Articles | Volume 22, issue 18
https://doi.org/10.5194/acp-22-12543-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-22-12543-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
26 Sep 2022
Research article |
| 26 Sep 2022
| 26 Sep 2022
Correcting ozone biases in a global chemistry–climate model: implications for future ozone
School of GeoSciences, The University of Edinburgh, Edinburgh, UK
Ruth M. Doherty
School of GeoSciences, The University of Edinburgh, Edinburgh, UK
Oliver Wild
Lancaster Environment Centre, Lancaster University, Lancaster, UK
Fiona M. O'Connor
Met Office Hadley Centre, Exeter, UK
Steven T. Turnock
Met Office Hadley Centre, Exeter, UK
University of Leeds Met Office Strategic Research Group, School of Earth and Environment, University of Leeds, Leeds, UK
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Cited
24 citations as recorded by crossref.
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al.
- Interrelationships Among Domestic Fossil Energy Supply, Photochemical Oxidant Indices, and the Asthma Prevalence Rate in Schoolchildren K. KIMURA & K. GONJO
- Historical and future changes of surface ozone over China from CMIP6 models, including an assessment of present-day uncertainties in model prediction S. Li et al.
- Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning K. Miyazaki et al.
- Air Quality Alerts, Health Impacts, and Adaptation Implications Under Varying Climate Policy M. Sparks et al.
- Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
- Applying deep learning to a chemistry-climate model for improved ozone prediction Z. Liu et al.
- Benefits of net-zero policies for future ozone pollution in China Z. Liu et al.
- A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends L. Gouldsbrough et al.
- Machine Learning-Based Bias-Corrected Future Projections of Ozone Concentrations from a Chemistry-Climate Model Y. Ni et al.
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al.
- A gradient boosting-based machine learning framework for improving atmospheric visibility numerical prediction C. Han et al.
- Projections of Future Air Quality Are Uncertain. But Which Source of Uncertainty Is Most Important? R. Doherty et al.
- A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4) O. Clifton et al.
- Divergent Ozone Predictions in China Under Carbon Neutrality: Why Chemical Mechanisms Disagree X. Weng et al.
- Understanding drivers and biases of simulated CO emissions from the INFERNO fire model over South America M. Velásquez-García et al.
- Elucidating the Effects of COVID-19 Lockdowns in the UK on the O3-NOx-VOC Relationship R. Holland et al.
- Assessment of bias correction technique to improve ozone reanalysis dataset over India T. Gangwar et al.
- Opinion: The role of AerChemMIP in advancing climate and air quality research P. Griffiths et al.
- Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950–2014 Y. Tong et al.
- Tropospheric ozone trends and attributions over East and Southeast Asia in 1995–2019: an integrated assessment using statistical methods, machine learning models, and multiple chemical transport models X. Lu et al.
- Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry–climate model surface ozone fields C. Staehle et al.
- Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework F. Kleinert et al.
- Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future R. Crespo-Miguel et al.
24 citations as recorded by crossref.
- Applications of Machine Learning and Artificial Intelligence in Tropospheric Ozone Research S. Hickman et al.
- Interrelationships Among Domestic Fossil Energy Supply, Photochemical Oxidant Indices, and the Asthma Prevalence Rate in Schoolchildren K. KIMURA & K. GONJO
- Historical and future changes of surface ozone over China from CMIP6 models, including an assessment of present-day uncertainties in model prediction S. Li et al.
- Identifying drivers of surface ozone bias in global chemical reanalysis with explainable machine learning K. Miyazaki et al.
- Air Quality Alerts, Health Impacts, and Adaptation Implications Under Varying Climate Policy M. Sparks et al.
- Co-evolving emission controls and climate impacts: A multi-decadal machine learning decomposition of urban O3 and NO2 air quality measurements M. Brancher
- Applying deep learning to a chemistry-climate model for improved ozone prediction Z. Liu et al.
- Benefits of net-zero policies for future ozone pollution in China Z. Liu et al.
- A machine learning approach to downscale EMEP4UK: analysis of UK ozone variability and trends L. Gouldsbrough et al.
- Machine Learning-Based Bias-Corrected Future Projections of Ozone Concentrations from a Chemistry-Climate Model Y. Ni et al.
- Interactions between atmospheric composition and climate change – progress in understanding and future opportunities from AerChemMIP, PDRMIP, and RFMIP S. Fiedler et al.
- A gradient boosting-based machine learning framework for improving atmospheric visibility numerical prediction C. Han et al.
- Projections of Future Air Quality Are Uncertain. But Which Source of Uncertainty Is Most Important? R. Doherty et al.
- A single-point modeling approach for the intercomparison and evaluation of ozone dry deposition across chemical transport models (Activity 2 of AQMEII4) O. Clifton et al.
- Divergent Ozone Predictions in China Under Carbon Neutrality: Why Chemical Mechanisms Disagree X. Weng et al.
- Understanding drivers and biases of simulated CO emissions from the INFERNO fire model over South America M. Velásquez-García et al.
- Elucidating the Effects of COVID-19 Lockdowns in the UK on the O3-NOx-VOC Relationship R. Holland et al.
- Assessment of bias correction technique to improve ozone reanalysis dataset over India T. Gangwar et al.
- Opinion: The role of AerChemMIP in advancing climate and air quality research P. Griffiths et al.
- Machine-learning-based corrections of CMIP6 historical surface ozone in China during 1950–2014 Y. Tong et al.
- Tropospheric ozone trends and attributions over East and Southeast Asia in 1995–2019: an integrated assessment using statistical methods, machine learning models, and multiple chemical transport models X. Lu et al.
- Technical note: An assessment of the performance of statistical bias correction techniques for global chemistry–climate model surface ozone fields C. Staehle et al.
- Representing chemical history in ozone time-series predictions – a model experiment study building on the MLAir (v1.5) deep learning framework F. Kleinert et al.
- Large-scale ozone episodes in Europe: Decreasing sizes in the last decades but diverging changes in the future R. Crespo-Miguel et al.
Saved (final revised paper)
Latest update: 16 May 2026
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.
Weaknesses in process representation in chemistry–climate models lead to biases in simulating...
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