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影响长江中下游夏季复合高温干旱的关键环流型及其变化归因

Attribution of Changes in Key Circulation Patterns Driving Summer Compound Hot–Dry Events over the Middle and Lower Reaches of the Yangtze River

  • 摘要: 在全球增暖背景下,长江中下游夏季复合高温干旱(compound hot-dry events)愈发严重,但其背后大气环流和人类活动的贡献仍缺系统量化。本文基于CN05.1、ERA5再分析及CMIP6模式资料,利用Copula构建的复合高温干旱指数刻画其严重程度,通过环流相似法来量化环流和背景场增暖对复合高温干旱的影响;进一步使用自组织映射(Self-Organizing Map,SOM)获取与复合高温干旱相关的关键环流型,并探究人类活动对其频率的影响。结果表明:(1)1961年以来长江中下游夏季复合高温干旱的严重程度发生增强,且在20世纪80年代以来,随着气候变暖加剧,其严重程度呈现显著增强趋势;(2)“东亚双阻型—偶极型”环流结构,即中高纬阻塞脊与西太平洋副高西伸北抬的协同增强导致该区域下沉增温与水汽亏损,有利于复合事件发生;(3)基于相似环流法揭示此类典型环流的动力作用造成了温度异常增高与降水异常减少,而背景增暖的热力作用加剧了温度的异常。(4)SOM 聚类结果为4×4个环流型(A1-D4),其中D2–D4为典型的干热环流型,对 CHDE 影响最大且整体频率呈现上升趋势,自1980年以来,人类活动显著提高了此类干热型环流的发生概率。

     

    Abstract: Under the background of global warming, compound hot-dry events (CHDEs) in the middle and lower reaches of the Yangtze River have become increasingly severe. However, the respective contributions of atmospheric circulation and human activities to these events have not been systematically quantified. Based on CN05.1 observations, ERA5 reanalysis data, and CMIP6 model outputs, this study uses a Copula-based compound hot-dry index to describe the severity of CHDEs. The impacts of atmospheric circulation and background warming are quantified using a circulation-analogue approach. In addition, the Self-Organizing Map (SOM) method is applied to identify key circulation patterns associated with CHDEs and to explore the influence of human activities on their frequency. The main results are as follows: (1) Since 1961, the severity of summer CHDEs in the middle and lower reaches of the Yangtze River has increased, showing a significant upward trend since the 1980s as climate warming has intensified. (2) The “East Asia double-blocking-dipole” circulation pattern—characterized by the joint strengthening of the mid–high-latitude blocking ridge and the westward extension and northward shift of the Western Pacific Subtropical High (WPSH)—enhances subsidence, warming, and moisture deficit over the region, favoring the occurrence of CHDEs. (3) The circulation-analogue analysis reveals that such typical circulation patterns dynamically cause higher temperatures and reduced precipitation, while background warming thermodynamically amplifies the temperature anomalies. (4) The SOM clustering identifies 16 circulation patterns (A1–D4), among which D2–D4 represent typical hot-dry circulation types. These have the strongest influence on CHDEs and show an overall increasing frequency. Since the 1980s, human activities have significantly increased the occurrence probability of these hot-dry circulation types.

     

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