Mode-Decomposition Diagnosis for the Dynamical Processes of Sudden Stratospheric Warming Events

Abstract:

Sudden stratospheric warmings (SSWs) are extreme wintertime circulation events of the Arctic stratosphere that are accompanied by a disruption of the polar vortex, strong westerly winds around the winter pole. The disruption can be classified into split and displaced events based on the vortex geometry. SSWs can have long-lasting impacts of several weeks to months on surface weather. However, it is difficult to forecast SSWs at lead times longer than 1-2 weeks. Therefore, understanding the dynamical development of the vortex disruption will be crucial to improve the predictability of SSWs, and ultimately, the weather at the Earth’s surface. To this end, we combine dynamical analysis and machine learning techniques to better identify, classify, and predict SSWs, with the goal of successful probabilistic predictions at lead times beyond 1-2 weeks. We employ a mode-decomposition diagnosis for the principal components (PCs) of the potential vorticity (PV) equation at 850 K. The time tendency of the PCs is separated into contributions from linear and nonlinear advection terms, which consist of different empirical orthogonal functions (EOFs) at low and high frequency power. The results show a clear signal for the development of SSWs around 20 days before the onset of the events, with a dominant contribution from the linear interaction between low modes. Further analyses indicate that linear advection is the dominant contribution to the PC tendency for displacement events, while the nonlinear advection is as important as linear terms for split events. The differences in the dynamical processes for the development of split and displacement SSWs could be used to classify the type of event around 1-2 weeks before the events. The mode contributions in linear and nonlinear terms for split or displacement SSWs versus non-SSWs can be used as input to different classification algorithms (e.g., Support Vector Machine) to identify and classify SSWs and to distinguish them from non-SSWs. By incorporating the knowledge of the dynamical processes that lead to the vortex disruption into machine learning algorithms, processes related to SSWs could be identified around 20 days ahead of the events, which is beyond the current predictability limit for SSWs and which therefore promises to improve the prediction of surface weather.