Self-Organizing Maps Identify Windows of Opportunity for Seasonal European Summer Predictions

• Published in: Frontiers in Climate
• Main Authors: Carvalho-Oliveira, Julianna, Borchert, Leonard F., Zorita, Eduardo, Baehr, Johanna
• Other Authors: European Commission, European Research Council, Deutsche Forschungsgemeinschaft
• Format: Article in Journal/Newspaper
• Language: unknown
• Published: Frontiers Media SA 2022
• Subjects: Management, Monitoring, Policy and Law; Atmospheric Science; Pollution; Environmental Science (miscellaneous); Global and Planetary Change; North Atlantic; North Atlantic oscillation
• Online Access: http://dx.doi.org/10.3389/fclim.2022.844634; https://www.frontiersin.org/articles/10.3389/fclim.2022.844634/full

We combine a machine learning method and ensemble climate predictions to investigate windows of opportunity for seasonal predictability of European summer climate associated with the North Atlantic jet stream. We particularly focus on the impact of North Atlantic spring sea surface temperatures (SST) on the four dominant atmospheric teleconnections associated with the jet stream: the summer North Atlantic Oscillation (NAO) in positive and negative phases, the Atlantic Ridge (At. Ridge), and Atlantic Low (At. Low). We go beyond standard forecast practices by not only identifying these atmospheric teleconnections and their SST precursors but by making use of these identified precursors in the analysis of a dynamical forecast ensemble. Specifically, we train the neural network-based classifier Self-Organizing Maps (SOM) with ERA-20C reanalysis and combine it with model simulations from the Max Planck Institute Earth System Model in mixed resolution (MPI-ESM-MR). We use two different sets of 30-member hindcast ensembles initialized every May, one for training and evaluation between 1902 and 2008, and one for verification between 1980–2016, respectively. Among the four summer atmospheric teleconnections analyzed here, we find that At. Ridge simulated by MPI-ESM-MR shows the best agreement with ERA-20C, thereby representing with its occurrence windows of opportunity for skillful summer predictions. Conversely, At. Low shows the lowest agreement, which might limit the model skill for early warning of warmer than average summers. In summary, we find that spring SST patterns identified with a SOM analysis can be used to guess the dominant summer atmospheric teleconnections at initialization and guide a sub-selection of potential skillful ensemble members. This holds especially true for At. Ridge and At. Low and is unclear for summer NAO. We show that predictive skill in the selected ensemble exceeds that of the full ensemble over regions in the Euro-Atlantic domain where spring SST significantly correlates with summer ...