Articles | Volume 18, issue 21
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.
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.
Research article
| 
13 Nov 2018
Research article |
| 13 Nov 2018
| 13 Nov 2018
Tropospheric ozone in CCMI models and Gaussian process emulation to understand biases in the SOCOLv3 chemistry–climate model
Laura E. Revell
CORRESPONDING AUTHOR
School of Physical and Chemical Sciences, University of Canterbury, Christchurch, New Zealand
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Bodeker Scientific, Christchurch, New Zealand
Andrea Stenke
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Fiona Tummon
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
now at: Biosciences, Fisheries, and Economics Faculty, University of Tromsø, Tromsø, Norway
Aryeh Feinberg
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Eugene Rozanov
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
Physical-Meteorological Observatory/World Radiation Center, Davos, Switzerland
Thomas Peter
Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
N. Luke Abraham
Department of Chemistry, University of Cambridge, Cambridge, UK
National Centre for Atmospheric Science (NCAS), Cambridge, UK
Hideharu Akiyoshi
National Institute of Environmental Studies (NIES), Tsukuba, Japan
Alexander T. Archibald
Department of Chemistry, University of Cambridge, Cambridge, UK
National Centre for Atmospheric Science (NCAS), Cambridge, UK
Neal Butchart
Met Office Hadley Centre (MOHC), Exeter, UK
Makoto Deushi
Meteorological Research Institute (MRI), Tsukuba, Japan
Patrick Jöckel
Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Oberpfaffenhofen, Germany
Douglas Kinnison
National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Martine Michou
CNRM UMR 3589, Météo-France/CNRS, Toulouse, France
Olaf Morgenstern
National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
Fiona M. O'Connor
Met Office Hadley Centre (MOHC), Exeter, UK
Luke D. Oman
National Aeronautics and Space Administration Goddard Space Flight Center (NASA GSFC), Greenbelt, Maryland, USA
Giovanni Pitari
Department of Physical and Chemical Sciences, Universitá dell'Aquila, L'Aquila, Italy
David A. Plummer
Environment and Climate Change Canada, Montréal, Canada
Robyn Schofield
School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia
ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australia
Kane Stone
School of Earth Sciences, University of Melbourne, Melbourne, Victoria, Australia
ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, Australia
now at: Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology (MIT), Cambridge, Massachusetts, USA
Simone Tilmes
National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
Daniele Visioni
Department of Physical and Chemical Sciences, Universitá dell'Aquila, L'Aquila, Italy
now at: Sibley School of Mechanical and Aerospace Engineering, Cornell University, Ithaca, New York, USA
Yousuke Yamashita
National Institute of Environmental Studies (NIES), Tsukuba, Japan
now at: Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan
Guang Zeng
National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
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Cited
26 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
- Signal‐To‐Noise Calculations of Emergence and De‐Emergence of Stratospheric Ozone Depletion F. Robertson et al. 10.1029/2023GL104246
- A cautious note advocating the use of ensembles of models and driving data in modeling of regional ozone burdens J. Karlický et al. 10.1007/s11869-024-01516-3
- 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
- Iodine chemistry in the chemistry–climate model SOCOL-AERv2-I A. Karagodin-Doyennel et al. 10.5194/gmd-14-6623-2021
- Cause of a Lower‐Tropospheric High‐Ozone Layer in Spring Over Hanoi S. Ogino et al. 10.1029/2021JD035727
- Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites L. Kuai et al. 10.5194/acp-20-281-2020
- Tropospheric ozone production and chemical regime analysis during the COVID-19 lockdown over Europe C. Nussbaumer et al. 10.5194/acp-22-6151-2022
- The historical ozone trends simulated with the SOCOLv4 and their comparison with observations and reanalyses A. Karagodin-Doyennel et al. 10.5194/acp-22-15333-2022
- Benefits of net-zero policies for future ozone pollution in China Z. Liu et al. 10.5194/acp-23-13755-2023
- 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
- A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2 S. Vattioni et al. 10.5194/gmd-17-7767-2024
- Causes of growing middle-to-upper tropospheric ozone over the northwest Pacific region X. Ma et al. 10.5194/acp-25-943-2025
- 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
- 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
- Construction of maximum projection Latin hypercube designs using number‐theoretic methods Y. Ye et al. 10.1111/sjos.70014
- Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. 10.5194/acp-20-1363-2020
- Updated Simulation of Tropospheric Ozone and Its Radiative Forcing over the Globe and China Based on a Newly Developed Chemistry-Climate Model A. Qi et al. 10.1007/s13351-022-1187-2
- Utilizing Novel Field and Data Exploration Methods to Explore Hot Moments in High-Frequency Soil Nitrous Oxide Emissions Data: Opportunities and Challenges C. O’Connell et al. 10.3389/ffgc.2022.674348
- 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
26 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
- Signal‐To‐Noise Calculations of Emergence and De‐Emergence of Stratospheric Ozone Depletion F. Robertson et al. 10.1029/2023GL104246
- A cautious note advocating the use of ensembles of models and driving data in modeling of regional ozone burdens J. Karlický et al. 10.1007/s11869-024-01516-3
- 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
- Iodine chemistry in the chemistry–climate model SOCOL-AERv2-I A. Karagodin-Doyennel et al. 10.5194/gmd-14-6623-2021
- Cause of a Lower‐Tropospheric High‐Ozone Layer in Spring Over Hanoi S. Ogino et al. 10.1029/2021JD035727
- Attribution of Chemistry-Climate Model Initiative (CCMI) ozone radiative flux bias from satellites L. Kuai et al. 10.5194/acp-20-281-2020
- Tropospheric ozone production and chemical regime analysis during the COVID-19 lockdown over Europe C. Nussbaumer et al. 10.5194/acp-22-6151-2022
- The historical ozone trends simulated with the SOCOLv4 and their comparison with observations and reanalyses A. Karagodin-Doyennel et al. 10.5194/acp-22-15333-2022
- Benefits of net-zero policies for future ozone pollution in China Z. Liu et al. 10.5194/acp-23-13755-2023
- 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
- A fully coupled solid-particle microphysics scheme for stratospheric aerosol injections within the aerosol–chemistry–climate model SOCOL-AERv2 S. Vattioni et al. 10.5194/gmd-17-7767-2024
- Causes of growing middle-to-upper tropospheric ozone over the northwest Pacific region X. Ma et al. 10.5194/acp-25-943-2025
- 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
- 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
- Construction of maximum projection Latin hypercube designs using number‐theoretic methods Y. Ye et al. 10.1111/sjos.70014
- Mapping the drivers of uncertainty in atmospheric selenium deposition with global sensitivity analysis A. Feinberg et al. 10.5194/acp-20-1363-2020
- Updated Simulation of Tropospheric Ozone and Its Radiative Forcing over the Globe and China Based on a Newly Developed Chemistry-Climate Model A. Qi et al. 10.1007/s13351-022-1187-2
- Utilizing Novel Field and Data Exploration Methods to Explore Hot Moments in High-Frequency Soil Nitrous Oxide Emissions Data: Opportunities and Challenges C. O’Connell et al. 10.3389/ffgc.2022.674348
- 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
Latest update: 31 Oct 2025
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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