A NAO-based subsampling approach for winter seasonal prediction

The North Atlantic Oscillation (NAO) represents the dominant mode of atmospheric circulation variability over the North Atlantic, driving winter weather conditions over a large part of the Euro-Mediterranean sector. Seasonal forecast systems have demonstrated some predictive skill for wintertime NAO, related to the enhanced ability of dynamical models to correctly represent possible sources of NAO predictability. However, increasing the predictive skill at the seasonal timescale over the European domain is still considered a major challenge.

In this work the aim is to extract the potential hidden skill in a dynamical seasonal forecast ensemble by properly selecting relevant realizations. The idea is to define a reduced ensemble better performing in terms of NAO predictions and to assess the performance of this subsample compared to the full ensemble.

Different subsampling criteria have been tested and verified. On the one hand, under the assumption that the ensemble average represents the most predictable path, the members simulating at the beginning of the forecast a NAO state closest to the ensemble mean NAO are selected. On the other hand, the realizations that resemble a reliable and independent estimate of the winter NAO, derived from the autumn conditions of a set of established dynamical predictors, are taken into consideration.

The comparison between the results obtained with the full ensemble and these subsampled ensembles reveals the potential for a significant improvement in the prediction of 2m temperature and precipitation anomalies, therefore representing a valuable strategy for possible real-time operational applications both in a multi-model and in a single model framework.