Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1029/2018JD028549 |
Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate | |
Herger, Nadja1,2; Angelil, Oliver1,2; Abramowitz, Gab1,3; Donat, Markus1,2; Stone, Daithi4,5; Lehmann, Karsten6 | |
2018-06-16 | |
发表期刊 | JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES |
ISSN | 2169-897X |
EISSN | 2169-8996 |
出版年 | 2018 |
卷号 | 123期号:11页码:5988-6004 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia; USA; England; Germany |
英文摘要 | Climate models serve as indispensable tools to investigate the effect of anthropogenic emissions on current and future climate, including extremes. However, as low-dimensional approximations of the climate system, they will always exhibit biases. Several attempts have been made to correct for biases as they affect extremes prediction, predominantly focused on correcting model-simulated distribution shapes. In this study, the effectiveness of a recently published quantile-based bias correction scheme, as well as a new subset selection method introduced here, are tested out-of-sample using model-as-truth experiments. Results show that biases in the shape of distributions tend to persist through time, and therefore, correcting for shape bias is useful for past and future statements characterizing the probability of extremes. However, for statements characterized by a ratio of the probabilities of extremes between two periods, we find that correcting for shape bias often provides no skill improvement due to the dominating effect of bias in the long-term trend. Using a toy model experiment, we examine the relative importance of the shape of the distribution versus its position in response to long-term changes in radiative forcing. It confirms that the relative position of the two distributions, based on the trend, is at least as important as the shape. We encourage the community to consider all model biases relevant to their metric of interest when using a bias correction procedure and to construct out-of-sample tests that mirror the intended application. |
英文关键词 | multimodel ensemble extremes attribution calibration bias correction prediction |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000436110800014 |
WOS关键词 | 2 DEGREES-C ; BIAS CORRECTION ; TEMPERATURE ; ATTRIBUTION ; CMIP5 ; SIMULATIONS ; STATISTICS ; WEATHER ; EVENTS ; 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/33995 |
专题 | 气候变化 |
作者单位 | 1.UNSW, Climate Change Res Ctr, Sydney, NSW, Australia; 2.ARC Ctr Excellence Climate Syst Sci, Sydney, NSW, Australia; 3.ARC Ctr Excellence Climate Extremes, Sydney, NSW, Australia; 4.Lawrence Berkeley Natl Lab, Berkeley, CA USA; 5.Global Climate Adaptat Partnership, Oxford, England; 6.Satalia, Berlin, Germany |
推荐引用方式 GB/T 7714 | Herger, Nadja,Angelil, Oliver,Abramowitz, Gab,et al. Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate[J]. JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,2018,123(11):5988-6004. |
APA | Herger, Nadja,Angelil, Oliver,Abramowitz, Gab,Donat, Markus,Stone, Daithi,&Lehmann, Karsten.(2018).Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate.JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES,123(11),5988-6004. |
MLA | Herger, Nadja,et al."Calibrating Climate Model Ensembles for Assessing Extremes in a Changing Climate".JOURNAL OF GEOPHYSICAL RESEARCH-ATMOSPHERES 123.11(2018):5988-6004. |
条目包含的文件 | 条目无相关文件。 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论