Open AccessDesigning Detection and Attribution Simulations for CMIP6 to Optimize the Estimation of Greenhouse Gas–Induced Warming
Author(s) -
Aurélien Ribes,
Nathan P. Gillett,
Francis W. Zwiers
Publication year - 2015
Publication title -
journal of climate
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.315
H-Index - 287
eISSN - 1520-0442
pISSN - 0894-8755
DOI - 10.1175/jcli-d-14-00691.1
Subject(s) - greenhouse gas , environmental science , forcing (mathematics) , climatology , climate model , transient climate simulation , climate change , monte carlo method , atmospheric sciences , radiative forcing , meteorology , coupled model intercomparison project , global warming , aerosol , statistics , mathematics , ecology , physics , biology , geology
Climate 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.