Metric to Quantify the Importance of Mixing State for CCN Activity

The mixing state of the aerosol population depends on the distribution of chemical compounds in individual particles contained in the population. Field observations demonstrate that ambient aerosol mixing states can be complex; moreover the mixing state is always evolving due to atmospheric aging processes such as coagulation, condensation of secondary aerosol species, and heterogeneous reactions. This mixing state evolution modifies CCN properties of the aerosol population.

It is a conventional belief that models are more prone to errors in predicted CCN concentrations when the aerosol populations are externally mixed. However, it has been difficult to rigorously investigate this assumption because appropriate metrics for mixing state were lacking and metrics needed to quantify the error in CCN concentrations due to mixing state effects were unavailable. In this work we use the mixing state index (χ) proposed by Riemer and West (ACP, 2013) to rigorously quantify the degree of external/internal mixing of aerosol populations. We combine this metric with particle-resolved model simulations to quantify error in CCN predictions when mixing state information is neglected, exploring about 400 scenarios that cover different extent of aerosol aging. We show that mixing state information does indeed become unimportant for more internally-mixed populations, more precisely for populations with χ larger than 0.6. For more externally-mixed populations (χ below 0.2) the relationship of χ and the error in CCN predictions is not unique, but ranges from about −40% to about 150%, depending on the underlying aerosol population and the environmental supersaturation. We explain the reasons for this behavior with detailed process analyses.

This study presents an objective method to quantify the impacts of mixing state for estimating CCN concentrations. With increasing availability of particle-level measurements from field campaigns, the approach is useful in quantifying mixing state diversity in different environments and provides further insights about the evolution of and the uncertainties in aerosol composition-dependent quantities, such as CCN concentrations and optical properties pertaining to aerosol-cloud-climate interactions.