Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

Abstract

Neural networks can learn predictable signals of internal sea surface temperature variability at 1-3, 1-5, and 3-7 year lead times • Neural networks trained on climate model output can skillfully predict sea surface temperature variability in reconstructed observations • Neural network skill in predicting observed sea surface temperature variability depends on the climate model used for training