Earths Future最新文献

Better understanding how fires respond to climate variability is an issue of current interest in light of ongoing climate change. However, evaluating the global-scale temporal variability of fires in response to climate presents a challenge due to the intricate processes at play and the limitation of fire data. Here, we investigate the links between year-to-year variability of burned area (BA) and climate using BA data, the Fire Weather Index (FWI), and the Standardized Precipitation Evapotranspiration Index (SPEI) from 2001 to 2021 at ecoregion scales. Our results reveal complex spatial patterns in the dependence of BA variability on antecedent and concurrent weather conditions, highlighting where BA is mostly influenced by either FWI or SPEI and where the combined effect of both indicators must be considered. Our findings indicate that same-season weather conditions have a more pronounced relationship with BA across various ecoregions, particularly in climatologically wetter areas. Additionally, we note that BA is also significantly associated with periods of antecedent wetness and coolness, with this association being especially evident in more arid ecoregions. About 60% of the interannual variations in BA can be explained by climatic variability in a large fraction (∼77%) of the world's burnable regions.

Aerosol increases over the 20th century delayed the rate at which Earth warmed as a result of increases in greenhouse gases (GHGs). Aggressive aerosol mitigation policies arrested aerosol radiative forcing from ∼1980 to ∼2010. Recent evidence supports decreases in forcing magnitude since then. Using the approximate partial radiative perturbation (APRP) method, future shortwave aerosol effective radiative forcing changes are isolated from other shortwave changes in an 18-member ensemble of ScenarioMIP projections from phase 6 of the Coupled Model Intercomparison Project (CMIP6). APRP-derived near-term (2020–2050) aerosol forcing trends are correlated with published model emulation values but are 30%–50% weaker. Differences are likely explained by location shifts of aerosol-impacting emissions and their resultant influences on susceptible clouds. Despite weaker changes, implementation of aggressive aerosol cleanup policies will have a major impact on global warming rates over 2020–2050. APRP-derived aerosol radiative forcings are used together with a forcing and impulse response model to estimate global temperature trends. Strong mitigation of GHGs, as in SSP1-2.6, likely prevents warming exceeding 2C since preindustrial but the strong aerosol cleanup in this scenario increases the probability of exceeding 2C by 2050 from near zero without aerosol changes to 6% with cleanup. When the same aerosol forcing is applied to a more likely GHG forcing scenario (i.e., SSP2-4.5), aggressive aerosol cleanup more than doubles the probability of reaching 2C by 2050 from 30% to 80%. It is thus critical to quantify and simulate the impacts of changes in aerosol radiative forcing over the next few decades.

Because of the climatological prevalence of hot, humid conditions, moist heat extremes are a significant challenge to the health and wellbeing of the people in India. While research has demonstrated the importance of summer monsoon to moist heat in India, impact of monsoon-break and warm spells in modulating extreme moist heat regionally has not been fully investigated. Here we investigate moist heat extremes, as measured by the Wet-Bulb Globe Temperature (WBGT) metric, specifically during monsoon and monsoon-break periods and find that they pose a major threat to physical labor and health relative to other seasons. During the 1951–2020 break period, an increase in area exposed (∼42.76 million km²), representing at least 670 million people, to extreme and detrimental WBGT values >31°C occur. Our results imply that future studies on extreme moist heat must pay close attention to the variation of weather systems on synoptic to subseasonal time scales that are superimposed on the seasonal monsoon migration.

A changing climate and growing coastal populations exacerbate the outcomes of environmental hazards. Large-scale flooding and acute disasters have been extensively studied through historic and current data. Chronic coastal flooding is less well understood and poses a substantial threat to future coastal populations. This paper presents a novel technique to record chronic coastal flooding using inexpensive accelerometers. This technique was tested in Key West, FL, USA using storm drains to deploy HOBO pendant G data loggers. The accuracy and feasibility of the method was tested through four deployments performed by a team of local stakeholders and researchers between July 2019–November 2021 resulting in 22 sensors successfully recording data, with 15 of these sensors recording flooding. Sensors captured an average of 13.58 inundation events, an average of 12.07% of the deployment time. Measured flooding events coincided with local National Oceanic and Atmospheric Administration (NOAA) water level measurements of high tides. Multiple efforts to predict coastal flooding were compared. Sensors recorded flooding even when NOAA water levels did not exceed the elevation or flooding thresholds set by the National Weather Service (NWS), indicating that NOAA water levels alone were not sufficient in predicting flooding. Access to an effective and inexpensive sensor, such as the one tested here, for measuring flood events can increase opportunities to measure chronic flood hazards and assess local vulnerabilities with stakeholder participation. The ease of use and successful recording of loggers can give communities an increased capacity to make data-informed decisions surrounding sea level rise adaptation.

Despite increasing exposure to flooding and associated financial damages, estimates suggest more than two-thirds of flood-exposed properties are currently uninsured. This low adoption rate could undermine the climate resilience of communities and weaken the financial solvency of the United States National Flood Insurance Program. We study whether repeated exposure to flood events, especially the disaster-scale floods that are expected to become more frequent in a warming climate, could spur insurance adoption. Using improved estimates of residential insurance take-up in locations where such insurance is voluntary, and exploiting variation in the frequency and severity of flood events over time, we quantify how flood events impact local insurance demand. We find that a flood disaster declaration in a given year increases the take-up rate of insurance by 7% in the following year, but that the effect diminishes in subsequent years and is gone after 5 years. This effect is more short-lived in counties in inland states that do not border the Gulf and Atlantic coasts. We also find that the effect of a flood on take-up is substantially larger if there was also a flood in the previous year, and that recent disasters are more salient for homeowners whose primary residences are exposed to a disaster declaration compared to non-primary residences. Overall, these findings suggest that relying on households to self-adapt to increasing flood risks in a changing climate is insufficient for closing the insurance protection gap.

In the face of a changing climate, the understanding, predictions, and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways, and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support next generation climate services.

Wildfires in the snow zone can brighten winter and spring landscapes by removing forest canopy, revealing underlying snow cover. Land surface albedo (LSA) alterations associated with transitioning from a canopied, snow-hiding vegetation regime to a snow-revealing landscape have impacts on the surface energy balance, with implications for climate and water supply. Forest fires are increasing in frequency, size, and elevation, but the change in LSA due to fire in the seasonal snow zone (SSZ) is poorly understood. This study addresses this knowledge gap for the Sierra Nevada, where recent climatic changes have contributed to droughts, earlier and more rapidly declining snowpacks, and worsening wildfire impacts. Remotely sensed snow fraction and LSA data from Moderate Resolution Imaging Spectrometer were used to assess the impact of wildfire on landscapes in the Sierra Nevada SSZ by comparing LSA in burn scars to unburned control areas and the historical average LSA, then quantifying the surface radiative forcing (RF) associated with change in LSA. Among high and moderate burn severity fires, winter LSA varied depending on snow cover, land characteristics, and burn severity, ranging from 0.12 in low-snow fire scars to 0.47 in snow-covered fire scars. This study adds to understanding of how landscapes respond to wildfires and the subsequent impacts on the surface energy balance.

Extreme precipitation events are projected to increase in frequency across much of the land-surface as the global climate warms, but such projections have typically relied on coarse-resolution (100–250 km) general circulation models (GCMs). The ensemble of HighResMIP GCMs presents an opportunity to evaluate how a more finely resolved atmosphere and land-surface might enhance the fidelity of the simulated contribution of large-magnitude storms to total precipitation, particularly across topographically complex terrain. Here, the simulation of large-storm dominance, that is, the number of wettest days to reach half of the total annual precipitation, is quantified across the western United States (WUS) using four GCMs within the HighResMIP ensemble and their coarse resolution counterparts. Historical GCM simulations (1950–2014) are evaluated against a baseline generated from station-observed daily precipitation (4,803 GHCN-D stations) and from three gridded, observationally based precipitation data sets that are coarsened to match the resolution of the GCMs. All coarse-resolution simulations produce less large-storm dominance than in observations across the WUS. For two of the four GCMs, bias in the median large-storm dominance is reduced in the HighResMIP simulation, decreasing by as much as 62% in the intermountain west region. However, the other GCMs show little change or even an increase (+28%) in bias of median large-storm dominance across multiple sub-regions. The spread in differences with resolution amongst GCMs suggests that, in addition to resolution, model structure and parameterization of precipitation generating processes also contribute to bias in simulated large-storm dominance.

An accurate quantification of global methane sources and sinks is imperative for assessing realistic pathways to mitigate climate change. A key challenge of quantifying the Global Methane Budget (Saunois et al., 2020, https://doi.org/10.5194/essd-12-1561-2020) is the lack of consistency in uncertainties between sectors. Here we provide a new perspective on bottom-up (BU) and top-down (TD) methane uncertainties by using an expert opinion analysis based on a questionnaire conducted in 2021. Expectedly, experts rank highest uncertainty and lowest confidence levels in the Global Methane Budget related to natural sources in BU budgets. Here, we further reveal specific uncertainty types and introduce a ranking system for uncertainties in each sector. We find that natural source uncertainty is related particularly to driver data uncertainty in freshwater, vegetation, and coastal/ocean sources, as well as parameter uncertainty in wetland models. Reducing uncertainties, most notably in aquatic and wetland sources will help balance future BU and TD global methane budgets. We suggest a new methane source partitioning over gradients of human disturbance and demonstrate that 76.3% (75.8%–79.4%) or 561 (443–700) Tg CH₄ yr⁻¹ of global emissions can be attributed to moderately impacted, man-made, artificial, or fully anthropogenic sources and 23.7% (20.6%–24.2%) or 174 (115–223) Tg CH₄ yr⁻¹ to natural and low impacted methane sources. Finally, we identify current research gaps and provide a plan of action to reduce current uncertainties in the Global Methane Budget.

Lake-effect snow (LES) storms, characterized by heavy convective precipitation downwind of large lakes, pose significant coastal hazards with severe socioeconomic consequences in vulnerable areas. In this study, we investigate how devastating LES storms could evolve in the future by employing a storyline approach, using the LES storm that occurred over Buffalo, New York, in November 2022 as an example. Using a Pseudo-Global Warming method with a fully three-dimensional two-way coupled lake-land-atmosphere modeling system at a cloud-resolving 4 km resolution, we show a 14% increase in storm precipitation under the end-century warming. This increase in precipitation is accompanied by a transition in the precipitation form from predominantly snowfall to nearly equal parts snowfall and rainfall. Through additional simulations with isolated atmospheric and lake warming, we discerned that the warmer lake contributes to increased storm precipitation through enhanced evaporation while the warmer atmosphere contributes to the increase in the storm's rainfall, at the expense of snowfall. More importantly, this shift from snowfall to rainfall was found to nearly double the area experiencing another winter hazard, Rain-on-Snow. Our study provides a plausible future storyline for the Buffalo LES storm, focusing on understanding the intricate interplay between atmospheric and lake warming in shaping the future dynamics of LES storms. It emphasizes the importance of accurately capturing the changing lake-atmosphere dynamics during LES storms under future warming.