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Title: Metrics to quantify the importance of mixing state for CCN activity

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.
Authors:
ORCiD logo [1] ;  [1] ; ORCiD logo [2] ; ORCiD logo [3]
  1. Pacific Northwest National Lab. (PNNL), Richland, WA (United States). Atmospheric Sciences and Global Change Division
  2. University of Illinois at Urbana–Champaign, Urbana (United States). Department of Mechanical Science and Engineering
  3. University of Illinois at Urbana–Champaign, Urbana (United States). Department of Atmospheric Sciences
Publication Date:
Report Number(s):
PNNL-SA-122612
Journal ID: ISSN 1680-7324; KP1701000
Grant/Contract Number:
AC05-76RL01830; SC0011771
Type:
Accepted Manuscript
Journal Name:
Atmospheric Chemistry and Physics (Online)
Additional Journal Information:
Journal Name: Atmospheric Chemistry and Physics (Online); Journal Volume: 17; Journal Issue: 12; Journal ID: ISSN 1680-7324
Publisher:
European Geosciences Union
Research Org:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org:
USDOE Office of Science (SC)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; CCN; mixing state; aerosol; particles
OSTI Identifier:
1367378

Ching, Joseph, Fast, Jerome, West, Matthew, and Riemer, Nicole. Metrics to quantify the importance of mixing state for CCN activity. United States: N. p., Web. doi:10.5194/acp-17-7445-2017.
Ching, Joseph, Fast, Jerome, West, Matthew, & Riemer, Nicole. Metrics to quantify the importance of mixing state for CCN activity. United States. doi:10.5194/acp-17-7445-2017.
Ching, Joseph, Fast, Jerome, West, Matthew, and Riemer, Nicole. 2017. "Metrics to quantify the importance of mixing state for CCN activity". United States. doi:10.5194/acp-17-7445-2017. https://www.osti.gov/servlets/purl/1367378.
@article{osti_1367378,
title = {Metrics to quantify the importance of mixing state for CCN activity},
author = {Ching, Joseph and Fast, Jerome and West, Matthew and Riemer, Nicole},
abstractNote = {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.},
doi = {10.5194/acp-17-7445-2017},
journal = {Atmospheric Chemistry and Physics (Online)},
number = 12,
volume = 17,
place = {United States},
year = {2017},
month = {6}
}