Atmos. Chem. Phys., 11, 7781-7816, 2011
www.atmos-chem-phys.net/11/7781/2011/
doi:10.5194/acp-11-7781-2011
© Author(s) 2011. This work is distributed
under the Creative Commons Attribution 3.0 License.
Global dust model intercomparison in AeroCom phase I
N. Huneeus1, M. Schulz1,2, Y. Balkanski1, J. Griesfeller1,2, J. Prospero3, S. Kinne4, S. Bauer5,6, O. Boucher8,*, M. Chin9, F. Dentener10, T. Diehl11,12, R. Easter13, D. Fillmore14, S. Ghan13, P. Ginoux15, A. Grini16,17, L. Horowitz15, D. Koch5,6,7, M. C. Krol18,19, W. Landing20, X. Liu13,21, N. Mahowald22, R. Miller6,23, J.-J. Morcrette24, G. Myhre16,25, J. Penner21, J. Perlwitz6,23, P. Stier26, T. Takemura27, and C. S. Zender28
1Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, France
2Meteorological Institut, Oslo, Norway
3Rosenstiel School of Marine and Atmospheric Science, University of Miami, Miami, FL., USA
4Max-Planck-Institut für Meteorologie, Hamburg, Germany
5The Earth Institute, Columbia University, New York, USA
6NASA Goddard Institute for Space Studies, New York, NY, USA
7US Department of Energy, Washington, DC, USA
8Met Office, Hadley Centre, Exeter, UK
9NASA Goddard Space Flight Center, Greenbelt, MD, USA
10European Comission, Joint Research Centre, Institute for Environment and Sustainability, Italy
11NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
12Universities Space Research Association, Columbia, Maryland, USA
13Pacific Northwest National Laboratory, Richland, WA, USA
14NCAR, Boulder, Colorado, USA
15NOAA, Geophysical Fluid Dynamics Laboratory, Princeton, New Jersey, USA
16Department of Geosciences, University of Oslo, Oslo, Norway
17Kongsberg Oil & Gas Technologies, Norway
18Utrecht University, Institute for Marine and Atmospheric Research, Utrecht, The Netherlands
19Wageningen University, Meteorology and Air Quality, Wageningen, The Netherlands
20Departement of Earth, Ocean and Atmospheric Science, Florida State University, Tallahassee, FL, USA
21Department of Atmospheric, Oceanic and Space Sciences, University of Michigan, Ann Arbor, MI, USA
22Departement of Earth and Atmospheric Sciences, Cornell University, Ithaca, New York, USA
23Department of Applied Physics and Applied Mathematics, Columbia University, New York, USA
24European Centre for Medium-Range Weather Forecasts, Reading, UK
25Center for International Climate and Environmental Research – Oslo (CICERO) Oslo, Norway
26Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, UK
27Research Institute for Applied Mechanics, Kyushu University, Fukuoka, Japan
28Department of Earth System Science, University of California, Irvine, USA
*now at: Laboratoire de Météorologie Dynamique, IPSL, CNRS/UPMC, Paris, France

Abstract. This study presents the results of a broad intercomparison of a total of 15 global aerosol models within the AeroCom project. Each model is compared to observations related to desert dust aerosols, their direct radiative effect, and their impact on the biogeochemical cycle, i.e., aerosol optical depth (AOD) and dust deposition. Additional comparisons to Angström exponent (AE), coarse mode AOD and dust surface concentrations are included to extend the assessment of model performance and to identify common biases present in models. These data comprise a benchmark dataset that is proposed for model inspection and future dust model development. There are large differences among the global models that simulate the dust cycle and its impact on climate. In general, models simulate the climatology of vertically integrated parameters (AOD and AE) within a factor of two whereas the total deposition and surface concentration are reproduced within a factor of 10. In addition, smaller mean normalized bias and root mean square errors are obtained for the climatology of AOD and AE than for total deposition and surface concentration. Characteristics of the datasets used and their uncertainties may influence these differences. Large uncertainties still exist with respect to the deposition fluxes in the southern oceans. Further measurements and model studies are necessary to assess the general model performance to reproduce dust deposition in ocean regions sensible to iron contributions. Models overestimate the wet deposition in regions dominated by dry deposition. They generally simulate more realistic surface concentration at stations downwind of the main sources than at remote ones. Most models simulate the gradient in AOD and AE between the different dusty regions. However the seasonality and magnitude of both variables is better simulated at African stations than Middle East ones. The models simulate the offshore transport of West Africa throughout the year but they overestimate the AOD and they transport too fine particles. The models also reproduce the dust transport across the Atlantic in the summer in terms of both AOD and AE but not so well in winter-spring nor the southward displacement of the dust cloud that is responsible of the dust transport into South America. Based on the dependency of AOD on aerosol burden and size distribution we use model bias with respect to AOD and AE to infer the bias of the dust emissions in Africa and the Middle East. According to this analysis we suggest that a range of possible emissions for North Africa is 400 to 2200 Tg yr−1 and in the Middle East 26 to 526 Tg yr−1.

Citation: Huneeus, N., Schulz, M., Balkanski, Y., Griesfeller, J., Prospero, J., Kinne, S., Bauer, S., Boucher, O., Chin, M., Dentener, F., Diehl, T., Easter, R., Fillmore, D., Ghan, S., Ginoux, P., Grini, A., Horowitz, L., Koch, D., Krol, M. C., Landing, W., Liu, X., Mahowald, N., Miller, R., Morcrette, J.-J., Myhre, G., Penner, J., Perlwitz, J., Stier, P., Takemura, T., and Zender, C. S.: Global dust model intercomparison in AeroCom phase I, Atmos. Chem. Phys., 11, 7781-7816, doi:10.5194/acp-11-7781-2011, 2011.
 
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