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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 9 | Copyright
Atmos. Meas. Tech., 9, 4843-4859, 2016
https://doi.org/10.5194/amt-9-4843-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 28 Sep 2016

Research article | 28 Sep 2016

Evaluation of column-averaged methane in models and TCCON with a focus on the stratosphere

Andreas Ostler1, Ralf Sussmann1, Prabir K. Patra2, Sander Houweling3,4, Marko De Bruine3, Gabriele P. Stiller5, Florian J. Haenel5, Johannes Plieninger5, Philippe Bousquet6,7, Yi Yin6,7, Marielle Saunois6,7, Kaley A. Walker8, Nicholas M. Deutscher9,10, David W. T. Griffith9, Thomas Blumenstock6, Frank Hase6, Thorsten Warneke10, Zhiting Wang10, Rigel Kivi11, and John Robinson12 Andreas Ostler et al.
  • 1Karlsruhe Institute of Technology, IMK-IFU, 82467 Garmisch-Partenkirchen, Germany
  • 2Research Institute for Global Change, JAMSTEC, Yokohama, 236-0001, Japan
  • 3Institute for Marine and Atmospheric Research Utrecht, Utrecht University, 3584 CC Utrecht, the Netherlands
  • 4SRON Netherlands Institute for Space Research, 3584 CA Utrecht, the Netherlands
  • 5Karlsruhe Institute of Technology, IMK-ASF, 76021 Karlsruhe, Germany
  • 6Laboratoire des Sciences du Climat et de l'Environnement, IPSL-LSCE, CEA-CNRS-UVSQ, UMR8212, 91191 Gif-sur-Yvette, France
  • 7Université de Versailles Saint Quentin en Yvelines, 78000 Versaille, France
  • 8Department of Physics, University of Toronto, Toronto, Ontario M5S 1A7, Canada
  • 9School of Chemistry, University of Wollongong, Wollongong, NSW 2522, Australia
  • 10Institute of Environmental Physics, University of Bremen, 28334 Bremen, Germany
  • 11Finnish Meteorological Institute, Arctic Research Center, 99600 Sodankylä, Finland
  • 12Department of Atmospheric Research, National Institute of Water and Atmospheric Research (NIWA) Ltd, Wellington 6021, New Zealand

Abstract. The distribution of methane (CH4) in the stratosphere can be a major driver of spatial variability in the dry-air column-averaged CH4 mixing ratio (XCH4), which is being measured increasingly for the assessment of CH4 surface emissions. Chemistry-transport models (CTMs) therefore need to simulate the tropospheric and stratospheric fractional columns of XCH4 accurately for estimating surface emissions from XCH4. Simulations from three CTMs are tested against XCH4 observations from the Total Carbon Column Network (TCCON). We analyze how the model–TCCON agreement in XCH4 depends on the model representation of stratospheric CH4 distributions. Model equivalents of TCCON XCH4 are computed with stratospheric CH4 fields from both the model simulations and from satellite-based CH4 distributions from MIPAS (Michelson Interferometer for Passive Atmospheric Sounding) and MIPAS CH4 fields adjusted to ACE-FTS (Atmospheric Chemistry Experiment Fourier Transform Spectrometer) observations. Using MIPAS-based stratospheric CH4 fields in place of model simulations improves the model–TCCON XCH4 agreement for all models. For the Atmospheric Chemistry Transport Model (ACTM) the average XCH4 bias is significantly reduced from 38.1 to 13.7ppb, whereas small improvements are found for the models TM5 (Transport Model, version 5; from 8.7 to 4.3ppb) and LMDz (Laboratoire de Météorologie Dynamique model with zooming capability; from 6.8 to 4.3ppb). Replacing model simulations with MIPAS stratospheric CH4 fields adjusted to ACE-FTS reduces the average XCH4 bias for ACTM (3.3ppb), but increases the average XCH4 bias for TM5 (10.8ppb) and LMDz (20.0ppb). These findings imply that model errors in simulating stratospheric CH4 contribute to model biases. Current satellite instruments cannot definitively measure stratospheric CH4 to sufficient accuracy to eliminate these biases. Applying transport diagnostics to the models indicates that model-to-model differences in the simulation of stratospheric transport, notably the age of stratospheric air, can largely explain the inter-model spread in stratospheric CH4 and, hence, its contribution to XCH4. Therefore, it would be worthwhile to analyze how individual model components (e.g., physical parameterization, meteorological data sets, model horizontal/vertical resolution) impact the simulation of stratospheric CH4 and XCH4.

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Our evaluation of column-averaged methane (XCH4) in models and TCCON reveals latitudinal biases between 0.4 % and 2.1 % originating from an inter-model spread in stratospheric CH4. Substituting model stratospheric CH4 fields by satellite data significantly reduces the large XCH4 bias observed for one model. For other models, showing only minor biases, the impact is ambiguous; i.e., the satellite uncertainty range hinders a more accurate model evaluation needed to improve inverse modeling.
Our evaluation of column-averaged methane (XCH4) in models and TCCON reveals latitudinal biases...
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