Stochastic particle-resolved models for simulating atmospheric aerosol

Abstract:
Observations show that atmospheric aerosols can be composed of a complex mix of compounds such as soluble inorganic salts and acids, insoluble crustal materials, trace metals, and carbonaceous materials. The per-particle composition of aerosols, the so-called mixing state, is of crucial importance for assessing their macroscopic impacts on climate. However, tracking the mixing state in conventional aerosol models requires treating a multidimensional size distribution, which is computationally prohibitive. Therefore current models usually assume an internal mixture within one mode or size section. The uncertainties associated with this assumption, which artificially ages freshly emitted particles instantly, are not well quantified.

In this seminar I will present an aerosol model of a new type, the stochastic particle- resolved model PartMC-MOSAIC. This model tracks individual particles as they undergo chemical and physical transformations in the atmosphere. Hence aerosol impacts that depend on per-particle composition, e.g. optical properties and cloud condensation activity, can be represented in detail. I will illustrate the usefulness of this new approach by focusing on the aging process of black-carbon-containing particles. Field observations reveal that the composition of carbonaceous aerosol particles changes continuously after emission during their transport in the atmosphere. The adequate representation of these aging processes in models is a key challenge in determining the climate-relevant properties of black carbon, a short-lived climate forcer. Using PartMC-MOSAIC we are able to quantify the individual processes that contribute to the aging of the aerosol distribution and demonstrate the effect of aerosol mixing state on aging time scales, optical properties, and CCN activation properties.