Predictability of the Tropospheric NAM and Sudden Stratospheric Warmings in the NMME Phase-2 Models

The Northern Annular Mode (NAM) dominates variability of the Northern Hemisphere (NH) wintertime extratropical circulation in the troposphere and stratosphere. Changes in the tropospheric NAM (i.e., changes in the position and strength of the polar jet stream) directly alter NH mid-latitude temperature and precipitation patterns and potentially increase chances for extreme winter weather in major population centers. These features make NAM predictability a significant priority for subseasonal-to-seasonal (S2S) forecasts during the winter months.

This study examines the predictability of the wintertime tropospheric NAM in the hindcast simulations of the North American Multi-Model Ensemble Phase-2 (NMME-2) model suite, specifically through examining how the models capture the evolution of sudden stratospheric warming (SSW) events. SSW events are well-known to precede large changes in the tropospheric NAM by 2-6 weeks, thereby offering extended predictability for mid-latitude winter weather. Findings indicate that the NMME-2 models have an overall mixed performance in capturing the spatiotemporal characteristics of the near-surface NAM and its teleconnections. Marked biases are apparent in the persistence of positive vs negative NAM regimes, the strength of the Atlantic jet stream, and temperature patterns across Eurasia. We then investigate the models’ ability to simulated the lifecycle of SSW events, including pre- and post-SSW circulation patterns and sensible weather, for (1) NMME-identified SSW events and (2) actual SSW events (i.e., those identified from reanalysis/observations). Model biases are quantified to show inherent issues with some stratosphere-troposphere coupling dynamics in the NMME-2 models (e.g., downward propagation, inconsistent precursor geopotential height fields). We also present the forecast skill score for actual SSW events from the models and briefly detail why certain models may have missed certain events over the last 35 years. Together, the results offer potential pathways forward for predicting SSW events and hopefully improving subseasonal temperature and precipitation forecasts using dynamical models.