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DOI | 10.1175/JCLI-D-18-0606.1 |
Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections Using Data Augmentation | |
Evin, Guillaume1; Hingray, Benoit2; Blanchet, Juliette2; Eckert, Nicolas1; Morin, Samuel3; Verfaillie, Deborah3 | |
2019-04-01 | |
发表期刊 | JOURNAL OF CLIMATE |
ISSN | 0894-8755 |
EISSN | 1520-0442 |
出版年 | 2019 |
卷号 | 32期号:8页码:2423-2440 |
文章类型 | Article |
语种 | 英语 |
国家 | France |
英文摘要 | The quantification of uncertainty sources in ensembles of climate projections obtained from combinations of different scenarios and climate and impact models is a key issue in climate impact studies. The small size of the ensembles of simulation chains and their incomplete sampling of scenario and climate model combinations makes the analysis difficult. In the popular single-time ANOVA approach for instance, a precise estimate of internal variability requires multiple members for each simulation chain (e.g., each emission scenario-climate model combination), but multiple members are typically available for a few chains only. In most ensembles also, a precise partition of model uncertainty components is not possible because the matrix of available scenario/models combinations is incomplete (i.e., projections are missing for many scenario-model combinations). The method we present here, based on data augmentation and Bayesian techniques, overcomes such limitations and makes the statistical analysis possible for single-member and incomplete ensembles. It provides unbiased estimates of climate change responses of all simulation chains and of all uncertainty variables. It additionally propagates uncertainty due to missing information in the estimates. This approach is illustrated for projections of regional precipitation and temperature for four mountain massifs in France. It is applicable for any kind of ensemble of climate projections, including those produced from ad hoc impact models. |
英文关键词 | Bayesian methods Error analysis Risk assessment Statistical techniques Climate models Climate variability |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000464476700001 |
WOS关键词 | INTERNAL VARIABILITY ; EURO-CORDEX ; FUTURE ; DISTRIBUTIONS ; CMIP5 |
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/182152 |
专题 | 气候变化 |
作者单位 | 1.Univ Grenoble Alpes, Irstea, UR ETGR, Grenoble, France; 2.Univ Grenoble Alpes, CNRS, IRD, Grenoble INP,IGE, Grenoble, France; 3.Univ Toulouse, Univ Grenoble Alpes, CNRS, Meteo France,CNRM,CEN, Grenoble, France |
推荐引用方式 GB/T 7714 | Evin, Guillaume,Hingray, Benoit,Blanchet, Juliette,et al. Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections Using Data Augmentation[J]. JOURNAL OF CLIMATE,2019,32(8):2423-2440. |
APA | Evin, Guillaume,Hingray, Benoit,Blanchet, Juliette,Eckert, Nicolas,Morin, Samuel,&Verfaillie, Deborah.(2019).Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections Using Data Augmentation.JOURNAL OF CLIMATE,32(8),2423-2440. |
MLA | Evin, Guillaume,et al."Partitioning Uncertainty Components of an Incomplete Ensemble of Climate Projections Using Data Augmentation".JOURNAL OF CLIMATE 32.8(2019):2423-2440. |
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