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

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Published
  • Matt Amos
  • Paul Young
  • J. S. Hosking
  • Jean-François Lamarque
  • N. L. Abraham
  • Hideharu Akiyoshi
  • Alex Archibald
  • Slimane Bekki
  • Makoto Deushi
  • Patrick Jöckel
  • Douglas E. Kinnison
  • Ole Kirner
  • Markus Kunze
  • Marion Marchand
  • David A Plummer
  • D. Saint-Martin
  • Kengo Sudo
  • Simone Tilmes
  • Yousuke Yamashita
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<mark>Journal publication date</mark>26/08/2020
<mark>Journal</mark>Atmospheric Chemistry and Physics
Volume20
Number of pages17
Pages (from-to)9961–9977
Publication StatusPublished
<mark>Original language</mark>English

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.