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

• Main Authors: Amos, Matt, Young, Paul J., Hosking, J. Scott, Lamarque, Jean-François, Abraham, N. 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
• Format: Article in Journal/Newspaper
• Language: English
• Published: European Geosciences Union 2020
• Subjects: ddc:004; DATA processing & computer science; info:eu-repo/classification/ddc/004; Antarctic; Antarc*
• Online Access: https://publikationen.bibliothek.kit.edu/1000123312; https://publikationen.bibliothek.kit.edu/1000123312/86536349; https://doi.org/10.5445/IR/1000123312

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.