Hidden Potential in Predicting Wintertime Temperature Anomalies in the Northern Hemisphere - Dobrynin, Mikhail; Düsterhus, André; Fröhlich, Kristina; Athanasiadis, Panos; Ruggieri, Paolo; Müller, Wolfgang A.; Baehr, Johanna; Düsterhus, André; 3 ICARUS Department of Geography Maynooth University Maynooth Ireland; Fröhlich, Kristina; 1 Deutscher Wetterdienst (DWD) Hamburg Germany; Athanasiadis, Panos; 4 Department of Physics and Astronomy University of Bologna Bologna Italy; Ruggieri, Paolo; 4 Department of Physics and Astronomy University of Bologna Bologna Italy; Müller, Wolfgang A.; 6 Max Planck Institute for Meteorology Hamburg Germany; et al. (Geophysical Research Letters, 2022)
Variability of the North Atlantic Oscillation (NAO) drives wintertime temperature anomalies in the Northern Hemisphere. Dynamical seasonal prediction systems can skilfully predict the winter NAO. However, prediction of the NAO‐dependent air temperature anomalies remains elusive, partially due to the low variability of predicted NAO. Here, we demonstrate a hidden potential of a multi‐model ensemble of operational seasonal prediction systems for predicting wintertime temperature by increasing the variability of predicted NAO. We identify and subsample those ensemble members which are close to NAO index statistically estimated from initial autumn conditions. In our novel multi‐model approach, the correlation prediction skill for wintertime Central Europe temperature is improved from 0.25 to 0.66, accompanied by an increased winter NAO prediction skill of 0.9. Thereby, temperature anomalies can be skilfully predicted for the upcoming winter over a large part of the Northern Hemisphere through increased variability and skill of predicted NAO. Plain Language Summary: Wintertime temperature in the Northern Hemisphere is regulated by the variations of atmospheric pressure, represented by the so‐called North Atlantic Oscillation (NAO). The NAO's phase—negative or positive—is associated with the pathways of cold and warm air masses leading to cold or warm winters in Europe. While the NAO phase can be predicted well, predictions of the NAO‐dependent air temperature remain elusive. Specifically, it is challenging to predict the strength of the NAO, the most important requirement for the accurate prediction of wintertime temperature. Here, we improve wintertime temperature prediction by increasing the strength of the predicted NAO. We use observation based autumn Northern Hemisphere ocean and air temperature, as well as ice and snow cover for statistical estimation of the first guess NAO for the upcoming winter. Then, we sub‐select only those simulations from the multi‐model ensemble, which are consistent with our first ...