Kinne, S. and Schulz, M. and Textor, C. and Guibert, S. and Balkanski, Y. and Bauer, S. E. and Berntsen, T. and Berglen, T. F. and Boucher, O. and Chin, M. and Collins, W. and Dentener, F. and Diehl, T. and Easter, R. and Feichter, J. and Fillmore, D. and Ghan, S. and Ginoux, P. and Gong, S. and Grini, A. and Hendricks, J. and Herzog, M. and Horowitz, L. and Isaksen, I. and Iversen, T. and Kirkevåg, A. and Kloster, S. and Koch, D. and Kristjansson, J. E. and Krol, M. and Lauer, A. and Lamarque, J. F. and Lesins, G. and Liu, X. and Lohmann, U. and Montanaro, V. and Myhre, G. and Penner, J. E. and Pitari, G. and Reddy, S. and Seland, O. and Stier, P. and Takemura, T. and Tie, X. (2006) An AeroCom initial assessment – optical properties in aerosol component modules of global models. Atmospheric Chemistry and Physics, 6 (7). pp. 1815-1834. ISSN 1680-7316 http://resolver.caltech.edu/CaltechAUTHORS:KINacp06
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The AeroCom exercise diagnoses multi-component aerosol modules in global modeling. In an initial assessment simulated global distributions for mass and mid-visible aerosol optical thickness (aot) were compared among 20 different modules. Model diversity was also explored in the context of previous comparisons. For the component combined aot general agreement has improved for the annual global mean. At 0.11 to 0.14, simulated aot values are at the lower end of global averages suggested by remote sensing from ground (AERONET ca. 0.135) and space (satellite composite ca. 0.15). More detailed comparisons, however, reveal that larger differences in regional distribution and significant differences in compositional mixture remain. Of particular concern are large model diversities for contributions by dust and carbonaceous aerosol, because they lead to significant uncertainty in aerosol absorption (aab). Since aot and aab, both, influence the aerosol impact on the radiative energy-balance, the aerosol (direct) forcing uncertainty in modeling is larger than differences in aot might suggest. New diagnostic approaches are proposed to trace model differences in terms of aerosol processing and transport: These include the prescription of common input (e.g. amount, size and injection of aerosol component emissions) and the use of observational capabilities from ground (e.g. measurements networks) or space (e.g. correlations between aerosol and clouds).
|Additional Information:||© 2006 Author(s). This work is licensed under a Creative Commons License. Published by Copernicus GmbH on behalf of the European Geosciences Union. Received: 25 May 2005 – Published in Atmos. Chem. Phys. Discuss.: 8 September 2005; Revised: 6 February 2006 – Accepted: 13 February 2006 – Published: 29 May 2006 The data comparisons would not have been possible without the support by the various satellite retrievals groups and data-centers in the US and the support of the AERONET community. In particular, we like to acknowledge the support by the AERONET staff lead by Brent Holben in the US and Philippe Goloub in Europe and we thank the site managers of the 12 AERONET sites, whose data were used in local comparisons. We also acknowledge access and assessment help for the many global aerosol data-sets from satellite retrievals, including O. Torres for the TOMS data, L. Remer for the MODIS data, R. Kahn and J. Martonchik for the MISR data, J.-L. Deuzé and P. Lallart for the POLDER data, I. Geogdzhayev and M. Mishchenko for the 2-channel AVHRR data and A. Ignatov for the 1-channel AVHRR data. Also acknowledged is the support for this study by the EU PHOENICS project. The contributions of O. Boucher forms part of the Climate Prediction Programme of the UK Department for the Environment, Food and Rural Affairs (DEFRA) under contract PECD 7/12/37.|
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|Deposited On:||08 Aug 2006|
|Last Modified:||26 Dec 2012 08:58|
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