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

Matt Amos (1) , Paul J. Young (2, 1) , J. Scott Hosking (3) , Jean-François Lamarque (4) , Nathan Luke Abraham (5, 6) , Hideharu Akiyoshi (7) , Alexander T. Archibald (5, 6) , Slimane Bekki (8) , Makoto Deushi (9) , Patrick Jöckel (10) , Douglas Kinnison (11) , Ole Kirner (12) , Markus Kunze (13) , Marion Marchand (8) , David A. Plummer (14) , David Saint-Martin (15) , Kengo Sudo (16, 17) , Simone Tilmes (4) , Yousuke Yamashita (17)

1 Lancaster Environment Centre
2 CEEDS - Centre for Excellence in Environmental Data Science
3 BAS - British Antarctic Survey
4 ACOML - Atmospheric Chemistry Observations and Modeling Laboratory
5 Department of Chemistry [Cambridge, UK]
6 NCAS - National Centre for Atmospheric Science [Leeds]
7 NIES - National Institute for Environmental Studies
8 STRATO - LATMOS
9 MRI - Meteorological Research Institute [Tsukuba]
10 IPA - DLR Institut für Physik der Atmosphäre = DLR Institute of Atmospheric Physics
11 NCAR - National Center for Atmospheric Research [Boulder]
12 SCC - Steinbuch Centre for Computing [Karlsruhe]
13 Institut für Meteorologie [Berlin]
14 CCCma - Canadian Centre for Climate Modelling and Analysis
15 CNRM - Centre national de recherches météorologiques
16 Graduate School of Environmental Studies [Nagoya]
17 JAMSTEC - Japan Agency for Marine-Earth Science and Technology

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.

Domaines: Physique Atmosphérique et Océanique [physics.ao-ph]; Climatologie

Matt Amos, Paul J. Young, J. Scott Hosking, Jean-François Lamarque, Nathan Luke Abraham, et al.. Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence. Atmospheric Chemistry and Physics, 2020, 20 (16), pp.9961-9977. ⟨10.5194/acp-20-9961-2020⟩. ⟨insu-02464298⟩