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.
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
Data sets
BADC data access IGAC/SPARC Chemistry-Climate Model Initiative http://blogs.reading.ac.uk/ccmi/badc-data-access
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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