Articles | Volume 17, issue 12
Atmos. Chem. Phys., 17, 7445–7458, 2017
https://doi.org/10.5194/acp-17-7445-2017
Atmos. Chem. Phys., 17, 7445–7458, 2017
https://doi.org/10.5194/acp-17-7445-2017

Research article 21 Jun 2017

Research article | 21 Jun 2017

Metrics to quantify the importance of mixing state for CCN activity

Joseph Ching1,a, Jerome Fast1, Matthew West2, and Nicole Riemer3 Joseph Ching et al.
  • 1Atmospheric Sciences and Global Change Division, Pacific Northwest National Laboratory, Richland, WA, 99354, USA
  • 2Department of Mechanical Science and Engineering, University of Illinois at Urbana–Champaign, 1206 W. Green St., Urbana, IL 61801, USA
  • 3Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, 105 S Gregory St., Urbana, IL 61801, USA
  • anow at: Meteorological Research Institute, Japan Meteorological Agency, 1-1 Nagamine, Tsukuba 305-0052, Japan

Abstract. It is commonly assumed that models are more prone to errors in predicted cloud condensation nuclei (CCN) concentrations when the aerosol populations are externally mixed. In this work we investigate this assumption by using the mixing state index (χ) proposed by Riemer and West (2013) to quantify the 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 χ larger than 75 %. For more externally mixed populations (χ below 20 %) the relationship of χ and the error in CCN predictions is not unique and ranges from lower than −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.

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Short summary
The composition of individual aerosols affects their cloud condensation nuclei (CCN) properties, but is challenging to represent in models. This study quantifies the error in CCN calculations when per-particle information is neglected by using a metric for the composition diversity within a population. With more particle-level measurements from field campaigns, the approach is useful for quantifying uncertainties in composition-dependent quantities regarding aerosol–cloud–climate interactions.
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