Preprints
https://doi.org/10.5194/acp-2022-196
https://doi.org/10.5194/acp-2022-196
 
07 Apr 2022
07 Apr 2022
Status: this preprint is currently under review for the journal ACP.

Correcting ozone biases in a global chemistry-climate model: implications for future ozone

Zhenze Liu1, Ruth M. Doherty1, Oliver Wild2, Fiona M. O’Connor3, and Steven T. Turnock3,4 Zhenze Liu et al.
  • 1School of GeoSciences, The University of Edinburgh, Edinburgh, UK
  • 2Lancaster Environment Centre, Lancaster University, Lancaster, UK
  • 3Met Office Hadley Centre, Exeter, UK
  • 4University of Leeds Met Office Strategic Research Group, School of Earth and Environment, University of Leeds, Leeds, UK

Abstract. 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 in a global chemistry-climate model. We apply this approach to identify the key factors causing ozone biases and to correct projections of future surface ozone. Temperature and the related geographic variables latitude and month show the strongest relationship with ozone biases. This indicates that ozone biases are sensitive to temperature and suggests weaknesses in representation of temperature-sensitive physical or chemical processes. Photolysis rates are also an important factor highlighting the sensitivity of biases to simulated cloud cover and insolation. Atmospheric chemical species such as the hydroxyl radical, nitric acid and peroxyacyl nitrate show strong positive relationships with ozone biases on a regional scale. We correct model projections of future ozone under different climate and emission scenarios following the shared socio-economic pathways. We find that changes in seasonal ozone mixing ratios from the present day to the future are generally smaller than those simulated without bias correction, especially in high-emission regions. This suggests that the ozone sensitivity to changing emissions and climate may be overestimated with chemistry-climate models. Given the uncertainty in simulating future ozone, we show that deep learning approaches can provide improved assessment of the impacts of climate and emission changes on future air quality, along with valuable information to guide future model development.

Zhenze Liu et al.

Status: open (extended)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-196', Anonymous Referee #2, 01 May 2022 reply

Zhenze Liu et al.

Zhenze Liu et al.

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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 the capability of providing improved assessments of the impacts of climate and emission changes on future air quality, along with valuable information to guide future model development.
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