Abstract
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.
| Original language | English |
|---|---|
| Pages (from-to) | 4382-4394 |
| Number of pages | 13 |
| Journal | Journal of Geophysical Research: Atmospheres |
| Volume | 124 |
| Issue number | 8 |
| DOIs | |
| Publication status | Published - Jan 1 2019 |
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All Science Journal Classification (ASJC) codes
- Geophysics
- Forestry
- Oceanography
- Aquatic Science
- Ecology
- Water Science and Technology
- Soil Science
- Geochemistry and Petrology
- Earth-Surface Processes
- Atmospheric Science
- Space and Planetary Science
- Earth and Planetary Sciences (miscellaneous)
- Palaeontology
Cite this
Comparison of Effective Radiative Forcing Calculations Using Multiple Methods, Drivers, and Models. / Tang, T.; Shindell, D.; Faluvegi, G.; Myhre, G.; Olivié, D.; Voulgarakis, A.; Kasoar, M.; Andrews, T.; Boucher, O.; Forster, P. M.; Hodnebrog; Iversen, T.; Kirkevåg, A.; Lamarque, J. F.; Richardson, T.; Samset, B. H.; Stjern, C. W.; Takemura, T.; Smith, C.
In: Journal of Geophysical Research: Atmospheres, Vol. 124, No. 8, 01.01.2019, p. 4382-4394.Research output: Contribution to journal › Article
}
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.
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.
UR - http://www.scopus.com/inward/record.url?scp=85064596208&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85064596208&partnerID=8YFLogxK
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 -