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DOI | 10.1007/s00382-019-04635-1 |
Uncertainty component estimates in transient climate projections Precision of estimators in a single time or time series approach | |
Hingray, Benoit1; Blanchet, Juliette1; Evin, Guillaume2; Vidal, Jean-Philippe3 | |
2019-09-01 | |
发表期刊 | CLIMATE DYNAMICS |
ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 53页码:2501-2516 |
文章类型 | Article |
语种 | 英语 |
国家 | France |
英文摘要 | Quantifying model uncertainty and internal variability components in climate projections has been paid a great attention in the recent years. For multiple synthetic ensembles of climate projections, we compare the precision of uncertainty component estimates obtained respectively with the two Analysis of Variance (ANOVA) approaches mostly used in recent works: the popular Single Time approach (STANOVA), based on the data available for the considered projection lead time and a time series based approach (QEANOVA), which assumes quasi-ergodicity of climate outputs over the available simulation period. We show that the precision of all uncertainty estimates is higher when more members are used, when internal variability is smaller and/or the response-to-uncertainty ratio is higher. QEANOVA estimates are much more precise than STANOVA ones: QEANOVA simulated confidence intervals are roughly 3-5 times smaller than STANOVA ones. Except for STANOVA when less than three members is available, the precision is rather high for total uncertainty and moderate for internal variability estimates. For model uncertainty or response-to-uncertainty ratio estimates, the precision is low for QEANOVA to very low for STANOVA. In the most unfavorable configurations (small number of members, large internal variability), large over- or underestimation of uncertainty components is thus very likely. In a number of cases, the uncertainty analysis should thus be preferentially carried out with a time series approach or with a local-time series approach, applied to all predictions available in the temporal neighborhood of the target prediction lead time. |
英文关键词 | Uncertainty sources Climate projections ANOVA Internal variability Model uncertainty Scenario uncertainty Precision of estimates |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000483626900002 |
WOS关键词 | INTERNAL VARIABILITY ; FUTURE CHANGES ; PRECIPITATION ; VARIANCE ; ENSEMBLE |
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/186330 |
专题 | 气候变化 |
作者单位 | 1.Univ Grenoble Alpes, CNRS, IGE UMR 5001, F-38000 Grenoble, France; 2.Univ Grenoble Alpes, Irstea, UR ETNA, F-38000 Grenoble, France; 3.Ctr Lyon Villeurbanne, UR RiverLy, Irstea, F-69625 Villeurbanne, France |
推荐引用方式 GB/T 7714 | Hingray, Benoit,Blanchet, Juliette,Evin, Guillaume,et al. Uncertainty component estimates in transient climate projections Precision of estimators in a single time or time series approach[J]. CLIMATE DYNAMICS,2019,53:2501-2516. |
APA | Hingray, Benoit,Blanchet, Juliette,Evin, Guillaume,&Vidal, Jean-Philippe.(2019).Uncertainty component estimates in transient climate projections Precision of estimators in a single time or time series approach.CLIMATE DYNAMICS,53,2501-2516. |
MLA | Hingray, Benoit,et al."Uncertainty component estimates in transient climate projections Precision of estimators in a single time or time series approach".CLIMATE DYNAMICS 53(2019):2501-2516. |
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