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DOI | 10.1007/s00382-019-04646-y |
Bias adjustment for decadal predictions of precipitation in Europe from CCLM | |
Li, Jingmin1,3; Pollinger, Felix1; Panitz, Hans-Juergen2; Feldmann, Hendrik2; Paeth, Heiko1 | |
2019-08-01 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 53页码:1323-1340 |
文章类型 | Article |
语种 | 英语 |
国家 | Germany |
英文摘要 | A cross-validated model output statistics (MOS) approach is applied to precipitation data from the high-resolution regional climate model CCLM for Europe. The aim is to remove systematic errors of simulated precipitation in decadal climate predictions. We developed a two-step bias-adjustment approach. In step one, we estimate model errors based on a long-term CCLM assimilation run' (regionalizing data from a global assimilation run) and observational data. In step two, the resulting transfer function is applied to the complete set of decadal hindcast simulations (285 individual runs). In contrast to lead-time-dependent bias-adjustment approaches, this one is designed for variables with poor decadal prediction skill and without dominant lead-time-dependent bias. In terms of the CCLM assimilation run, MOS is shown to be effective in predictor selection, model skill improvement, and model bias reduction. Yet, the positive effect of MOS correction is accompanied with an underestimation of precipitation variability. After MOS application, an estimated mean square skill score of more than 0.5 is observed regionally. Simulated precipitation in decadal hindcasts is further improved when the MOS is trained on the basis of other decadal hindcasts from the same regional climate model but with a large underestimation in forecast uncertainty. Our results suggest that the MOS system derived from the assimilation run is less effective but allows the potential climate predictability in decadal hindcasts and forecasts to be retained. Using hindcasts itself for training is recommended unless a statistical method is capable of distinguishing biases and predictions within a hindcasts dataset. |
英文关键词 | Bias-adjustment CCLM Hindcasts Decadal prediction Precipitation Model output statistics |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000475558800006 |
WOS关键词 | WIND STRESS ; CLIMATE ; FORECAST ; HINDCASTS ; ENSEMBLE ; DEPENDENCE ; IMPACT |
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/185400 |
专题 | 气候变化 |
作者单位 | 1.Univ Wurzburg, Inst Geog & Geol, Wurzburg, Germany; 2.Karlsruhe Inst Technol, Inst Meteorol & Climate Res, Karlsruhe, Germany; 3.German Aerosp Ctr, Inst Atmospher Phys, Oberpfaffenhofen, Germany |
推荐引用方式 GB/T 7714 | Li, Jingmin,Pollinger, Felix,Panitz, Hans-Juergen,et al. Bias adjustment for decadal predictions of precipitation in Europe from CCLM[J]. CLIMATE DYNAMICS,2019,53:1323-1340. |
APA | Li, Jingmin,Pollinger, Felix,Panitz, Hans-Juergen,Feldmann, Hendrik,&Paeth, Heiko.(2019).Bias adjustment for decadal predictions of precipitation in Europe from CCLM.CLIMATE DYNAMICS,53,1323-1340. |
MLA | Li, Jingmin,et al."Bias adjustment for decadal predictions of precipitation in Europe from CCLM".CLIMATE DYNAMICS 53(2019):1323-1340. |
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