Earths Future最新文献

{"title":"Changes in Global Heatwave Risk and Its Drivers Over One Century","authors":"Chuan Wang,&nbsp;Zhi Li,&nbsp;Yaning Chen,&nbsp;Yupeng Li,&nbsp;Lin Ouyang,&nbsp;Jianyu Zhu,&nbsp;Fan Sun,&nbsp;Shiran Song,&nbsp;Hongwei Li","doi":"10.1029/2024EF004430","DOIUrl":"https://doi.org/10.1029/2024EF004430","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Heatwaves represent a significant and growing threat to natural ecosystems and socio-economic structures, making heatwave risk mitigation and prevention an important area of research. In exploring heatwave frequency and intensity from 1901 to 2020, the present study finds a sharp increase in both. The study also finds that the spatial distribution of heatwaves is unequal, the volatility of intensity characteristics has become more prominent over time, and the Gini coefficients of four key heatwave indictors have become larger due to increasing dryness. Although heatwaves occur more frequently in drylands, there is greater cumulative heat in humid areas, resulting in a higher heatwave risk in those areas. The global heatwave risk over the past three decades (1991–2020) has increased nearly five-fold compared to the early 20th century (1901–1930). Furthermore, GeoDetector analysis indicates that the Palmer drought severity index (PDSI) and downward surface shortwave radiation (Srad) contributing the most in drylands and humid areas (0.29 and 0.41, respectively). The contribution of relative humidity (RH), wind speed (WS), soil moisture (SM), and the normalized difference vegetation index (NDVI) is also significant in humid areas, but is much smaller in drylands. Composite analysis shows that the years with anomalously high heatwave risk correspond to positive anomalies of 500hPa geopotential height and surface pressure. The inhibition of cloud formation due to sinking air and the resulting increase in temperature in the atmosphere may be increasing the risk of heatwave occurrence. This study emphasizes the urgent need to address worsening climate change impacts.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004430","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Distinct Role for Aerosol and GHG Forcing in Historical CMIP6 Evapotranspiration Trends","authors":"Marius Egli,&nbsp;Vincent Humphrey,&nbsp;Sebastian Sippel,&nbsp;Reto Knutti","doi":"10.1029/2024EF004973","DOIUrl":"https://doi.org/10.1029/2024EF004973","url":null,"abstract":"<p>Evapotranspiration (ET) is crucial for the global water balance, plant growth, and freshwater availability. It connects the surface water balance with surface energy fluxes, making its accurate representation vital for climate projections. However, global climate models (GCMs) struggle with ET representation due to resolution limitations and simplified depictions of soil, plant, and atmosphere interactions. Simulated future changes in ET are uncertain, and the role of driving processes remain unclear. Here, we explore the utility of a simple and interpretable method to disentangle these varying drivers. We investigate the sensitivity of JJA ET to different atmospheric variables through simple linear models predicting ET from atmospheric variables only. The model consistently yields good results across GCMs or forcing scenarios. We find that GCMs have shown strong decreases and subsequent increases in ET over the historical period, related to changes in net surface radiation. For future climate projections, decreases in water availability compete with higher available surface radiation, making future projections uncertain. Single forcing GCM realizations show that historical ET trends in densely populated regions have been more influenced by aerosol emissions than greenhouse gases. Finally, we investigate which atmospheric variables explain most short-term (year-to-year) and long-term (decadal) changes. While water availability may be the most important driver of short-term variability, for certain regions, radiation trends dominate long-term forcing. This paper leverages a simple approach to provide a comprehensive and understandable view into recent and future changes in ET, reconciling the evidence provided by more complex case studies.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004973","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525505","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Persistently Elevated High-Latitude Ocean Temperatures and Global Sea Level Following Temporary Temperature Overshoots","authors":"Fabrice Lacroix,&nbsp;Friedrich A. Burger,&nbsp;Yona Silvy,&nbsp;Carl-F. Schleussner,&nbsp;Thomas L. Frölicher","doi":"10.1029/2024EF004862","DOIUrl":"https://doi.org/10.1029/2024EF004862","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>As exceeding the 1.5°C level of global warming is likely to happen in the near future, understanding the response of the ocean-climate system to temporarily overshooting this warming level is of critical importance. Here, we apply the Adaptive Emissions Reduction Approach to the Earth System Model GFDL-ESM2M to conduct novel overshoot scenarios that reach 2.0, 2.5 and 3.0°C of global warming before returning to 1.5°C over the time period of 1861–2500. We also perform a complementary scenario that stabilizes global temperature at 1.5°C, allowing to isolate impacts caused by the temperature overshoots alone, both during their peaks and after their reversals. The simulations indicate that substantial residual ocean surface warming persists in the high latitudes after the overshoots, with most notable regional anomalies occurring in the North Atlantic (up to +3.1°C in the 3°C overshoot scenario compared to the 1.5°C stabilization scenario) and the Southern Ocean (+1.2°C). The residual warming is primarily driven by the recoveries of the Atlantic and Southern Ocean meridional overturning circulation and associated increases in ocean heat transport. Excess subsurface heat storage in low and mid-latitudes prevents steric sea level rise (SLR) from reverting to 1.5°C stabilization levels in any overshoot scenario, with steric sea level remaining up to 32% higher in the 3°C overshoot scenario on centennial time scales. Both peak impacts and persistent changes after overshoot reversal bear significant implications for future assessments of coastlines, regional climates, marine ecosystems, and ice sheets.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004862","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525536","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Wildfire Emissions Offset More Permafrost Ecosystem Carbon Sink in the 21st Century","authors":"Xingru Zhu,&nbsp;Gensuo Jia,&nbsp;Xiyan Xu","doi":"10.1029/2024EF005098","DOIUrl":"https://doi.org/10.1029/2024EF005098","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Permafrost ecosystems in high-latitudes stock a large amount of carbon and are vulnerable to wildfires under climate warming. However, major knowledge gap remains in the effects of direct carbon loss from increasing wildfire biomass burning on permafrost ecosystem carbon sink. In this study, we used observation-derived data sets and Coupled Model Intercomparison Project Phase 6 (CMIP6) simulations to investigate how carbon emissions from wildfire biomass burning offset permafrost ecosystem carbon sink under climate warming in the 21st century. We show that the fraction of permafrost ecosystem carbon sink offset by wildfire emissions was 14%–25% during the past two decades. The fraction is projected to be 28%–45% at the end of this century under different warming scenarios. The weakening carbon sink is caused by greater increase in wildfire emissions than net ecosystem production in permafrost regions under climate warming. The increased fraction of ecosystem carbon sink offset by wildfire carbon loss is especially pronounced in continuous permafrost region during the past two decades. Although uncertainties exist in simulations of wildfire emissions and ecosystem carbon budget, results from different models still show that wildfire emissions offset more permafrost ecosystem carbon sink in the 21st century. These findings highlight that carbon sink capacity of permafrost ecosystems is increasingly threatened by wildfires under the warming climate.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005098","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Large Ensemble Exploration of Global Energy Transitions Under National Emissions Pledges","authors":"Jacob Wessel,&nbsp;Gokul Iyer,&nbsp;Thomas Wild,&nbsp;Yang Ou,&nbsp;Haewon McJeon,&nbsp;Jonathan Lamontagne","doi":"10.1029/2024EF004754","DOIUrl":"https://doi.org/10.1029/2024EF004754","url":null,"abstract":"<p>Global climate goals require a transition to a deeply decarbonized energy system. Meeting the objectives of the Paris Agreement through countries' nationally determined contributions and long-term strategies represents a complex problem with consequences across multiple systems shrouded by deep uncertainty. Robust, large-ensemble methods and analyses mapping a wide range of possible future states of the world are needed to help policymakers design effective strategies to meet emissions reduction goals. This study contributes a scenario discovery analysis applied to a large ensemble of 5,760 model realizations generated using the Global Change Analysis Model. Eleven energy-related uncertainties are systematically varied, representing national mitigation pledges, institutional factors, and techno-economic parameters, among others. The resulting ensemble maps how uncertainties impact common energy system metrics used to characterize national and global pathways toward deep decarbonization. Results show globally consistent but regionally variable energy transitions as measured by multiple metrics, including electricity costs and stranded assets. Larger economies and developing regions experience more severe economic outcomes across a broad sampling of uncertainty. The scale of CO₂ removal globally determines how much the energy system can continue to emit, but the relative role of different CO₂ removal options in meeting decarbonization goals varies across regions. Previous studies characterizing uncertainty have typically focused on a few scenarios, and other large-ensemble work has not (to our knowledge) combined this framework with national emissions pledges or institutional factors. Our results underscore the value of large-ensemble scenario discovery for decision support as countries begin to design strategies to meet their goals.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004754","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Uncovering the Dynamic Drivers of Floods Through Interpretable Deep Learning","authors":"Yuanhao Xu,&nbsp;Kairong Lin,&nbsp;Caihong Hu,&nbsp;Xiaohong Chen,&nbsp;Jingwen Zhang,&nbsp;Mingzhong Xiao,&nbsp;Chong-Yu Xu","doi":"10.1029/2024EF004751","DOIUrl":"https://doi.org/10.1029/2024EF004751","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The formation of floods, as a complex physical process, exhibits dynamic changes in its driving factors over time and space under climate change. Due to the black-box nature of deep learning, its use alone does not enhance understanding of hydrological processes. The challenge lies in employing deep learning to uncover new knowledge on flood formation mechanism. This study proposes an interpretable framework for deep learning flood modeling that employs interpretability techniques to elucidate the inner workings of a peak-sensitive Informer, revealing the dynamic response of floods to driving factors in 482 watersheds across the United States. Accurate simulation is a prerequisite for interpretability techniques to provide reliable information. The study reveals that comparing the Informer with Transformer and LSTM, the former showed superior performance in peak flood simulation (Nash-Sutcliffe Efficiency over 0.6 in 70% of watersheds). By interpreting Informer's decision-making process, three primary flood-inducing patterns were identified: Precipitation, excess soil water, and snowmelt. The controlling effect of dominant factors is regional, and their impact on floods in time steps shows significant differences, challenging the traditional understanding that variables closer to the timing of flood event occurrence have a greater impact. Over 40% of watersheds exhibited shifts in dominant driving factors between 1981 and 2020, with precipitation-dominated watersheds undergoing more significant changes, corroborating climate change responses. Additionally, the study unveils the interplay and dynamic shifts among variables. These findings suggest that interpretable deep learning, through reverse deduction, transforms data-driven models from merely fitting nonlinear relationships to effective tools for enhancing understanding of hydrological characteristics.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004751","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Streamflow Response to Multi-Day Warm Anomaly Events: Sensitivity to Future Warming and Spatiotemporal Variability by Event Magnitude","authors":"Sam Anderson,&nbsp;Shawn Chartrand","doi":"10.1029/2024EF004962","DOIUrl":"https://doi.org/10.1029/2024EF004962","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Persistent warm temperature anomalies can drive streamflow in regions where snow and glacier melt are important constituents of streamflow. However, the spatiotemporal variability of the streamflow response depends on both the magnitude of the forcing temperature anomalies and the nature of the underlying hydrological system. Here we ask: when, where, and for what magnitude of temperature anomalies will the streamflow response change most rapidly under warming? We use observed streamflow and temperature for 868 basins across Canada to quantify the streamflow response during warm temperature anomalies and how such responses vary in space, time, and by anomaly magnitude. We first identify two temporal modes of the streamflow response, one in autumn and one in spring, the relative strength of which varies by climate. We then use sinusoidal approximations of seasonal temperature cycles to characterize the sensitivity of such modes to changes in annual temperature. At individual basins, we find that relative to moderate warm events, the streamflow response to more extreme warm events is more sensitive to changes in mean annual temperatures, and this sensitivity is greatest in the coastal, southern, and central regions of Canada. Our results have implications for how the hydrological impacts of extreme events, such as heatwaves, will change in space and time under future climate change.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004962","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Role of Initial Conditions and Meteorological Drought in Soil Moisture Drought Propagation: An Event-Based Causal Analysis Over South Asia","authors":"Amitesh Gupta,&nbsp;L. Karthikeyan","doi":"10.1029/2024EF004674","DOIUrl":"https://doi.org/10.1029/2024EF004674","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>The role of meteorological droughts and initial conditions (land and atmosphere) in soil moisture drought (SMD) propagation are not yet fully understood. This work uses a drought event-based causal framework to investigate the relative importance of meteorological drought (MD) duration and intensity and initial conditions that result in surface and rootzone SMD, considering their event-level propagation time (PT) over South Asia. Initially, spatial variability of drought propagation is assessed by the Propagation Ratio (PR) computed based on MD counts that trigger SMD at various lags. PR depicts 2–3 months slower rootzone propagation than at surface. The gradual decrease in PR with increasing regional aridity indicates faster propagation over humid regions. The causal impact of initial conditions and MD parameters on propagating SMD are evaluated using normalized mutual information and a newly proposed normalized conditional mutual information. We found greater importance of triggering MD parameters followed by initial soil moisture condition on propagating SMD. This behavior is more evident for the surface layer propagation at shorter PT. There is a confounding effect of initial atmospheric conditions on drought propagation through initial soil moisture, depicting the significance of land-atmosphere interactions prior to propagation. In the rootzone propagation, initial soil moisture has a greater influence on propagation, especially at longer PT, indicating the significance of soil moisture persistence. Stronger causal links obtained through the joint influence of MD parameters on SMD suggest the importance of accounting for MD duration and intensity simultaneously, which are not considered in drought index-based propagation studies.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004674","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524711","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"High Resolution (30 m) Burned Area Product Improves the Ability for Carbon Emission Estimation in Africa","authors":"Baoye Qi,&nbsp;Zhaoming Zhang,&nbsp;Tengfei Long,&nbsp;Guojin He,&nbsp;Guizhou Wang,&nbsp;Yan Peng,&nbsp;Zekun Xu","doi":"10.1029/2024EF005051","DOIUrl":"https://doi.org/10.1029/2024EF005051","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <p>Fire significantly contributes to greenhouse gas emissions. The current global burned area (BA) products mainly have coarse native spatial resolution, which leads to underestimation of global BA and carbon emissions from biomass burning. Performances of BA products in Africa from GABAM (30 m), MCD64A1 (500 m), GFED4s (0.25°), FireCCI51 (250 m), and GFED5 (0.25°) were compared. From 2014 to 2020, GFED5 detected the most BA, 1.58 times more than GABAM during the same period. GABAM detected 0.09 Mkm² more burned area than FireCCI51 on average. From 2014 to 2016, GABAM detected an average of 2.99 Mkm² of BA in Africa, which was 1.03 times more than GFED4s. From 2014 to 2021, the average African BA derived from GABAM was 2.89 Mkm², 1.22 times more than MCD64A1. The increase in BA will inevitably lead to an increase in the estimation of carbon emissions from biomass burning. Based on GABAM products and GFED framework, we estimated the average vegetation burning carbon emissions in Africa from 2014 to 2021 to be 1113.25 Tg, which is higher than GFED4s' carbon emissions in the same time period. This shows that the use of high-resolution (30 m) burned area products to estimate carbon emissions can effectively avoid the underestimation of overall fire carbon emissions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF005051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524673","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projecting Large Fires in the Western US With an Interpretable and Accurate Hybrid Machine Learning Method","authors":"Fa Li,&nbsp;Qing Zhu,&nbsp;Kunxiaojia Yuan,&nbsp;Fujiang Ji,&nbsp;Arindam Paul,&nbsp;Peng Lee,&nbsp;Volker C. Radeloff,&nbsp;Min Chen","doi":"10.1029/2024EF004588","DOIUrl":"https://doi.org/10.1029/2024EF004588","url":null,"abstract":"<p>More frequent and widespread large fires are occurring in the western United States (US), yet reliable methods for predicting these fires, particularly with extended lead times and a high spatial resolution, remain challenging. In this study, we proposed an interpretable and accurate hybrid machine learning (ML) model, that explicitly represented the controls of fuel flammability, fuel availability, and human suppression effects on fires. The model demonstrated notable accuracy with a <i>F</i>₁-score of 0.846 ± 0.012, surpassing process-driven fire danger indices and four commonly used ML models by up to 40% and 9%, respectively. More importantly, the ML model showed remarkably higher interpretability relative to other ML models. Specifically, by demystifying the “black box” of each ML model using the explainable AI techniques, we identified substantial structural differences across ML fire models, even among those with similar accuracy. The relationships between fires and their drivers, identified by our model, were aligned closer with established fire physical principles. The ML structural discrepancy led to diverse fire predictions and our model predictions exhibited greater consistency with actual fire occurrence. With the highly interpretable and accurate model, we revealed the strong compound effects from multiple climate variables related to evaporative demand, energy release component, temperature, and wind speed, on the dynamics of large fires and megafires in the western US. Our findings highlight the importance of assessing the structural integrity of models in addition to their accuracy. They also underscore the critical need to address the rise in compound climate extremes linked to large wildfires.</p>","PeriodicalId":48748,"journal":{"name":"Earths Future","volume":"12 10","pages":""},"PeriodicalIF":7.3,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1029/2024EF004588","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}