Abstract ID: 147
The Impact of Rossby Wave Breaking on the Subseasonal Forecast of the February 2021 Great Plains Cold Air Outbreak
Lead Author: Oliver T. Millin
School of Meteorology, University of Oklahoma, United States of America
Keywords: extreme events, Rossby wave breaking, blocking, subseasonal-to-seasonal prediction
Abstract: The February 2021 Great Plains cold air outbreak (CAO) was a high-impact event of historical significance in the South-Central Plains of the United States (US). This extreme weather event led to vast power outages throughout the region, huge economic losses in agriculture and other sectors, significant traffic incidents, and unfortunately loss of life. It is therefore of critical importance to understand the subseasonal forecast capability of predicting such an event, to provide potential for enhanced warning and impact mitigation. In this presentation we will outline the evolution of the February 2021 CAO before analyzing the subseasonal forecast of the event, with particular emphasis on Rossby wave breaking. We first use European Centre for Medium-Range Weather Forecasts (ECMWF) fifth reanalysis (ERA5) to highlight two Rossby wave breaks preceding the event – a anticyclonic wave break in the East Siberian Sea between 2-4 February and a cyclonic wave break in the Labrador Sea between 9-11 February. These wave events are associated with the development of blocking anticyclones. We also use ECMWF and National Center for Environmental Prediction (NCEP) subseasonal-to-seasonal (S2S) forecast models to analyze the impact of these wave breaking features on the forecast of the February 2021 CAO, at a lead time of 2-3 weeks. Ensemble members that successfully simulate these wave breaks produce more negative temperature anomalies across the Great Plains, linked to the correct positioning of high latitude blocking anticyclones. Ensemble members which fail to resolve these wave breaks instead miss the event or even forecast warmer-than-normal conditions for the Central US in mid-February 2021. Therefore, improving the dynamical representation of these features, and blocking regimes in general, in operational S2S prediction systems could yield better long-lead skillful forecasts of S2S extreme events.
Co-authors:
Jason C. Furtado (School of Meteorology, University of Oklahoma)
