Skillful long-lead prediction of summertime heavy rainfall in the US Midwest from sea surface salinity

• Published in: Geophysical Research Letters
• Main Authors: Li, Laifang, Schmitt, Raymond W., Ummenhofer, Caroline C.
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: American Geophysical Union 2022
• Subjects: Sea surface salinity; Midwest precipitation; Heavy rainfall; Long-lead prediction; Pacific; North Atlantic
• Online Access: https://hdl.handle.net/1912/29462

Author Posting. © American Geophysical Union, 2022. This article is posted here by permission of American Geophysical Union for personal use, not for redistribution. The definitive version was published in Geophysical Research Letters 49(13), (2022): e2022GL098554, https://doi.org/10.1029/2022GL098554. 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. This study is supported by the NSF PREEVENTS program under ICER-1663138 (LL) and ICER-1663704 (RWS and CCU). 2023-01-07