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  1. A deep neural network is trained to predict sea surface temperature variations at two important regions of the Atlantic ocean, using 800 years of simulated climate dynamics based on the first-principles physics models. This model is then tested against 60 years of historical data. Our statistical model learns to approximate the physical laws governing the simulation, providing significant improvement over simple statistical forecasts and comparable to most state-of-the-art dynamical/conventional forecast models for a fraction of the computational cost.