Skillful Long‐Lead Prediction of Summertime Heavy Rainfall in the US Midwest From Sea Surface Salinity
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 unsatisfac...
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Veröffentlicht in: | Geophysical research letters 2022-07, Vol.49 (13), p.n/a |
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Zusammenfassung: | 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.
Plain Language Summary
Predicting heavy rainfall one season ahead is challenging for the US Midwest, a primary crop production area in the world. In this study, we show a skillful prediction of Midwest heavy rain using sea surface salinity anomalies (SSSAs) in the western tropical Pacific and subtropical North Atlantic. Compared to sea surface temperature anomalies which traditionally form the basis for seasonal precipitation forecasts, our newly identified SSSA‐based predictors improve the accuracy of heavy rainfall prediction by 92%. This superior skill of SSSA‐based prediction appears to result from a close relationship between salinity variations and the oceanic water cycle, as well as related atmospheric circulation changes and soil moisture feedback.
Key Points
Heavy rainfall explains the majority of the year‐to‐year variation of summer precipitation in the US Midwest
SSS in the tropical Pacific and subtropical North Atlantic equally contribute to skillful predictions of Midwest heavy rain a season ahead
Skillful prediction from SSS is realized through tropical‐extratropical teleconnections and local soil moisture feedback |
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ISSN: | 0094-8276 1944-8007 |
DOI: | 10.1029/2022GL098554 |