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Comparison of Effective Radiative Forcing Calculations Using Multiple Methods, Drivers, and Models
Author(s) -
Tang T.,
Shindell D.,
Faluvegi G.,
Myhre G.,
Olivié D.,
Voulgarakis A.,
Kasoar M.,
Andrews T.,
Boucher O.,
Forster P.M.,
Hodnebrog Ø.,
Iversen T.,
Kirkevåg A.,
Lamarque J.F.,
Richardson T.,
Samset B.H.,
Stjern C.W.,
Takemura T.,
Smith C.
Publication year - 2019
Publication title -
journal of geophysical research: atmospheres
Language(s) - English
Resource type - Journals
eISSN - 2169-8996
pISSN - 2169-897X
DOI - 10.1029/2018jd030188
Subject(s) - radiative forcing , forcing (mathematics) , linear regression , radiative transfer , shortwave radiation , shortwave , environmental science , consistency (knowledge bases) , climatology , atmospheric sciences , mathematics , meteorology , statistics , radiation , aerosol , physics , geology , geometry , quantum mechanics
We compare six methods of estimating effective radiative forcing (ERF) using a set of atmosphere‐ocean general circulation models. This is the first multiforcing agent, multimodel evaluation of ERF values calculated using different methods. We demonstrate that previously reported apparent consistency between the ERF values derived from fixed sea surface temperature simulations and linear regression holds for most climate forcings, excluding black carbon (BC). When land adjustment is accounted for, however, the fixed sea surface temperature ERF values are generally 10–30% larger than ERFs derived using linear regression across all forcing agents, with a much larger (~70–100%) discrepancy for BC. Except for BC, this difference can be largely reduced by either using radiative kernel techniques or by exponential regression. Responses of clouds and their effects on shortwave radiation show the strongest variability in all experiments, limiting the application of regression‐based ERF in small forcing simulations.