Articles | Volume 22, issue 18
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,, 2021. a
Archibald, A., Neu, J., Elshorbany, Y., Cooper, O., Young,P., Akiyoshi, H., Cox, R., Coyle, M., Derwent, R., Deushi,M., Finco, A., Frost, G. J., Galbally, I. E., Gerosa, G., Granier, C., Griffiths, P. T., Hossaini, R., Hu, L., Jöckel, P., Josse, B., Lin, M. Y., Mertens, M., Morgenstern, O., Naja, M., Naik, V., Oltmans, S., Plummer, D. A., Revell, L. E., Saiz-Lopez, A., Saxena, P., Shin, Y. M., Shahid, I., Shallcross, D., Tilmes, S., Trickl, T., Wallington, T. J., Wang, T., Worden, H. M., and Zeng, G.: Tropospheric Ozone Assessment Report: A critical review of changes in the tropospheric ozone burden and budget from 1850 to 2100, Elementa, 8, 034,, 2020a. a, b
Archibald, A. T., O'Connor, F. M., Abraham, N. L., Archer-Nicholls, S., Chipperfield, M. P., Dalvi, M., Folberth, G. A., Dennison, F., Dhomse, S. S., Griffiths, P. T., Hardacre, C., Hewitt, A. J., Hill, R. S., Johnson, C. E., Keeble, J., Köhler, M. O., Morgenstern, O., Mulcahy, J. P., Ordóñez, C., Pope, R. J., Rumbold, S. T., Russo, M. R., Savage, N. H., Sellar, A., Stringer, M., Turnock, S. T., Wild, O., and Zeng, G.: Description and evaluation of the UKCA stratosphere–troposphere chemistry scheme (StratTrop vn 1.0) implemented in UKESM1, Geosci. Model Dev., 13, 1223–1266,, 2020b. a
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,, 2020c. a, b
Betancourt, C., Stomberg, T. T., Edrich, A.-K., Patnala, A., Schultz, M. G., Roscher, R., Kowalski, J., and Stadtler, S.: Global, high-resolution mapping of tropospheric ozone – explainable machine learning and impact of uncertainties, Geosci. Model Dev., 15, 4331–4354,, 2022. a
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.
Final-revised paper