PNNL

Abstract

It is commonly assumed 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 () proposed by Riemer andWest (ACP, 13, 11423-11439, 2013) to quantify the 5 degree of external and 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 a range of scenarios that cover different conditions of aerosol aging. We show that mixing state information does indeed become unimportant for more internally-mixed populations, more precisely for populations with larger than 60%. For more externally-mixed populations ( below 20%) the relationship of and the error in CCN predictions is not unique, and ranges from lower than 40% to about 10 150%, depending on the underlying aerosol population and the environmental supersaturation. We explain the reasons for this behavior with detailed process analyses.