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DOI10.5194/acp-20-9961-2020
Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence
Amos M.; Young P.J.; Scott Hosking J.; Lamarque J.-F.; Luke Abraham N.; Akiyoshi H.; Archibald A.T.; Bekki S.; Deushi M.; Jöckel P.; Kinnison D.; Kirner O.; Kunze M.; Marchand M.; Plummer D.A.; Saint-Martin D.; Sudo K.; Tilmes S.; Yamashita Y.
发表日期2020
ISSN1680-7316
起始页码9961
结束页码9977
卷号20期号:16
英文摘要Calculating a multi-model mean, a commonly used method for ensemble averaging, assumes model independence and equal model skill. Sharing of model components amongst families of models and research centres, conflated by growing ensemble size, means model independence cannot be assumed and is hard to quantify. We present a methodology to produce a weighted-model ensemble projection, accounting for model performance and model independence. Model weights are calculated by comparing model hindcasts to a selection of metrics chosen for their physical relevance to the process or phenomena of interest. This weighting methodology is applied to the Chemistry-Climate Model Initiative (CCMI) ensemble to investigate Antarctic ozone depletion and subsequent recovery. The weighted mean projects an ozone recovery to 1980 levels, by 2056 with a 95% confidence interval (2052-2060), 4 years earlier than the most recent study. Perfect-model testing and out-of-sample testing validate the results and show a greater projective skill than a standard multi-model mean. Interestingly, the construction of a weighted mean also provides insight into model performance and dependence between the models. This weighting methodology is robust to both model and metric choices and therefore has potential applications throughout the climate and chemistry-climate modelling communities. © 2020 Author(s).
语种英语
scopus关键词atmospheric chemistry; climate modeling; ozone; ozone depletion
来源期刊Atmospheric Chemistry and Physics
文献类型期刊论文
条目标识符http://gcip.llas.ac.cn/handle/2XKMVOVA/141139
作者单位Lancaster Environment Centre, Lancaster University, Lancaster, United Kingdom; Centre of Excellence in Environmental Data Science, Lancaster University, Lancaster, United Kingdom; British Antarctic Survey, Cambridge, United Kingdom; National Center for Atmospheric Research (NCAR), Boulder, CO, United States; Department of Chemistry, University of Cambridge, Cambridge, United Kingdom; National Centre for Atmospheric Science (NCAS), Leeds, LS29PH, United Kingdom; National Institute for Environmental Studies (NIES), Tsukuba, Japan; LATMOS, Institut Pierre Simon Laplace (IPSL), Paris, France; Meteorological Research Institute (MRI), Tsukuba, Japan; Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft-und Raumfahrt (DLR), Oberpfaffenhofen, Germany; Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany; Institut für Meteorologie, Freie Universität Berlin, Berlin, Germany; Environment and Climate Change Canada, Montreal, Canada; CNRM, Université de Toulouse, Méteó-France...
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Amos M.,Young P.J.,Scott Hosking J.,et al. Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence[J],2020,20(16).
APA Amos M..,Young P.J..,Scott Hosking J..,Lamarque J.-F..,Luke Abraham N..,...&Yamashita Y..(2020).Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence.Atmospheric Chemistry and Physics,20(16).
MLA Amos M.,et al."Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence".Atmospheric Chemistry and Physics 20.16(2020).
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