03 Feb 2023
 | 03 Feb 2023
Status: this preprint is currently under review for the journal ACP.

Impact of transport model resolution and a-priori assumptions on inverse modeling of Swiss F-gases emissions

Ioannis Katharopoulos, Dominique Rust, Martin K. Vollmer, Dominik Brunner, Stefan Reimann, Simon J. O'Doherty, Dickon Young, Kieran M. Stanley, Tanja Schuck, Jgor Arduini, Lukas Emmenegger, and Stephan Henne

Abstract. Inverse modeling is a widely used top-down method to infer greenhouse gas (GHG) emissions and their spatial distribution based on atmospheric observations. The errors associated with inverse modeling have multiple sources, such as observations and a-priori emission estimates, but they are often dominated by the transport model error. Here, we utilize the Lagrangian Particle Dispersion Model (LPDM) FLEXPART, driven by the meteorological fields of the regional numerical weather prediction model COSMO. The main source of errors in LPDMs is the turbulence diffusion parameterization and the meteorological fields. The latter are outputs of an Eulerian model. Recently, we introduced an improved parameterization scheme of the turbulence diffusion in FLEXPART, which significantly improves FLEXPART-COSMO simulations at 1 km resolution. We exploit F-gases measurements from two extended field campaigns on the Swiss Plateau (in Beromünster and Sottens) and we conduct both high- (1 km) and low-resolution (7 km) FLEXPART transport simulations that are then used in a Bayesian analytical inversion to estimate spatial emission distributions. Our results for four F-gases (HFC-134a, HFC-125, HFC-32, SF6) indicate that both high-resolution inversions and a dense measurement network significantly improve the ability to estimate the spatial distribution of emissions. Furthermore, the total emission estimates from the high-resolution inversions (351±44 Mg yr−1 for HFC-134a, 101±21 Mg yr−1 for HFC-125, 50±8 Mg yr−1 for HFC-32, 9.0±1.1 Mg yr−1 for SF6) are significantly higher compared to the low-resolution inversions (20–40 % increase) and result in total a-posteriori emission estimates that are closer to national inventory values as reported to the UNFCCC (10–20 % difference between high-resolution inversion estimates and inventory values compared to 30–40 % difference between the low-resolution inversion estimates and inventory values). Specifically, we attribute these improvements to a better representation of the atmospheric flow in complex terrain in the high-resolution model, partly induced by the more realistic topography. We further conduct numerous sensitivity inversions, varying different parameters and variables of our Bayesian inversion framework to explore the whole range of uncertainty in the inversion errors (e.g., inversion grid, spatial distribution of a-priori emissions, covariance parameters like baseline uncertainty and spatial correlation length, temporal resolution of the assimilated observations, observation network, seasonality of emissions). From the above-mentioned parameters, we find that the uncertainty of the mole fraction baseline and the spatial distribution of the a-priori emissions have the largest impact on the a-posteriori total emission estimates and their spatial distribution. This study is a step towards mitigating the errors associated with the transport models and better characterizing the uncertainty inherent in the inversion error. Improvements in the latter will facilitate the validation and standardization of the national GHG emission inventories and support policymakers.

Ioannis Katharopoulos et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on acp-2022-723', Anonymous Referee #1, 06 Feb 2023
  • RC2: 'Comment on acp-2022-723', Anonymous Referee #2, 21 Mar 2023

Ioannis Katharopoulos et al.

Data sets

Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions Rust, Dominique; Vollmer, Martin K.; Katharopoulos, Ioannis; Henne, Stephan; Reimann, Stefan

Ioannis Katharopoulos et al.


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Short summary
The effectiveness of climate change mitigation needs to be scrutinized by monitoring greenhouse gas (GHG) emissions. Countries report their emissions to the UN in a bottom-up manner. By combining atmospheric observations and transport models someone can independently validate emission estimates in a top-down fashion. We report Swiss emissions of synthetic GHGs based on kilometer-scale transport and inverse modeling, highlighting the role of appropriate resolution in complex terrain.