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© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 03 Feb 2020

Submitted as: research article | 03 Feb 2020

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A revised version of this preprint is currently under review for the journal ACP.

Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence

Matt Amos1, Paul J. Young1,2, J. Scott Hosking3, Jean-François Lamarque4, N. Luke Abraham5,6, Hideharu Akiyoshi7, Alexander T. Archibald5,6, Slimane Bekki8, Makoto Deushi9, Patrick Jöckel10, Douglas Kinnison4, Ole Kirner11, Markus Kunze12, Marion Marchand8, David A. Plummer13, David Saint-Martin14, Kengo Sudo15,16, Simone Tilmes4, and Yousuke Yamashita16 Matt Amos et al.
  • 1Lancaster University, Lancaster, UK
  • 2Centre for Excellence in Environmental Data Science, Lancaster University, Lancaster, UK
  • 3British Antarctic Survey, Cambridge, UK
  • 4National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
  • 5Department of Chemistry, University of Cambridge, Cambridge, UK
  • 6National Centre for Atmospheric Science (NCAS), UK
  • 7National Institute of Environmental Studies (NIES), Tsukuba, Japan
  • 8LATMOS, Institut Pierre Simon Laplace (IPSL), Paris, France
  • 9Meteorological Research Institute (MRI), Tsukuba, Japan
  • 10Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany
  • 11Steinbuch Centre for Computing, Karlsruhe Institute of Technology, Karlsruhe, Germany
  • 12Institut für Meteorologie, Freie Universität Berlin, Berlin, Germany
  • 13Environment and Climate Change Canada, Montréal, Canada
  • 14CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France
  • 15Graduate School of Environmental Studies, Nagoya University, Nagoya, Japan
  • 16Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan

Abstract. The current method for averaging model ensembles, which is to calculate a multi model mean, 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.

Matt Amos et al.

Interactive discussion

Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Matt Amos et al.

Model code and software

Weighting Analysis Scripts M. Amos

Matt Amos et al.


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Latest update: 02 Jun 2020
Publications Copernicus
Short summary
We present an updated projection of Antarctic ozone hole recovery using an ensemble of chemistry climate models. To do so we employ a method, more advanced and skilful than the current multi model mean standard, which is applicable to other ensemble analyses. It calculates the performance and similarity of the models, which we then use to weight the model. Calculating model similarity allows us to account for model which are constructed from similar components.
We present an updated projection of Antarctic ozone hole recovery using an ensemble of chemistry...