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 R² increases from 0.82 to 0.89).

中文翻译：

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基于集合的气候预测中被忽视的模型偏差的时空变化

贝叶斯模型平均 (BMA) 方法已广泛用于生成概率气候预测。然而，BMA 中使用的平均权重只能反映每个集合成员的空间和时间平均性能，而无法解决模型偏差的时空变化。这可能导致不可避免地夸大或低估单个成员对整体均值的贡献，从而降低所得概率预测的稳健性。在这里，我们提出了一种新方法来帮助解决模型偏差的被忽视的时空变化。通过所提出的方法，BMA 权重用作先验分布来驱动贝叶斯判别分析，以便根据其空间和时间聚类性能为各个集成模型生成精细的权重。通过将所提出的方法应用于加拿大，我们证明了它在生成稳健的概率气候预测（例如，平均R ²从 0.82 增加到 0.89)。