The skill of multi-model seasonal forecasts of the wintertime North Atlantic Oscillation

The skill assessment of a set of wintertime North Atlantic Oscillation (NAO) seasonal predictions in a multi-model ensemble framework has been carried out. The multi-model approach consists in merging the ensemble hindcasts of four atmospheric general circulation models forced with observed sea surface temperatures to create a multi-model ensemble. Deterministic (ensemble-mean based) and probabilistic (categorical) NAO hindcasts have been considered. Two different sets of NAO indices have been used to create the hindcasts. A first set is defined as the projection of model anomalies onto the NAO spatial pattern obtained from atmospheric analyses. The second set obtains the NAO indices by standardizing the leading principal component of each single-model ensemble. Positive skill is found with both sets of indices, especially in the case of the multi-model ensemble. In addition, the NAO definition based upon the single-model leading principal component shows a higher skill than the hindcasts obtained using the projection method. Using the former definition, the multi-model ensemble shows statistically significant (at 5% level) positive skill in a variety of probabilistic scoring measures. This is interpreted as a consequence of the projection method being less suitable because of the presence of errors in the spatial NAO patterns of the models. The positive skill of the seasonal NAO found here seems to be due not to the persistence of the long-term (decadal) variability specified in the initial conditions, but rather to a good simulation of the year-to-year variability. Nevertheless, most of the NAO seasonal predictability seems to be due to the correct prediction of particular cases such as the winter of 1989. The higher skill of the multi-model has been explained on the basis of a more reliable description of large-scale tropospheric wave features by the multi-model ensemble, illustrating the potential of multi-model experiments to better identify mechanisms that explain seasonal variability in the atmosphere.