Lagged influence of ENSO regimes on droughts over the Poyang Lake basin, China

厄尔尼诺南方涛动 气候学 遥相关 多元ENSO指数 环境科学 降水 可预测性 蒸散量 构造盆地 中国 南方涛动 地理 地质学 气象学 考古 生态学 古生物学 物理 量子力学 生物
作者
Zikang Xing,Zhongbo Yu,Jianhui Wei,Xuejun Zhang,Miaomiao Ma,Peng Yi,Qin Ju,Jiayi Wang,Patrick Laux,Harald Kunstmann
出处
期刊:Atmospheric Research [Elsevier]
卷期号:275: 106218-106218 被引量:4
标识
DOI:10.1016/j.atmosres.2022.106218
摘要

The regional drought predictability is strongly influenced by teleconnections factors. Among them, El Niño-Southern Oscillation (ENSO) is important to drought occurrence. However, it is still not fully understood how the probability of regional droughts corresponding to different ENSO regimes varies with time lags. In this study, the event-based coincidence analysis (ECA) is employed to statistically derive the relationships regarding time information and magnitude between conventional ENSO (c-ENSO)/ENSO Modoki (ENSO_M) episodes and drought events. The standardized precipitation evapotranspiration index (SPEI) based on station-records is used to determine meteorological droughts. Our study focuses on the largest freshwater lake of China, Poyang Lake basin (PLB), as severe droughts have occurred there frequently. Results indicate that the summer-autumn continuous drought dominates the severe droughts, with a frequent onset in August/September, and a cessation in October/November. The likelihood of drought occurrence is highest during cool phases of c-ENSO (CEN), followed by warm ENSO_M (WEM), warm c-ENSO (WEN), and cool ENSO_M (CEM). The influence of WEN/WEM on droughts is statistically significant mostly for a time lag of 10 months, whereas CEN/CEM shows no lagged influence. Furthermore, the variability of the 850 hPa wind anomalies during different ENSO episodes at various time lags is corresponding to the lagged influence of ENSO on droughts. The development and persistence of droughts is resulting from strengthened northeasterly wind anomalies over the PLB. Our findings demonstrate that the ENSO anomalies are early warning signals of droughts and hence can be beneficial for increasing the drought predictivity

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