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Supplementary material: Ensembling geophysical models with Bayesian Neural Networks Anonymous Author(s) Affiliation Address email

Neural Information Processing Systems

This is based on work from Knutti et al. The heteroscedastic loss function is prone to episodes of catastrophic forgetting. Synthetic experiment Ozone experimentSpatial coord scaling 2 2 Temporal coord scaling (month of year) 1 2 Temporal coord scaling (total months) 1 1 Number of physical models 4 15 Number of neural network ensemble members 50 65 Bias mean. Noise mean prior 0. 02 0 .015 In the following, we derive the anchored ensembling loss function for the heteroscedastic case.