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DOI | 10.1007/s00382-019-04690-8 |
Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change | |
Herger, Nadja1; Abramowitz, Gab2; Sherwood, Steven1; Knutti, Reto3; Angelil, Oliver1; Sisson, Scott A.4,5,6 | |
2019-08-01 | |
发表期刊 | CLIMATE DYNAMICS
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ISSN | 0930-7575 |
EISSN | 1432-0894 |
出版年 | 2019 |
卷号 | 53页码:1581-1596 |
文章类型 | Article |
语种 | 英语 |
国家 | Australia; Switzerland |
英文摘要 | Future climate is typically projected using multi-model ensembles, but the ensemble mean is unlikely to be optimal if models' skill at reproducing historical climate is not considered. Moreover, individual climate models are not independent. Here, we examine the interplay between the benefits of optimising an ensemble for the performance of its mean and the the effect this has on ensemble spread as an uncertainty estimate. Using future Australian precipitation change as a case study, we perform optimal subset selection based on present-day precipitation, sea surface temperature and/or 500 hPa eastward wind climatologies. We use either one, two, or all three variables as predictors. Out-of-sample projection skill is assessed using a model-as-truth approach (rather than observations). For multiple variables, multi-objective optimisation is used to obtain Pareto-optimal subsets (an ensemble of model subsets), to gauge the uncertainty in optimisation arising from the multiple constraints. We find that the spread of climate model subset averages typically under-represents the true projection uncertainty (overconfidence), but that the situation can be significantly improved using mixture distributions for uncertainty estimation. The single best predictor, present-day precipitation, gives the most accurate results but is still overconfident-a consequence of calibrating too specifically. It is only when all three constraints are used that projection skill is improved and overconfidence is eliminated, but at the cost of a poorer best estimate relative to one predictor. We thus identify an important trade-off between accuracy and precision, depending on the number of predictors, which is likely relevant for any subset selection or weighting strategy. |
英文关键词 | Multi-objective optimisation Pareto optimality Constraint Multi-model ensemble Prediction Model-as-truth experiments |
领域 | 气候变化 |
收录类别 | SCI-E |
WOS记录号 | WOS:000475558800020 |
WOS关键词 | CLIMATE PROJECTIONS ; MULTIMODEL ENSEMBLE ; INDEPENDENCE ; MODELS ; SKILL ; 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/185414 |
专题 | 气候变化 |
作者单位 | 1.UNSW Australia, Climate Change Res Ctr, ARC Ctr Excellence Climate Syst Sci, Sydney, NSW 2052, Australia; 2.UNSW Australia, Climate Change Res Ctr, ARC Ctr Excellence Climate Extremes, Sydney, NSW 2052, Australia; 3.Swiss Fed Inst Technol, Inst Atmospher & Climate Sci, Zurich, Switzerland; 4.UNSW Australia, Sch Math & Stat, Sydney, NSW 2052, Australia; 5.UNSW Australia, ARC Ctr Excellence Climate Extremes, Sydney, NSW 2052, Australia; 6.UNSW Australia, ARC Ctr Excellence Math & Stat Frontiers, Sydney, NSW 2052, Australia |
推荐引用方式 GB/T 7714 | Herger, Nadja,Abramowitz, Gab,Sherwood, Steven,et al. Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change[J]. CLIMATE DYNAMICS,2019,53:1581-1596. |
APA | Herger, Nadja,Abramowitz, Gab,Sherwood, Steven,Knutti, Reto,Angelil, Oliver,&Sisson, Scott A..(2019).Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change.CLIMATE DYNAMICS,53,1581-1596. |
MLA | Herger, Nadja,et al."Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change".CLIMATE DYNAMICS 53(2019):1581-1596. |
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