A multivariate regression model derived from climate model simulations is shown to produce skillful predictions of unforced, annual mean sea surface temperature variations on multiyear time scales in observations and climate model simulations. Patterns that can be predicted with skill are identified explicitly and shown to arise from a combination of persistence and coupled interactions in the Pacific Ocean. Adding the regression model predictions to an estimate of the response to anthropogenic and natural forcing yields a prediction with higher skill than either alone, demonstrating the contribution of initial condition information to skill on multiyear time scales.
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