  Blocking events are an important cause of extreme weather, especially
long-lasting blocking events that trap weather systems in place. The duration
of blocking events is, however, underestimated in climate models. Explainable
Artificial Intelligence are a class of data analysis methods that can help
identify physical causes of prolonged blocking events and diagnose model
deficiencies. We demonstrate this approach on an idealized quasigeostrophic
model developed by Marshall and Molteni (1993). We train a convolutional neural
network (CNN), and subsequently, build a sparse predictive model for the
persistence of Atlantic blocking, conditioned on an initial high-pressure
anomaly. Shapley Additive ExPlanation (SHAP) analysis reveals that
high-pressure anomalies in the American Southeast and North Atlantic, separated
by a trough over Atlantic Canada, contribute significantly to prediction of
sustained blocking events in the Atlantic region. This agrees with previous
work that identified precursors in the same regions via wave train analysis.
When we apply the same CNN to blockings in the ERA5 atmospheric reanalysis,
there is insufficient data to accurately predict persistent blocks. We
partially overcome this limitation by pre-training the CNN on the plentiful
data of the Marshall-Molteni model, and then using Transfer Learning to achieve
better predictions than direct training. SHAP analysis before and after
transfer learning allows a comparison between the predictive features in the
reanalysis and the quasigeostrophic model, quantifying dynamical biases in the
idealized model. This work demonstrates the potential for machine learning
methods to extract meaningful precursors of extreme weather events and achieve
better prediction using limited observational data.
