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

Related authors

Applying deep learning to a chemistry-climate model for improved ozone prediction
Zhenze Liu, Ke Li, Oliver Wild, Ruth M. Doherty, Fiona M. O’Connor, and Steven T. Turnock
EGUsphere, https://doi.org/10.5194/egusphere-2025-1250,https://doi.org/10.5194/egusphere-2025-1250, 2025
Short summary
Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research
Sebastian H. M. Hickman, Makoto Kelp, Paul T. Griffiths, Kelsey Doerksen, Kazuyuki Miyazaki, Elyse A. Pennington, Gerbrand Koren, Fernando Iglesias-Suarez, Martin G. Schultz, Kai-Lan Chang, Owen R. Cooper, Alexander T. Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
EGUsphere, https://doi.org/10.5194/egusphere-2024-3739,https://doi.org/10.5194/egusphere-2024-3739, 2025
Short summary
The effect of different climate and air quality policies in China on in situ ozone production in Beijing
Beth S. Nelson, Zhenze Liu, Freya A. Squires, Marvin Shaw, James R. Hopkins, Jacqueline F. Hamilton, Andrew R. Rickard, Alastair C. Lewis, Zongbo Shi, and James D. Lee
Atmos. Chem. Phys., 24, 9031–9044, https://doi.org/10.5194/acp-24-9031-2024,https://doi.org/10.5194/acp-24-9031-2024, 2024
Short summary
Benefits of net-zero policies for future ozone pollution in China
Zhenze Liu, Oliver Wild, Ruth M. Doherty, Fiona M. O'Connor, and Steven T. Turnock
Atmos. Chem. Phys., 23, 13755–13768, https://doi.org/10.5194/acp-23-13755-2023,https://doi.org/10.5194/acp-23-13755-2023, 2023
Short summary
Tropospheric ozone changes and ozone sensitivity from the present day to the future under shared socio-economic pathways
Zhenze Liu, Ruth M. Doherty, Oliver Wild, Fiona M. O'Connor, and Steven T. Turnock
Atmos. Chem. Phys., 22, 1209–1227, https://doi.org/10.5194/acp-22-1209-2022,https://doi.org/10.5194/acp-22-1209-2022, 2022
Short summary

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
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, https://doi.org/10.1525/elementa.2020.034, 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, https://doi.org/10.5194/gmd-13-1223-2020, 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, https://doi.org/10.1098/rsta.2019.0329, 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, https://doi.org/10.5194/gmd-15-4331-2022, 2022. a
Download
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.
Share
Altmetrics
Final-revised paper
Preprint