Articles | Volume 22, issue 18
https://doi.org/10.5194/acp-22-12543-2022
https://doi.org/10.5194/acp-22-12543-2022
Research article
 | 
26 Sep 2022
Research article |  | 26 Sep 2022

Correcting ozone biases in a global chemistry–climate model: implications for future ozone

Zhenze Liu, Ruth M. Doherty, Oliver Wild, Fiona M. O'Connor, and Steven T. Turnock

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Cited articles

Archer-Nicholls, S., Abraham, N. L., Shin, Y., Weber, J., Russo,M. R., Lowe, D., Utembe, S., O’Connor, F., Kerridge, B., Latter, B, Siddans, R., Jenkin, M., Wild, O., and Archibald, A. T.: The Common Representative Intermediates Mechanism version 2 in the United Kingdom Chemistry and Aerosols Model, J. Adv. Model. Earth Sy., 13, e2020MS002420, https://doi.org/10.1029/2020MS002420, 2021. a
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Archibald, A. T., Turnock, S. T., Griffiths, P. T., Cox, T., Derwent, R. G., Knote, C., and Shin, M.: On the changes in surface ozone over the twenty-first century: sensitivity to changes in surface temperature and chemical mechanisms, Philos. T. Roy. Soc. A, 378, 20190329, https://doi.org/10.1098/rsta.2019.0329, 2020c. a, b
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Short summary
Weaknesses in process representation in chemistry–climate models lead to biases in simulating surface ozone and to uncertainty in projections of future ozone change. We develop a deep learning model to demonstrate the feasibility of ozone bias correction and show its capability in providing improved assessments of the impacts of climate and emission changes on future air quality, along with valuable information to guide future model development.
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