科学评估苹果关键生育期的干旱灾害风险，对降低苹果因旱损失具有重要意义。本文基于河北省气象资料、苹果种植资料、高程、河流密度和植被覆盖度等多源数据，通过分析干旱灾害发生的致灾因子危险性、孕灾环境敏感性和承灾体暴露性，系统评估了河北省苹果关键生育期的干旱灾害风险特征。结果表明：河北省苹果在萌芽—幼果期干旱发生概率最高，普遍超过60%，且以重旱和特旱为主。致灾因子危险性由高到低依次为：萌芽—幼果期>着色—成熟期>果实膨大期，其中西北部在各生育期均为危险性高值区。孕灾环境敏感性呈现由东南向西北递增的空间分布特征，承灾体暴露性在东部和南部局地相对较高。萌芽—幼果期、果实膨大期、着色—成熟期干旱灾害风险指数大于0.800的区域分别占比20.8%、8.7%和8.5%，且西北部在各生育期均为风险指数的高值区。苹果全生育期干旱灾害风险的高值区、中值区和低值区分别占总面积的14.2%、27.2%、58.6%，整体呈由东南向西北逐渐升高趋势。因此，需重点关注河北省西北部高风险区的干旱灾害防御工作，为苹果产业抗旱减灾提供科学依据。

在全球气候变化背景下，极端高温事件频发，且自21世纪以来其发生频率明显增加，对农业生产及人体健康造成深远影响。为探究地形复杂的伊犁河流域极端高温时空演变规律，本文利用1991—2020年该流域11个气象站逐日气温数据，计算夏季日数（SU25）、热夜日数（TR20）、暖昼日数（TX90p）、暖夜日数（TN90p）、日最高气温极高值（TXx）和日最低气温极高值（TNx）6个极端高温指数，并通过线性趋势分析、Mann-Kendall突变检验、经验正交函数分解（Empirical Orthogonal Function decomposition，EOF）以及克里金插值法（Kriging），系统分析伊犁河流域6个极端高温指数的时空变化特征。 结果表明：伊犁河流域大部分极端高温指数整体呈快速增长趋势，且在20世纪90年代至21世纪初发生显著突变。其中SU25、TX90p、TXx和TNx的增长速率突出，2015年后各指数处于加速增长阶段。空间分布上，流域大部分极端高温指数呈现明显的“西北高、东南低”格局：西北部为高温指数高值区，昭苏东北部、特克斯、巩留及尼勒克西南部形成稳定低值中心。由EOF分解得到TXx与TNx的空间分布存在两种典型模态，其演变特征与流域极端高温整体变化趋势具有高度一致性。

陇南输电线路沿线地质灾害频发易发，严重威胁线路安全稳定运行。为有效提升陇南电网防灾减灾能力，选取2018—2022年5—10月降水数据，利用降水分布特征和有效雨量致灾概率评估地质灾害致灾因子危险性，利用信息量-层次分析耦合模型评估孕灾环境暴露度，利用地质灾害脆弱性简化评估模型评估承灾体脆弱性，并以陇南±800 kV青豫线、祁韶线为例，利用地质灾害个例检验强降水诱发电网地质灾害风险预警效果。结果表明，陇南短时强降水和暴雨局地性强，发生频次自西北向东南呈增加趋势，灾情点大多发生在较高和高危险性区域。地质灾害灾情点大多位于海拔较高、坡度较陡区域，易发坡向为北坡、南坡并以凸型坡为主，旱地、中度及以下植被覆盖度区域地质灾害易发，67.4%的灾情点位于较高暴露度以上区域。陇南±800 kV青豫线、祁韶线电线杆塔不处于极高暴露度与极高脆弱性区域。地质灾害气象风险模型能够捕捉到较密集的地质灾害事件，有效雨量致灾概率对降水诱发的地质灾害预警效果较好。

利用1961—2010年我国北方沙尘源区134个地面气象站沙尘暴、扬沙和浮尘发生频率逐月资料，结合定义的沙尘指数，系统地分析了中国北方7大主要沙尘源区沙尘强度的时空分布特征及变化趋势。结果表明：西部的塔克拉玛干沙漠沙尘强度最高，多年平均沙尘指数高达235，其次是中部的阿拉善高原和鄂尔多斯高原沙漠群（182），东北部的呼伦贝尔沙地沙尘强度最小，多年平均沙尘指数仅为23。总体来看，近50 a来中国沙尘源区的沙尘强度呈明显减小趋势，沙尘指数在1972、1987和2000年出现突变。其中，中部的阿拉善高原和鄂尔多斯高原沙漠群沙尘强度减小趋势最为显著（-6.3 a-1），其次是西部的塔克拉玛干沙漠（-5.9 a-1）。EOF分析结果表明，中国北方沙尘源区各地的沙尘强度整体变化一致，塔克拉玛干沙漠、阿拉善高原和鄂尔多斯高原沙漠群是沙尘强度变化中心；在此基础上，东西部地区的沙尘强度呈明显的反相变化；此外，中国北方沙尘源区的沙尘强度在1980年代后期发生显著变化，沙尘指数至今处于较低值。

This paper proposes a revised vertical diffusion package with a nonlocal turbulent mixing coefficient in the planetary boundary layer (PBL). Based on the study of Noh et al. and accumulated results of the behavior of the Hong and Pan algorithm, a revised vertical diffusion algorithm that is suitable for weather forecasting and climate prediction models is developed. The major ingredient of the revision is the inclusion of an explicit treatment of entrainment processes at the top of the PBL. The new diffusion package is called the Yonsei University PBL (YSU PBL). In a one-dimensional offline test framework, the revised scheme is found to improve several features compared with the Hong and Pan implementation. The YSU PBL increases boundary layer mixing in the thermally induced free convection regime and decreases it in the mechanically induced forced convection regime, which alleviates the well-known problems in the Medium-Range Forecast (MRF) PBL. Excessive mixing in the mixed layer in the presence of strong winds is resolved. Overly rapid growth of the PBL in the case of the Hong and Pan is also rectified. The scheme has been successfully implemented in the Weather Research and Forecast model producing a more realistic structure of the PBL and its development. In a case study of a frontal tornado outbreak, it is found that some systematic biases of the large-scale features such as an afternoon cold bias at 850 hPa in the MRF PBL are resolved. Consequently, the new scheme does a better job in reproducing the convective inhibition. Because the convective inhibition is accurately predicted, widespread light precipitation ahead of a front, in the case of the MRF PBL, is reduced. In the frontal region, the YSU PBL scheme improves some characteristics, such as a double line of intense convection. This is because the boundary layer from the YSU PBL scheme remains less diluted by entrainment leaving more fuel for severe convection when the front triggers it.

Extreme heat events (EHEs) are becoming prevalent across the globe and are a major factor in terms of temperature‐related mortality in the United States (US). In this study, we compare trends in extreme temperature events (ETEs) across the Contiguous US, from 3 reanalysis products, namely: European Centre for Medium‐Range Weather Forecasts Reanalysis Version 5 (ERA5), Modern‐Era Retrospective Analysis for Research and Applications Version 2 (MERRA2) and North American Regional Reanalysis (NARR). We focused on the trends (1980–2022) in absolute extreme heat and cold events (ECE) as well as seasonally relative extreme heat and cold events (REHE and RECE). ETEs are defined based on a duration‐intensity metric calculated from excess apparent temperature factors, based on the exceedance of apparent temperature beyond local percentile thresholds while incorporating an acclimatisation factor. Our results show that the reanalysis data sets generally produced consistent climatology of ETEs, though with some inconsistencies in their number and spatial distribution. ETE trends in the study region are spatially heterogeneous and were more consistent between MERRA2 and ERA5. Nonetheless, all data sets agree that the frequency of EHEs is significantly increasing in the western parts of the US, whereas REHEs are significantly increasing in the southern parts. The highest increase in the frequency of EHEs occurs in southern California and Nevada, while REHE trends are maximal in Florida. RECEs are significantly decreasing more in spatial scale and magnitude than ECEs, especially towards the coastal regions. The highest decrease in RECEs is in Florida peninsula, southern California and Nevada. The data sets show inconsistency in ECE trends. Trends in excess temperature factors further indicated that extreme cold conditions are decreasing faster compared to the increasing trends of extreme heat conditions. Our findings highlight the need for improving the monitoring of ETEs across the US and for policies that mitigate the impact of ETEs on biological systems.

Dust aerosols significantly affect the Earth’s climate, not only as a source of radiation, but also as ice nuclei, cloud condensation nuclei and thus affect CO2 exchange between the atmosphere and the ocean. However, there are large deviations in dust model simulations due to limited observations on a global scale. Based on ten initial Climate Models Intercomparison Project Phase Six (CMIP6) models, the multi-model ensemble (MME) approximately underestimates future changes in global dust mass loading (DML) by 7–21%, under four scenarios of shared socioeconomic pathways (SSPs). Therefore, this study primarily constrains the CMIP6 simulations under various emission scenarios by applying an equidistant cumulative distribution function (EDCDF) method combined with the Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) datasets based on observation assimilation. We find that the results (19.0–26.1 Tg) for 2000–2014 are closer to MERRA2 (20.0–24.8 Tg) than the initial simulations (4.4–37.5 Tg), with model deviation reduced by up to 75.6%. We emphasize that the DML during 2081–2100 is expected to increase significantly by 0.023 g m–2 in North Africa and the Atlantic region, while decreasing by 0.006 g m–2 in the Middle East and East Asia. In comparison with internal variability and scenario uncertainty, model uncertainty accounts for more than 70% of total uncertainty. When bias correction is applied, model uncertainty significantly decreases by 65% to 90%, resulting in a similar variance contribution to internal variability.

The role of vertical mesh spacing in the convergence of full-physics global atmospheric model solutions is examined for synoptic, mesoscale, and convective-scale horizontal resolutions. Using the MPAS-Atmosphere model, convergence is evaluated for three solution metrics: the horizontal kinetic energy spectrum, the Richardson number probability density function, and resolved flow features. All three metrics exhibit convergence in the free atmosphere for a 15-km horizontal mesh when the vertical grid spacing is less than or equal to 200 m. Nonconvergence is accompanied by noise, spurious structures, reduced levels of mesoscale kinetic energy, and reduced Richardson number peak frequencies. Coarser horizontal mesh solutions converge in a similar manner but contain much less noise than the 15-km solutions for coarse vertical resolution. For convective-scale resolution simulations with 3-km cell spacing on a variable-resolution mesh, solution convergence is almost attained with a vertical mesh spacing of 200 m. The boundary layer scheme is the dominant source of vertical filtering in the free atmosphere. Although the increased vertical mixing at coarser vertical mesh spacing depresses the kinetic energy spectra and Richardson number convergence, it does not produce sufficient dissipation to effectively halt scale collapse. These results confirm and extend the results from a number of previous studies, and further emphasize the sensitivity of the energetics to the vertical mixing formulations in the model.

Extreme temperature events are becoming more frequent due to global warming, and have critical effects on natural ecosystems, social and economic spheres, human production and life. Predicting changes in temperature extremes and trends under future climate scenarios helps to assess the impact of climate change accurately. Based on climate observations from 54 meteorological stations in the North China Plain and the projection data from seven general circulation models (GCMs) from the Coupled Model Intercomparison Project phase 6 (CMIP6), this paper researches nine representative extreme temperature indices under four typical climate scenarios. The aim is to reveal the temporal and spatial variations in extreme temperature indices in the North China Plain during the past (1971–2010) and the future (2061–2100). The results show that: using a support vector machine (SVM) to perform regression analysis on the multi-GCMs prediction results, the root mean square error (RMSE) and relative standard deviation (RSD) of the multi-model ensemble simulations obtained by the SVM method are lower than those of the arithmetic mean method and can better match the trend of the historical extreme temperature index; the extreme high temperature index is predicted to show a significant upward trend in the future, while the extreme low temperature index will decrease significantly; and there are significant spatial differences in the extreme temperature index in both historical and future periods, with the extreme temperature index under the high radiation forcing scenario (SSP585) showing the most considerable variation and the most significant spatial differences.

Accurate initial condition is prerequisite to an accurate prediction/forecast of near-surface meteorological elements and wind power. Although assimilating wind tower observations of wind farms in the initial condition has been proved feasible to improve the prediction, how and why assimilating wind tower observation improves initial condition are unclear. This study aims to improve the prediction by obtaining effective information from the initial conditions of assimilating wind tower observations. Results suggest that assimilating wind tower observations has local impacts on the initial condition, and the improvement of the initial condition is significantly related to the observed near-surface atmospheric stability. The forecast improvement by assimilating wind tower observations depends on two effective information: determining whether the initial condition is improved and the length of the improved initial condition can positively impact the forecast results. When the initial condition improves, forecast improvements can persist for about 12 h and are most distinct in the ultra-short term (0–4 h). Using the two effective information not only can distinctly improve the forecasts of near-surface wind, temperature, relative humidity, and wind power, but also can eliminate the stochastic forecasts. When the initial condition improves by assimilation, enhancing nudging intensity will have more positively impact on the prediction, and the root mean square error and mean absolute error in the ultra-short term can be reduced by 0.39 and 0.44 m s−1 for near-surface wind and 66.64 and 79.09 kW for wind power, respectively.

Understanding the changing patterns of extreme temperatures is important for taking measures to reduce their associated negative impacts. Based on daily temperature data derived from 2272 meteorological stations in China, the spatiotemporal variations in temperature extremes were examined with respect to covariates by means of the Mann–Kendall test and a spatiotemporal model during 1960–2018. The results indicated that the temporal changes in cold extremes showed decreasing trends and warm extremes experienced increasing trends across almost all of China, with mean change rates of −3.9 days, −1.8 days, 3.7 days and 2.3 days per decade for TN10p, TX10p, TN90p and TX90p, respectively. Nighttime warming/cooling was higher than daytime warming/cooling, which indicated that trends in minimum temperature extremes are more rapid than trends in maximum temperature extremes. In addition, the temporal effect on the temperature extremes varied throughout the year, with significant increasing trends in the temporal heterogeneity of warm extremes occurring during 1992–2018. The areas with strong spatial heterogeneity of cool nights mainly included northeastern and central China, and the spatial variation on cool days was more prominent in northern China. For warm nights, the areas showing high spatial heterogeneity were mainly located in the northwestern part of China, while areas for warm days were distributed in northern China. Our results provide meaningful information for a deeper understanding of the spatiotemporal variations in temperature extremes across mainland China.