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Discussion papers
https://doi.org/10.5194/acp-2019-149
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-149
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 28 Feb 2019

Submitted as: research article | 28 Feb 2019

Review status
This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Chemistry and Physics (ACP).

Estimating CCN number concentrations using aerosol optical properties: Role of particle number size distribution and parameterization

Yicheng Shen1,2, Aki Virkkula2,1,3, Aijun Ding1, Krista Luoma2, Helmi Keskinen2,4, Pasi P. Aalto2, Xuguang Chi1, Ximeng Qi1, Wei Nie1, Xin Huang1, Tuukka Petäjä2,1, Markku Kulmala2, and Veli-Matti Kerminen2 Yicheng Shen et al.
  • 1Joint International Research Laboratory of Atmospheric Sciences, School of Atmospheric Sciences, Nanjing University, Nanjing, 210023, China
  • 2Institute for Atmospheric and Earth System Research, University of Helsinki, Helsinki, Finland
  • 3Finnish Meteorological Institute, Helsinki, Finland
  • 4Hyytiälä Forestry Field Station, Hyytiäläntie 124, Korkeakoski 35500, Finland

Abstract. The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol-cloud interactions within warm clouds. Long-term CCN number concentration (NCCN) data are scarce, there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating NCCN from AOP measurements. Such parameterizatios have been made earlier, in the present work a new one is presented. The relationships between AOPs, NCCN and particle number size distributions were investigated based on in-situ measurement data from six stations in very different environments around the world. The parameterization derived here depends on the scattering Ångström exponent (SAE), backscatter fraction (BSF) and total scattering coefficient (σsp) of PM10 particles. The analysis showed that the dependence of NCCN on supersaturation SS% is logarithmic:
NCCN ≈ ((287 ± 45)SAE10ln(SS%/(0.093 ± 0.006))(BSF − BSFmin) + (5.2 ± 3.3))σsp.
At the lowest supersaturations of each site (SS% ≈ 0.1) the average bias, defined as the ratio of the AOP-derived and measured NCCN varied from ~ 0.7 to ~ 1.5 at most sites except at a Himalayan site where bias was > 4. At SS% > 0.3 the average bias ranged from ~ 0.7 to ~ 1.3 at all sites. In other words, at SS% > 0.3 NCCN was estimated with an average uncertainty of approximately 30 % by using nephelometer data. The squared correlation coefficients between the AOP-derived and measured NCCN varied from ~ 0.5 to ~ 0.8. The coefficients of the parameterization derived for the different sites were linearly related to each other. To study the explanation of this, lognormal unimodal particle size distributions were generated and NCCN and AOPs were calculated. The simulation yielded similar relationships between the coefficients as in the field data. It also showed that the relationships of the coefficients are affected by the geometric mean diameter and width of the size distribution and the activation diameter.

Yicheng Shen et al.
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Short summary
The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting aerosol-cloud interactions. Long-term CCN number concentration (NCCN) data are scarce, there are a lot more data on aerosol optical properties (AOPs). It is therefore valuable to derive parameterizations for estimating NCCN from AOP measurements. In the present work a new one is presented. With it NCCN can be estimated from measurements of light scattering by aerosols.
The concentration of cloud condensation nuclei (CCN) is an essential parameter affecting...
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