为研究我国干旱半干旱区连续无雨日气候特征的变化趋势，利用该区域74个气象站1961—2022年逐日降水观测资料，对研究区连续无雨日变化特征及其在1961—1990年和1991—2020年前后两个时段的差异等进行分析，重点关注16 d及以上连续无雨日，并将16~25 d、26~40 d、41~60 d、60 d以上连续无雨日分别定义为轻旱、中旱、重旱和特旱。结果表明：我国干旱半干旱气候区16 d及以上连续无雨日的发生次数和日数存在明显差异，尤其是重旱、特旱对应的连续无雨日发生次数和日数，干旱区分别是半干旱区的2.0倍与6.0倍左右；1991—2020年与1961—1990年相比，研究区西部不同等级气象干旱发生次数明显减少，而中东部有所增加；研究区西部不同等级干旱发生日数同样减少明显，中部轻旱和中旱发生日数减少，而重旱和特旱发生日数有所增加；研究时段内，该区域绝大部分气象站点不同等级干旱发生次数和日数均未发生突变。

在气候变暖背景下，2022年夏季我国出现1961年以来平均气温最高和降水量次少的气候异常，并伴有最强的全国性（东北地区除外）高温过程和长江中下游及川渝地区大范围强伏旱。针对这次高温干旱的持续性和极端性，本文基于2022年6—8月全国2162个气象站逐日最高气温和降水量以及NCEP（National Centers for Environmental Prediction）/NCAR（National Center for Atmospheric Research）逐日再分析资料等，分析其时空分布特征及环流形势，将对今后我国南方地区夏季高温干旱不同时间尺度的预报预测有一定参考价值。结果表明：2022年夏季，全国76.0%的站共出现48 198次高温，其中36.6%的站累计出现3001次极端高温事件，20次以上极端高温事件的站点均分布在四川盆地，高温状况远超21世纪以来的典型高温年份。全国性的高温过程从6月13日持续到8月30日，共计79 d，高温最强时段在8月11—24日。按照高温发生站次、持续时间、影响范围、强度等由强到弱综合排序，依次是华东、西南、华中、西北、华北和华南地区，其中西南地区极端性最强，而东北地区未出现高温。干旱时空分布特征与高温基本相似，全国最强干旱时段在8月中旬。2022年夏季，500 hPa欧亚中高纬度呈“两脊一槽”型，尤其在7—8月乌拉尔山和鄂霍次克海附近高压脊形成阶段性阻塞高压，强盛的副热带系统将两高之间活跃的冷空气大部分时段阻挡在50°N以北，造成我国“北涝南旱”的格局；低纬度的伊朗高压异常东伸，西太平洋副热带高压略偏北且异常西伸，两高压长时间贯通形成的高压带控制区气流辐散下沉，并持续阻碍水汽向中纬度输送，不利于长江流域产生降水。同时，对流层高层南亚高压异常偏东，与中层的西太平洋副热带高压相向而行，于8月中下旬在80°E—120°E范围内叠加，致使控制我国大范围的高压系统呈稳定正压结构，中心位于川渝上空，致使川渝地区成为高温日数和极端高温事件次数的高值中心。

在全球气候变暖的严峻形势下，区域性高温干旱事件愈发频繁，对生态环境、粮食安全、经济发展和生命健康构成重大威胁。2024年4—6月，我国华北、西北及西南地区再度遭遇高温干旱侵袭，农业生产遭受明显损失。本研究综合多种数据资料剖析上述3个区域高温干旱事件的演变特征及成因。结果表明，西南地区干旱主要发生在4月，而华北和西北地区自4月起旱情显现、5—6月旱情逐渐加剧（强度增强、范围扩大）。伴随旱情加剧，区域最高气温异常范围明显扩展，西北地区高温日数创历史新高，5月最高气温达到峰值，较旱情最为严重的6月提前一个月；西南和华北地区高温接近历史极值。进一步分析表明，华北地区干旱主要受太平洋地区环流调控，而高温则主要受低纬度太平洋环流及西太平洋暖池影响；西北地区的干旱主要与西太平洋副热带高压及北半球极涡密切相关，高温则主要来自北大西洋的影响；西南地区高温干旱的成因更为复杂，但主要聚焦于北半球副热带高压和低纬度太平洋、印度洋。从大气环流和水汽输送的角度审视，华北和西北旱情的主导因素为大陆高压的发展和维持，而西南地区的干旱则受偏北的西太平洋副热带高压引导，致使来自印度大陆的干热气流控制这一区域，造成水汽辐散，进而引发高温干旱灾害。

Changes in global fire activity are influenced by a multitude of factors including land-cover change, policies, and climatic conditions. This study uses 17 climate models to evaluate when changes in fire weather, as realized through the Fire Weather Index, emerge from the expected range of internal variability due to anthropogenic climate change using the time of emergence framework. Anthropogenic increases in extreme Fire Weather Index days emerge for 22% of burnable land area globally by 2019, including much of the Mediterranean and the Amazon. By the midtwenty-first century, emergence among the different Fire Weather Index metrics occurs for 33-62% of burnable lands. Emergence of heightened fire weather becomes more widespread as a function of global temperature change. At 2 degrees C above preindustrial levels, the area of emergence is half that for 3 degrees C. These results highlight increases in fire weather conditions with human-caused climate change and incentivize local adaptation efforts to limit detrimental fire impacts. Plain Language Summary Observed increases in the frequency and severity of fire weather have been observed across portions of the globe over the past half century. We used climate models to identify where and when anthropogenic climate change causes fire weather conditions to exceed that of natural variability. Modeling results show that emergence for some fire weather indices is already under way for a sizable portion of the globe, including much of southern Europe and the Amazon, and with an expansion of this area with continued warming over the twenty-first century. These findings suggest substantial increases in fire potential in regions where vegetation abundance and ignitions are not limiting, highlighting the urgency to adapt to changes in fire disturbances and hazards.

Wildfire is an integral part of the Earth system, but at the same time it can pose serious threats to human society and to certain types of terrestrial ecosystems. Meteorological conditions are a key driver of wildfire activity and extent, which led to the emergence of the use of fire danger indices that depend solely on weather conditions. The Canadian Fire Weather Index (FWI) is a widely used fire danger index of this kind. Here, we evaluate how well the FWI, its components, and the climate variables from which it is derived, correlate with observation-based burned area (BA) for a variety of world regions. We use a novel technique, according to which monthly BA are grouped by size for each Global Fire Emissions Database (GFED) pyrographic region. We find strong correlations of BA anomalies with the FWI anomalies, as well as with the underlying deviations from their climatologies for the four climate variables from which FWI is estimated, namely, temperature, relative humidity, precipitation, and wind. We quantify the relative sensitivity of the observed BA to each of the four climate variables, finding that this relationship strongly depends on the pyrographic region and land type. Our results indicate that the BA anomalies strongly correlate with FWI anomalies at a GFED region scale, compared to the strength of the correlation with individual climate variables. Additionally, among the individual climate variables that comprise the FWI, relative humidity and temperature are the most influential factors that affect the observed BA. Our results support the use of the composite fire danger index FWI, as well as its sub-indices, the Build-Up Index (BUI) and the Initial Spread Index (ISI), comparing to single climate variables, since they are found to correlate better with the observed forest or non-forest BA, for the most regions across the globe.

In recent decades boreal wildfires have occurred frequently over eastern Siberia, leading to increased emissions of carbon dioxide and pollutants. However, it is unclear what factors have contributed to recent increases in these wildfires. Here, using the data we show that background eastern Siberian Arctic warming (BAW) related to summer Russian Arctic sea-ice decline accounts for ~79% of the increase in summer vapor pressure deficit (VPD) that controls wildfires over eastern Siberia over 2004-2021 with the remaining ~21% related to internal atmospheric variability associated with changes in Siberian blocking events. We further demonstrate that Siberian blocking events are occurring at higher latitudes, are more persistent and have larger zonal scales and slower decay due to smaller meridional potential vorticity gradients caused by stronger BAW under lower sea-ice. These changes lead to more persistent, widespread and intense high-latitude warming and VPD, thus contributing to recent increases in eastern Siberian high-latitude wildfires.© 2024. The Author(s).

The Fire Weather Index (FWI) is widely used to assess the meteorological fire danger in several ecosystems worldwide. One shortcoming of the FWI is that only surface weather conditions are considered, despite the important role often played by atmospheric instability in the development of very large wildfires. In this work, we focus on the Iberian Peninsula for the period spanning 2004–2018. We show that atmospheric instability, assessed by the Continuous Haines Index (CHI), can be used to improve estimates of the probability of exceedance of energy released by fires. To achieve this, we consider a Generalized Pareto (GP) model and we show that by stepwisely introducing the FWI and then the CHI as covariates of the GP parameters, the model is improved at each stage. A comprehensive comparison of results using the GP with the FWI as a covariate and the GP with both the FWI and CHI as covariates allowed us to then define a correction to the FWI, dependent on the CHI, that we coined enhanced FWI (FWIe). Besides ensuring a better performance of this improved FWI version, it is important to stress that the proposed FWIe incorporates efficiently the effect of atmospheric instability into an estimation of fire weather danger and can be easily incorporated into existing systems.

. Climate-driven changes in the fire regime within boreal forest ecosystems are likely to have important effects on carbon cycling and species composition. In the context of improving fire management options and developing more realistic scenarios of future change, it is important to understand how meteorology regulates different aspects of fire dynamics, including ignition, daily fire spread, and cumulative annual burned area. Here we combined Moderate-Resolution Imaging Spectroradiometer (MODIS) active fires (MCD14ML), MODIS imagery (MOD13A1) and ancillary historic fire perimeter information to produce a data set of daily fire spread maps for Alaska during 2002–2011. This approach provided a spatial and temporally continuous representation of fire progression and a precise identification of ignition and extinction locations and dates for each wildfire. The fire-spread maps were analyzed with daily vapor pressure deficit (VPD) observations from the North American Regional Reanalysis (NARR) and lightning strikes from the Alaska Lightning Detection Network (ALDN). We found a significant relationship between daily VPD and likelihood that a lightning strike would develop into a fire ignition. In the first week after ignition, above average VPD increased the probability that fires would grow to large or very large sizes. Strong relationships also were identified between VPD and burned area at several levels of temporal and spatial aggregation. As a consequence of regional coherence in meteorology, ignition, daily fire spread, and fire extinction events were often synchronized across different fires in interior Alaska. At a regional scale, the sum of positive VPD anomalies during the fire season was positively correlated with annual burned area during the NARR era (1979–2011; R2 = 0.45). Some of the largest fires we mapped had slow initial growth, indicating opportunities may exist for suppression efforts to adaptively manage these forests for climate change. The results of our spatiotemporal analysis provide new information about temporal and spatial dynamics of wildfires and have implications for modeling the terrestrial carbon cycle.\n

Global warming is expected to alter wildfire potential and fire season severity, but the magnitude and location of change is still unclear. Here, we show that climate largely determines present fire-prone regions and their fire season. We categorize these regions according to the climatic characteristics of their fire season into four classes, within general Boreal, Temperate, Tropical and Arid climate zones. Based on climate model projections, we assess the modification of the fire-prone regions in extent and fire season length at the end of the 21st century. We find that due to global warming, the global area with frequent fire-prone conditions would increase by 29%, mostly in Boreal (+111%) and Temperate (+25%) zones, where there may also be a significant lengthening of the potential fire season. Our estimates of the global expansion of fire-prone areas highlight the large but uneven impact of a warming climate on Earth’s environment.

This data descriptor documents a dataset containing over 38 years of global reanalysis of wildfire danger. It consists of seven fields to assess fuel moisture as well as fire behavior. The methodology employed to generate these data is based on the Canadian Forest Fire Weather Danger Rating and utilizes weather forcing from ERA-Interim, a global reanalysis dataset produced by the European Centre for Medium-range Weather Forecasts. Global fire danger reanalysis data are used to quantify the climatological expectation of fire danger at a certain time of the year and for any location on the globe. It can be regarded as a complementary product to the fire danger forecasts issued daily by the Global Wildfire Information System (GWIS) under the umbrella of the European Copernicus program.

A new Ensemble Empirical Mode Decomposition (EEMD) is presented. This new approach consists of sifting an ensemble of white noise-added signal (data) and treats the mean as the final true result. Finite, not infinitesimal, amplitude white noise is necessary to force the ensemble to exhaust all possible solutions in the sifting process, thus making the different scale signals to collate in the proper intrinsic mode functions (IMF) dictated by the dyadic filter banks. As EEMD is a time–space analysis method, the added white noise is averaged out with sufficient number of trials; the only persistent part that survives the averaging process is the component of the signal (original data), which is then treated as the true and more physical meaningful answer. The effect of the added white noise is to provide a uniform reference frame in the time–frequency space; therefore, the added noise collates the portion of the signal of comparable scale in one IMF. With this ensemble mean, one can separate scales naturally without any a priori subjective criterion selection as in the intermittence test for the original EMD algorithm. This new approach utilizes the full advantage of the statistical characteristics of white noise to perturb the signal in its true solution neighborhood, and to cancel itself out after serving its purpose; therefore, it represents a substantial improvement over the original EMD and is a truly noise-assisted data analysis (NADA) method.

Extreme wildfires are becoming more common and increasingly affecting Earth’s climate. Wildfires in boreal forests have attracted much less attention than those in tropical forests, although boreal forests are one of the most extensive biomes on Earth and are experiencing the fastest warming. We used a satellite-based atmospheric inversion system to monitor fire emissions in boreal forests. Wildfires are rapidly expanding into boreal forests with emerging warmer and drier fire seasons. Boreal fires, typically accounting for 10% of global fire carbon dioxide emissions, contributed 23% (0.48 billion metric tons of carbon) in 2021, by far the highest fraction since 2000. 2021 was an abnormal year because North American and Eurasian boreal forests synchronously experienced their greatest water deficit. Increasing numbers of extreme boreal fires and stronger climate–fire feedbacks challenge climate mitigation efforts.