Explainable AI for Identifying Precursors of Extreme Oceanic Events in the North Atlantic

 Ocean physical and biogeochemical extremes, such as marine heatwaves (MHWs), deoxygenation, and acidification events have significant impacts on the marine environment, ecosystems, and economic livelihoods. In recent decades, the frequency, intensity and spatial extent of these extremes have been amplified (Capotondi et al., 2024; Shu et al., 2025; Gruber et al., 2021). Hence, a deeper understanding of the processes and precursors leading to extreme events remains crucial for improving and forecasting risk assessment.

In this study, we apply interpretable machine learning approaches to investigate which oceanic and atmospheric variables, as well as their lag effects, are most relevant for the extreme events in the North Atlantic, a relevant region for their occurrence in recent decades (England et al., 2025). Our framework combines high-resolution ocean model simulations with explainable artificial intelligence (XAI) techniques (He et al., 2024, Camps-Valls, 2025), allowing us to examine where, when, and which model variables are more important when identifying extreme events.

Rather than focusing on predictive skill, the emphasis of this study lies on identifying the underlying physics of precursor patterns leading to ocean extremes across different spatial and temporal scales. By integrating XAI into the analysis, this approach provides a more transparent and interpretable perspective on the decision-making processes of machine learning models, offering insights into the key variables and structures associated with the occurrence of ocean extremes. The outcomes of this study improve the interpretable assessment of potential precursors of MHWs, ocean deoxygenation and acidification extremes.

 

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