A Priori Identification of Skillful Extratropical Subseasonal Forecasts

Abstract

The current generation of subseasonal operational model forecasts has, on average, low skill for leads beyond 3 weeks. This is likely a fundamental property of the climate system, due to the relative high amplitude of unpredictable weather variability compared to potentially predictable, but generally weaker, climate signals. Thus, for subseasonal forecasts to be useful, their high versus low skill events should be identified at time of forecast. We show that a linear inverse model (LIM), an empirical‐dynamical model constructed from covariability statistics of wintertime (December–March) weekly averaged observational analyses, can be used to identify, a priori, the expected extratropical subseasonal surface and midtropospheric forecast skill. The LIM's predicted signal‐to‐noise ratio identifies the subset (10%–30%) of Weeks 3–6 forecasts—of the LIM and two operational models from the National Centers for Environmental Prediction and the European Centre for Medium‐Range Weather Forecasts—with relatively higher skill versus the much larger remainder of forecasts whose skill cannot be distinguished from random chance.

Windows of Opportunity for Skillful Forecasts Subseasonal to Seasonal and Beyond

“…What predictability remains can be well approximated by much simpler linear dynamics and a residual random "noise" (e.g., Hasselmann 1976;Penland and Sardeshmukh 1995). This approximation, empirically determined using the linear inverse model (LIM) technique (Penland and Sardeshmukh 1995), generates S2S forecasts about as skillful as those from operational dynamical models at both NCEP and ECMWF (e.g., Newman et al 2003;Albers and Newman 2019). The LIM technique also allows identification of high skill cases or forecasts of opportunity ahead of time (Albers and Newman 2019; see example in Fig.…”

Introduction to Special Collection: “Bridging Weather and Climate: Subseasonal‐to‐Seasonal (S2S) Prediction”

“…Several papers in this Special Collection explore these potentially more predicable periods and windows of opportunity for prediction. For example, Albers and Newman () identify a priori 10–30% of Week 3–6 forecasts for the extratropics constitute such forecasts of opportunity.…”

Attribution of NAO Predictive Skill Beyond 2 Weeks in Boreal Winter

“…Extreme stratospheric polar vortex states (i.e., weak and strong vortex events) are followed by anomalous near‐surface NAO conditions (Baldwin & Dunkerton, 2001) implying enhanced NAO prediction skill could occur as forecasts‐of‐opportunity contingent on initial stratospheric states (Albers & Newman, 2019). For instance, Sigmond et al (2013) demonstrated that the skill of NAO forecasts averaged over 15‐ to 60‐day periods is substantially enhanced when forecasts are initialized at the onset time of weak stratospheric polar vortex events.…”