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Preprints
https://doi.org/10.5194/acp-2019-786
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-786
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  15 Nov 2019

15 Nov 2019

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This preprint was under review for the journal ACP. A revision for further review has not been submitted.

Characterizing model errors in chemical transport modelling of methane: Using GOSAT XCH4 data with weak constraint four-dimensional variational data assimilation

Ilya Stanevich1, Dylan B. A. Jones1, Kimberly Strong1, Martin Keller1, Daven K. Henze2,3, Robert J. Parker4,5, Hartmut Boesch4,5, Debra Wunch1, Justus Notholt6, Christof Petri6, Thorsten Warneke6, Ralf Sussmann7, Matthias Schneider8, Frank Hase8, Rigel Kivi9, Nicholas M. Deutscher10, Voltaire A. Velazco10, Kaley A. Walker1, and Feng Deng1 Ilya Stanevich et al.
  • 1Department of Physics, University of Toronto, Toronto, Ontario, Canada
  • 2Department of Mechanical Engineering, University of Colorado Boulder, Boulder, CO, USA
  • 3California Institute of Technology, Pasadena, CA, USA
  • 4Earth Observation Science, Department of Physics and Astronomy, University of Leicester, Leicester, UK
  • 5National Centre for Earth Observation (NCEO), University of Leicester, Leicester, UK
  • 6Institute of Environmental Physics, University of Bremen, Bremen, Germany
  • 7Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), Garmisch-Partenkirchen, Germany
  • 8Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
  • 9Finnish Meteorological Institute, Sodankylä, Finland
  • 10Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, NSW, Australia

Abstract. We examined biases in the global GEOS-Chem chemical transport model for the period of February–May 2010 using weak constraint (WC) four-dimensional variational (4D-Var) data assimilation and dry-air mole fractions of CH4 (XCH4) from the Greenhouse gases Observing SATellite (GOSAT). The ability of the observations and the WC 4D-Var method to mitigate model errors in CH4 concentrations was first investigated in a set of observing system simulation experiments (OSSEs). We then assimilated the GOSAT XCH4 retrievals and found that they were capable of differentiating the vertical distribution of model errors and of removing a significant portion of biases in the modelled CH4 state. In the WC 4D-Var assimilation, corrections were added to the modeled CH4 state at each model time step to account for model errors and improve the model fit to the assimilated observations. Compared to the conventional strong constraint (SC) 4D-Var assimilation, the WC method was able to significantly improve the model fit to independent observations. Examination of the WC state corrections suggested that a significant source of the model errors was associated with discrepancies in the model CH4 in the stratosphere. The WC state corrections also suggested that the model vertical transport in the troposphere at mid- and high-latitudes is too weak. The problem was traced back to biases in the uplift of CH4 over the source regions in eastern China and North America. In the tropics, the WC assimilation pointed to the possibility of biased CH4 outflow from the African continent to the Atlantic in the mid-troposphere. The WC assimilation in this region would greatly benefit from glint observations over the ocean to provide additional constraints on the vertical structure of the model errors in the tropics. We also compared the WC assimilation at the 4° × 5° and 2° × 2.5° horizontal resolutions and found that the WC corrections to mitigate the model errors were significantly larger at 4° × 5° than at 2° × 2.5° resolution, indicating the presence of resolution-dependent model errors. Our results illustrate the potential utility of the WC 4D-Var approach for characterizing model errors. However, a major limitation of this approach is the need to better characterize the specified model error covariance in the assimilation scheme.

Ilya Stanevich et al.

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Ilya Stanevich et al.

Ilya Stanevich et al.

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Short summary
We explore the utility of a weak constraint (WC) four-dimensional variational (4D-Var) data assimilation scheme for mitigating systematic errors in the methane simulation in the GEOS-Chem model. We use data from the Greenhouse Gases Observing Satellite (GOSAT) and show that, compared to the traditional 4D-Var approach, the WC scheme improves the agreement between the model and independent observations. We find that the WC corrections to the model provide insight into the source of the errors.
We explore the utility of a weak constraint (WC) four-dimensional variational (4D-Var) data...
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