Atmospheric Chemistry and Physics
Atmospheric Chemistry and Physics
ACP
Articles | Volume 25, issue 22
Articles | Volume 25, issue 22
https://doi.org/10.5194/acp-25-16969-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-25-16969-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 25, issue 22
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

Viewed

Total article views: 1,166 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
1,032 107 27 1,166 26 40
  • HTML: 1,032
  • PDF: 107
  • XML: 27
  • Total: 1,166
  • BibTeX: 26
  • EndNote: 40
Views and downloads (calculated since 10 Jun 2025)
Cumulative views and downloads (calculated since 10 Jun 2025)

Viewed (geographical distribution)

Total article views: 1,166 (including HTML, PDF, and XML) Thereof 1,129 with geography defined and 37 with unknown origin.
Country # Views %
  • 1
1
 
 
 
 
Latest update: 27 Nov 2025
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.
Read more
Share
Altmetrics
Final-revised paper
Preprint