  Ensembles of geophysical models improve projection accuracy and express
uncertainties. We develop a novel data-driven ensembling strategy for combining
geophysical models using Bayesian Neural Networks, which infers
spatiotemporally varying model weights and bias while accounting for
heteroscedastic uncertainties in the observations. This produces more accurate
and uncertainty-aware projections without sacrificing interpretability. Applied
to the prediction of total column ozone from an ensemble of 15
chemistry-climate models, we find that the Bayesian neural network ensemble
(BayNNE) outperforms existing ensembling methods, achieving a 49.4% reduction
in RMSE for temporal extrapolation, and a 67.4% reduction in RMSE for polar
data voids, compared to a weighted mean. Uncertainty is also
well-characterized, with 90.6% of the data points in our extrapolation
validation dataset lying within 2 standard deviations and 98.5% within 3
standard deviations.
