Detecting and quantifying the global teleconnections with flash droughts (FDs) and understanding their causal relationships is crucial to improve their predictability. This study employs causal effect networks (CENs) to explore the global predictability sources of subseasonal soil moisture FDs in three regions of the United States (US): upper Mississippi, South Atlantic Gulf (SAG), and upper and lower Colorado river basins. We analyzed the causal relationships of FD events with global 2-m air temperature, sea surface temperature, water deficit (precipitation minus evaporation), and geopotential height at 500 hPa at the weekly timescale over the warm season (April to September) from 1982 to 2018. CENs revealed that the Indian Ocean Dipole, Pacific North Atlantic patterns, Bermuda high-pressure system, and teleconnection patterns via Rossby wave train and jet streams strongly influence FDs in these regions. Moreover, a strong link from South America suggests that atmospheric circulation forcings could affect the SAG through the low-level atmospheric flow, reducing inland moisture transport, and leading to a precipitation deficit. Machine learning utilizing the identified causal regions and factors can well predict major FD events up to 4 weeks in advance, providing useful insights for improved subseasonal forecasting and early warnings.

中文翻译：

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因果发现分析揭示了区域性突发干旱的全球可预测性来源


检测和量化具有突发干旱 （FD） 的全球遥相关并了解它们的因果关系对于提高其可预测性至关重要。本研究采用因果效应网络 （CEN） 来探索美国 （US） 三个地区的亚季节土壤水分 FD 的全球可预测性来源：密西西比河上游、南大西洋湾 （SAG） 以及科罗拉多河流域上游和下游。我们分析了 FD 事件与 1982 年至 2018 年暖季（4 月至 9 月）每周时间尺度上全球 2 m 气温、海面温度、缺水（降水减去蒸发）和 500 hPa 位势高度的因果关系。CEN 显示，印度洋偶极子、太平洋北大西洋模式、百慕大高压系统以及通过罗斯比波列和急流的遥相关模式强烈影响了这些地区的 FD。此外，来自南美洲的强联系表明，大气环流强迫可以通过低层大气流影响 SAG，减少内陆水分输送，并导致降水不足。利用已确定的因果区域和因素的机器学习可以很好地提前 4 周预测重大 FD 事件，为改进次季节性预测和早期预警提供有用的见解。