Neglected Spatiotemporal Variations of Model Biases in Ensemble-Based Climate Projections

The Bayesian model averaging (BMA) method has been widely used for generating probabilistic climate projections. However, the averaging weights used in BMA can only reflect the spatially- and temporally-averaged performance of each ensemble member, without the ability to address the spatiotemporal variations of model biases. This can lead to inevitable exaggeration or understatement of the contributions of individual members to the ensemble mean, thus reducing the robustness of the resulting probabilistic projections. Here we propose a new method to help address the neglected spatiotemporal variations of model biases. Through the proposed method, the BMA weights are used as prior distributions to drive the Bayesian discriminant analysis in order to generate refined weights for individual ensemble models according to their spatially- and temporally-clustered performance. Through applying the proposed method to Canada, we demonstrate its effectiveness in generating robust probabilistic climate projections (e.g., the average R-2 increases from 0.82 to 0.89).