Earths Future最新文献

The use of satellite-based thermal infrared remote sensing has facilitated the assessment of surface water temperature on a large scale. However, the inherent limitations of this remote sensing technique make it difficult to assess rivers unless ambient conditions are cloud-free, devoid of steep terrain and the rivers are at least 60 m wide. To address these challenges that limit the spatiotemporal continuity of satellite-based hydro-thermal data, we harnessed the extensive coverage from the Landsat missions' thermal infrared sensors and data-driven techniques to estimate surface water temperature of rivers. Out of the tested data-driven techniques, we selected the Random Forest Regressor as our prime non-linear approach for estimation of surface water temperature in rivers. Using the selected technique, proposed as THORR (Thermal History of Regulated Rivers), we successfully reconstructed a multi-decadal, continuous spatiotemporal surface water temperature record for regulated rivers in the Columbia River Basin. Using 42 years of data, the surface water temperature could be predicted on average with 0.71° C of absolute error regardless of the dam's potential thermal influence in the downstream reaches. The reconstructed hydro-thermal behavior generated from THORR revealed a long-term downstream warming trend along the Columbia River. The open-source THORR tool can be extended to any river system around the world that is not gauged with in-situ temperature measurements for the reconstruction of hydro-thermal behavior.

Over the past four decades, global temperatures have increased more rapidly than before, potentially reducing vegetation activity if temperatures exceed the optimum temperature (T_opt). However, plants have the capacity to acclimate to rising temperatures by adjusting T_opt, thereby maintaining or even enhancing photosynthesis and carbon uptake. Despite this, it remains unclear how T_opt of vegetation activity changes over time and to what extent global vegetation can acclimate to current temperature changes. In this study, we evaluated the temporal trends of T_opt of vegetation activity and the thermal acclimation magnitudes globally using three remote-sensed vegetation indices and eddy-covariance observations of gross primary productivity from 1982 to 2020. We found that the global T_opt of vegetation activity has increased at an average rate of 0.63°C per decade over the past four decades. The increase in T_opt closely tracked the rise in annual maximum daily mean temperature (T_max), indicating that thermal acclimation has occurred widely across the globe. Globally, we found an average thermal acclimation magnitude of 0.38°C per 1°C increase in T_max. Notably, polar and continental regions exhibited the highest thermal acclimation magnitudes, while arid areas showed the lowest. Additionally, the thermal acclimation magnitude was positively affected by interannual temperature variability and negatively affected by soil moisture and vapor pressure deficits. Our findings indicate that terrestrial ecosystems have acclimated to current climate warming trends with varying degrees, suggesting a greater potential for land carbon uptake. Moreover, these results highlight the necessity for earth system models to integrate the thermal acclimation of T_opt to better forecast the global carbon cycle.

High Mountain Asia has long been known as a hotspot for landslide risk, and studies have suggested that landslide hazard is likely to increase in this region over the coming decades. Extreme precipitation may become more frequent, with a nonlinear response relative to increasing global temperatures. However, these changes are geographically varied. This article maps probable changes to landslide hazard, as shown by a landslide hazard indicator (LHI) derived from downscaled precipitation and temperature. In order to capture the nonlinear response of slopes to extreme precipitation, a simple machine-learning model was trained on a database of landslides across High Mountain Asia to develop a regional LHI. This model was applied to statistically downscaled data from the 30 members of the Seamless System for Prediction and Earth System Research large ensembles to produce a range of possible outcomes under the Shared Socioeconomic Pathways 2-4.5 and 5-8.5. The LHI reveals that landslide hazard will increase in most parts of High Mountain Asia. Absolute increases will be highest in already hazardous areas such as the Central Himalaya, but relative change is greatest on the Tibetan Plateau. Even in regions where landslide hazard declines by year 2100, it will increase prior to the mid-century mark. However, the seasonal cycle of landslide occurrence will not change greatly across High Mountain Asia. Although substantial uncertainty remains in these projections, the overall direction of change seems reliable. These findings highlight the importance of continued analysis to inform disaster risk reduction strategies for stakeholders across High Mountain Asia.

Mangrove-saltmarsh ecotones are experiencing rapid alterations due to climate change and human activities, however, the ecological and morphological implications of these shifts remain largely unknown. This study systematically explores how interspecific interactions and herbivory influence the dominant wetland species, as well as the resultant morphological evolution and landscape configuration. To achieve this, we develop a new eco-morphodynamic model that integrates hydrodynamics, sediment transport, bed-level change, and vegetation dynamics. The novelty of the current model lies in newly incorporated modules to simulate biotic interactions between mangroves and saltmarshes, enabling exploration of eco-morphodynamic feedback in mangrove-saltmarsh ecotones in response to tidal flows and species interactions. Our results show that vertical growth rates of coexisting vegetation species are dominant factors in determining wetland dominance. When mangroves and saltmarshes exhibit comparable growth rates, mangroves typically become the dominant wetland species. Conversely, if mangroves grow more slowly than saltmarshes, they are unable to outcompete saltmarshes. Additionally, herbivory can fundamentally alter wetland dominance depending on herbivore food preferences. Our simulations further underline that saltmarsh-dominated wetlands develop channel networks more extensively and rapidly than mangrove-dominated systems. This pattern is also observed during species invasions, with invading saltmarshes extending channel networks, while invading mangroves inhibit ongoing network expansion. This study highlights the pivotal roles of relative growth properties and herbivory in driving ecotone development in respect to wetland dominance and channel network development at the intertidal scale.

Wind renewable energy (WRE) is an essential component of the global sustainable energy portfolio. Recently, there has been increasing discussion on the potential supplementation of this conventional mitigation portfolio with Solar Radiation Modification (SRM). However, the impact of SRM on conventional mitigation measures has received limited attention to date. In this study, we explore one part of this impact, the potential effect of one type of SRM, Stratospheric Aerosol Injections (SAI), on WRE. Using hourly output from the Earth System Model CNRM-ESM2-1, we compare WRE potential under a medium emission scenario (SSP245) and a high emission scenario (SSP585) with an SRM scenario that has SSP585 baseline conditions and uses SAI to offset warming to approximately SSP245 global warming levels. Our results suggest that SAI may affect surface wind resources by modifying large-scale circulation patterns, such as a significant poleward jet-shift in the Southern Hemisphere. The modeled total global WRE potential is negligibly reduced under SAI compared to the SSP-scenarios. However, regional trends are highly variable, with large increases and decreases in WRE potential frequently reaching 12% across the globe with SAI. This study highlights potential downstream effects of SRM on climatic elements, such as wind patterns, and offers perspectives on its implications for our mitigation efforts.

The management of developed barrier islands is often piece-meal and reactionary despite the complex, dynamic nature of these systems, and sustainable practices will become increasingly difficult due to heightened pressures of climate change. Adaptation actions, including nature-based solutions, need to be thoroughly evaluated prior to implementation to understand system-wide impacts and avoid maladaptation. Anarde et al. (2024a), (https://doi.org/10.1029/2023ef003672), Anarde et al. (2024b), (https://doi.org/10.1029/2023ef004200) is the latest important contribution in a growing body of transdisciplinary research that more robustly evaluates the complex physical process-and-response relationship of barrier systems via sophisticated numerical modeling approaches that also interface with socioeconomic models to inform coastal management actions in response to mitigating coastal risk. This new research indicates the importance of coordinated system-scale barrier island management, as strategies to reduce coastal hazard risk in one location will directly affect adjacent communities. Further, this work demonstrates that reducing barrier management interventions may actually promote barrier recovery and sustainability in the face of sea level rise. In addition, recent advances in the analysis and application of remotely sensed data from satellites and oblique aerial photography provide scientists an unprecedented opportunity to track coastal evolution over a wide range of spatial and temporal scales at minimal cost. As sea level rise and changing storm patterns challenge the sustainable management of barrier island systems, integrating these advanced, transdisciplinary tools will enable scientists and coastal practitioners to more thoroughly evaluate coastal adaptation options, efficiently invest limited resources to mitigate coastal hazard risk for communities, support healthy ecosystems, and reduce system-wide impacts.

Climate change threatens the resource adequacy of future power systems. Existing research and practice lack frameworks for identifying decarbonization pathways that are robust to climate-related uncertainty. We create such an analytical framework, then use it to assess the robustness of alternative pathways to achieving 60% emissions reductions from 2022 levels by 2040 for the Western U.S. power system. Our framework integrates power system planning and resource adequacy models with 100 climate realizations from a large climate ensemble. Climate realizations drive electricity demand; thermal plant availability; and wind, solar, and hydropower generation. Among five initial decarbonization pathways, all exhibit modest to significant resource adequacy failures under climate realizations in 2040, but certain pathways experience significantly less resource adequacy failures at little additional cost relative to other pathways. By identifying and planning for an extreme climate realization that drives the largest resource adequacy failures across our pathways, we produce a new decarbonization pathway that has no resource adequacy failures under any climate realizations. This new pathway is roughly 5% more expensive than other pathways due to greater capacity investment, and shifts investment from wind to solar and natural gas generators. Our analysis suggests modest increases in investment costs can add significant robustness against climate change in decarbonizing power systems. Our framework can help power system planners adapt to climate change by stress testing future plans to potential climate realizations, and offers a unique bridge between energy system and climate modeling.

The climate and land use changes caused by the natural environment and socioeconomic development have potential impacts on the green and sustainable development of agriculture. To accommodate agricultural production under multiple scenarios of future climate and land-use change, this study proposes a “simulation–optimization” modeling approach based on a crop growth model with a synergistic “carbon emission–economic benefit” approach. This approach is based on climate change conditions and it accurately simulates future land use changes and crop growth processes, establishes a carbon emission intensity optimization model, and generates a spatial planting structure optimization and regulation scheme based on intelligent optimization algorithms under changing scenarios. The results of the model application show that the planting structure option in the future scenario can increase economic benefit by up to 14.8% compared to the current scenario while simultaneously reducing total greenhouse gas emissions by 6.77%. Correlation analysis of planting area, irrigation water volume, carbon intensity value and unilateral water use efficiency can be used to obtain the coordination level of each county under different regulation scenarios. This “simulation–optimization” modeling approach provides an effective approach to achieve synergistic and coordinated development of regional agricultural benefits and carbon reduction by fine-tuning the planting structure, which promotes low-carbon and high-quality development of regional agriculture.

Climate change induces significant changes in storm characteristics, particularly for short-duration extreme storms (heavy rain features), impacting their intensity and spatio-temporal distribution. Although alterations in precipitation intensity are well documented, past studies examining changes in spatio-temporal distribution of storms (storm rainrates) were region-specific and focused on isolated aspects of change in space or time, eluding a comprehensive understanding of the precise nature and extent of these changes. Bridging this gap, this study introduces a novel grid-based measure of storm homogeneity, “spatio-temporal homogeneity” metric and investigates the global patterns of change in combined spatio-temporal characteristics of extreme storms. Analyzing the 30 min × 0.1° × 0.1° resolution Global Precipitation Measurements, the study finds that extreme storms are shrinking in both space and time due to rising surface air temperatures, predominantly in tropics. In contrast, temperate regions experience expanded extreme storms with increasing temperatures. The study also identifies a global trend toward more front-loading in storms with rising temperatures, driven by a substantial increase in tropics and southern temperate regions. Conversely, storms in northern temperate regions become slightly more rear-loaded as temperature increases. Furthermore, the study finds that characteristics of short–duration storms (6–12 hr) are more sensitive to temperature changes. Overall, this study contributes valuable insights into the global spatio-temporal changes of extreme storms, highlighting regions most susceptible to alterations in storm patterns due to climate change. These findings are essential for developing effective adaptation strategies and flood management practices to cope with the changing nature of extreme storms in a warming climate.