Skillful Long‐Lead Prediction of Summertime Heavy Rainfall in the US Midwest From Sea Surface Salinity
Skillful Long‐Lead Prediction of Summertime Heavy Rainfall in the US Midwest From Sea Surface Salinity
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DOI：
10.1029/2022gl098554
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发表时间：
2022-07
影响因子：
5.2
通讯作者：
Laifang Li;R. Schmitt;C. Ummenhofer
中科院分区：
文献类型：
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作者：
Laifang Li;R. Schmitt;C. Ummenhofer
美国中西部是世界主要农作物产区之一，夏季暴雨及其引发的洪水是最有害的自然灾害之一。然而，目前基于季前海表温度异常（SSTA）的暴雨季节性预报仍然不能令人满意。在这里，我们提出的证据表明，海表盐度异常（SSSAs）在热带西太平洋和亚热带北大西洋夏季强降雨的一个季节提前熟练的预测。热带西太平洋SSSA的一个标准差变化与当地降水量每天增加1.8毫米有关，这激发了北太平洋外的遥相关型。通过海-气相互作用和中纬度海温异常的长记忆，诱发了一个有利于美国中西部暴雨的波列。结合土壤水分反馈，弥补了春季北大西洋盐度的不足，基于SSSA的统计预测模型将中西部强降雨预测提高了92%，补充了现有的基于SSTA的框架。
Summertime heavy rainfall and its resultant floods are among the most harmful natural hazards in the US Midwest, one of the world's primary crop production areas. However, seasonal forecasts of heavy rain, currently based on preseason sea surface temperature anomalies (SSTAs), remain unsatisfactory. Here, we present evidence that sea surface salinity anomalies (SSSAs) over the tropical western Pacific and subtropical North Atlantic are skillful predictors of summer time heavy rainfall one season ahead. A one standard deviation change in tropical western Pacific SSSA is associated with a 1.8 mm day−1 increase in local precipitation, which excites a teleconnection pattern to extratropical North Pacific. Via extratropical air‐sea interaction and long memory of midlatitude SSTA, a wave train favorable for US Midwest heavy rain is induced. Combined with soil moisture feedbacks bridging the springtime North Atlantic salinity, the SSSA‐based statistical prediction model improves Midwest heavy rainfall forecasts by 92%, complementing existing SSTA‐based frameworks.