A hybrid statistical-dynamical approach for seasonal prediction of the boreal winter stratosphere

The winter seasonal variability of the Northern Hemisphere Stratospheric Polar Vortex
(SPV) is characterized by extreme events known as Sudden Stratospheric Warmings
(SSWs). SSWs feature a rapid increase of the temperature of the stratospheric polar
cap and a reversal of the zonal winds, exerting a downward impact on the troposphere
that makes their prediction societally relevant. Yet, despite their importance, current
seasonal prediction systems (SPSs) show only limited skill in forecasting SPV
variability, and SSWs occurrence. This study quantifies to what extent existing
statistical--dynamical approaches, that were designed for the prediction of tropospheric
modes of variability, can improve SPV seasonal predictions. More specifically, we
investigate the efficacy of a teleconnection-based subsampling for the North Atlantic
Oscillation (NAO), which selects a subset of ensemble members based on their NAO
representation. We do this by combining the Copernicus Climate Change Service
(C3S) dynamical forecasts with statistical predictive models for the NAO index. This
approach is motivated by the established link between NAO and stratospheric
variability. Results show that the teleconnection-based subsampling technique
increases the prediction skill of winter SPV variability and of the number of SSW days
in a season, with improvements that are robust across all C3S individual models.
These improvements come together with a better representation of extra-tropical lower-
stratosphere wave activity during December and January (DJ). However, the study
demonstrates that only some of the NAO statistical predictors correlate with SPV
variability, and others show contrasting behavior. This has implications for future
development of hybrid forecast systems targeting both NAO and SPV.