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Richard J. Hall
,
Adam A. Scaife
,
Edward Hanna
,
Julie M. Jones
, and
Robert Erdélyi

Abstract

The variability of the North Atlantic Oscillation (NAO) is a key aspect of Northern Hemisphere atmospheric circulation and has a profound impact upon the weather of the surrounding landmasses. Recent success with dynamical forecasts predicting the winter NAO at lead times of a few months has the potential to deliver great socioeconomic impacts. Here, a linear regression model is found to provide skillful predictions of the winter NAO based on a limited number of statistical predictors. Identified predictors include El Niño, Arctic sea ice, Atlantic SSTs, and tropical rainfall. These statistical models can show significant skill when used to make out-of-sample forecasts, and the method is extended to produce probabilistic predictions of the winter NAO. The statistical hindcasts can achieve similar levels of skill to state-of-the-art dynamical forecast models, although out-of-sample predictions are less skillful, albeit over a small period. Forecasts over a longer out-of-sample period suggest there is true skill in the statistical models, comparable with that of dynamical forecasting models. They can be used both to help evaluate and to offer insight into the sources of predictability and limitations of dynamical models.

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