Quantifying the practical local predictability of the 2021 sudden stratospheric warming event using a nonlinear method

Sudden stratospheric warming (SSW) is identified as key sources of skill in winter subseasonal-to-seasonal (S2S) forecasts because of their surface impacts lasting up to 30–60 days through stratosphere troposphere coupling, despite their typical prediction being limited about 10 days. A better understanding of the predictability of the SSW itself, thus, is fundamental. Most of the previous studies investigate the predictability of SSW events using linear approaches, which are insufficient given the inherently chaotic and nonlinear nature of SSWs. In the study, we apply a nonlinear method—Backward Searching for the Initial Condition (BaSIC)—to quantify the local predictability limit the 2021 SSW event, which caused cold extremes across East Asia and North America. Using ERA5 reanalysis and the S2S reforecasts data, BaSIC estimates the maximum prediction lead time of this 2021 SSW event to be 17 days. To explore sensitive region of forecast uncertainty, we identify regions of fastest error growth via error tracking in S2S systems using BaSIC method. Forecast errors during the SSW event are small across the polar stratosphere after initiation but grow gradually over two weeks, accelerating rapidly over central Eurasia (30°-60°E) and spreading across the continent. This points to central Eurasia at high altitudes as a critical region for SSW forecast error development. • A nonlinear BaSIC method estimates the maximum prediction lead time of the 2021 sudden stratospheric warming as 17 days. • BaSIC is able to reveal extended predictability via nonlinear error tracking in S2S systems. • Forecast errors grow rapidly over central Eurasia, making it as the sensitive region of forecast error.