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

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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.
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