AAAR 34th Annual Conference
October 12 - October 16, 2015
Hyatt Regency
Minneapolis, Minnesota, USA
Abstract View
Metrics to Quantify the Importance of Mixing State for CCN Activity
Joseph Ching, NICOLE RIEMER, Jeffrey H. Curtis, Jerome Fast, University of Illinois at Urbana-Champaign
Abstract Number: 684 Working Group: Aerosols, Clouds, and Climate
Abstract The mixing state of the aerosol population is the distribution of chemical compounds across the population. Field observations reveal that ambient aerosol mixing states can be complex. Even freshly emitted particles can contain multiple chemical species depending on the source characteristics, and the initial particle composition is further modified in the atmosphere as a result of aging processes such as coagulation, condensation of secondary aerosol species, and heterogeneous reactions. This has profound impacts on the evolution of cloud condensation nuclei (CCN) activity of aerosol populations.
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 (chi) proposed by Riemer and West (ACP, 13, 11423-11439, 2013) to rigorously quantify the degree of external/internal mixing of aerosol populations. This mixing state index is a scalar quantity, and varies between 0 (for completely external mixtures) and 1 (for completely internal mixtures) for any given aerosol population. 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 chi larger than 0.6. For more externally-mixed populations (chi below 0.2) the relationship of chi and the error in CCN predictions is not unique, and ranges from lower than 10% to about 150%, depending on the underlying aerosol population. We explain the reasons for this behavior with detailed process analyses.