Extreme Meteorological Drought Events over China(1951-2022):Migration Patterns,Diversity of Temperature Extremes,and Decadal Variations

Recently,extreme meteorological droughts have affected China,causing terrible socioeconomic impacts.Despite previous research on the spatiotemporal characteristics and mechanisms of drought,two crucial issues remain seldom explored.First,an event-oriented drought chronology with detailed spatiotemporal evolutions is urgently required.Second,the complex migration patterns and diversity of synchronous temperature extremes need to be quantitatively investigated.Accordingly,the main achievements of our investigation are as follows.We produced an event-oriented set of extreme meteorological droughts over China through the application of a newly developed 3D DBSCAN-based detection method(deposited on https://doi.org/10.25452/figshare.plus.25512334),which was verified with a historical atlas and monographs on a case-by-case basis.In addition,distinctive migration patterns(i.e.,stationary/propagation types)are identified and ranked,considering the differences in latitudinal zones and coastal/inland locations.We also analyze the diversity of synchronous temperature extremes(e.g.,hotness and coldness).Notably,an increasing trend in hot droughts occurred over China since the late 1990s,predominantly appearing to the south of 30°N and north of 40°N.All drought events and synchronous temperature extremes are ranked using a comprehensive magnitude index,with the 2022 summer-autumn Yangtze River hot drought being the hottest.Furthermore,Liang-Kleeman information flow-based causality analysis emphasizes key areas where the PDO and AMO influenced decadal variations in coverages of droughts and temperature extremes.We believe that the achievements in this study may offer new insights into sequential mechanism exploration and prediction-related issues.

3D DBSCAN detection and parameter sensitivity of the 2022 Yangtze river summertime heatwave and drought

极端气候事件的精准识别是机理分析的重要前提.本研究借助无监督机器学习中经典的DBSCAN密度聚类算法,发展了在三维(经度-纬度-时间)空间内进行目标事件识别和参数敏感性分析的研究方案.在2022年长江全域高温伏秋旱事件识别中的应用表明,本次天气尺度极端热浪和季节尺度重旱事件的产生发展,空间传播模式不同.天气尺度热浪信号自6月底从北太平洋向西南方向延伸,直至8月中旬覆盖长江全域;季节重旱信号于7月中旬从孟加拉湾陆面区域向东北向延伸,直至9月中旬覆盖长江全域.同时,本研究中亦进行了相关参数敏感性的详细分析,对算法应用,结果理解亦有帮助.

Global Seasonal-Scale Meteorological Droughts. Part Ⅰ: Detection, Metrics, and Inland/Coastal Types

Knowledge of spatiotemporal characteristics and process evolutions is the fundamental basis of understanding drought mechanisms,especially from a global perspective.For a comprehensive investigation,we implemented event detection,type grouping,and spatiotemporal metrics from 3-dimensional(3D,longitude-latitude-time)perspectives.The major procedures and achievements were as follows.First,we identified global-scale seasonal-scale meteorological drought events following the recently proposed 3D DBSCAN(Density-Based Spatial Clustering of Applications with Noise)-based workflow of event detection.The 3D DBSCAN clustering algorithm can directly obtain arbitrarily shaped point collections over a given 3D space,as drought events can spread over space and evolve over time.Subsequently,these detected drought events are further grouped into inland and coastal types,as the observations revealed that some droughts over coastal regions originate from,extend to,or are accompanied by long-term precipitation deficits over adjacent oceans.Third,typical spatiotemporal characteristics(e.g.,lifetime,genesis locations,migration/local developments,and process evolutions)were investigated with coastal/inland-type differences considered.The drought ratios originating from continents in all coastal-type droughts were~50%over Africa,Asia,and South America,indicating the nonnegligible extension from continents to oceans.Additionally,process evolution-based analysis revealed intensity variations in intensification or recession phases,and coastal types overall displayed larger intensity variations than inland types.Moreover,~92%of inland types and~70%of coastal types can be treated as having symmetric development.Notably,the grouping type and spatiotemporal metrics herein can provide adequate preliminary knowledge for global-scale drought mechanism exploration.

Global Seasonal-Scale Meteorological Droughts. Part Ⅱ: Temperature Anomaly-Based Classifications

Although compound drought and heatwave extremes have recently drawn much attention,whether droughts are always concurrent with heatwaves remains unknown.Moreover,how temperature abnormalities evolve spatiotemporally during drought development and how their associated categories are distributed globally are not fully understood.Part II investigated this fundamental issue from the perspectives of temperature abnormality-based drought classification and statistical characteristics of process evolution.The major procedures and achievements were as follows.First,the detected global-scale drought events in Part I were employed and assigned to Hot,Cold,Normal,and Hybrid categories utilizing a self-designed temperature abnormality-based classification algorithm;the associated global-scale occurrences of these 4 event categories were approximately 40%,10%,30%,and 20%,respectively,and in turn,they displayed statistically significant(P value<0.05)increasing,decreasing,decreasing,and increasing trends,respectively,during 1980 to 2020.The Hot and Normal types appeared mostly within 45°S to 45°N in warm seasons,with Cold types over mid-high latitudes in cold seasons.In relation to coastal/inland group differences,Hot and Normal types were the common major features of the 2 drought groups(i.e.,inland and coastal),while most Cold types were mostly associated with coastal droughts.Second,through self-designed intensity-based parameters reflecting extreme values and phase variation,we found that 60%of Hot types and 75%of Cold types experienced prominent intensification with~0.5 values of standardized temperature.Regarding approximately 80%of Hot types,hot extremes followed drought extremes.The achievements may provide robust event-based insights into the physical mechanisms behind global droughts and concurrent temperature anomalies.