A Priori Identification of Skillful Extratropical Subseasonal Forecasts
Tuesday, Mar 03, 2020, 2:00 pm
DSRC Room GC402
The current generation of extratropical 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 synoptic 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. Unfortunately, spread-skill type relationships typically provide relatively limited guidance beyond forecast Week 3. 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 subseasonal surface and mid-tropospheric forecast skill. Using the LIM’s predicted signal-to-noise ratio, we identify the small subset (10-30%) of Weeks 3-6 forecasts - of the LIM and two operational models from NCEP and ECMWF - with relatively higher skill versus the much larger remainder of forecasts whose skill cannot be distinguished from random chance.
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