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Title: Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration

Abstract

A 4-year record of aerosol size and hygroscopic growth factor distributions measured at the Department of Energy’s Southern Great Plains (SGP) site in Oklahoma, U.S. were used to estimate supersaturation (S)-dependent cloud condensation nuclei concentrations (N CCN). Baseline or reference N CCN(S) spectra were estimated using κ-Köhler Theory without any averaging of the measured distributions by creating matrices of size- and hygroscopicity-dependent number concentration (N) and then integrating for S > critical supersaturation (S c) calculated for the same size and hygroscopicity pairs. Moreover, those estimates were first compared with directly measured N CCN at the same site. Subsequently, N CCN was calculated using the same dataset but with an array of simplified treatments in which the aerosol was assumed to be either an internal or an external mixture and the hygroscopicity either assumed or based on averages derived from the growth factor distributions. The CCN spectra calculated using the simplified treatments were compared with those calculated using the baseline approach to evaluate the error introduced with commonly used approximations.

Authors:
ORCiD logo [1];  [2];  [1]
  1. Texas A & M Univ., College Station, TX (United States)
  2. Univ. of Colorado, Boulder, CO (United States); ; National Oceanic and Atmospheric Administration (NOAA), Boulder, CO (United States). Earth System Research Lab.
Publication Date:
Research Org.:
Colorado State Univ., Fort Collins, (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Atmospheric Radiation Measurement (ARM)
OSTI Identifier:
1501858
Grant/Contract Number:  
SC0016051
Resource Type:
Accepted Manuscript
Journal Name:
Atmosphere (Basel)
Additional Journal Information:
Journal Name: Atmosphere (Basel); Journal Volume: 9; Journal Issue: 2; Journal ID: ISSN 2073-4433
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; aerosol; CCN; hygroscopicity; mixing state

Citation Formats

Mahish, Manasi, Jefferson, Anne, and Collins, Don. Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration. United States: N. p., 2018. Web. doi:10.3390/atmos9020054.
Mahish, Manasi, Jefferson, Anne, & Collins, Don. Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration. United States. doi:10.3390/atmos9020054.
Mahish, Manasi, Jefferson, Anne, and Collins, Don. Thu . "Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration". United States. doi:10.3390/atmos9020054. https://www.osti.gov/servlets/purl/1501858.
@article{osti_1501858,
title = {Influence of Common Assumptions Regarding Aerosol Composition and Mixing State on Predicted CCN Concentration},
author = {Mahish, Manasi and Jefferson, Anne and Collins, Don},
abstractNote = {A 4-year record of aerosol size and hygroscopic growth factor distributions measured at the Department of Energy’s Southern Great Plains (SGP) site in Oklahoma, U.S. were used to estimate supersaturation (S)-dependent cloud condensation nuclei concentrations (NCCN). Baseline or reference NCCN(S) spectra were estimated using κ-Köhler Theory without any averaging of the measured distributions by creating matrices of size- and hygroscopicity-dependent number concentration (N) and then integrating for S > critical supersaturation (Sc) calculated for the same size and hygroscopicity pairs. Moreover, those estimates were first compared with directly measured NCCN at the same site. Subsequently, NCCN was calculated using the same dataset but with an array of simplified treatments in which the aerosol was assumed to be either an internal or an external mixture and the hygroscopicity either assumed or based on averages derived from the growth factor distributions. The CCN spectra calculated using the simplified treatments were compared with those calculated using the baseline approach to evaluate the error introduced with commonly used approximations.},
doi = {10.3390/atmos9020054},
journal = {Atmosphere (Basel)},
number = 2,
volume = 9,
place = {United States},
year = {2018},
month = {2}
}

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