Articles | Volume 9, issue 1
Geosci. Model Dev., 9, 111–124, 2016
Geosci. Model Dev., 9, 111–124, 2016

Model description paper 19 Jan 2016

Model description paper | 19 Jan 2016

Prediction of cloud condensation nuclei activity for organic compounds using functional group contribution methods

M. D. Petters1, S. M. Kreidenweis2, and P. J. Ziemann3 M. D. Petters et al.
  • 1Department of Marine, Earth, and Atmospheric Sciences, North Carolina State University, Raleigh, NC, USA
  • 2Department of Atmospheric Sciences, Colorado State University, Fort Collins, CO, USA
  • 3Department of Chemistry and Biochemistry, Colorado University, Boulder, CO, USA

Abstract. A wealth of recent laboratory and field experiments demonstrate that organic aerosol composition evolves with time in the atmosphere, leading to changes in the influence of the organic fraction to cloud condensation nuclei (CCN) spectra. There is a need for tools that can realistically represent the evolution of CCN activity to better predict indirect effects of organic aerosol on clouds and climate. This work describes a model to predict the CCN activity of organic compounds from functional group composition. Following previous methods in the literature, we test the ability of semi-empirical group contribution methods in Köhler theory to predict the effective hygroscopicity parameter, kappa. However, in our approach we also account for liquid–liquid phase boundaries to simulate phase-limited activation behavior. Model evaluation against a selected database of published laboratory measurements demonstrates that kappa can be predicted within a factor of 2. Simulation of homologous series is used to identify the relative effectiveness of different functional groups in increasing the CCN activity of weakly functionalized organic compounds. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote CCN activity while methylene and nitrate moieties inhibit CCN activity. The model can be incorporated into scale-bridging test beds such as the Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere (GECKO-A) to evaluate the evolution of kappa for a complex mix of organic compounds and to develop suitable parameterizations of CCN evolution for larger-scale models.

Short summary
Organic particles suspended in air serve as nucleation seeds for droplets in atmospheric clouds. Over time their chemical composition changes towards more functionalized compounds. This work presents a model that can predict an organic compounds' ability promote the nucleation of cloud drops from its functional group composition. Hydroxyl, carboxyl, aldehyde, hydroperoxide, carbonyl, and ether moieties promote droplet nucleation. Methylene and nitrate moieties inhibit droplet nucleation.