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Date
2020-11-01Type
- Journal Article
Abstract
The North Atlantic Oscillation (NAO) is the main driver of weather variability in parts of Eurasia, Greenland, North America and North Africa on a range of timescales. Successful extended-range NAO predictions would equate to improved predictions of precipitation and temperature in these regions. It has become clear that the NAO is influenced by the stratosphere, but as this downward coupling is not fully reproduced by all forecast models, the potential for improved NAO forecasts has not been fully realized. Here, an analysis of 21 winters of subseasonal forecast data from the European Center for Medium-Range Weather Forecasts (ECMWF) monthly forecasting system is presented. By dividing the forecasts into clusters according to their errors in North Atlantic sea level pressure 15–30 days into the forecasts, we identify relationships between these errors and the state of the stratospheric polar vortex when the forecasts were initialized. A key finding is that the model overestimates the persistence of both the negative NAO response following a weak polar vortex and the positive NAO response following a strong polar vortex. A case in point is the sudden stratospheric warming in early 2019, which was followed by five consecutive weeks of an overestimation of the negative NAO regime. A consequence on the ground was too cold temperature predictions for northern Europe. Another important finding is that the model appears to misrepresent the gradual downward impact of stratospheric vortex anomalies. This suggests that an improved representation and prediction of stratosphere–troposphere coupling in models might yield substantial benefits for extended-range weather forecasting in the Northern Hemisphere midlatitudes. Show more
Publication status
publishedExternal links
Journal / series
Journal of ClimateVolume
Pages / Article No.
Publisher
American Meteorological SocietySubject
North Atlantic Oscillation; Stratosphere-troposphere coupling; Climate prediction; Seasonal forecastingOrganisational unit
09612 - Domeisen, Daniela / Domeisen, Daniela
Funding
170523 - Improving the Prediction of Sub-seasonal to Seasonal Weather and Climate – From Theory to Application (SNF)
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