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Abstract This study illustrates the considerable improvement in accuracy achievable for long‐lead forecasts (18 months) of the Ocean Niño Index (ONI) through the utilization of a long short‐term memory (LSTM) machine learning algorithm. The research assesses the predictive potential of eight predictors from both tropical and extratropical regions constructed based on sea surface temperature, outgoing longwave radiation, sea surface height and zonal and meridional wind anomalies. In comparison to linear regression model forecasts, the LSTM model outperforms them for both the tropical and extratropical predictor sets. Among all the predictors, the western North Pacific (WNP) index demonstrates the highest prediction skill in ONI forecasts, followed by the North Tropical Atlantic (NTA) index and then the sea surface height index. While other predictors help the LSTM model to forecast either the phase variation of the amplitude variation of the observed ONI, the extratropical WNP predictor enables the LSTM model to forecast both variations. This superiority can be attributed to the involvement of SST anomalies in the WNP region in both tropical and extratropical El Niño–Southern Oscillation (ENSO) dynamics, allowing for the utilization of predictive potential from both components of ENSO dynamics. The study also concludes that the extratropical ENSO dynamics provide a robust source of predictability for long‐lead ENSO forecasts, which can be effectively harnessed using the LSTM model.
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Skillful Long‐Lead Prediction of Summertime Heavy Rainfall in the US Midwest From Sea Surface Salinity
Abstract 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−1increase 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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- Award ID(s):
- 1663704
- PAR ID:
- 10370192
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Geophysical Research Letters
- Volume:
- 49
- Issue:
- 13
- ISSN:
- 0094-8276
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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