A Spatiotemporal-Aware Climate Model Ensembling Method for Improving
Precipitation Predictability
Abstract
Multimodel ensembling has been widely used to improve climate model
predictions, and the improvement strongly depends on the ensembling
scheme. In this work, we propose a Bayesian neural network (BNN)
ensembling method, which combines climate models within a Bayesian model
averaging framework, to improve the predictive capability of model
ensembles. Our proposed BNN approach calculates spatiotemporally varying
model weights and biases by leveraging individual models’ simulation
skill, calibrates the ensemble prediction against observations by
considering observation data uncertainty, and quantifies epistemic
uncertainty when extrapolating to new conditions. More importantly, the
BNN method provides interpretability about which climate model
contributes more to the ensemble prediction at which locations and
times. Thus, beyond its predictive capability, the method also brings
insights and understanding of the models to guide further model and data
development. In this study, we apply the BNN weighting scheme to an
ensemble of CMIP6 climate models for monthly precipitation prediction
over the conterminous United States. In both synthetic and real case
studies, we demonstrate that BNN produces predictions of monthly
precipitation with higher accuracy than three baseline ensembling
methods. BNN can correctly assign a larger weight to the regions and
seasons where the individual model fits the observation better.
Moreover, its offered interpretability is consistent with our
understanding of localized climate model performance. Additionally, BNN
shows an increasing uncertainty when the prediction is farther away from
the period with constrained data, which appropriately reflects our
predictive confidence and trustworthiness of the models in the changing
climate.