ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-10-2467-2010 Comprehensively accounting for the effect of giant CCN in cloud activation parameterizations Barahona D. 1 West R. E. L. 3 Stier P. 3 Romakkaniemi S. 4 Kokkola H. 5 Nenes A. 1 2 School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, USA School of Earth and Atmospheric Sciences, Georgia Institute of Technology, USA Atmospheric, Oceanic and Planetary Physics, Department of Physics, University of Oxford, UK Department of Physics and Mathematics, University of Eastern Finland, Finland Finnish Meteorological Institute, Kuopio Unit, Finland 11 03 2010 10 5 2467 2473 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/10/2467/2010/acp-10-2467-2010.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/10/2467/2010/acp-10-2467-2010.pdf

Large cloud condensation nuclei (CCN) (e.g., aged dust particles and seasalt) cannot attain their equilibrium size during the typical timescale of cloud droplet activation. Cloud activation parameterizations applied to aerosol with a large fraction of large CCN often do not account for this limitation adequately and can give biased predictions of cloud droplet number concentration (CDNC). Here we present a simple approach to address this problem that can easily be incorporated into cloud activation parameterizations. This method is demonstrated with activation parameterizations based on the "population splitting" concept of Nenes and Seinfeld (2003); it is shown that accounting for large CCN effects eliminates a positive bias in CDNC where the aerosol dry geometric diameter is greater than 0.5 μm. The method proposed here can also be extended to include the water vapor depletion from pre-existing droplets and ice crystals in global and regional atmospheric models.

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