Submitted as: model evaluation paper
16 Mar 2023
Submitted as: model evaluation paper |  | 16 Mar 2023
Status: a revised version of this preprint is currently under review for the journal GMD.

Implementation of a Satellite-Based Tool for the Quantification of CH4 emissions over Europe (AUMIA v1.0) – Part 1: Forward Modeling Evaluation against Near-Surface and Satellite Data

Angel Liduvino Vara-Vela, Christoffer Karoff, Noelia Rojas Benavente, and Janaina Nascimento

Abstract. Methane is the second most important greenhouse gas after carbon dioxide, and accounts for around 10 % of total European Union greenhouse gases emissions. Given that the atmospheric methane budget over a region depends on its terrestrial and aquatic methane sources, inverse modeling techniques appear as a powerful tools for identifying critical areas that can later be submitted to emission mitigation strategies. In this regard, an inverse modeling system of methane emissions for Europe is being implemented based on the Weather Research and Forecasting (WRF) model: the Aarhus University Methane Inversion Algorithm (AUMIA) v1.0. The forward modeling component of AUMIA consists of the WRF model coupled to a multipurpose global database of methane anthropogenic emissions. To assure transport consistency during the inversion process, the backward modeling component will be based on the WRF model coupled to a lagrangian particle dispersion module. A description of the modeling tools, input data sets and one-year forward modeling evaluation from April 01, 2018 to March 31, 2019 is provided in this paper. The a posteriori methane emission estimates, including a more focused inverse modeling for Denmark, will be provided in a second paper. A good general agreement is found between the modeling results and observations based on the TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite. Model-observation discrepancies for summer peak season are in line with previous studies conducted over urban areas in central Europe, with relative differences between simulated concentrations and observational data in this study ranging from study ranging from 1 to 2 %. Domain-wide correlation coefficients and root-mean-square-errors for summer months ranged from 0.4 to 0.5 and from 27 to 30 ppb, respectively. For winter months, otherwise, model-observation discrepancies show a significant overestimation of anthropogenic emissions over the study region, with relative differences ranging from 2 to 3 %. Domain-wide correlation coefficients and root-mean-square-errors in this case ranged from 0.1 to 0.4 and from 33 to 50 ppb, respectively, indicating that a more refined inverse analysis assessment will be required for this season. According to modeling results, the methane enhancement above the background concentrations came almost entirely from anthropogenic sources; however, these sources contributed with only up to 2 % to the methane total column concentration. Contributions from natural sources (wetlands and termites) and biomass burning were not relevant during the study period. The results found in this study contribute with a new model evaluation of methane concentrations over Europe, and demonstrate a huge and under explored potential for methane inverse modeling using improved TROPOMI products in large-scale applications.

Angel Liduvino Vara-Vela et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • CEC1: 'Comment on gmd-2023-9', Juan Antonio Añel, 05 May 2023
    • AC1: 'Reply on CEC1', Angel Liduvino Vara-Vela, 05 May 2023
      • EC1: 'Reply on AC1', Fiona O'Connor, 05 May 2023
        • AC2: 'Reply on EC1', Angel Liduvino Vara-Vela, 06 May 2023
          • CEC2: 'Reply on AC2', Juan Antonio Añel, 10 May 2023
  • RC1: 'Comment on gmd-2023-9', Anonymous Referee #1, 12 May 2023
    • AC3: 'Reply on RC1', Angel Liduvino Vara-Vela, 25 May 2023
  • RC2: 'Comment on gmd-2023-9', Anonymous Referee #2, 01 Jun 2023
    • AC4: 'Reply on RC2', Angel Liduvino Vara-Vela, 08 Jun 2023

Angel Liduvino Vara-Vela et al.

Angel Liduvino Vara-Vela et al.


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Short summary
One-year simulation of atmospheric CH4 over Europe is performed and evaluated against observations based on the TROPOspheric Monitoring Instrument (TROPOMI). A good general model-observation agreement is found, with discrepancies reaching their minimum and maximum values during summer peak season and winter months, respectively. A huge and under explored potential for CH4 inverse modeling using improved TROPOMI XCH4 data sets in large-scale applications, is identified.