An Earth-System-Oriented View of the S2S Predictability of North American Weather Regimes

Abstract

It is largely understood that subseasonal-to-seasonal (S2S) predictability arises from the atmospheric initial state during early lead times, the land during intermediate lead times, and the ocean during later lead times. We examine whether this hypothesis holds for the S2S prediction of weather regimes by training a set of XGBoost models to predict weekly weather regimes over North America at 1-to-8-week lead times. Each model used a different predictor from one of the three considered Earth system components (atmosphere, ocean, or land) sourced from reanalyses. Three additional models were trained using land-, ocean-, or atmosphere-only predictors to capture process interactions and leverage multiple signals within the respective Earth system component. We found that each Earth system component performed more skillfully at different forecast horizons, with sensitivity to seasonality and observed (i.e., ground truth) weather regime. S2S predictability from the atmosphere was higher during winter, from the ocean during summer, and from land during spring and summer. Ocean heat content was the best predictor for most seasons and weather regimes beyond week 2, highlighting the importance of sub-surface ocean conditions for S2S predictability. Soil temperature and water content were also important predictors. Climate patterns were associated with changes in the likelihood of occurrence for specific weather regimes, including the El Ni\~no-Southern Oscillation, Madden Julian Oscillation, North Pacific Gyre, and Indian Ocean dipole. This study quantifies predictability from some previously identified processes on the large-scale atmospheric circulation and gives insight into new sources for future study.