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DOI | 10.1175/JCLI-D-17-0054.1 |
Do Statistical Pattern Corrections Improve Seasonal Climate Predictions in the North American Multimodel Ensemble Models? | |
Barnston, Anthony G.1; Tippett, Michael K.2,3 | |
2017-10-01 | |
发表期刊 | JOURNAL OF CLIMATE
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ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2017 |
卷号 | 30期号:20 |
文章类型 | Article |
语种 | 英语 |
国家 | USA; Saudi Arabia |
英文摘要 | Canonical correlation analysis (CCA)-based statistical corrections are applied to seasonal mean precipitation and temperature hindcasts of the individual models from the North American Multimodel Ensemble project to correct biases in the positions and amplitudes of the predicted large-scale anomaly patterns. Corrections are applied in 15 individual regions and then merged into globally corrected forecasts. The CCA correction dramatically improves the RMS error skill score, demonstrating that model predictions contain correctable systematic biases in mean and amplitude. However, the corrections do not materially improve the anomaly correlation skills of the individual models for most regions, seasons, and lead times, with the exception of October-December precipitation in Indonesia and eastern Africa. Models with lower uncorrected correlation skill tend to benefit more from the correction, suggesting that their lower skills may be due to correctable systematic errors. Unexpectedly, corrections for the globe as a single region tend to improve the anomaly correlation at least as much as the merged corrections to the individual regions for temperature, and more so for precipitation, perhaps due to better noise filtering. The lack of overall improvement in correlation may imply relatively mild errors in large-scale anomaly patterns. Alternatively, there may be such errors, but the period of record is too short to identify them effectively but long enough to find local biases in mean and amplitude. Therefore, statistical correction methods treating individual locations (e.g., multiple regression or principal component regression) may be recommended for today's coupled climate model forecasts. The findings highlight that the performance of statistical postprocessing can be grossly overestimated without thorough cross validation or evaluation on independent data. |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000411438000017 |
WOS关键词 | FALSE DISCOVERY RATE ; FORECAST SKILL ; FIELD SIGNIFICANCE ; SYSTEMATIC-ERROR ; PRECIPITATION ; HEMISPHERE ; ATMOSPHERE ; REGRESSION |
WOS类目 | Meteorology & Atmospheric Sciences |
WOS研究方向 | Meteorology & Atmospheric Sciences |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/20774 |
专题 | 气候变化 |
作者单位 | 1.Columbia Univ, Int Res Inst Climate & Soc, Palisades, NY 10964 USA; 2.Columbia Univ, Dept Appl Phys & Appl Math, New York, NY USA; 3.King Abdulaziz Univ, Ctr Excellence Climate Change Res, Dept Meteorol, Jeddah, Saudi Arabia |
推荐引用方式 GB/T 7714 | Barnston, Anthony G.,Tippett, Michael K.. Do Statistical Pattern Corrections Improve Seasonal Climate Predictions in the North American Multimodel Ensemble Models?[J]. JOURNAL OF CLIMATE,2017,30(20). |
APA | Barnston, Anthony G.,&Tippett, Michael K..(2017).Do Statistical Pattern Corrections Improve Seasonal Climate Predictions in the North American Multimodel Ensemble Models?.JOURNAL OF CLIMATE,30(20). |
MLA | Barnston, Anthony G.,et al."Do Statistical Pattern Corrections Improve Seasonal Climate Predictions in the North American Multimodel Ensemble Models?".JOURNAL OF CLIMATE 30.20(2017). |
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