CERA uses an autoencoder with latent-space alignment to learn climate-invariant representations, improving generalization of moist-physics parameterizations to a +4K climate without warmer-climate labels.
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CERA: A Framework for Improved Generalization of Machine Learning Models to Changed Climates
CERA uses an autoencoder with latent-space alignment to learn climate-invariant representations, improving generalization of moist-physics parameterizations to a +4K climate without warmer-climate labels.