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

• 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), Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Sorbonne Université (SU)-Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - 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)-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
• Subjects: [PHYS.PHYS.PHYS-AO-PH]Physics [physics]/Physics [physics]/Atmospheric and Oceanic Physics [physics.ao-ph]; [SDU.STU.CL]Sciences of the Universe [physics]/Earth Sciences/Climatology; Antarctic; Antarc*
• Online Access: https://hal-insu.archives-ouvertes.fr/insu-02464298; https://hal-insu.archives-ouvertes.fr/insu-02464298/document; https://hal-insu.archives-ouvertes.fr/insu-02464298/file/acp-20-9961-2020.pdf; https://doi.org/10.5194/acp-20-9961-2020

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.