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

Amos, Matt and Young, Paul and Hosking, J. S. and Lamarque, Jean-François and Abraham, N. L. and Akiyoshi, Hideharu and Archibald, Alex and Bekki, Slimane and Deushi, Makoto and Jöckel, Patrick and Kinnison, Douglas E. and Kirner, Ole and Kunze, Markus and Marchand, Marion and Plummer, David A and Saint-Martin, D. and Sudo, Kengo and Tilmes, Simone and Yamashita, Yousuke (2020) Projecting ozone hole recovery using an ensemble of chemistry-climate models weighted by model performance and independence. Atmospheric Chemistry and Physics, 20. 9961–9977. ISSN 1680-7316

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Abstract

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.