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

International audience 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...

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Bibliographic Details
Published in:Atmospheric Chemistry and Physics
Main Authors: Amos, Matt, Young, Paul J., Hosking, J. Scott, Lamarque, Jean-François, Abraham, Nathan Luke, Akiyoshi, Hideharu, Archibald, Alexander T., Bekki, Slimane, Deushi, Makoto, Jöckel, Patrick, Kinnison, Douglas, Kirner, Ole, Kunze, Markus, Marchand, Marion, Plummer, David A., Saint-Martin, David, Sudo, Kengo, Tilmes, Simone, Yamashita, Yousuke
Other Authors: Lancaster Environment Centre, Lancaster University, Centre for Excellence in Environmental Data Science (CEEDS), Lancaster University-UK Centre of Ecology and Hydrology (UKCEH), British Antarctic Survey (BAS), Natural Environment Research Council (NERC), Atmospheric Chemistry Observations and Modeling Laboratory (ACOML), National Center for Atmospheric Research Boulder (NCAR), Department of Chemistry Cambridge, UK, University of Cambridge UK (CAM), National Centre for Atmospheric Science Leeds (NCAS), National Institute for Environmental Studies (NIES), STRATO - LATMOS, Laboratoire Atmosphères, Milieux, Observations Spatiales (LATMOS), Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Meteorological Research Institute Tsukuba (MRI), Japan Meteorological Agency (JMA), DLR Institut für Physik der Atmosphäre (IPA), Deutsches Zentrum für Luft- und Raumfahrt Oberpfaffenhofen-Wessling (DLR), Steinbuch Centre for Computing Karlsruhe (SCC), Karlsruher Institut für Technologie (KIT), Institut für Meteorologie Berlin, Freie Universität Berlin, Canadian Centre for Climate Modelling and Analysis (CCCma), Environment and Climate Change Canada, Centre national de recherches météorologiques (CNRM), Institut national des sciences de l'Univers (INSU - CNRS)-Observatoire Midi-Pyrénées (OMP), Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Institut de Recherche pour le Développement (IRD)-Université Toulouse III - Paul Sabatier (UT3), Université de Toulouse (UT)-Université de Toulouse (UT)-Institut national des sciences de l'Univers (INSU - CNRS)-Centre National d'Études Spatiales Toulouse (CNES)-Centre National de la Recherche Scientifique (CNRS)-Météo-France-Centre National de la Recherche Scientifique (CNRS), Graduate School of Environmental Studies Nagoya, Nagoya University, Japan Agency for Marine-Earth Science and Technology (JAMSTEC)
Format: Article in Journal/Newspaper
Language:English
Published: HAL CCSD 2020
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Online Access:https://insu.hal.science/insu-02464298
https://insu.hal.science/insu-02464298/document
https://insu.hal.science/insu-02464298/file/acp-20-9961-2020.pdf
https://doi.org/10.5194/acp-20-9961-2020
Description
Summary:International audience 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.