TY - JOUR
T1 - Comparison of Effective Radiative Forcing Calculations Using Multiple Methods, Drivers, and Models
AU - Tang, T.
AU - Shindell, D.
AU - Faluvegi, G.
AU - Myhre, G.
AU - Olivié, D.
AU - Voulgarakis, A.
AU - Kasoar, M.
AU - Andrews, T.
AU - Boucher, O.
AU - Forster, P. M.
AU - Hodnebrog,
AU - Iversen, T.
AU - Kirkevåg, A.
AU - Lamarque, J. F.
AU - Richardson, T.
AU - Samset, B. H.
AU - Stjern, C. W.
AU - Takemura, T.
AU - Smith, C.
N1 - Funding Information:
We would like to acknowledge the helpful and constructive reviews that improved earlier version of this manuscript. The PDRMIP model output used in this study are available to public through the Norwegian NORSTORE data storage facility. We acknowledge the NASA High-End Computing Program through the NASA Center for Climate Simulation at Goddard Space Flight Center for computational resources to run the GISS-E2R model and support from NASA GISS. PDRMIP is partly funded through the Norwegian Research Council project NAPEX (project 229778). O. Boucher acknowledges HPC resources from CCRT under the gencmip6 allocation provided by GENCI (Grand Equipement National de Calcul Intensif). P. Forster and T. Richardson are supported by NERC grants NE/K007483/1 and NE/N006038/1. Ø. Hodnebrog was partly funded through the Norwegian Research Council project HYPRE (project 243942). A. Voulgarakis and M. Kasoar are supported by NERC under grant NE/K500872/1. HadGEM3-GA4 simulations used the MONSooN system supplied under the Joint Weather and Climate Research Programme of the Met Office and NERC. D. Olivié, A. Kirkevåg, and T. Iversen were supported by the Norwegian Research Council through the projects EVA (grant 229771), EarthClim (207711/E10), NOTUR (nn2345k), and NorStore (ns2345k). T. Takemura was supported by the supercomputer system of the National Institute for Environmental Studies, Japan, the Environment Research and Technology Development Fund (S-12-3) of the Ministry of the Environment, Japan and JSPS KAKENHI grant 15H01728 and 15 K12190. Computing resources for CESM1-CAM5 (ark:/85065/d7wd3xhc) simulations were provided by the Climate Simulation Laboratory at NCAR Computational and Information System Laboratory, sponsored by the National Science Foundation and other agencies.
Publisher Copyright:
©2019. American Geophysical Union. All Rights Reserved.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
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U2 - 10.1029/2018JD030188
DO - 10.1029/2018JD030188
M3 - Article
AN - SCOPUS:85064596208
VL - 124
SP - 4382
EP - 4394
JO - Journal of Geophysical Research
JF - Journal of Geophysical Research
SN - 0148-0227
IS - 8
ER -
