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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 11, issue 11 | Copyright

Special issue: The Lagrangian particle dispersion model FLEXPART

Geosci. Model Dev., 11, 4469-4487, 2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development and technical paper 08 Nov 2018

Development and technical paper | 08 Nov 2018

Three-dimensional methane distribution simulated with FLEXPART 8-CTM-1.1 constrained with observation data

Christine D. Groot Zwaaftink1, Stephan Henne2, Rona L. Thompson1, Edward J. Dlugokencky3, Toshinobu Machida4, Jean-Daniel Paris5, Motoki Sasakawa4, Arjo Segers6, Colm Sweeney3, and Andreas Stohl1 Christine D. Groot Zwaaftink et al.
  • 1Norwegian Institute for Air Research NILU, Kjeller, Norway
  • 2Empa, Swiss Federal Laboratories for Materials Science and Technology, Air Pollution/Environmental Technology, Dübendorf, Switzerland
  • 3NOAA Earth System Research Laboratory, Boulder, CO, USA
  • 4National Institute for Environmental Studies, Tsukuba, Japan
  • 5Laboratoire des Sciences du Climat et de l'Environnement, Gif-sur-Yvette, France
  • 6Netherlands Organisation for Applied Scientific Research (TNO), Utrecht, the Netherlands

Abstract. A Lagrangian particle dispersion model, the FLEXible PARTicle dispersion chemical transport model (FLEXPART CTM), is used to simulate global three-dimensional fields of trace gas abundance. These fields are constrained with surface observation data through nudging, a data assimilation method, which relaxes model fields to observed values. Such fields are of interest to a variety of applications, such as inverse modelling, satellite retrievals, radiative forcing models and estimating global growth rates of greenhouse gases. Here, we apply this method to methane using 6 million model particles filling the global model domain. For each particle, methane mass tendencies due to emissions (based on several inventories) and loss by reaction with OH, Cl and O(1D), as well as observation data nudging were calculated. Model particles were transported by mean, turbulent and convective transport driven by 1° × 1° ERA-Interim meteorology. Nudging is applied at 79 surface stations, which are mostly included in the World Data Centre for Greenhouse Gases (WDCGG) database or the Japan–Russia Siberian Tall Tower Inland Observation Network (JR-STATION) in Siberia. For simulations of 1 year (2013), we perform a sensitivity analysis to show how nudging settings affect modelled concentration fields. These are evaluated with a set of independent surface observations and with vertical profiles in North America from the National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL), and in Siberia from the Airborne Extensive Regional Observations in SIBeria (YAK-AEROSIB) and the National Institute for Environmental Studies (NIES). FLEXPART CTM results are also compared to simulations from the global Eulerian chemistry Transport Model version 5 (TM5) based on optimized fluxes. Results show that nudging strongly improves modelled methane near the surface, not only at the nudging locations but also at independent stations. Mean bias at all surface locations could be reduced from over 20 to less than 5ppb through nudging. Near the surface, FLEXPART CTM, including nudging, appears better able to capture methane molar mixing ratios than TM5 with optimized fluxes, based on a larger bias of over 13ppb in TM5 simulations. The vertical profiles indicate that nudging affects model methane at high altitudes, yet leads to little improvement in the model results there. Averaged from 19 aircraft profile locations in North America and Siberia, root mean square error (RMSE) changes only from 16.3 to 15.7ppb through nudging, while the mean absolute bias increases from 5.3 to 8.2ppb. The performance for vertical profiles is thereby similar to TM5 simulations based on TM5 optimized fluxes where we found a bias of 5ppb and RMSE of 15.9ppb. With this rather simple model setup, we thus provide three-dimensional methane fields suitable for use as boundary conditions in regional inverse modelling as a priori information for satellite retrievals and for more accurate estimation of mean mixing ratios and growth rates. The method is also applicable to other long-lived trace gases.

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Short summary
A Lagrangian particle dispersion model is used to simulate global fields of methane, constrained by observations through nudging. We show that this rather simple and computationally inexpensive method can give results similar to or as good as a computationally expensive Eulerian chemistry transport model with a data assimilation scheme. The three-dimensional methane fields are of interest to applications such as inverse modelling and satellite retrievals.
A Lagrangian particle dispersion model is used to simulate global fields of methane, constrained...