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

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

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. ... mehr

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Verlagsausgabe §
DOI: 10.5445/IR/1000123312
Veröffentlicht am 03.09.2020
Originalveröffentlichung
DOI: 10.5194/acp-20-9961-2020
Scopus
Zitationen: 3
Web of Science
Zitationen: 3
Dimensions
Zitationen: 4
Cover der Publikation
Zugehörige Institution(en) am KIT Steinbuch Centre for Computing (SCC)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1680-7324
KITopen-ID: 1000123312
HGF-Programm 46.11.01 (POF III, LK 01) Computational Science and Mathematical Methods
Erschienen in Atmospheric chemistry and physics
Verlag European Geosciences Union (EGU)
Band 20
Heft 16
Seiten 9961–9977
Vorab online veröffentlicht am 26.08.2020
Nachgewiesen in Web of Science
Dimensions
Scopus
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