Articles | Volume 18, issue 21
Atmos. Chem. Phys., 18, 16155–16172, 2018
https://doi.org/10.5194/acp-18-16155-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue: Chemistry–Climate Modelling Initiative (CCMI) (ACP/AMT/ESSD/GMD...
Research article
13 Nov 2018
Research article
| 13 Nov 2018
Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model
Laura E. Revell et al.
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Cited
15 citations as recorded by crossref.
- Intercomparison of the representations of the atmospheric chemistry of pre-industrial methane and ozone in earth system and other global chemistry-transport models R. Derwent et al. 10.1016/j.atmosenv.2021.118248
- Monte Carlo analyses of the uncertainties in the predictions from global tropospheric ozone models: Tropospheric burdens and seasonal cycles R. Derwent 10.1016/j.atmosenv.2020.117545
- Atmosphere–ocean–aerosol–chemistry–climate model SOCOLv4.0: description and evaluation T. Sukhodolov et al. 10.5194/gmd-14-5525-2021
- On the Changing Role of the Stratosphere on the Tropospheric Ozone Budget: 1979–2010 P. Griffiths et al. 10.1029/2019GL086901
- Evaluation of the Total Column Ozone and Tropospheric Ozone in the CCMI-1 Models over East Asia S. Kim et al. 10.15531/KSCCR.2021.12.3.215
- Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period Y. Zhao et al. 10.5194/acp-19-13701-2019
- Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. 10.5194/acp-20-1363-2020
- Iodine chemistry in the chemistry–climate model SOCOL-AERv2-I A. Karagodin-Doyennel et al. 10.5194/gmd-14-6623-2021
- Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites L. Kuai et al. 10.5194/acp-20-281-2020
- Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes R. Williams et al. 10.5194/acp-19-3589-2019
- A machine-learning-based global sea-surface iodide distribution T. Sherwen et al. 10.5194/essd-11-1239-2019
- Improved tropospheric and stratospheric sulfur cycle in the aerosol–chemistry–climate model SOCOL-AERv2 A. Feinberg et al. 10.5194/gmd-12-3863-2019
- Carbon and health implications of trade restrictions J. Lin et al. 10.1038/s41467-019-12890-3
- Global Warming Potential (GWP) for Methane: Monte Carlo Analysis of the Uncertainties in Global Tropospheric Model Predictions R. Derwent 10.3390/atmos11050486
- Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers D. Anderson et al. 10.5194/acp-21-6481-2021
15 citations as recorded by crossref.
- Intercomparison of the representations of the atmospheric chemistry of pre-industrial methane and ozone in earth system and other global chemistry-transport models R. Derwent et al. 10.1016/j.atmosenv.2021.118248
- Monte Carlo analyses of the uncertainties in the predictions from global tropospheric ozone models: Tropospheric burdens and seasonal cycles R. Derwent 10.1016/j.atmosenv.2020.117545
- Atmosphere–ocean–aerosol–chemistry–climate model SOCOLv4.0: description and evaluation T. Sukhodolov et al. 10.5194/gmd-14-5525-2021
- On the Changing Role of the Stratosphere on the Tropospheric Ozone Budget: 1979–2010 P. Griffiths et al. 10.1029/2019GL086901
- Evaluation of the Total Column Ozone and Tropospheric Ozone in the CCMI-1 Models over East Asia S. Kim et al. 10.15531/KSCCR.2021.12.3.215
- Inter-model comparison of global hydroxyl radical (OH) distributions and their impact on atmospheric methane over the 2000–2016 period Y. Zhao et al. 10.5194/acp-19-13701-2019
- Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. 10.5194/acp-20-1363-2020
- Iodine chemistry in the chemistry–climate model SOCOL-AERv2-I A. Karagodin-Doyennel et al. 10.5194/gmd-14-6623-2021
- Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites L. Kuai et al. 10.5194/acp-20-281-2020
- Characterising the seasonal and geographical variability in tropospheric ozone, stratospheric influence and recent changes R. Williams et al. 10.5194/acp-19-3589-2019
- A machine-learning-based global sea-surface iodide distribution T. Sherwen et al. 10.5194/essd-11-1239-2019
- Improved tropospheric and stratospheric sulfur cycle in the aerosol–chemistry–climate model SOCOL-AERv2 A. Feinberg et al. 10.5194/gmd-12-3863-2019
- Carbon and health implications of trade restrictions J. Lin et al. 10.1038/s41467-019-12890-3
- Global Warming Potential (GWP) for Methane: Monte Carlo Analysis of the Uncertainties in Global Tropospheric Model Predictions R. Derwent 10.3390/atmos11050486
- Spatial and temporal variability in the hydroxyl (OH) radical: understanding the role of large-scale climate features and their influence on OH through its dynamical and photochemical drivers D. Anderson et al. 10.5194/acp-21-6481-2021
Discussed (final revised paper)
Latest update: 08 Aug 2022
Short summary
Global models such as those participating in the Chemistry-Climate Model Initiative (CCMI) consistently simulate biases in tropospheric ozone compared with observations. We performed an advanced statistical analysis with one of the CCMI models to understand the cause of the bias. We found that emissions of ozone precursor gases are the dominant driver of the bias, implying either that the emissions are too large, or that the way in which the model handles emissions needs to be improved.
Global models such as those participating in the Chemistry-Climate Model Initiative (CCMI)...
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Final-revised paper
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