A Bayesian Deep Learning Approach to Near-Term Climate Prediction. (arXiv:2202.11244v1 [physics.ao-ph])

Since model bias and associated initialization shock are serious shortcomings
that reduce prediction skills in state-of-the-art decadal climate prediction
efforts, we pursue a complementary machine-learning-based approach to climate
prediction. The example problem setting we consider consists of predicting
natural variability of the North Atlantic sea surface temperature on the
interannual timescale in the pre-industrial control simulation of the Community
Earth System Model (CESM2). While previous works have considered the use of
recurrent networks such as convolutional LSTMs and reservoir computing …

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