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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 8
Geosci. Model Dev., 11, 3109-3130, 2018
https://doi.org/10.5194/gmd-11-3109-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
Geosci. Model Dev., 11, 3109-3130, 2018
https://doi.org/10.5194/gmd-11-3109-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Model experiment description paper 03 Aug 2018

Model experiment description paper | 03 Aug 2018

Age of air as a diagnostic for transport timescales in global models

Maarten Krol1,2,3, Marco de Bruine2, Lars Killaars4, Huug Ouwersloot5, Andrea Pozzer5, Yi Yin6,a, Frederic Chevallier6, Philippe Bousquet6, Prabir Patra7, Dmitry Belikov8, Shamil Maksyutov9, Sandip Dhomse10, Wuhu Feng11, and Martyn P. Chipperfield10,11 Maarten Krol et al.
  • 1Meteorology and Air Quality, Wageningen University, Wageningen, the Netherlands
  • 2Institute for Marine and Atmospheric Research, Utrecht University, Utrecht, the Netherlands
  • 3Netherlands Institute for Space Research SRON, Utrecht, the Netherlands
  • 4Faculty of Science and Engineering, University of Groningen, Groningen, the Netherlands
  • 5Max Planck Institute for Chemistry, Mainz, Germany
  • 6Laboraroire de Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France
  • 7Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama City, Japan
  • 8Hokkaido University, Sapporo, Hokkaido, Japan
  • 9Center for Global Environmental Research, National Institute for Environmental Studies, Tsukuba, Ibaraki, Japan
  • 10School of Earth and Environment, University of Leeds, Leeds, UK
  • 11National Centre for Atmospheric Science, University of Leeds, Leeds, UK
  • anow at: Jet Propulsion Laboratory, Pasadena, California, USA

Abstract. This paper presents the first results of an age-of-air (AoA) inter-comparison of six global transport models. Following a protocol, three global circulation models and three chemistry transport models simulated five tracers with boundary conditions that grow linearly in time. This allows for an evaluation of the AoA and transport times associated with inter-hemispheric transport, vertical mixing in the troposphere, transport to and in the stratosphere, and transport of air masses between land and ocean. Since AoA is not a directly measurable quantity in the atmosphere, simulations of 222Rn and SF6 were also performed. We focus this first analysis on averages over the period 2000–2010, taken from longer simulations covering the period 1988–2014. We find that two models, NIES and TOMCAT, show substantially slower vertical mixing in the troposphere compared to other models (LMDZ, TM5, EMAC, and ACTM). However, while the TOMCAT model, as used here, has slow transport between the hemispheres and between the atmosphere over land and ocean, the NIES model shows efficient horizontal mixing and a smaller latitudinal gradient in SF6 compared to the other models and observations. We find consistent differences between models concerning vertical mixing of the troposphere, expressed as AoA differences and modelled 222Rn gradients between 950 and 500hPa. All models agree, however, on an interesting asymmetry in inter-hemispheric mixing, with faster transport from the Northern Hemisphere surface to the Southern Hemisphere than vice versa. This is attributed to a rectifier effect caused by a stronger seasonal cycle in boundary layer venting over Northern Hemispheric land masses, and possibly to a related asymmetric position of the intertropical convergence zone. The calculated AoA in the mid–upper stratosphere varies considerably among the models (4–7 years). Finally, we find that the inter-model differences are generally larger than differences in AoA that result from using the same model with a different resolution or convective parameterisation. Taken together, the AoA model inter-comparison provides a useful addition to traditional approaches to evaluate transport timescales. Results highlight that inter-model differences associated with resolved transport (advection, reanalysis data, nudging) and parameterised transport (convection, boundary layer mixing) are still large and require further analysis. For this purpose, all model output and analysis software are available.

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The TransCom inter-comparison project regularly carries out studies to quantify errors in simulated atmospheric transport. This paper presents the first results of an age of air (AoA) inter-comparison of six global transport models. Following a protocol, six models simulated five tracers from which atmospheric transport times can easily be deduced. Results highlight that inter-model differences associated with atmospheric transport are still large and require further analysis.
The TransCom inter-comparison project regularly carries out studies to quantify errors in...
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