Deep learning reveals enhanced ENSO predictability under historical anthropogenic forcing Science Advances

Understanding how ENSO predictability responds to climate change is essential for improving future climate projections. In this study, we apply a deep learning model-convolutional neural network with a leave-one-out model strategy to Coupled Model Intercomparison Project Phase 6 (CMIP6) historical and preindustrial control simulations. We find that El Niño-Southern Oscillation (ENSO) predictability is statistically enhanced by 14.0 ± 1.8% enhancement under historical anthropogenic forcing. This improvement is linked to changes in key ocean-atmosphere feedbacks. Under historical forcing, the equatorial Pacific shows a shoaling of the thermocline. As a result, the surface ocean becomes more sensitive to wind forcing, and subsurface temperature becomes more responsive to thermocline variations. These processes strengthen the three-dimensional advective and thermocline feedbacks, which together enhance ENSO predictability. These findings highlight the importance of anthropogenic forcing in shaping ENSO predictability in a warming climate.