温带气旋
气候学
马登-朱利安振荡
构造盆地
联轴节(管道)
长江
中纬度
环境科学
热带气旋
大气科学
地质学
气象学
地理
对流
机械工程
古生物学
考古
中国
工程类
作者
Ke-Yue Zhang,Pang‐Chi Hsu
标识
DOI:10.1175/jcli-d-24-0362.1
摘要
Abstract The Yangtze River Basin (YRB), one of the most densely populated regions in China, is prone to compound cold–wet extreme (CCWE) events during winter. Previous case studies emphasized different tropical and extratropical precursory signals of such events. Using k -means clustering analysis and temperature/moisture diagnostics, this study systematically investigated the relative effects of various atmospheric modes and their combined impacts on historical YRB CCWE events. Two intraseasonal wave trains traversing southern Eurasia (EUS) and northern Eurasia (EUN) along polar and extratropical jets, and two opposing tropical patterns featuring wet and dry intraseasonal convective phases over the Maritime Continent (MCW and MCD, respectively), were identified as key regulators of both the occurrence and the severity of YRB CCWE events. The EUS and EUN wave trains induce freezing temperatures with comparable amplitude over the YRB by deepening the East Asian trough, thereby promoting near-surface cold advection and adiabatic cooling. In contrast, the moisture sources promoting the occurrence of CCWE events induced by the MCW and MCD patterns differ. Moisture channels associated with the MCW pattern originate from the Bay of Bengal, whereas the MCD pattern facilitates moisture transport from the tropical western Pacific toward the YRB. Compared with the former, the latter supports a longer and more intense preconditioning stage that accumulates abundant moisture, leading to strong moisture convergence during the period of CCWE event occurrence. Therefore, CCWE events induced by the MCD pattern tend to be more severe, regardless of their co-occurrence with either the EUS or the EUN wave train. The findings of this study have implications for closer monitoring and accurate prediction of these tropical and extratropical intraseasonal modes, and reduction of the risk associated with YRB CCWE events.
科研通智能强力驱动
Strongly Powered by AbleSci AI