past ecosystem states - a crucial step for building confidence in future projections. To address these issues, we have developed FishMIP 2.0 comprising a two-track framework for: (a) Model evaluation and attribution of past changes and (b) future climate and socioeconomic scenario projections. Key advances include improved historical climate forcing, which captures oceanographic features not previously resolved, and standardized global fishing forcing to test fishing effects systematically across models. FishMIP 2.0 is a crucial step toward a detection and attribution framework for changing marine ecosystems and toward enhanced policy relevance through increased confidence in future ensemble projections. Our results will help elucidate pathways toward achieving sustainable development goals.