Exploring Euro-Atlantic Winter Seasonal Predictability: A Comparative Analysis of Deep Learning and Maximum Covariance Analysis

The seasonal predictability of wintertime atmospheric patterns is determined, to a large extent, by the anomalous ocean surface thermal conditions. Specifically, the sea surface temperatures (SST) appears as a significant contributor to the predictability of wintertime atmospheric patterns in the Euro-Atlantic region (EAR). Current seasonal prediction systems rely significantly on the interannual phenomenon known as the El Niño-Southern Oscillation (ENSO).

On the one hand, current seasonal prediction systems predominantly rely on dynamical models, which propagate the signals associated to these forcings to both local and remote areas. However, the complexity of atmospheric processes, the important biases in reproducing SST in the extratropics and the interaction of signals make this propagation much more challenging. On the other hand, traditional statistical techniques (i.e. Maximum Covariance Analysis (MCA)), allows the possibility of making seasonal predictions with less bias, by focusing on the relationship between the predictor and the predictand. Nevertheless, there is a growing interest in exploring non-linear relationships between seasonal anomalies of various physical variables. Deep learning approaches offer promising avenues for modeling such complex relationships. Therefore, this study aims to assess the predictive capability of autumntime (September-October) SST anomalies in forecasting wintertime (November-December and January-February) sea level pressure (SLP) anomalies across the EAR by using two different statistical prediction techniques.

Specifically, the MCA will be used to identify and analyse the dominant patterns of co-variability between SST anomalies in different ocean basins and EAR atmospheric conditions. Additionally, several deep neural network models are developed to capture the complex non-linear atmospheric teleconnections associated with SST anomalies, and their predictive performance is rigorously evaluated over the EAR region. The assessment highlights regions with higher prediction accuracy for the different methods and identifies key sources of skill, particularly over the Pacific basin. Concretely, certain regions show higher skill in the EAR than the one from the ECMWF seasonal forecasting model (SEAS5).

By comparing deep learning methodologies with traditional statistical techniques such as MCA, this study provides a comprehensive analysis of wintertime atmospheric predictability over the EAR. The findings contribute to advancing our understanding of oceanic forced atmospheric teleconnections, not only by establishing windows of opportunity for seasonal forecasts but also by means of analysing possible drivers of these teleconnections. All of these aid in the development of more accurate and reliable prediction models for managing climatological risks in the Euro-Atlantic region.