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 blocking based
on expert judgement. We then demonstrate 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. Using ERA5 reanalysis data (1979–2019), we find that
the SOM-BI identifies blocking events with a higher precision and recall than other BIs. In particular, SOM-BI already
performs well using only around 20 years of training data so
that observational records are long enough to train our new
method. We present case studies of the 2003 and 2019 European heat waves and highlight that well-defined groups of
SOM nodes can be an effective tool to diagnose such weather
events, although the domain-based approach can still lead to
errors in the identification of certain events in a fashion similar to the other BIs. We further test the red blocking detection
skill of SOM-BI depending on the meteorological variable
used to study blocking, including geopotential height, sea
level pressure and four variables related to potential vorticity,
and the 500 hPa geopotential height anomaly field provides
the best results with our new approach. We also demonstrate
how SOM-BI can be used to identify different types of blocking events and their associated trends. Finally, we evaluate
the SOM-BI performance on around 100 years of climate
model data from a pre-industrial simulation with the new
UK Earth System Model (UKESM1-0-LL). For the model
data, all blocking detection methods have lower skill than for
the ERA5 reanalysis, but SOM-BI performs noticeably better than the conventional indices. Overall, our results demonstrate the significant potential for unsupervised learning to
complement the study of blocking events in both reanalysis
and climate modelling contexts.