TY - JOUR
T1 - Efficacy of Climate Forcings in PDRMIP Models
AU - Richardson, T. B.
AU - Forster, P. M.
AU - Smith, C. J.
AU - Maycock, A. C.
AU - Wood, T.
AU - Andrews, T.
AU - Boucher, O.
AU - Faluvegi, G.
AU - Fläschner, D.
AU - Hodnebrog,
AU - Kasoar, M.
AU - Kirkevåg, A.
AU - Lamarque, J. F.
AU - Mülmenstädt, J.
AU - Myhre, G.
AU - Olivié, D.
AU - Portmann, R. W.
AU - Samset, B. H.
AU - Shawki, D.
AU - Shindell, D.
AU - Stier, P.
AU - Takemura, T.
AU - Voulgarakis, A.
AU - Watson-Parris, D.
N1 - Funding Information:
The PDRMIP model output is publicly available (for data access, visit http://www.cicero.uio.no/en/PDRMIP/PDRMIP‐data‐access ). T. B. R. and P. M. F. were supported by Natural Environment Research Council Grant NE/N006038/1. P. M. F., A. C. M., G. M., B. H. S., O. B., C. J. S., and T. A. acknowledge support from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 820829 (CONSTRAIN). A. C. M. was supported by a NERC Independent Research Fellowship (NE/M018199/1). T. A. was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). B. H. S., G. M., and Ø. H. were funded by the Research Council of Norway, through the grant NAPEX (229778). D. S. and G. F. thank NASA GISS for funding and acknowledge the NASA High‐End Computing Program through the NASA Center for Climate Simulation at Goddard Space Flight Center for computational resources. O. B. acknowledges HPC resources from TGCC under the gencmip6 allocation provided by GENCI (Grand Equipement National de Calcul Intensif). T. T. is supported by the NEC SX‐ACE supercomputer system of the National Institute for Environmental Studies, Japan, the Environmental Research and Technology Development Fund (S‐12‐3) of the Ministry of Environment, Japan, and JSPS KAKENHI Grants JP15H01728 and JP15K12190. M. K., D. S., and A. V. were supported by the Natural Environment Research Council under Grant NE/K500872/1 and from the Grantham Institute at Imperial College. Simulations with HadGEM2 and HadGEM3‐GA4 were performed using the MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, which is a strategic partnership between the Met Office and the Natural Environment Research Council. D. O. and A. K. were supported by the Norwegian Research Council through the projects EVA (229771), EarthClim (207711/E10), NOTUR (nn2345k), and NorStore (ns2345k). D. W. P. and P. S. acknowledge funding from Natural Environment Research Council Projects NE/J022624/1 (GASSP), NE/L01355X/1 (CLARIFY), and NE/P013406/1 (A‐CURE). P. S. also acknowledges funding from the European Research Council project RECAP under the European Union's Horizon 2020 Research and Innovation Programme with Grant Agreement 724602. The ECHAM6‐HAM2 simulations were performed using the ARCHER UK National Supercomputing Service. The ECHAM‐HAMMOZ model is developed by a consortium composed of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute, and the Leibniz Institute for Tropospheric Research and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich.
Funding Information:
The PDRMIP model output is publicly available (for data access, visit http://www.cicero.uio.no/en/PDRMIP/PDRMIP-data-access). T. B. R. and P. M. F. were supported by Natural Environment Research Council Grant NE/N006038/1. P. M. F., A. C. M., G. M., B. H. S., O. B., C. J. S., and T. A. acknowledge support from the European Union's Horizon 2020 Research and Innovation Programme under Grant Agreement 820829 (CONSTRAIN). A. C. M. was supported by a NERC Independent Research Fellowship (NE/M018199/1). T. A. was supported by the Joint UK BEIS/Defra Met Office Hadley Centre Climate Programme (GA01101). B. H. S., G. M., and Ø. H. were funded by the Research Council of Norway, through the grant NAPEX (229778). D. S. and G. F. thank NASA GISS for funding and acknowledge the NASA High-End Computing Program through the NASA Center for Climate Simulation at Goddard Space Flight Center for computational resources. O. B. acknowledges HPC resources from TGCC under the gencmip6 allocation provided by GENCI (Grand Equipement National de Calcul Intensif). T. T. is supported by the NEC SX-ACE supercomputer system of the National Institute for Environmental Studies, Japan, the Environmental Research and Technology Development Fund (S-12-3) of the Ministry of Environment, Japan, and JSPS KAKENHI Grants JP15H01728 and JP15K12190. M. K., D. S., and A. V. were supported by the Natural Environment Research Council under Grant NE/K500872/1 and from the Grantham Institute at Imperial College. Simulations with HadGEM2 and HadGEM3-GA4 were performed using the MONSooN system, a collaborative facility supplied under the Joint Weather and Climate Research Programme, which is a strategic partnership between the Met Office and the Natural Environment Research Council. D. O. and A. K. were supported by the Norwegian Research Council through the projects EVA (229771), EarthClim (207711/E10), NOTUR (nn2345k), and NorStore (ns2345k). D. W. P. and P. S. acknowledge funding from Natural Environment Research Council Projects NE/J022624/1 (GASSP), NE/L01355X/1 (CLARIFY), and NE/P013406/1 (A-CURE). P. S. also acknowledges funding from the European Research Council project RECAP under the European Union's Horizon 2020 Research and Innovation Programme with Grant Agreement 724602. The ECHAM6-HAM2 simulations were performed using the ARCHER UK National Supercomputing Service. The ECHAM-HAMMOZ model is developed by a consortium composed of ETH Zurich, Max Planck Institut für Meteorologie, Forschungszentrum Jülich, University of Oxford, the Finnish Meteorological Institute, and the Leibniz Institute for Tropospheric Research and managed by the Center for Climate Systems Modeling (C2SM) at ETH Zurich.
Publisher Copyright:
©2019. The Authors.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Quantifying the efficacy of different climate forcings is important for understanding the real-world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near-surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy-induced bias of ±0.4 K in equilibrium climate sensitivity to CO2 doubling when estimated using the historical GSAT trend.
AB - Quantifying the efficacy of different climate forcings is important for understanding the real-world climate sensitivity. This study presents a systematic multimodel analysis of different climate driver efficacies using simulations from the Precipitation Driver and Response Model Intercomparison Project (PDRMIP). Efficacies calculated from instantaneous radiative forcing deviate considerably from unity across forcing agents and models. Effective radiative forcing (ERF) is a better predictor of global mean near-surface air temperature (GSAT) change. Efficacies are closest to one when ERF is computed using fixed sea surface temperature experiments and adjusted for land surface temperature changes using radiative kernels. Multimodel mean efficacies based on ERF are close to one for global perturbations of methane, sulfate, black carbon, and insolation, but there is notable intermodel spread. We do not find robust evidence that the geographic location of sulfate aerosol affects its efficacy. GSAT is found to respond more slowly to aerosol forcing than CO2 in the early stages of simulations. Despite these differences, we find that there is no evidence for an efficacy effect on historical GSAT trend estimates based on simulations with an impulse response model, nor on the resulting estimates of climate sensitivity derived from the historical period. However, the considerable intermodel spread in the computed efficacies means that we cannot rule out an efficacy-induced bias of ±0.4 K in equilibrium climate sensitivity to CO2 doubling when estimated using the historical GSAT trend.
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U2 - 10.1029/2019JD030581
DO - 10.1029/2019JD030581
M3 - Article
AN - SCOPUS:85076347242
SN - 2169-897X
VL - 124
SP - 12824
EP - 12844
JO - Journal of Geophysical Research: Atmospheres
JF - Journal of Geophysical Research: Atmospheres
IS - 23
ER -
