Articles | Volume 18, issue 21
Research article
13 Nov 2018
Research article |  | 13 Nov 2018

Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model

Laura E. Revell, Andrea Stenke, Fiona Tummon, Aryeh Feinberg, Eugene Rozanov, Thomas Peter, N. Luke Abraham, Hideharu Akiyoshi, Alexander T. Archibald, Neal Butchart, Makoto Deushi, Patrick Jöckel, Douglas Kinnison, Martine Michou, Olaf Morgenstern, Fiona M. O'Connor, Luke D. Oman, Giovanni Pitari, David A. Plummer, Robyn Schofield, Kane Stone, Simone Tilmes, Daniele Visioni, Yousuke Yamashita, and Guang Zeng


Interactive discussion

Status: closed
Status: closed
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
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Peer-review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Laura Revell on behalf of the Authors (12 Sep 2018)  Author's response    Manuscript
ED: Referee Nomination & Report Request started (12 Sep 2018) by Paul Young
RR by Anonymous Referee #2 (24 Sep 2018)
RR by Edmund Ryan (25 Sep 2018)
ED: Publish subject to minor revisions (review by editor) (11 Oct 2018) by Paul Young
AR by Laura Revell on behalf of the Authors (14 Oct 2018)  Author's response    Manuscript
ED: Publish as is (02 Nov 2018) by Paul Young
Short summary
Global models such as those participating in the Chemistry-Climate Model Initiative (CCMI) consistently simulate biases in tropospheric ozone compared with observations. We performed an advanced statistical analysis with one of the CCMI models to understand the cause of the bias. We found that emissions of ozone precursor gases are the dominant driver of the bias, implying either that the emissions are too large, or that the way in which the model handles emissions needs to be improved.
Final-revised paper