Contributions of the troposphere and stratosphere to CH4 model biases
Zhiting Wang1,Thorsten Warneke1,Nicholas M. Deutscher1,2,Justus Notholt1,Ute Karstens3,Marielle Saunois4,Matthias Schneider5,Ralf Sussmann6,Harjinder Sembhi7,David W. T. Griffith2,Dave F. Pollard8,Rigel Kivi9,Christof Petri1,Voltaire A. Velazco2,Michel Ramonet4,and Huilin Chen10,11Zhiting Wang et al. Zhiting Wang1,Thorsten Warneke1,Nicholas M. Deutscher1,2,Justus Notholt1,Ute Karstens3,Marielle Saunois4,Matthias Schneider5,Ralf Sussmann6,Harjinder Sembhi7,David W. T. Griffith2,Dave F. Pollard8,Rigel Kivi9,Christof Petri1,Voltaire A. Velazco2,Michel Ramonet4,and Huilin Chen10,11
1Institute of Environmental Physics, University of Bremen, Bremen, Germany
2Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, New South Wales, Australia
3Max Planck Institute for Biogeochemistry, Jena, Germany
4Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRSUVSQ), Université Paris-Saclay, 91 191 Gif Sur Yvette, France
5Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, Germany
6Karlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, Germany
7Earth Observation Science, Department of physics and Astronomy, University of Leicester, Leicester, UK
8National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
9Finnish Meteorological Institute Arctic Research Center, FMI-ARC, Sodankylä, Finland
10Center for Isotope Research (CIO), University of Groningen, Groningen, the Netherlands
11Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA
1Institute of Environmental Physics, University of Bremen, Bremen, Germany
2Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, New South Wales, Australia
3Max Planck Institute for Biogeochemistry, Jena, Germany
4Laboratoire des Sciences du Climat et de l'Environnement, LSCE-IPSL (CEA-CNRSUVSQ), Université Paris-Saclay, 91 191 Gif Sur Yvette, France
5Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, Germany
6Karlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, Germany
7Earth Observation Science, Department of physics and Astronomy, University of Leicester, Leicester, UK
8National Institute of Water and Atmospheric Research (NIWA), Wellington, New Zealand
9Finnish Meteorological Institute Arctic Research Center, FMI-ARC, Sodankylä, Finland
10Center for Isotope Research (CIO), University of Groningen, Groningen, the Netherlands
11Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado, Boulder, CO, USA
Correspondence: Zhiting Wang (zhiting@iup.physik.uni-bremen.de)
Received: 22 Nov 2016 – Discussion started: 23 Dec 2016 – Revised: 12 Sep 2017 – Accepted: 03 Oct 2017 – Published: 09 Nov 2017
Abstract. Inverse modelling is a useful tool for retrieving CH4 fluxes; however, evaluation of the applied chemical transport model is an important step before using the inverted emissions. For inversions using column data one concern is how well the model represents stratospheric and tropospheric CH4 when assimilating total column measurements. In this study atmospheric CH4 from three inverse models is compared to FTS (Fourier transform spectrometry), satellite and in situ measurements. Using the FTS measurements the model biases are separated into stratospheric and tropospheric contributions. When averaged over all FTS sites the model bias amplitudes (absolute model to FTS differences) are 7.4 ± 5.1, 6.7 ± 4.8, and 8.1 ± 5.5 ppb in the tropospheric partial column (the column from the surface to the tropopause) for the models TM3, TM5-4DVAR, and LMDz-PYVAR, respectively, and 4.3 ± 9.9, 4.7 ± 9.9, and 6.2 ± 11.2 ppb in the stratospheric partial column (the column from the tropopause to the top of the atmosphere). The model biases in the tropospheric partial column show a latitudinal gradient for all models; however there are no clear latitudinal dependencies for the model biases in the stratospheric partial column visible except with the LMDz-PYVAR model. Comparing modelled and FTS-measured tropospheric column-averaged mole fractions reveals a similar latitudinal gradient in the model biases but comparison with in situ measured mole fractions in the troposphere does not show a latitudinal gradient, which is attributed to the different longitudinal coverage of FTS and in situ measurements. Similarly, a latitudinal pattern exists in model biases in vertical CH4 gradients in the troposphere, which indicates that vertical transport of tropospheric CH4 is not represented correctly in the models.
In this paper we separate the biases of atmospheric methane models into stratospheric and tropospheric parts. It is observed in other studies that simulated total columns of atmospheric methane present a latitudinal bias compared to measurements. The latitudinal gradients are considered to be from the stratosphere. However, our results show that the latitudinal biases could come from the troposphere in two of three models evaluated in this study.
In this paper we separate the biases of atmospheric methane models into stratospheric and...
