Abstract
A model weighting scheme is important in multi-model climate ensembles for projecting future changes. The climate model output typically needs to be bias corrected before it can be used. When a bias-correction (BC) is applied, equal model weights are usually derived because some BC methods cause the observations and historical simulation to match perfectly. This equal weighting is sometimes criticized because it does not take into account the model performance. Unequal weights reflecting model performance may be obtained from raw data before BC is applied. However, we have observed that certain models produce excessively high weights, while the weights generated in all other models are extremely low. This phenomenon may be partly due to the fact that some models are more fit or calibrated to the observations for a given applications. To address these problems, we consider, in this study, a hybrid weighting scheme including both equal and unequal weights. The proposed approach applies an “imperfect” correction to the historical data in computing their weights, while it applies ordinary BC to the future data in computing the ensemble prediction. We employ a quantile mapping method for the BC and a Bayesian model averaging for performance-based weighting. Furthermore, techniques for selecting the optimal correction rate based on the chi-square test statistic and the continuous ranked probability score are examined. Comparisons with ordinary ensembles are provided using a perfect model test. The usefulness of the proposed method is illustrated using the annual maximum daily precipitation as observed in the Korean peninsula and simulated by 21 models from the Coupled Model Intercomparison Project Phase 6.
Highlights
Over the last few decades, ensemble methods based on global climate models have become an important part of climate forecasting owing to their ability to reduce uncertainty in prediction.The multi-model ensemble (MME) methods in climatic projection have proven to improve the systematic bias and general limitations of single simulation models
Multi-model ensemble methods in climatic projection have proved to improve upon the systematic bias and general limitations of a single simulation model
It has been argued that model uncertainty can be reduced by attributing more weight to those models that are more skillful and realistic for a specific process or application
Summary
Over the last few decades, ensemble methods based on global climate models have become an important part of climate forecasting owing to their ability to reduce uncertainty in prediction.The multi-model ensemble (MME) methods in climatic projection have proven to improve the systematic bias and general limitations of single simulation models. Of the many possible ensemble methods, the method we apply here is Bayesian model averaging (BMA), which determines static weights for each model using the posterior probability [3,7,8,9,10]. Other weighting methods such as reliability ensemble averaging [1] and combined performance-independence weighting [6,11] are applicable to the proposed method in this
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