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Volume 11, issue 9
Atmos. Chem. Phys., 11, 4073–4083, 2011
https://doi.org/10.5194/acp-11-4073-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Special issue: European Integrated Project on Aerosol-Cloud-Climate and Air...

Atmos. Chem. Phys., 11, 4073–4083, 2011
https://doi.org/10.5194/acp-11-4073-2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 04 May 2011

Research article | 04 May 2011

A simple representation of surface active organic aerosol in cloud droplet formation

N. L. Prisle1,2, M. Dal Maso1, and H. Kokkola2 N. L. Prisle et al.
  • 1University of Helsinki, Department of Physics, P.O. Box 48, 00014, University of Helsinki, Finland
  • 2Finnish Meteorological Institute, Kuopio Unit, P.O. Box 1627, 70211, Kuopio, Finland

Abstract. Atmospheric aerosols often contain surface active organics. Surface activity can affect cloud droplet formation through both surface partitioning and surface tension reduction in activating droplets. However, a comprehensive thermodynamic account for these effects in Köhler modeling is computationally demanding and requires knowledge of both droplet composition and component molecular properties, which is generally unavailable. Here, a simple representation of activation properties for surface active organics is introduced and compared against detailed model predictions and laboratory measurements of CCN activity for mixed surfactant-salt particles from the literature. This simple organic representation is seen to work well for aerosol organic-inorganic composition ranges typically found in the atmosphere, and agreement with both experiments and detailed model predictions increases with surfactant strength. The simple representation does not require resolution of the organic aerosol composition and relies solely on properties of the organic fraction that can be measured directly with available techniques. It can thus potentially be applied to complex and ambient surface active aerosols. It is not computationally demanding, and therefore has high potential for implementation to atmospheric models accounting for cloud microphysics.

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