以落叶松(Larix gmelinii)叶片、白桦(Betula platyphylla)叶片、落叶松-白桦叶片混合物为例, 初步研究了气温和空气相对湿度对地表细小死可燃物平衡含水率和时滞的影响, 对这3种可燃物在不同气温、不同空气相对湿度条件下(共20个温湿度组合)失水过程中的含水率进行了测定。通过统计软件建立了相应条件下3种类型可燃物含水率时间动态方程, 并利用此方程估算了3种可燃物平衡含水率和时滞, 同时用估算值分别建立了3种可燃物的平衡含水率-气温模型、平衡含水率-湿度模型、时滞-气温模型、时滞-湿度模型等4种模型。并用已知的4个可燃物平衡含水率模型拟合了该研究得到的平衡含水率数据, 其中, 用Van Wanger模型得到的平均绝对误差和均方根误差均不超过0.01, 拟合效果最好; Nelson模型的拟合效果最差。对气温和空气相对湿度对3种可燃物平衡含水率和时滞的影响的研究结果表明: 气温和空气相对湿度对3种可燃物的平衡含水率和时滞有显著影响, 气温与平衡含水率和时滞呈负相关关系, 而空气相对湿度与二者均呈正相关关系。其中, 时滞-湿度模型高估了可燃物的时滞。该研究具有一定的局限性与不确定性, 在未来的工作中, 应选择在更宽的可燃物类型范围内, 结合更全面的影响因子进行研究。

干旱是影响范围最广的自然灾害之一。2022年夏季发生在长江流域的异常高温干旱事件不仅强度大，而且持续时间长，是一次罕见的重大干旱事件，对我国的社会经济造成了十分严重的影响。鉴于这次事件的极端性，本文在客观分析此次事件演变特征的基础上，揭示大气环流和外强迫异常对此次高温干旱的可能影响。研究发现，气象干旱指数及土壤湿度监测结果一致表明本次旱情从6月开始出现，7月迅速发展，进入8月后范围进一步扩展、强度进一步加剧。与此同时，流域内整体气温偏高，部分地区高温日数超过40 d。此外，夏季整个流域的蒸散量距平是1960年以来的历史第二高值（仅次于2013年高温伏旱），进一步加剧了长江流域的水分亏缺程度。从环流特征来看，夏季西太平洋副热带高压异常偏强西伸、极涡面积偏小及强度偏弱、南亚高压偏强东移，共同导致长江流域的水汽输送条件偏弱、下沉气流盛行，使得整体条件不利于降水发生。而前期拉尼娜事件的持续、印度洋偶极子负位相的出现以及春季青藏高原西北部积雪负异常的持续，可能是导致今年夏季环流异常的主要外强迫因子。

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。

The Fire Weather Index System has been in use across Canada for the past 30 years in the daily operations of fire management agencies. As part of this system, the Drought Code (DC) was developed to act as a daily index of water stored in the soil. A major obstacle to the completion of climate risk analyses on the DC is that lengthy series of daily temperature and precipitation are not available for large portions of the circumboreal forest. Here the authors present a methodological modification to the daily DC to allow its approximation using monthly data. This new Monthly Drought Code (MDC) still retains its ability to capture moisture trends in deep organic layers. On the basis of high-resolution temperature and precipitation data, an analysis of summer moisture availability across Canada over 1901–2002 is presented. The driest periods on record were from the 1920s to the early 1960s, with the driest years being 1955, 1958, and 1961. The wettest period was from the mid-1960s to the 1980s. For the century-long period, drying was statistically significant in northern Canada. Locations south of the Hudson Bay, in the eastern Maritimes, and in western Canada recorded a trend toward decreasing dryness. When analyzed over 1951–2002, trends could hardly be distinguished from the (multi) decadal variability. Annual values of a spatial average of all July MDC grid cells showed an excellent fit against fire statistics: 63% of the variance in the Canada-wide annual area burned from 1959 to 1999 was explained by summer moisture availability.

California has experienced devastating autumn wildfires in recent years. These autumn wildfires have coincided with extreme fire weather conditions during periods of strong offshore winds coincident with unusually dry vegetation enabled by anomalously warm conditions and late onset of autumn precipitation. In this study, we quantify observed changes in the occurrence and magnitude of meteorological factors that enable extreme autumn wildfires in California, and use climate model simulations to ascertain whether these changes are attributable to human-caused climate change. We show that state-wide increases in autumn temperature (∼1 °C) and decreases in autumn precipitation (∼30%) over the past four decades have contributed to increases in aggregate fire weather indices (+20%). As a result, the observed frequency of autumn days with extreme (95th percentile) fire weather—which we show are preferentially associated with extreme autumn wildfires—has more than doubled in California since the early 1980s. We further find an increase in the climate model-estimated probability of these extreme autumn conditions since ∼1950, including a long-term trend toward increased same-season co-occurrence of extreme fire weather conditions in northern and southern California. Our climate model analyses suggest that continued climate change will further amplify the number of days with extreme fire weather by the end of this century, though a pathway consistent with the UN Paris commitments would substantially curb that increase. Given the acute societal impacts of extreme autumn wildfires in recent years, our findings have critical relevance for ongoing efforts to manage wildfire risks in California and other regions.

. While the use and data assimilation (DA) of operational Moderate\nResolution Imaging Spectroradiometer (MODIS) aerosol data is commonplace,\nMODIS is scheduled to sunset in the next year. For data continuity, focus\nhas turned to the development of next-generation aerosol products and\nsensors such as those associated with the Visible Infrared Imaging\nRadiometer Suite (VIIRS) on Suomi NPOESS Preparation Project (S-NPP) and\nNOAA-20. Like MODIS algorithms, products from these sensors require their\nown set of extensive error characterization and correction exercises. This\nis particularly true in the context of monitoring significant aerosol events that tax an algorithm's ability to separate cloud from aerosol and account for multiple scattering related errors exacerbated by uncertainties in aerosol optical properties. To investigate the performance of polar-orbiting satellite algorithms to monitor and characterize significant events, a level 3 (L3) product has been developed using a consistent aggregation methodology for 4 years of observations (2016–2019) that is referred to as the SSEC/NRL L3 product. Included in this product are the AErosol RObotic NETwork (AERONET), MODIS Dark Target, Deep Blue, and Multi-Angle\nImplementation of Atmospheric Correction (MAIAC) algorithms. These MODIS\n“baseline algorithms” are compared to NASA's recently released NASA Deep\nBlue algorithm for use with VIIRS. Using this new dataset, the relative\nperformance of the algorithms for both land and ocean were investigated with a focus on the relative skill of detecting severe events and accuracy of the retrievals using AERONET. Maps of higher-percentile aerosol optical\ndepth (AOD) regions of the world by product identified those with the highest measured AODs and determined what is high by local standards. While patterns in AOD match across products and median to moderate AOD values match well, there are regionally correlated biases between products based on sampling, algorithm differences, and AOD range – in particular for higher AOD events. Most notable are differences in boreal biomass burning and Saharan dust. Significant percentile biases must be accounted for when data are used in trend studies, data assimilation, or inverse modeling. These biases vary by aerosol regime and are likely due to retrieval assumptions in lower boundary conditions and aerosol optical models.\n

The monthly global 2° × 2° Extended Reconstructed Sea Surface Temperature (ERSST) has been revised and updated from version 4 to version 5. This update incorporates a new release of ICOADS release 3.0 (R3.0), a decade of near-surface data from Argo floats, and a new estimate of centennial sea ice from HadISST2. A number of choices in aspects of quality control, bias adjustment, and interpolation have been substantively revised. The resulting ERSST estimates have more realistic spatiotemporal variations, better representation of high-latitude SSTs, and ship SST biases are now calculated relative to more accurate buoy measurements, while the global long-term trend remains about the same. Progressive experiments have been undertaken to highlight the effects of each change in data source and analysis technique upon the final product. The reconstructed SST is systematically decreased by 0.077°C, as the reference data source is switched from ship SST in ERSSTv4 to modern buoy SST in ERSSTv5. Furthermore, high-latitude SSTs are decreased by 0.1°–0.2°C by using sea ice concentration from HadISST2 over HadISST1. Changes arising from remaining innovations are mostly important at small space and time scales, primarily having an impact where and when input observations are sparse. Cross validations and verifications with independent modern observations show that the updates incorporated in ERSSTv5 have improved the representation of spatial variability over the global oceans, the magnitude of El Niño and La Niña events, and the decadal nature of SST changes over 1930s–40s when observation instruments changed rapidly. Both long- (1900–2015) and short-term (2000–15) SST trends in ERSSTv5 remain significant as in ERSSTv4.

Jolly, W. Matt; Freeborn, Patrick H. US Forest Serv, Rocky Mt Res Stn, Fire Sci Lab, Missoula, MT 59803 USA. Cochrane, Mark A.; Freeborn, Patrick H. S Dakota State Univ, GSCE, Brookings, SD 57007 USA. Holden, Zachary A. US Forest Serv Reg 1, Missoula, MT 59802 USA. Brown, Timothy J. Western Reg Climate Ctr, DRI, Reno, NV 89512 USA. Williamson, Grant J.; Bowman, David M. J. S. Univ Tasmania, Sch Biol Sci, Hobart, Tas 7001, Australia.

We estimate the impacts of drought, as defined by the U.S. Drought Monitor (USDM), on crop yields and farm income in the United States during the 2001–2013 time period. Our empirical strategy relies on panel data models with fixed effects that exploit spatial and temporal variability in drought conditions and agricultural outcomes at the county level. We find negative and statistically significant effects of drought on crop yields equal to reductions in the range of 0.1% to 1.2% for corn and soybean yields for each additional week of drought in dryland counties, and 0.1% to 0.5% in irrigated counties. Region‐specific results vary, with some regions experiencing no yield impacts from drought, while yield reductions as high as 8.0% are observed in dryland counties in the Midwest for every additional week of drought in the highest USDM severity category. Despite this impact on crop yields, we find that additional weeks of drought have little to no effect on measures of farm income. While precipitation and temperature explain most of the variability in crop yields, we find that the USDM captures additional negative impacts of drought on yields.

A series of large wildfires began over the terrain north of San Francisco, California, during the evening of 8 October 2017 and spread across nearly 250,000 acres, including areas near the towns of Santa Rosa and Napa. These “Wine Country” wildfires were the most destructive in California history, with 44 deaths; the loss of 9,000 buildings; damage to approximately 21,000 structures; $10 billion of insured losses; and substantially greater total economic loss.

. Framed within the Copernicus Climate Change Service (C3S) of the European Commission,\nthe European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies.\nThis is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5\nmost of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models.\nA main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution\nis hourly as in ERA5. Evaluation against independent in situ observations\nand global model or satellite-based reference datasets shows the added value\nof ERA5-Land in the description of the hydrological cycle, in particular\nwith enhanced soil moisture and lake description, and an overall better agreement of\nriver discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude.\nThe description of the\nenergy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as\nreference MODIS data, mainly due to the contribution of\ncoastal points where spatial resolution is important.\nSince January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a\n2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021.\nThe high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies,\nto initialize NWP and climate models,\nand to support diverse applications dealing with water resource, land, and environmental management. The full ERA5-Land hourly (Muñoz-Sabater, 2019a) and monthly (Muñoz-Sabater, 2019b) averaged datasets presented in this paper are available through the C3S Climate Data Store at https://doi.org/10.24381/cds.e2161bac and https://doi.org/10.24381/cds.68d2bb30, respectively.\n

Wildfires are important ecosystem processes that have a significant impact on terrestrial vegetation, environment, and climate. This study investigates how future wildfire risk and activities could change under 1.5 °C and 2.0 °C warming scenarios relative to pre-industrial levels using a modified McArthur Forest Fire Danger Index (FFDIn) and the CLM4.5-BGC land surface model. Sixteen Earth System Models (ESMs) from CMIP5 and CMIP6 were employed to supply the variables of climate change under low, middle, and high greenhouse emission scenarios in the 1.5 °C and 2.0 °C scenarios. The ensemble means from the FFDIn and results from the CLM4.5-BGC with multiple forcings show that the dry areas in the southwestern US, Brazilian Highlands, and Arabian islands are projected to face higher wildfire risk with larger burned areas and more carbon emissions under a warmer climate. The Congo Basin and part of the Amazon could have a lower wildfire risk with smaller burned areas and less carbon emissions. The absolute changes in the projected FFDIn are small, although large increases are observed in boreal areas, particularly in the winter and spring. Burned area and carbon emissions are projected to increase in general in the boreal area but decrease in northeastern Asia. Compared to the 1.5 °C scenario, the wildfire risk and burned area levels are projected to increase under the 2.0 °C scenario except in the western Amazon. However, fire carbon emissions are projected to decrease more in tropical areas under the 2.0 °C scenario. The different change directions in eastern North America and eastern China produced by the FFDIn and CLM4.5-BGC suggest the potential effect of non-meteorological elements on fire activities.

This study presents a spatial and temporal climatology of major wildfire events, defined as >100 acres burned (>40.47 ha, where 1 ha = 2.47 acre), in the northeast United States from 1999 to 2009 and the meteorological conditions associated with these events. The northeast United States is divided into two regions: region 1 is centered over the higher terrain of the northeast United States and region 2 is primarily over the coastal plain. About 59% of all wildfire events in these two regions occur in April and May, with ~76% in region 1 and ~53% in region 2. There is large interannual variability in wildfire frequency, with some years having 4–5 times more fire events than other years. The synoptic flow patterns associated with northeast United States wildfires are classified using the North American Regional Reanalysis. The most common synoptic pattern for region 1 is a surface high pressure system centered over the northern Appalachians, which occurred in approximately 46% of all events. For region 2, the prehigh anticyclone type extending from southeast Canada and the Great Lakes to the northeast United States is the most common pattern, occurring in about 46% of all events. A trajectory analysis highlights the influence of large-scale subsidence and decreasing relative humidity during the events, with the prehigh pattern showing the strongest subsidence and downslope drying in the lee of the Appalachians.

State climatologists and other expert drought observers have speculated about the value of monitoring Twitter for #drought and related hashtags. This study statistically examines the relationships between the rate of tweeting using #drought and related hashtags, within states, accounting for drought status and news coverage of drought. We collected and geolocated tweets, 2017–18, and used regression analysis and a diversity statistic to explain expected and identify unexpected volumes of tweets. This provides a quantifiable means to detect state-weeks with a volume of tweets that exceeds the upper limit of the prediction interval. To filter out instances where a high volume of tweets is related to the activities of one person or very few people, a diversity statistic was used to eliminate anomalous state-weeks where the diversity statistic did not exceed the 75th percentile of the range for that state’s diversity statistic. Anomalous state-weeks in a few cases preceded the onset of drought but more often coincided with or lagged increases in drought. Tweets are both a means of sharing original experience and a means of discussing news and other recent events, and anomalous weeks occurred throughout the course of a drought, not just at the beginning. A sum-to-zero contrast coefficient for each state revealed a difference in the propensity of different states to tweet about drought, apparently reflecting recent and long-term experience in those states, and suggesting locales that would be most predisposed to drought policy innovation.

Recent fire seasons have fueled intense speculation regarding the effect of anthropogenic climate change on wildfire in western North America and especially in California. During 1972–2018, California experienced a fivefold increase in annual burned area, mainly due to more than an eightfold increase in summer forest‐fire extent. Increased summer forest‐fire area very likely occurred due to increased atmospheric aridity caused by warming. Since the early 1970s, warm‐season days warmed by approximately 1.4 °C as part of a centennial warming trend, significantly increasing the atmospheric vapor pressure deficit (VPD). These trends are consistent with anthropogenic trends simulated by climate models. The response of summer forest‐fire area to VPD is exponential, meaning that warming has grown increasingly impactful. Robust interannual relationships between VPD and summer forest‐fire area strongly suggest that nearly all of the increase in summer forest‐fire area during 1972–2018 was driven by increased VPD. Climate change effects on summer wildfire were less evident in nonforested lands. In fall, wind events and delayed onset of winter precipitation are the dominant promoters of wildfire. While these variables did not change much over the past century, background warming and consequent fuel drying is increasingly enhancing the potential for large fall wildfires. Among the many processes important to California's diverse fire regimes, warming‐driven fuel drying is the clearest link between anthropogenic climate change and increased California wildfire activity to date.