Preprints
https://doi.org/10.5194/gmd-2021-106
https://doi.org/10.5194/gmd-2021-106

Submitted as: development and technical paper 28 May 2021

Submitted as: development and technical paper | 28 May 2021

Review status: this preprint is currently under review for the journal GMD.

Variational inverse modelling within the Community Inversion Framework to assimilate δ13C(CH4) and CH4: a case study with model LMDz-SACS

Joël Thanwerdas1, Marielle Saunois1, Antoine Berchet1, Isabelle Pison1, Bruce H. Vaughn2, Sylvia Englund Michel2, and Philippe Bousquet1 Joël Thanwerdas et al.
  • 1Laboratoire des Sciences du Climat et de l’Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, France
  • 2INSTAAR - University of Colorado, Boulder, CO, United States

Abstract. Atmospheric CH4 mixing ratios resumed their increase in 2007 after a plateau during the period 1999–2006, suggesting varying sources and sinks as main drivers. Estimating sources by exploiting observations within an inverse modeling framework (top-down approaches) is a powerful approach. It is nevertheless challenging to efficiently differentiate co-located emission categories and sinks by using CH4 observations alone. As a result, top-down approaches are limited when it comes to fully understanding CH4 burden changes and attribute these changes to specific source variations. CH4 source isotopic signatures differ between emission categories (biogenic, thermogenic and pyrogenic), and can therefore be used to address this limitation. Here, a new 3-D variational inverse modeling framework designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. This system is capable of optimizing both emissions and associated source signatures of multiple emission categories. We present the technical implementation of joint CH4 and δ13C(CH4) constraints in a variational system, and analyze how sensitive the system is to the setup controlling the optimization using the 3-D Chemistry-Transport Model LMDz-SACS. We find that assimilating δ13C(CH4) observations and allowing the system to adjust source isotopic signatures provide relatively large differences in global flux estimates for wetlands (5 Tg yr−1), microbial (6 Tg yr−1), fossil fuels (8 Tg yr−1) and biofuels-biomass burning (4 Tg yr−1) categories compared to the results inferred without assimilating δ13C(CH4) observations. More importantly, when assimilating both CH4 and δ13C(CH4) observations, but assuming source signatures are perfectly known increase these differences between the system with CH4 and the enhanced one with δ13C(CH4) by a factor 3 or 4, strengthening the importance of having as accurate as possible signatures. Initial conditions, uncertainties on δ13C(CH4) observations or the number of optimized categories have a much smaller impact (less than 2 Tg yr−1).

Joël Thanwerdas et al.

Status: open (until 23 Jul 2021)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • AC1: 'Comment on gmd-2021-106', Joel Thanwerdas, 28 May 2021 reply
  • CEC1: 'Comment on gmd-2021-106', Astrid Kerkweg, 08 Jun 2021 reply

Joël Thanwerdas et al.

Joël Thanwerdas et al.

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Short summary
Estimating CH4 sources by exploiting observations within an inverse modeling framework is a powerful approach. Here, a new system designed to assimilate δ13C(CH4) observations together with CH4 observations is presented. By optimizing both emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.