Earths Future最新文献

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.

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.

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.

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.

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.

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.