TY - JOUR
T1 - Skillful Long-Lead Prediction of Summertime Heavy Rainfall in the US Midwest From Sea Surface Salinity
AU - Li, Laifang
AU - Schmitt, Raymond W.
AU - Ummenhofer, Caroline C.
N1 - Funding Information:
This study is supported by the NSF PREEVENTS program under ICER‐1663138 (LL) and ICER‐1663704 (RWS and CCU). Helpful comments from two anonymous reviewers on an earlier version of the manuscript are gratefully acknowledged.
Publisher Copyright:
© 2022. American Geophysical Union. All Rights Reserved.
PY - 2022/7/16
Y1 - 2022/7/16
N2 - 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.
AB - 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.
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U2 - 10.1029/2022GL098554
DO - 10.1029/2022GL098554
M3 - Article
AN - SCOPUS:85133926670
SN - 0094-8276
VL - 49
JO - Geophysical Research Letters
JF - Geophysical Research Letters
IS - 13
M1 - e2022GL098554
ER -