Dynamical Precursors for Statistical Prediction of Stratospheric Sudden Warming Events

Abstract

This work explores dynamical arguments for statistical prediction of stratospheric sudden warming events (SSWs). Based on climate model output, it focuses on two predictors, upward wave activity in the lower stratosphere and meridional potential vorticity gradient in the upper stratosphere, and detects large values of these predictors. Then it quantifies how many SSWs are preceded by predictor events and, inversely, how many events are followed by SSWs. This allows to compute conditional probabilities of future SSW occurrence. It is found that upward wave activity leads to important increases in SSW probability within the following 3 weeks but is less important thereafter. A weak potential vorticity gradient is associated with increased SSW probability at short lags and, perhaps more importantly, decreased SSW probability at long lags. Finally, when both predictors are considered in combination, the information gain is large on the weekly and small but significant on the intraseasonal time scale.