Atmos. Chem. Phys., 9, 8697-8717, 2009
www.atmos-chem-phys.net/9/8697/2009/
doi:10.5194/acp-9-8697-2009
© Author(s) 2009. This work is distributed
under the Creative Commons Attribution 3.0 License.
Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data
J. Quaas1, Y. Ming2, S. Menon3,4, T. Takemura5, M. Wang6,13, J. E. Penner6, A. Gettelman7, U. Lohmann8, N. Bellouin9, O. Boucher9, A. M. Sayer10, G. E. Thomas10, A. McComiskey11, G. Feingold11, C. Hoose12, J. E. Kristjánsson12, X. Liu13, Y. Balkanski14, L. J. Donner2, P. A. Ginoux2, P. Stier10, B. Grandey10, J. Feichter1, I. Sednev3, S. E. Bauer4, D. Koch4, R. G. Grainger10, A. Kirkevåg15, T. Iversen12,15, Ø. Seland15, R. Easter13, S. J. Ghan13, P. J. Rasch13, H. Morrison7, J.-F. Lamarque7, M. J. Iacono16, S. Kinne1, and M. Schulz14
1Max Planck Institute for Meteorology, Hamburg, Germany
2Geophysical Fluid Dynamics Laboratory/NOAA, Princeton, USA
3Lawrence Berkeley National Laboratory, Berkeley, USA
4Goddard Institute for Space Studies/NASA, New York, USA
5Kyushu University, Fukoka, Japan
6University of Michigan, Ann Arbor, USA
7National Center for Atmospheric Research, Boulder, USA
8Institute for Atmospheric and Climate Science/ETH Zurich, Switzerland
9Met Office Hadley Centre, Exeter, UK
10Atmospheric, Oceanic and Planetary Physics, University of Oxford, UK
11NOAA Earth System Research Laboratory, Boulder, USA
12Department of Geosciences, University of Oslo, Norway
13Pacific Northwest National Laboratory, Richland, USA
14Laboratoire des Sciences du Climat et de l'Environnement/IPSL, Gif-sur-Yvette, France
15Norwegian Meteorological Institute, Oslo, Norway
16Atmospheric and Environmental Research, Inc., Lexington, USA

Abstract. Aerosol 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.

Citation: Quaas, J., Ming, Y., Menon, S., Takemura, T., Wang, M., Penner, J. E., Gettelman, A., Lohmann, U., Bellouin, N., Boucher, O., Sayer, A. M., Thomas, G. E., McComiskey, A., Feingold, G., Hoose, C., Kristjánsson, J. E., Liu, X., Balkanski, Y., Donner, L. J., Ginoux, P. A., Stier, P., Grandey, B., Feichter, J., Sednev, I., Bauer, S. E., Koch, D., Grainger, R. G., Kirkevåg, A., Iversen, T., Seland, Ø., Easter, R., Ghan, S. J., Rasch, P. J., Morrison, H., Lamarque, J.-F., Iacono, M. J., Kinne, S., and Schulz, M.: Aerosol indirect effects – general circulation model intercomparison and evaluation with satellite data, Atmos. Chem. Phys., 9, 8697-8717, doi:10.5194/acp-9-8697-2009, 2009.
 
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