Earths Future最新文献

Climate risks are growing. Research is increasingly important to inform the design of risk-management strategies. Assessing such strategies necessarily brings values into research. But the values assumed within research (often only implicitly) may not align with those of stakeholders and decision makers. These misalignments are often invisible to researchers and can severely limit research relevance or lead to inappropriate policy advice. Aligning strategy assessments with stakeholders' values requires a holistic approach to research design that is oriented around those values from the start. Integrating values into research in this way requires collaboration with stakeholders, integration across disciplines, and attention to all aspects of research design. Here we describe and demonstrate a qualitative conceptual tool called a values-informed mental model (ViMM) to support such values-centered research design. ViMMs map stakeholders' values onto a conceptual model of a study system to visualize the intersection of those values with coupled natural-human system dynamics. Through this mapping, ViMMs integrate inputs from diverse collaborators to support the design of research that assesses risk-management strategies in light of stakeholders' values. We define a visual language for ViMMs, describe accompanying practices and workflows, and present an illustrative application to the case of flood-risk management in a small community along the Susquehanna river in the Northeast United States.

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.

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.