A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-07-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/11/8/775 |
| _version_ | 1827712540550889472 |
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| author | Yonggwan Shin Youngsaeng Lee Jeong-Soo Park |
| author_facet | Yonggwan Shin Youngsaeng Lee Jeong-Soo Park |
| author_sort | Yonggwan Shin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T18:17:12Z |
| format | Article |
| id | doaj.art-afb20c80a37d46608a89fb551d393c48 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T18:17:12Z |
| publishDate | 2020-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-afb20c80a37d46608a89fb551d393c482023-11-20T07:38:28ZengMDPI AGAtmosphere2073-44332020-07-0111877510.3390/atmos11080775A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate SimulationYonggwan Shin0Youngsaeng Lee1Jeong-Soo Park2Department of Statistics, Chonnam National University, Gwangju 500-757, KoreaDepartment of Statistics, Chonnam National University, Gwangju 500-757, KoreaDepartment of Statistics, Chonnam National University, Gwangju 500-757, KoreaA 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.https://www.mdpi.com/2073-4433/11/8/775α-correctionα-weightsclimate changegeneralized extreme value distributionL-moments estimationleave-one-out cross-validation |
| spellingShingle | Yonggwan Shin Youngsaeng Lee Jeong-Soo Park A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation Atmosphere α-correction α-weights climate change generalized extreme value distribution L-moments estimation leave-one-out cross-validation |
| title | A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation |
| title_full | A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation |
| title_fullStr | A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation |
| title_full_unstemmed | A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation |
| title_short | A Weighting Scheme in A Multi-Model Ensemble for Bias-Corrected Climate Simulation |
| title_sort | weighting scheme in a multi model ensemble for bias corrected climate simulation |
| topic | α-correction α-weights climate change generalized extreme value distribution L-moments estimation leave-one-out cross-validation |
| url | https://www.mdpi.com/2073-4433/11/8/775 |
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