气候变化领域集成服务门户(CCPortal): Ensemble optimisation; multiple constraints and overconfidence: a case study with future Australian precipitation change

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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 N.; Abramowitz G.; Sherwood S.; Knutti R.; Angélil O.; Sisson S.A.
发表日期	2019
ISSN	0930-7575
起始页码	1581
结束页码	1596
卷号	53 期号:2020-03-04
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. © 2019, Springer-Verlag GmbH Germany, part of Springer Nature.
英文关键词	Constraint; Model-as-truth experiments; Multi-model ensemble; Multi-objective optimisation; Pareto optimality; Prediction
语种	英语
scopus关键词	climate change; climate modeling; ensemble forecasting; multiobjective programming; optimization; precipitation assessment; sea surface temperature; Australia
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/146079
作者单位	Climate Change Research Centre, ARC Centre of Excellence for Climate System Science, UNSW Australia, Sydney, NSW 2052, Australia; Climate Change Research Centre, ARC Centre of Excellence for Climate Extremes, UNSW Australia, Sydney, NSW 2052, Australia; Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland; School of Mathematics and Statistics, UNSW Australia, Sydney, NSW 2052, Australia; ARC Centre of Excellence for Climate Extremes, UNSW Australia, Sydney, NSW 2052, Australia; ARC Centre of Excellence for Mathematical and Statistical Frontiers, UNSW Australia, Sydney, NSW 2052, Australia
推荐引用方式 GB/T 7714	Herger N.,Abramowitz G.,Sherwood S.,et al. Ensemble optimisation; multiple constraints and overconfidence: a case study with future Australian precipitation change[J],2019,53(2020-03-04).
APA	Herger N.,Abramowitz G.,Sherwood S.,Knutti R.,Angélil O.,&Sisson S.A..(2019).Ensemble optimisation; multiple constraints and overconfidence: a case study with future Australian precipitation change.Climate Dynamics,53(2020-03-04).
MLA	Herger N.,et al."Ensemble optimisation; multiple constraints and overconfidence: a case study with future Australian precipitation change".Climate Dynamics 53.2020-03-04(2019).