Correcting ozone biases in a global chemistry–climate model: implications for future ozone
<p>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 here develop a deep learning model to demonstrate the feasibility of ozone bias correction in a global chemistry–climate m...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2022-09-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://acp.copernicus.org/articles/22/12543/2022/acp-22-12543-2022.pdf |
| _version_ | 1798001270957539328 |
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| author | Z. Liu R. M. Doherty O. Wild F. M. O'Connor S. T. Turnock S. T. Turnock |
| author_facet | Z. Liu R. M. Doherty O. Wild F. M. O'Connor S. T. Turnock S. T. Turnock |
| author_sort | Z. Liu |
| collection | DOAJ |
| description | <p>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 here 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. These relationships reveal the conditions under which ozone biases occur, although they reflect association rather than direct causation. 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.</p> |
| first_indexed | 2024-04-11T11:33:37Z |
| format | Article |
| id | doaj.art-7ded6bff1bbd47139d3601ac7c5374cc |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-04-11T11:33:37Z |
| publishDate | 2022-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-7ded6bff1bbd47139d3601ac7c5374cc2022-12-22T04:26:02ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242022-09-0122125431255710.5194/acp-22-12543-2022Correcting ozone biases in a global chemistry–climate model: implications for future ozoneZ. Liu0R. M. Doherty1O. Wild2F. M. O'Connor3S. T. Turnock4S. T. Turnock5School of GeoSciences, The University of Edinburgh, Edinburgh, UKSchool of GeoSciences, The University of Edinburgh, Edinburgh, UKLancaster Environment Centre, Lancaster University, Lancaster, UKMet Office Hadley Centre, Exeter, UKMet Office Hadley Centre, Exeter, UKUniversity of Leeds Met Office Strategic Research Group, School of Earth and Environment, University of Leeds, Leeds, UK<p>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 here 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. These relationships reveal the conditions under which ozone biases occur, although they reflect association rather than direct causation. 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.</p>https://acp.copernicus.org/articles/22/12543/2022/acp-22-12543-2022.pdf |
| spellingShingle | Z. Liu R. M. Doherty O. Wild F. M. O'Connor S. T. Turnock S. T. Turnock Correcting ozone biases in a global chemistry–climate model: implications for future ozone Atmospheric Chemistry and Physics |
| title | Correcting ozone biases in a global chemistry–climate model: implications for future ozone |
| title_full | Correcting ozone biases in a global chemistry–climate model: implications for future ozone |
| title_fullStr | Correcting ozone biases in a global chemistry–climate model: implications for future ozone |
| title_full_unstemmed | Correcting ozone biases in a global chemistry–climate model: implications for future ozone |
| title_short | Correcting ozone biases in a global chemistry–climate model: implications for future ozone |
| title_sort | correcting ozone biases in a global chemistry climate model implications for future ozone |
| url | https://acp.copernicus.org/articles/22/12543/2022/acp-22-12543-2022.pdf |
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