Metrics to quantify the importance of mixing state for CCN activity

Ching, Joseph; Fast, Jerome; West, Matthew; Riemer, Nicole

doi:https://doi.org/10.5194/acp-17-7445-2017

Articles | Volume 17, issue 12

https://doi.org/10.5194/acp-17-7445-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/acp-17-7445-2017

© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 17, issue 12

Research article

|

21 Jun 2017

Research article |

| 21 Jun 2017

Metrics to quantify the importance of mixing state for CCN activity

Joseph Ching, Jerome Fast, Matthew West, and Nicole Riemer

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Final revised paper (published on 21 Jun 2017)
Preprint (discussion started on 23 Dec 2016)

Interactive discussion

Status: closed

AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment

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- Supplement

RC1: 'Review of "Metrics to quantify the importance of mixing state for CCN activity"', Anonymous Referee #1, 16 Feb 2017
- AC1: 'Response to RC1', Joseph Ping Pui Ching, 02 May 2017
RC2: 'Comments on "Metrics to quantify the importance of mixing state for CCN activity" by Ching et al.', Anonymous Referee #2, 07 Mar 2017
- AC2: 'Response to RC2', Joseph Ping Pui Ching, 02 May 2017

Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision

AR by Anna Mirena Feist-Polner on behalf of the Authors (04 May 2017) Author's response Manuscript

ED: Publish as is (09 May 2017) by Holger Tost

AR by Joseph Ching on behalf of the Authors (18 May 2017) Author's response Manuscript

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.