Using Explainable AI and Transfer Learning to Understand and Predict the Maintenance of Atlantic Blocking With Limited Observational Data

• Published in: Journal of geophysical research. Machine learning and computation Vol. 1; no. 4
• Main Authors: Zhang, Huan, Finkel, Justin, Abbot, Dorian S., Gerber, Edwin P., Weare, Jonathan
• Format: Journal Article
• Language: English
• Published: Wiley 01.12.2024
• Subjects: blocking; explainable AI; extremes; neural networks; transfer learning
• ISSN: 2993-5210; 2993-5210
• DOI: 10.1029/2024JH000243

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 (QG) model developed by Marshall and Molteni (1993), https://doi.org/10.1175/1520‐0469(1993)050<1792:taduop>2.0.co;2. 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 (TL) to achieve better predictions than direct training. SHAP analysis before and after TL allows a comparison between the predictive features in the reanalysis and the QG 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. Plain Language Summary 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, systematically underestimated in climate models. Using data generated by a simplified atmospheric model we demonstrate that, given sufficient training data, convolutional neural networks can predict the maintenance of Atlantic blocking from an initial blocked state. Next, we show that first training the neural network on data from the simplified model and then fine tuning the training using real world weather data enables prediction even with few examples of long‐lasting blocking events in the observational record. Subsequent feature analysis of the resulting neural networks identifies the input variables that most strongly impact their predictions, revealing that areas of high pressure in certain parts of North America and the North Atlantic Ocean are important for predicting long‐lasting blocking events and quantifying biases in the idealized model relative to real weather. Key Points Given sufficient training data, convolutional neural networks can predict the maintenance of Atlantic blocking from an initial blocked state Transfer learning from an idealized model to reanalysis data enables predictive skill in the low data regime of the observational record Feature importance analysis reveals the influence of upstream flow on blocking persistence and quantifies biases in the idealized model