Explainable neural nets for disentangling sources of predictability in the North Atlantic Sea Surface Temperature (NASST)

North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, exhibit some of the highest predictability across global oceanic systems. However, the relative contributions of atmospheric versus oceanic influences on the long term NASST variability remains ambiguous. In this study, we utilize neural networks (NNs) to assess the significance of various atmospheric and oceanic predictors in forecasting the state of NASST within the CANARI Large Ensemble, which employs the Met Office CMIP6 physical climate model (HadGEM3-GC3.1) at a high-resolution atmospheric scale (N216, approximately 60 km at midlatitudes) and a 1/4° resolution for oceanic data. The ensemble comprises forty members, driven by CMIP6 historical data and SSP3-7.0 scenarios for the period from 1950 to 2099. First, we evaluate the ability of the NNs to anticipate the phases of long term (multidecadal variability) using observational datasets, thereby investigating the consistency of physical processes influencing NASST variability between modeled predictions and real-world observations. Second, the research delves into how the interplay between oceanic and atmospheric predictors, alongside external forcings and internal variability (atmospheric noise), impacts the machine learning-based predictions and we use explainable AI techniques to identify the sources of predictability and to pinpoint physical mechanisms and regions crucial for accurate NN forecasts.