Subseasonal Prediction of Impactful California Winter Weather in a Hybrid Dynamical‐Statistical Framework

Abstract

Atmospheric rivers (ARs) and Santa Ana winds (SAWs) are impactful weather events for California communities. Emergency planning efforts and resource management would benefit from extending lead times of skillful prediction for these and other types of extreme weather patterns. Here we describe a methodology for subseasonal prediction of impactful winter weather in California, including ARs, SAWs and heat extremes. The hybrid approach combines dynamical model and historical information to forecast probabilities of impactful weather outcomes at weeks 1–4 lead. This methodology uses dynamical model information considered most reliable, that is, planetary/synoptic‐scale atmospheric circulation, filters for dynamical model error/uncertainty at longer lead times and increases the sample of likely outcomes by utilizing the full historical record instead of a more limited suite of dynamical forecast model ensemble members. We demonstrate skill above climatology at subseasonal timescales, highlighting potential for use in water, health, land, and fire management decision support.

Disentangling the roles of internal atmospheric variability and ENSO on California precipitation to explain recent seasonal forecast errors

“…The new knowledge of how ENSO modulates high-impact weather regimes could add value to management and planning efforts given that weather regimes demonstrate some predictability at subseasonal timescales (Guirguis et al, 2023). This research aims to inform improved predictability on subseasonal-to-seasonal (S2S) timescales to prepare for upcoming events and communicate locally-relevant nuance about how certain storm events may differ from normal.…”

Reinterpreting ENSO's Role in Modulating Impactful Precipitation Events in California

“…are known to be important for modulating precipitation in the West (e.g., Gershunov & Barnett, 1998;Liu et al, 2018;Paek et al, 2017;Wang et al, 2024). Understanding how ENSO and other climate modes influence high-impact weather regimes could enhance management efforts, as weather regimes demonstrate some predictability at subseasonal timescales (Guirguis et al, 2023c). This research aims to inform improved predictability to prepare for upcoming events and communicate relevant information about how certain storms may differ from normal.…”