Preprints
https://doi.org/10.5194/egusphere-2022-275
https://doi.org/10.5194/egusphere-2022-275
 
16 Jun 2022
16 Jun 2022

A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions

Martin Vojta1, Andreas Plach1,2, Rona L. Thompson3, and Andreas Stohl1 Martin Vojta et al.
  • 1Department of Meteorology and Geophysics, University of Vienna, Vienna, Austria
  • 2Physics Institute, Climate and Environmental Physics, University of Bern, Bern, Switzerland
  • 3Norwegian Institute for Air Research NILU, Kjeller, Norway

Abstract. Using the example of sulfur hexafluoride (SF6) we investigate the use of Lagrangian Particle Dispersion Models (LPDMs) for inverse modeling of greenhouse gas (GHG) emissions and explore the limitations of this approach. We put the main focus on the impacts of baseline methods and the LPDM backward simulation period on the a posteriori emissions determined by the inversion. We consider baseline methods that are based on a statistical selection of observations at individual measurement sites and a global distribution based (GDB) approach, where global mixing ratio fields are coupled to the LPDM back-trajectories at their termination points. We show that purely statistical baseline methods cause large systematical errors, which lead to inversion results that are highly sensitive to the LPDM backward simulation period and can generate unrealistic global total a posteriori emissions. The GDB method produces a posteriori emissions that are far less sensitive to the backward simulation period and that are consistent with recognized global total emissions. Our results show that longer backward simulation periods, beyond the often used 5 to 10 days, reduce the mean squared error and increase the correlation between a priori modeled and observed mixing ratios. Also, the inversion becomes less sensitive to biases in the a priori emissions and the global mixing ratio fields for longer backward simulation periods. Further, longer periods help to better constrain emissions in regions poorly covered by the global SF6 monitoring network (e.g., Africa, South America). We find that the inclusion of existing flask measurements in the inversion helps to further close these gaps and suggest that a few additional and well placed flask sampling sites would have great value for improving global a posteriori emission fields.

Journal article(s) based on this preprint

18 Nov 2022
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
Geosci. Model Dev., 15, 8295–8323, https://doi.org/10.5194/gmd-15-8295-2022,https://doi.org/10.5194/gmd-15-8295-2022, 2022
Short summary

Martin Vojta et al.

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-275', Anonymous Referee #1, 18 Jul 2022
    • AC1: 'Reply on RC1', Martin Vojta, 28 Sep 2022
  • RC2: 'Comment on egusphere-2022-275', Anonymous Referee #2, 11 Sep 2022
    • AC2: 'Reply on RC2', Martin Vojta, 28 Sep 2022

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on egusphere-2022-275', Anonymous Referee #1, 18 Jul 2022
    • AC1: 'Reply on RC1', Martin Vojta, 28 Sep 2022
  • RC2: 'Comment on egusphere-2022-275', Anonymous Referee #2, 11 Sep 2022
    • AC2: 'Reply on RC2', Martin Vojta, 28 Sep 2022

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Martin Vojta on behalf of the Authors (28 Sep 2022)  Author's response    Author's tracked changes    Manuscript
ED: Referee Nomination & Report Request started (30 Sep 2022) by Andrea Stenke
RR by Anonymous Referee #1 (05 Oct 2022)
ED: Publish subject to technical corrections (21 Oct 2022) by Andrea Stenke
AR by Martin Vojta on behalf of the Authors (24 Oct 2022)  Author's response    Manuscript

Journal article(s) based on this preprint

18 Nov 2022
A comprehensive evaluation of the use of Lagrangian particle dispersion models for inverse modeling of greenhouse gas emissions
Martin Vojta, Andreas Plach, Rona L. Thompson, and Andreas Stohl
Geosci. Model Dev., 15, 8295–8323, https://doi.org/10.5194/gmd-15-8295-2022,https://doi.org/10.5194/gmd-15-8295-2022, 2022
Short summary

Martin Vojta et al.

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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.

Short summary
In light of recent global warming, we aim to improve the methods for modeling greenhouse gas emissions in order to support the successful implementation of the Paris Agreement. In this study, we investigate certain aspects of a Bayesian inversion method that uses computer simulations and atmospheric observations to improve estimates of greenhouse gas emissions. We explore method limitations, discuss problems, and suggest improvements.