Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data

dc.contributor.authorQuaas, J.
dc.contributor.authorMing, Y.
dc.contributor.authorMenon, S.
dc.contributor.authorTakemura, T.
dc.contributor.authorWang, M.
dc.contributor.authorPenner, J. E.
dc.contributor.authorGettelman, A.
dc.contributor.authorLohmann, U.
dc.contributor.authorBellouin, N.
dc.contributor.authorBoucher, O.
dc.contributor.authorSayer, Andrew
dc.contributor.authorThomas, G. E.
dc.contributor.authorMcComiskey, A.
dc.contributor.authorFeingold, G.
dc.contributor.authorHoose, C.
dc.contributor.authorKristjánsson, J. E.
dc.contributor.authorLiu, X.
dc.contributor.authorBalkanski, Y.
dc.contributor.authorDonner, L. J.
dc.contributor.authorGinoux, P. A.
dc.contributor.authorStier, P.
dc.contributor.authorGrandey, B.
dc.contributor.authorFeichter, J.
dc.contributor.authorSednev, I.
dc.contributor.authorBauer, S. E.
dc.contributor.authorKoch, D.
dc.contributor.authorGrainger, R. G.
dc.contributor.authorKirkev&aring
dc.contributor.authorG, A.
dc.contributor.authorIversen, T.
dc.contributor.authorSeland, Ø
dc.contributor.authorEaster, R.
dc.contributor.authorGhan, S. J.
dc.contributor.authorRasch, P. J.
dc.contributor.authorMorrison, H.
dc.contributor.authorLamarque, J.-F.
dc.contributor.authorIacono, M. J.
dc.contributor.authorKinne, S.
dc.contributor.authorSchulz, M.
dc.date.accessioned2024-04-29T17:01:33Z
dc.date.available2024-04-29T17:01:33Z
dc.date.issued2009-11-16
dc.description.abstractAerosol indirect effects continue to constitute one of the most important uncertainties for anthropogenic climate perturbations. Within the international AEROCOM initiative, the representation of aerosol-cloud-radiation interactions in ten different general circulation models (GCMs) is evaluated using three satellite datasets. The focus is on stratiform liquid water clouds since most GCMs do not include ice nucleation effects, and none of the model explicitly parameterises aerosol effects on convective clouds. We compute statistical relationships between aerosol optical depth (τa) and various cloud and radiation quantities in a manner that is consistent between the models and the satellite data. It is found that the model-simulated influence of aerosols on cloud droplet number concentration (Nd) compares relatively well to the satellite data at least over the ocean. The relationship between τa and liquid water path is simulated much too strongly by the models. This suggests that the implementation of the second aerosol indirect effect mainly in terms of an autoconversion parameterisation has to be revisited in the GCMs. A positive relationship between total cloud fraction (fcld) and τa as found in the satellite data is simulated by the majority of the models, albeit less strongly than that in the satellite data in most of them. In a discussion of the hypotheses proposed in the literature to explain the satellite-derived strong fcld–τa relationship, our results indicate that none can be identified as a unique explanation. Relationships similar to the ones found in satellite data between τa and cloud top temperature or outgoing long-wave radiation (OLR) are simulated by only a few GCMs. The GCMs that simulate a negative OLR–τa relationship show a strong positive correlation between τa and fcld. The short-wave total aerosol radiative forcing as simulated by the GCMs is strongly influenced by the simulated anthropogenic fraction of τa, and parameterisation assumptions such as a lower bound on Nd. Nevertheless, the strengths of the statistical relationships are good predictors for the aerosol forcings in the models. An estimate of the total short-wave aerosol forcing inferred from the combination of these predictors for the modelled forcings with the satellite-derived statistical relationships yields a global annual mean value of −1.5±0.5 Wm⁻² . In an alternative approach, the radiative flux perturbation due to anthropogenic aerosols can be broken down into a component over the cloud-free portion of the globe (approximately the aerosol direct effect) and a component over the cloudy portion of the globe (approximately the aerosol indirect effect). An estimate obtained by scaling these simulated clear- and cloudy-sky forcings with estimates of anthropogenic τa and satellite-retrieved Nd–τa regression slopes, respectively, yields a global, annual-mean aerosol direct effect estimate of −0.4±0.2 Wm⁻² and a cloudy-sky (aerosol indirect effect) estimate of −0.7±0.5 Wm⁻² , with a total estimate of −1.2±0.4 Wm⁻²
dc.description.sponsorshipCERES SSF data were obtained from the US National Aeronautics and Space Agency (NASA) Langley Research Center Atmospheric Sciences Data Center. The MODIS data used in this study were acquired as part of NASA’s Earth Science Enterprise. The MODIS Science Teams developed the algorithms for the ?a retrievals. The authors would like to thank the data distribution centers for their support. Computing time for the ECHAM5 model was provided by the German High Performance Computing Centre for Climate and Earth System Research (Deutsches Klimarechenzentrum, DKRZ). J. Q. was supported by an Emmy Noether grant of the German Research Foundation (DFG). The Met Office Hadley Centre is funded by the Joint DECC, Defra and MoD Integrated Climate Programme – (DECC/Defra) GA01101, (MoD) CBC/2B/0417 Annex C5. The work with CAM-Oslo was supported by the projects EUCAARI (European Integrated project No. 036833-2), IPY POLARCAT and NorClim (Norwegian Research Council grant No. 178246), and supported by the Norwegian Research Council’s program for Supercomputing through a grant of computer time. The work with LMDzT-INCA was supported by EUCAARI. The GFDL model was developed collectively by the GFDL Global Atmospheric Model Development Team (GAMDT). The Pacific Northwest National Laboratory is operated for the DOE by Battelle Memorial Institute under contract DE-AC06-76RLO 1830. The work at Lawrence Berkeley National Laboratory was supported by the US Department of Energy under Contract No. DE-AC02-05CH11231. S. M. acknowledges funding from the NASA MAP and DOE ARM program. The work of X. L., S. G. and R. E. was funded by US Department of Energy Atmospheric Radiation Measurement program and Scientific Discovery through Advanced Computing program. J. E. P. and M. W. were funded by NSF grant ATM 0609836 and NASA grant NNX08AL83G and acknowledge computer time provided by the NCAR CISL. The authors thank Leon Rotstayn, Ralph Kahn, Bjorn Stevens and Robert Wood for their helpful comments.
dc.description.urihttps://acp.copernicus.org/articles/9/8697/2009/
dc.format.extent21 pages
dc.genrejournal articles
dc.identifierdoi:10.13016/m2wqbq-sq2s
dc.identifier.citationQuaas, J., Y. Ming, S. Menon, T. Takemura, M. Wang, J. E. Penner, A. Gettelman, et al. “Aerosol Indirect Effects – General Circulation Model Intercomparison and Evaluation with Satellite Data.” Atmospheric Chemistry and Physics 9, no. 22 (November 16, 2009): 8697–8717. https://doi.org/10.5194/acp-9-8697-2009.
dc.identifier.urihttps://doi.org/10.5194/acp-9-8697-2009
dc.identifier.urihttp://hdl.handle.net/11603/33441
dc.language.isoen_US
dc.publisherEGU
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC GESTAR II
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rightsPublic Domain
dc.rights.urihttps://creativecommons.org/publicdomain/mark/1.0/
dc.titleAerosol indirect effects – general circulation model intercomparison and evaluation with satellite data
dc.typeText
dcterms.creatorhttps://orcid.org/0000-0001-9149-1789

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