Preprints
https://doi.org/10.5194/acp-2022-510
https://doi.org/10.5194/acp-2022-510
 
01 Aug 2022
01 Aug 2022
Status: a revised version of this preprint is currently under review for the journal ACP.

Impact of atmospheric transport on CO2 flux estimates derived from the atmospheric tracer inversions

Saqr Munassar1,2, Guillaume Monteil3, Marko Scholze3, Ute Karstens4, Christian Rödenbeck1, Frank-Thomas Koch1,5, Kai Uwe Totsche6, and Christoph Gerbig1 Saqr Munassar et al.
  • 1Department of Biogeochemical Signals, Max-Planck Institute for Biogeochemistry, Jena, Germany
  • 2Department of Physics, Faculty of Sciences, Ibb University, Ibb, Yemen
  • 3Department of Physical Geography and Ecosystem Science, Lund University, Lund, Sweden
  • 4ICOS Carbon Portal at Lund University, Lund, Sweden
  • 5Meteorological Observatory Hohenpeissenberg, Deutscher Wetterdienst, Hohenpeißenberg, Germany
  • 6Institute of Geoscience, Friedrich Schiller University, Jena, Germany

Abstract. We present an analysis of atmospheric transport impact on estimating CO2 fluxes using two atmospheric inversion systems (CarboScope Regional (CSR) and LUMIA) over Europe for 2018. The main focus of this study is to quantify the dominant drivers of spread amid CO2 estimates derived from atmospheric tracer inversions. The Lagrangian transport models STILT and FLEXPART were used to assess the impact of mesoscale transport. The impact of lateral boundary conditions for CO2 was assessed by applying the global transport models TM3 and TM5. CO2 estimates calculated with an ensemble of eight inversions differing in the regional and global transport models, as well as the inversion systems show a relatively large spread for the annual domain wide flux ranging between -0.72 and 0.20 PgC yr-1 with a mean estimate of -0.29 PgC. The largest discrepancies resulted from varying the mesoscale transport model, which amounted to a difference of 0.51 (PgC yr-1), in comparison with 0.23 and 0.10 (PgC yr-1) that resulted from the far-field contributions and the inversion systems, respectively. Additionally, varying the mesoscale transport caused large discrepancies in spatial and temporal patterns, while changing the lateral boundary conditions lead to more homogeneous spatial and temporal impact. We further investigated the origin of the discrepancies between transport models. The meteorological forcing parameters (forecasts versus reanalysis obtained from ECMWF data products) used to drive the transport models are responsible for a small part of the differences in CO2 estimates, but the largest impact seems to come from the models themselves. Although a good convergence in the differences between the inversion systems was achieved by applying a strict protocol of using identical priors, and atmospheric datasets, there was a non-negligible impact arising from applying a different inversion system. Specifically, the choice of prior error structure accounted for a large part of system-to-system differences.

Saqr Munassar 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-2022-510', Anonymous Referee #1, 24 Aug 2022
  • RC2: 'Comment on acp-2022-510', Anonymous Referee #2, 23 Sep 2022
  • AC1: 'ACs on RC 1 and RC2', Saqr Munassar, 21 Nov 2022

Saqr Munassar et al.

Saqr Munassar et al.

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Short summary
Using different transport models results in large errors in optimized fluxes in the inversion frameworks. Boundary conditions and inversion system configurations lead to a smaller, but non-negligible impact. The findings highlight the importance to validate transport models for further developments, but also to properly account for such errors in inverse modelling. This will help narrow the convergence of GHG estimates reported in the scientific literature from different inversion frameworks.
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