气候变化领域集成服务门户(CCPortal): Bias adjustment for decadal predictions of precipitation in Europe from CCLM

CCPortal

DOI	10.1007/s00382-019-04646-y
	Bias adjustment for decadal predictions of precipitation in Europe from CCLM
	Li J.; Pollinger F.; Panitz H.-J.; Feldmann H.; Paeth H.
发表日期	2019
ISSN	0930-7575
起始页码	1323
结束页码	1340
卷号	53 期号:2020-03-04
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. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Bias-adjustment; CCLM; Decadal prediction; Hindcasts; Model output statistics; Precipitation
语种	英语
scopus关键词	climate modeling; climate prediction; decadal variation; hindcasting; precipitation (climatology); regional climate; Europe
来源期刊	Climate Dynamics
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/146150
作者单位	Institute of Geography and Geology, University of Wuerzburg, Wuerzburg, Germany; Institute of Meteorology and Climate Research, Karlsruhe Institute of Technology, Karlsruhe, Germany; Institute of Atmospheric Physics, German Aerospace Center, Oberpfaffenhofen, Germany
推荐引用方式 GB/T 7714	Li J.,Pollinger F.,Panitz H.-J.,et al. Bias adjustment for decadal predictions of precipitation in Europe from CCLM[J],2019,53(2020-03-04).
APA	Li J.,Pollinger F.,Panitz H.-J.,Feldmann H.,&Paeth H..(2019).Bias adjustment for decadal predictions of precipitation in Europe from CCLM.Climate Dynamics,53(2020-03-04).
MLA	Li J.,et al."Bias adjustment for decadal predictions of precipitation in Europe from CCLM".Climate Dynamics 53.2020-03-04(2019).