Articles | Volume 25, issue 22
https://doi.org/10.5194/acp-25-16969-2025
https://doi.org/10.5194/acp-25-16969-2025
Research article
 | 
27 Nov 2025
Research article |  | 27 Nov 2025

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

Related authors

Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research
Sebastian H. M. Hickman, Makoto M. 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, Alex Archibald, Roberto Sommariva, David Carlson, Hantao Wang, J. Jason West, and Zhenze Liu
Geosci. Model Dev., 18, 8777–8800, https://doi.org/10.5194/gmd-18-8777-2025,https://doi.org/10.5194/gmd-18-8777-2025, 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
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
Atmos. Chem. Phys., 22, 12543–12557, https://doi.org/10.5194/acp-22-12543-2022,https://doi.org/10.5194/acp-22-12543-2022, 2022
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

Amos, M., Young, P. J., Hosking, J. S., Lamarque, J.-F., Abraham, N. L., Akiyoshi, H., Archibald, A. T., Bekki, S., Deushi, M., Jöckel, P., Kinnison, D., Kirner, O., Kunze, M., Marchand, M., Plummer, D. A., Saint-Martin, D., Sudo, K., Tilmes, S., and Yamashita, Y.: Projecting ozone hole recovery using an ensemble of chemistry–climate models weighted by model performance and independence, Atmos. Chem. Phys., 20, 9961–9977, https://doi.org/10.5194/acp-20-9961-2020, 2020. a, b
Archer-Nicholls, S., Abraham, N. L., Shin, Y. M., Weber, J., Russo, M. R., Lowe, D., Utembe, S. R., O'Connor, F. M., 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, Journal of Advances in Modeling Earth Systems, 13, e2020MS002420, https://doi.org/10.1029/2020MS002420, 2021. a
Archibald, A. T., Neu, J. L., Elshorbany, Y. F., Cooper, O. R., Young, P. J., Akiyoshi, H., Cox, R. A., Coyle, M., Derwent, R. G., 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: Science of the Anthropocene, 8, 34, https://doi.org/10.1525/elementa.2020.034, 2020a. a
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, 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
Chemistry-climate models have advanced substantially over the decades, yet they still exhibit substantial systematic biases in simulating atmospheric composition due to gaps in our understanding of underlying processes. We improve the predictions of an Earth system model using deep learning, and evaluate the performance of difference types of statistical models. We find that simulations of future surface ozone are likely to become less accurate under a warmer climate.
Share
Altmetrics
Final-revised paper
Preprint