Parameterization of cloud droplet formation for global and regional models: including adsorption activation from insoluble CCN
1School of Chemical and Biomolecular Engineering, Georgia Institute of Technology, Atlanta, GA, USA
2School of Earth and Atmospheric Sciences, Georgia Institute of Technology, Atlanta, GA, USA
Abstract. Dust and black carbon aerosol have long been known to have potentially important and diverse impacts on cloud droplet formation. Most studies to date focus on the soluble fraction of such particles, and ignore interactions of the insoluble fraction with water vapor (even if known to be hydrophilic). To address this gap, we develop a new parameterization framework that considers cloud droplet formation within an ascending air parcel containing insoluble (but wettable) particles mixed with aerosol containing an appreciable soluble fraction. Activation of particles with a soluble fraction is described through well-established Köhler Theory, while the activation of hydrophilic insoluble particles is treated by "adsorption-activation" theory. In the latter, water vapor is adsorbed onto insoluble particles, the activity of which is described by a multilayer Frankel-Halsey-Hill (FHH) adsorption isotherm modified to account for particle curvature. We further develop FHH activation theory, and i) find combinations of the adsorption parameters AFHH, BFHH for which activation into cloud droplets is not possible, and, ii) express activation properties (critical supersaturation) that follow a simple power law with respect to dry particle diameter.
Parameterization formulations are developed for sectional and lognormal aerosol size distribution functions. The new parameterization is tested by comparing the parameterized cloud droplet number concentration against predictions with a detailed numerical cloud model, considering a wide range of particle populations, cloud updraft conditions, water vapor condensation coefficient and FHH adsorption isotherm characteristics. The agreement between parameterization and parcel model is excellent, with an average error of 10% and R2 ~0.98.