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Subseasonal weather prediction can reduce economic disruption and loss of life, especially during “windows of opportunity” when noteworthy events in the Earth system are followed by characteristic weather patterns. Sudden stratospheric warmings (SSWs), breakdowns of the winter stratospheric polar vortex, are one such event. They often precede warm temperatures in Northern Canada and cold, stormy weather throughout Europe and the United States - including the most recent SSW on January 5th, 2021. Here we assess the drivers of surface weather in the weeks following the SSW through initial condition “scrambling” experiments using the real-time CESM2(WACCM6) Earth system prediction framework. We find that the SSW itself had a limited impact, and that stratospheric polar vortex stretching and wave reflection had no discernible contribution to the record cold in North America in February. Instead, the tropospheric circulation and bidirectional coupling between the troposphere and stratosphere were dominant contributors to variability.

The predictability of the middle atmosphere during major sudden stratospheric warmings (SSWs) is investigated based on subseasonal hindcasts in the Community Earth System Model, version 2 with the Whole Atmosphere Community Climate Model as its atmospheric component (CESM2[WACCM6]). The CESM2(WACCM6) hindcasts allow for the first comprehensive investigation into the predictability of the mesosphere and lower thermosphere (MLT) during SSWs. Analysis of 14 major SSWs demonstrates that CESM2(WACCM6) hindcasts initialized5–15 days prior to the SSW onset are able to predict the timing of the SSW, though they underestimate the strength of the SSW. Aspects of the MLT variability, such as the mesosphere cooling and enhanced semidiurnal tide, are found to be well predicted. The demonstrated ability to predict MLT variability during SSWs indicates the potential for improved multi‐day space weather forecasting. Improved space weather forecasting may be achieved by using whole atmosphere models that can predict the MLT variability that drives ionosphere‐thermosphere variability during SSWs.