Designing detection and attribution simulations for CMIP6 to optimize the estimation of greenhouse gas–induced warming

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dc.contributor.authorRibes, Aurélien
dc.contributor.authorGillett, Nathan P.
dc.contributor.authorZwiers, Francis W.
dc.date.accessioned2025-04-10T20:27:26Z
dc.date.available2025-04-10T20:27:26Z
dc.date.issued2015
dc.description.abstractClimate change detection and attribution studies rely on historical simulations using specified combinations of forcings to quantify the contributions from greenhouse gases and other forcings to observed climate change. In the last CMIP5 exercise, in addition to the so-called all-forcings simulations, which are driven with a combination of anthropogenic and natural forcings, natural forcings–only and greenhouse gas–only simulations were prioritized among other possible experiments. This study addresses the question of optimally designing this set of experiments to estimate the recent greenhouse gas–induced warming, which is highly relevant to the problem of constraining estimates of the transient climate response. Based on Monte Carlo simulations and considering experimental designs with a fixed budget for the number of simulations that modeling centers can perform, the most accurate estimate of historical greenhouse gas–induced warming is obtained with a design using a combination of all-forcings, natural forcings–only, and aerosol forcing–only simulations. An investigation of optimal ensemble sizes, given the constraint on the total number of simulations, indicates that allocating larger ensemble sizes to weaker forcings, such as natural-only, is optimal.
dc.description.reviewstatusReviewed
dc.description.scholarlevelFaculty
dc.description.sponsorshipAR gratefully acknowledges support from the Canadian Sea Ice and Snow Evolution network (CanSISE; https://www.cansise.ca), PCIC, and CNRM that facilitated a recent research visit toCCCMAand PCIC. The authors are grateful to Slava Kharin and Qiuzi Wen for useful comments on a previous version of this manuscript.
dc.identifier.citationRibes, A., Gillett, N. P., & Zwiers, F. W. (2015). Designing detection and attribution simulations for CMIP6 to optimize the estimation of greenhouse gas–induced warming. Journal of Climate, 28(8), 3435–3438. https://doi.org/10.1175/JCLI-D-14-00691.1
dc.identifier.urihttps://doi.org/10.1175/JCLI-D-14-00691.1
dc.identifier.urihttps://hdl.handle.net/1828/21788
dc.language.isoen
dc.publisherJournal of Climate
dc.subjectUN SDG 13: Climate Action
dc.subject#journal article
dc.subjectPacific Climate Impacts Consortium (PCIC)
dc.subjectCanadian Centre for Climate Modelling and Analysis (CCCma)
dc.titleDesigning detection and attribution simulations for CMIP6 to optimize the estimation of greenhouse gas–induced warming
dc.typeArticle

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