Earths Future最新文献

High-resolution climate projections are critical to assessing climate risk and developing climate resilience strategies. However, they remain limited in quality, availability, and/or geographic coverage. The Seasonal Trends and Analysis of Residuals empirical statistical downscaling model (STAR-ESDM) is a computationally-efficient, flexible approach to generating such projections that can be applied globally using predictands and predictors sourced from weather stations, gridded data sets, satellites, reanalysis, and global or regional climate models. It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi-Gaussian time series, gridded or point-based, into multi-decadal long-term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. Long-term predictor trends are bias-corrected and predictor components used to map predictand components to future conditions. Components are then recombined for each station or grid cell to produce a continuous, high-resolution bias-corrected and downscaled time series at the spatial and temporal scale of the predictand time series. Comparing STAR-ESDM output driven by coarse global climate model simulations with daily temperature and precipitation projections generated by a high-resolution version of the same global model demonstrates it is capable of accurately reproducing projected changes for all but the most extreme temperature and precipitation values. For most continental areas, biases in 1-in-1000 hottest and coldest temperatures are <0.5°C and biases in the 1-in-1000 wet day precipitation amounts are <5 mm/day. As climate impacts intensify, STAR-ESDM represents a significant advance in generating consistent high-resolution projections to comprehensively assess climate risk and optimize resilience globally.

Aedes sp. mosquitoes are changing their geographic range in response to climate change. This is of concern because these mosquitoes can carry dengue fever and other viral diseases. Changing weather patterns can also increase the numbers of Aedes mosquitoes, leading to greater human exposure and enhancing population health risks. We project the geographic distribution of Aedes and associated changes in populations exposed to dengue in Asian metropolitan areas under warming scenarios from 1.5°C to 5.0°C above pre-industrial temperatures, using multi-model ensembles. With global warming, the southern part of the Arabian Peninsula, the coast of the Arabian Sea in southern Iran, southern Pakistan in West Asia, the Korean Peninsula, most of the Japanese islands, and parts of North China in East Asia are projected to become suitable for dengue transmission. The numbers of metropolitan areas exposed to dengue is projected to change from 142 (48%) in the reference period (1995–2014) to 211 (71%) at 5.0°C warming. With the combined impact of socioeconomic and climate change, population exposure to dengue in Asian metropolitan areas is projected to increase from 263 (multi-model range 252–268) million in 1995–2014 to 411 (394–432) million, 446 (420–490) million, 509 (475–601), 558 (493–685) and 587 (529–773) million, respectively, at 1.5°C, 2.0°C, 3.0°C, 4.0°C and 5°C warming, with an average of 2.9 million new people exposed to dengue fever in metropolitan areas each year.

Urbanization alters the thermal and dynamic environment of the local climate system, resulting in significant impacts on precipitation in both urban and adjacent areas. Nevertheless, there remains a significant gap in our understanding of urbanization-induced effects on asymmetrical, symmetrical, and other precipitation patterns in urban agglomerations (UAs) with divergent background climates and geographic regions at different timescales. Specifically, this asymmetrical change pattern is characterized by an increase in heavy (or light) rainfall and a decrease in light (or heavy) rainfall. Here, we assessed the effects of urbanization on precipitation patterns across 18 UAs situated in diverse background climates and geographical areas in China at different timescales. The results demonstrate that urbanization predominantly alters precipitation patterns in UAs located in the humid region. Specifically, urbanization amplified asymmetrical changes in Yangtze River Delta, Pearl River Delta, Beibu Gulf, Middle Yangtze River, and Guanzhong, but exacerbated symmetrical changes in precipitation in some regions such as Chengdu-Chongqing. Notably, the urbanization effect demonstrates greater significance at the hourly scale, as exemplified in the Yangtze River Delta, Pearl River Delta, and Middle Yangtze River, where the urban impact is nearly twice as pronounced when compared to the daily scale. Moreover, urbanization had either no effect or has a negative impact on precipitation patterns in UAs located within continental and arid regions. This is related to the intensity of urbanization, background climate and complex topography. This finding implies that urban managers should consider the impact of urbanization on precipitation patterns in different contexts to provide scientific guidance for urban planning.

Changes in climate and land-use/land-cover will impact surface water dynamics throughout the 21st century and influence global surface water availability. However, most projections of surface water dynamics focus on climate drivers using local-scale hydrological models, with few studies accounting for climate and human drivers such as land-use/land-cover change. We used a data-driven, machine learning model to project seasonal surface water areas (SWAs) in the southeastern U.S. from 2006 to 2099 that combined land-cover and climate projections under eight different development and emissions scenarios. The model was fitted with historic Landsat imagery, land-use/land-cover, and climate observation data (mean squared error 0.14). We assessed the change in SWA for each scenario, and we compared the surface water projections from our data-driven model and a process-based model. We found that the scenario with the largest forest-dominated land cover loss and most extreme climate change had watersheds with the greatest projected increases (in the South Atlantic Gulf) and decreases (in the Lower Mississippi) in SWA. When compared to the increase or decrease in surface water projected by the process-based model, most of the watersheds across scenarios agreed on the direction of change. Our findings highlight the importance of forest-dominated land cover in maintaining stable surface water availability throughout the 21st century, which can inform land-use management policies for adaptation and water-stress mitigation as well as strategies to prepare for future flood and drought events.

Understanding the climate and carbon cycle response to negative CO₂ emissions is important for developing climate mitigation strategies that aim to limit global warming to a specific threshold. In this study, using a coupled climate and carbon cycle model, a novel set of nine stylized simulations are conducted with cumulative emissions of 1,000 GtC, 2,000 GtC, and 5,000 GtC over 150, 250, and 500 years, followed by identical cumulative negative emissions so that the net cumulative emissions are zero. On millennial-timescales, the climate system returns close to the preindustrial state, independent of the emission and removal pathways. However, the thermal and biogeochemical inertia of the ocean play an important role in determining the climate and carbon cycle response during the emission and removal phases. When zero net emissions are reached, surface air temperature is larger by 0–1°C than the preindustrial state, and the atmospheric CO₂ concentration is less by 12–29 ppm. These changes increase with both the magnitude and duration of the emission and removal pulses. In contrast, hysteresis in the relationship between global mean surface temperature and cumulative carbon emissions increases with the magnitude but decreases with the duration of emission and removal pulses. Our study highlights the role of ocean inertia in the asymmetry in climate response to emissions and removals and indicates that an earlier emission reduction implying emission/removal pathways with smaller magnitudes and shorter durations for the positive and negative emission pulses would avoid larger climate and carbon cycle impacts on centennial-timescales.

Forestation is a key strategy for climate mitigation in China through its biogeochemical (BGC) effect of ecosystem carbon sequestration. Additionally, the BGC effect of forestation can be either reinforced or counteracted by concurrent biogeophysical processes (BGP effect) resulting in local land surface warming or cooling, which can be translated into CO₂e (i.e., BGC effect) using a local transient climate response. Previous evaluations of the climate mitigation potential of future forestation in China have, however, focused on the BGC effect only and neglected the BGP effect, potentially leading to suboptimal forestation areas. Here, we determined priority forestation areas in China by incorporating both effects to maximize its global climate mitigation effect. Our results suggest an additional 167.2 Mha potentially suitable for forestation in China, exceeding the largest forestation target (86.8 Mha) possibly assumed by the government in 2060. The forestation-induced BGP effect (18.7 ± 61.9 tCO₂e ha⁻¹) largely reinforces the BGC effect (458.2 ± 92.6 tCO₂e ha⁻¹) in China, yielding a total climate mitigation effect of 476.9 ± 114.2 tCO₂e ha⁻¹ over 40 years (2021–2060). Under the 2060 forestation target, considering both BGC and BGP effects will displace 17.7% (15.3 Mha) of the forestation area derived by considering the BGC effect alone. Integrating both BGC and BGP effects will lead to a CO₂ uptake of 28.8 GtCO₂e by 2060, 3.9 GtCO₂e higher than the value obtained when considering the BGC effect only. Our results highlight the importance of considering BGP effect when making forestation policies for climate mitigation.

The changing climate and intensifying human activities have made an impact on the hydrological processes in the upper Yangtze River (UYR), but quantifying their effects remains uncertain. This study used the Budyko framework to investigate the response of runoff (Q) to climate change and human activities during 1956–2017 and evaluate the impacts of human activities, including land use/cover change, water use, dam construction, and vegetation change, on watershed characteristic. Results show that climate change is the dominant driver of Q variations in the Wujiang River (WJR), Jialing River (JLR), and Jinsha River (JSR) watersheds, with contributions of 58.6%, 66.9%, and 67.6%, respectively. However, in Mingjiang River (MJR) and UYR watersheds, human activities contribute more to Q variations with 55.2% and 51.2%, respectively. Human activities play important roles in variation of watershed characteristics, and they can explain 22%, 26%, 36%, 25%, and 53% of the watershed character change in UYR, WJR, JLR, MJR, and JSR, respectively. This study conducts a comprehensive analysis of the causes of Q change in UYR, and provides a new perspective to explore the effects of specific human activities on watershed characteristics.

Extreme flooding events are becoming more frequent and costly, and impacts have been concentrated in cities where exposure and vulnerability are both heightened. To manage risks, governments, the private sector, and households now rely on flood hazard data from national-scale models that lack accuracy in urban areas due to unresolved drainage processes and infrastructure. Here we assess the uncertainties of First Street Foundation (FSF) flood hazard data, available across the U.S., using a new model (PRIMo-Drain) that resolves drainage infrastructure and fine resolution drainage dynamics. Using the case of Los Angeles, California, we find that FSF and PRIMo-Drain estimates of population and property value exposed to 1%- and 5%-annual-chance hazards diverge at finer scales of governance, for example, by 4- to 18-fold at the municipal scale. FSF and PRIMo-Drain data often predict opposite patterns of exposure inequality across social groups (e.g., Black, White, Disadvantaged). Further, at the county scale, we compute a Model Agreement Index of only 24%—a ∼1 in 4 chance of models agreeing upon which properties are at risk. Collectively, these differences point to limited capacity of FSF data to confidently assess which municipalities, social groups, and individual properties are at risk of flooding within urban areas. These results caution that national-scale model data at present may misinform urban flood risk strategies and lead to maladaptation, underscoring the importance of refined and validated urban models.

Following the recent rejection of a formal Anthropocene series/epoch by the Subcommission on Quaternary Stratigraphy (SQS) of the International Commission on Stratigraphy (ICS), and its subsequent confirmation by the International Union of Geological Sciences (IUGS), the opportunity arises to reset the definition of the Anthropocene. The case for informally recognizing the Anthropocene to be a major planetary event of Earth system transformation offers a promising way forward, but this has been criticized by proponents of an Anthropocene series/epoch. In order to move on from the assumption that it must be a time interval, and to foster a more transdisciplinary and inclusive approach, the main points of the critique must be directly addressed.

The compound drought-extreme precipitation event (CDEP) is one of the most impactful successive compound events that shift from drought to extreme precipitation in the same location within a short period. Due to its dual characteristics of drought and flood, CDEP tends to be more destructive than the impact of individual drought or flood. Yet few studies have analyzed the likelihood of CDEP at different time intervals and their potential variations under global warming. In this study, we assessed the coincidence rate between droughts and extreme precipitation events at 1-month (CDEP-1), 2-month (CDEP-2), and 3-month (CDEP-3) intervals, as well as their potential changes in a 1.5 and 2°C warming world (under both SSP2-4.5 and SSP5-8.5 scenarios). Our results suggest that global droughts and extreme precipitation events have coincided more frequently at 1-month interval than at 2- and 3-month intervals during the period 1985–2014. The global average coincidence rates of CDEP-1, CDEP-2, and CDEP-3 are 24%, 10%, and 7%, respectively. Notably, the coincidence rate of CDEP-1 exceeded 40% in Eastern Asia, north-eastern North America, and India, indicating that more than 40% of droughts have been followed by extreme precipitation events in the next month after drought termination. Under both SSP2-4.5 and SSP5-8.5 scenarios, climate warming will increase the coincidence rate of CDEP-1, CDEP-2, and CDEP-3, especially will lead to higher values in the coincidence rate of CDEP-1. This study contributes to a better understanding of the patterns of CDEP and helps to develop more targeted risk management strategies.