Subseasonal Representation and Predictability of North American Weather Regimes
We use unsupervised learning (k-means clustering), ERA5 reanalysis, and the CESM2 subseasonal initialized prediction system (with 11 ensemble members) to assess the sources and limits of predictability of North American weather regimes.
Weather regimes help describe large amplitude flow patterns within lower temporal frequencies, which extend beyond the lifetime of individual weather disturbances. Weather regimes also have an imprint on weather at the surface, with certain regimes providing favorable large-scale environmental conditions for notable temperature and precipitation anomalies, along with impacts to storm tracks, moisture flux, and atmospheric rivers.
North American weather regimes are large-scale atmospheric patterns that can persist for several days. Their skillful subseasonal prediction can provide valuable lead time to prepare for temperature and precipitation anomalies that can stress energy and water resources. The purpose of this study was to assess the climatological representation and subseasonal predictability of North American weather regimes. We found that the Pacific trough and West Coast high regimes exhibited higher predictability and that skillful representation of conditions across the tropics and extratropics can increase predictability during later lead times.