An unsupervised learning approach to identifying blocking events: the case of European summer

Atmospheric blocking events are mid-latitude weather patterns, which obstruct the usual path of the polar jet streams. They are often associated with heat waves in summer and cold snaps in winter. Despite being central features of mid-latitude synoptic-scale weather, there is no well-defined historical dataset of blocking events. Various blocking indices (BIs) have thus been suggested for automatically identifying blocking events in observational and in climate model data. However, BIs show significant regional and seasonal differences so that several indices are typically applied in combination to ensure scientific robustness. Here, we introduce a new BI using self-organizing maps (SOMs), an unsupervised machine learning approach, and compare its detection skill to some of the most widely applied BIs. To enable this intercomparison, we first create a new ground truth time series classification of European summer blocking based on expert judgement. We have shown that our method (SOM-BI) has several key advantages over previous BIs because it exploits all of the spatial information provided in the input data and reduces the dependence on arbitrary thresholds. We then apply this SOM-BI to study long-term trends in European summer blocking events across climate models from the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP5 and CMIP6). Overall, our results demonstrate the significant potential for supervised and unsupervised learning to complement the study of blocking events in both reanalysis and climate modelling contexts.