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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 2
Geosci. Model Dev., 8, 129-150, 2015
https://doi.org/10.5194/gmd-8-129-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 8, 129-150, 2015
https://doi.org/10.5194/gmd-8-129-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 03 Feb 2015

Development and technical paper | 03 Feb 2015

Atmospheric transport and chemistry of trace gases in LMDz5B: evaluation and implications for inverse modelling

R. Locatelli1, P. Bousquet1, F. Hourdin2, M. Saunois1, A. Cozic1, F. Couvreux3, J.-Y. Grandpeix2, M.-P. Lefebvre2,3, C. Rio2, P. Bergamaschi4, S. D. Chambers5, U. Karstens6, V. Kazan1, S. van der Laan*, H. A. J. Meijer7, J. Moncrieff8, M. Ramonet1, H. A. Scheeren4,8, C. Schlosser9, M. Schmidt1,**, A. Vermeulen10, and A. G. Williams5 R. Locatelli et al.
  • 1Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette, France
  • 2Laboratoire de Meteorologie Dynamique (LMD) – Jussieu, Paris, France
  • 3Centre National de Recherches Météorologiques (CNRM-Game), Météo-France, Toulouse, France
  • 4Institute for Environmental and Sustainability, Joint Research Centre, European Commission, Ispra, Italy
  • 5Australian Nuclear Science and Technology Organisation, Locked Bag 2001, Kirrawee DC, NSW 2232, Australia
  • 6Max Planck Institute for Biogeochemistry, Jena, Germany
  • 7Centre for Isotope Research, ESRIG, University of Groningen, Nijenborgh 4, 9747 AG Groningen, the Netherlands
  • 8School of GeoSciences and Centre for Terrestrial Carbon Dynamics, University of Edinburgh, UK
  • 9Federal Office for Radiation Protection (BfS), Willy-Brandt-Str. 5, 38226 Salzgitter, Germany
  • 10Energy research Centre of the Netherlands (ECN), Petten, the Netherlands
  • *now at: Centre for Ocean and Atmospheric Sciences, School of Environmental Sciences, University of East Anglia, UK
  • **now at: Institut für Umweltphysik, Heidelberg University, INF 229, 69120 Heidelberg, Germany

Abstract. Representation of atmospheric transport is a major source of error in the estimation of greenhouse gas sources and sinks by inverse modelling. Here we assess the impact on trace gas mole fractions of the new physical parameterizations recently implemented in the atmospheric global climate model LMDz to improve vertical diffusion, mesoscale mixing by thermal plumes in the planetary boundary layer (PBL), and deep convection in the troposphere. At the same time, the horizontal and vertical resolution of the model used in the inverse system has been increased. The aim of this paper is to evaluate the impact of these developments on the representation of trace gas transport and chemistry, and to anticipate the implications for inversions of greenhouse gas emissions using such an updated model.

Comparison of a one-dimensional version of LMDz with large eddy simulations shows that the thermal scheme simulates shallow convective tracer transport in the PBL over land very efficiently, and much better than previous versions of the model. This result is confirmed in three-dimensional simulations, by a much improved reproduction of the radon-222 diurnal cycle. However, the enhanced dynamics of tracer concentrations induces a stronger sensitivity of the new LMDz configuration to external meteorological forcings. At larger scales, the inter-hemispheric exchange is slightly slower when using the new version of the model, bringing them closer to observations. The increase in the vertical resolution (from 19 to 39 layers) significantly improves the representation of stratosphere/troposphere exchange. Furthermore, changes in atmospheric thermodynamic variables, such as temperature, due to changes in the PBL mixing modify chemical reaction rates, which perturb chemical equilibriums of reactive trace gases.

One implication of LMDz model developments for future inversions of greenhouse gas emissions is the ability of the updated system to assimilate a larger amount of high-frequency data sampled at high-variability stations. Others implications are discussed at the end of the paper.

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