16 Jun 2022
16 Jun 2022
Status: this preprint is open for discussion.

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.

Martin Vojta et al.

Status: open (until 11 Aug 2022)

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Martin Vojta et al.


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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.