Articles | Volume 18, issue 21
https://doi.org/10.5194/acp-18-16155-2018
https://doi.org/10.5194/acp-18-16155-2018
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

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Latest update: 28 Feb 2024
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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.
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