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Volume 18, issue 9 | Copyright

Special issue: Chemistry–Climate Modelling Initiative (CCMI) (ACP/AMT/ESSD/GMD...

Atmos. Chem. Phys., 18, 6699-6720, 2018
https://doi.org/10.5194/acp-18-6699-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 14 May 2018

Research article | 14 May 2018

Quantifying the effect of mixing on the mean age of air in CCMVal-2 and CCMI-1 models

Simone Dietmüller1, Roland Eichinger2,1, Hella Garny1,2, Thomas Birner3,a, Harald Boenisch4, Giovanni Pitari5, Eva Mancini6, Daniele Visioni6, Andrea Stenke7, Laura Revell8, Eugene Rozanov7,9, David A. Plummer10, John Scinocca11, Patrick Jöckel1, Luke Oman12, Makoto Deushi13, Shibata Kiyotaka14, Douglas E. Kinnison15, Rolando Garcia15, Olaf Morgenstern16, Guang Zeng16, Kane Adam Stone17,18, and Robyn Schofield17,18 Simone Dietmüller et al.
  • 1Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 2Ludwig Maximilians University of Munich, Meteorological Institute Munich, Munich, Germany
  • 3Department of Atmospheric Science, Colorado State University, Fort Collins, Colorado, USA
  • 4Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research, Karlsruhe, Germany
  • 5Department of Physical and Chemical Sciences, Università dell'Aquila, L'Aquila, Italy
  • 6Department of Physical and Chemical Sciences and center of Excellence CETEMPS, Università dell'Aquila, L'Aquila, Italy
  • 7Institute for Atmospheric and Climate Science, ETH Zürich (ETHZ), Zürich, Switzerland
  • 8Bodeker Scientific, Christchurch, New Zealand
  • 9Physical-Meteorological Observatory/World Radiation Center, Davos, Switzerland
  • 10Environment and Climate Change Canada, Climate Research Division, Montréal, QC, Canada
  • 11Environment and Climate Change Canada, Climate Research Division, Victoria, BC, Canada
  • 12National Aeronautics and Space Administration Goddard Space Flight Center (NASA GSFC), Greenbelt, Maryland, USA
  • 13Meteorological Research Institute (MRI), Tsukuba, Japan
  • 14School of Environmental Science and Engineering, Kochi University of Technology, Kami, Japan
  • 15National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA
  • 16National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
  • 17School of Earth Sciences, University of Melbourne, Melbourne, Australia
  • 18ARC Centre of Excellence for Climate System Science, Sydney, Australia
  • acurrently at: Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany

Abstract. The stratospheric age of air (AoA) is a useful measure of the overall capabilities of a general circulation model (GCM) to simulate stratospheric transport. Previous studies have reported a large spread in the simulation of AoA by GCMs and coupled chemistry–climate models (CCMs). Compared to observational estimates, simulated AoA is mostly too low. Here we attempt to untangle the processes that lead to the AoA differences between the models and between models and observations. AoA is influenced by both mean transport by the residual circulation and two-way mixing; we quantify the effects of these processes using data from the CCM inter-comparison projects CCMVal-2 (Chemistry–Climate Model Validation Activity 2) and CCMI-1 (Chemistry–Climate Model Initiative, phase 1). Transport along the residual circulation is measured by the residual circulation transit time (RCTT). We interpret the difference between AoA and RCTT as additional aging by mixing. Aging by mixing thus includes mixing on both the resolved and subgrid scale. We find that the spread in AoA between the models is primarily caused by differences in the effects of mixing and only to some extent by differences in residual circulation strength. These effects are quantified by the mixing efficiency, a measure of the relative increase in AoA by mixing. The mixing efficiency varies strongly between the models from 0.24 to 1.02. We show that the mixing efficiency is not only controlled by horizontal mixing, but by vertical mixing and vertical diffusion as well. Possible causes for the differences in the models' mixing efficiencies are discussed. Differences in subgrid-scale mixing (including differences in advection schemes and model resolutions) likely contribute to the differences in mixing efficiency. However, differences in the relative contribution of resolved versus parameterized wave forcing do not appear to be related to differences in mixing efficiency or AoA.

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