Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4831-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-15-4831-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Variational inverse modeling within the Community Inversion Framework v1.1 to assimilate δ13C(CH4) and CH4: a case study with model LMDz-SACS
Joël Thanwerdas
CORRESPONDING AUTHOR
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, France
Marielle Saunois
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, France
Antoine Berchet
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, France
Isabelle Pison
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, France
Bruce H. Vaughn
INSTAAR, University of Colorado, Boulder, Boulder, CO, USA
Sylvia Englund Michel
INSTAAR, University of Colorado, Boulder, Boulder, CO, USA
Philippe Bousquet
Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif-sur-Yvette, France
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Cited
14 citations as recorded by crossref.
- Estimating emissions of methane consistent with atmospheric measurements of methane and δ13C of methane S. Basu et al. https://doi.org/10.5194/acp-22-15351-2022
- Investigation of the renewed methane growth post-2007 with high-resolution 3-D variational inverse modeling and isotopic constraints J. Thanwerdas et al. https://doi.org/10.5194/acp-24-2129-2024
- High-resolution regional inversion reveals overestimation of anthropogenic methane emissions in China S. Feng et al. https://doi.org/10.5194/acp-25-15121-2025
- Bottom‐Up Evaluation of the Methane Budget in Asia and Its Subregions A. Ito et al. https://doi.org/10.1029/2023GB007723
- Atmospheric Methane: Comparison Between Methane's Record in 2006–2022 and During Glacial Terminations E. Nisbet et al. https://doi.org/10.1029/2023GB007875
- How do Cl concentrations matter for the simulation of CH4 and δ13C(CH4) and estimation of the CH4 budget through atmospheric inversions? J. Thanwerdas et al. https://doi.org/10.5194/acp-22-15489-2022
- Global Methane Budget 2000–2020 M. Saunois et al. https://doi.org/10.5194/essd-17-1873-2025
- The global hydrogen budget Z. Ouyang et al. https://doi.org/10.1038/s41586-025-09806-1
- Exploring atmospheric CH4 monitoring network expansion in Italy using inverse modelling J. Thanwerdas et al. https://doi.org/10.5194/acp-26-10477-2026
- Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019 S. Wittig et al. https://doi.org/10.5194/acp-23-6457-2023
- Machine-learning-based estimates of global natural vegetated wetland methane emissions (2000–2025) M. Li et al. https://doi.org/10.5194/essd-18-3507-2026
- High-Resolution Global Methane Mapping: Advances in Satellite Remote Sensing, Machine Learning, and Policy Frameworks A. Singh & . Madhubala https://doi.org/10.3390/methane5030021
- A global dataset of δ13C-CH4 source signatures and associated uncertainties (1998–2022), with a sensitivity analysis to support isotopic inversions E. Tapin et al. https://doi.org/10.5194/essd-18-4793-2026
- Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01 J. Thanwerdas et al. https://doi.org/10.5194/gmd-18-1505-2025
14 citations as recorded by crossref.
- Estimating emissions of methane consistent with atmospheric measurements of methane and δ13C of methane S. Basu et al. https://doi.org/10.5194/acp-22-15351-2022
- Investigation of the renewed methane growth post-2007 with high-resolution 3-D variational inverse modeling and isotopic constraints J. Thanwerdas et al. https://doi.org/10.5194/acp-24-2129-2024
- High-resolution regional inversion reveals overestimation of anthropogenic methane emissions in China S. Feng et al. https://doi.org/10.5194/acp-25-15121-2025
- Bottom‐Up Evaluation of the Methane Budget in Asia and Its Subregions A. Ito et al. https://doi.org/10.1029/2023GB007723
- Atmospheric Methane: Comparison Between Methane's Record in 2006–2022 and During Glacial Terminations E. Nisbet et al. https://doi.org/10.1029/2023GB007875
- How do Cl concentrations matter for the simulation of CH4 and δ13C(CH4) and estimation of the CH4 budget through atmospheric inversions? J. Thanwerdas et al. https://doi.org/10.5194/acp-22-15489-2022
- Global Methane Budget 2000–2020 M. Saunois et al. https://doi.org/10.5194/essd-17-1873-2025
- The global hydrogen budget Z. Ouyang et al. https://doi.org/10.1038/s41586-025-09806-1
- Exploring atmospheric CH4 monitoring network expansion in Italy using inverse modelling J. Thanwerdas et al. https://doi.org/10.5194/acp-26-10477-2026
- Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019 S. Wittig et al. https://doi.org/10.5194/acp-23-6457-2023
- Machine-learning-based estimates of global natural vegetated wetland methane emissions (2000–2025) M. Li et al. https://doi.org/10.5194/essd-18-3507-2026
- High-Resolution Global Methane Mapping: Advances in Satellite Remote Sensing, Machine Learning, and Policy Frameworks A. Singh & . Madhubala https://doi.org/10.3390/methane5030021
- A global dataset of δ13C-CH4 source signatures and associated uncertainties (1998–2022), with a sensitivity analysis to support isotopic inversions E. Tapin et al. https://doi.org/10.5194/essd-18-4793-2026
- Improving the ensemble square root filter (EnSRF) in the Community Inversion Framework: a case study with ICON-ART 2024.01 J. Thanwerdas et al. https://doi.org/10.5194/gmd-18-1505-2025
Saved (final revised paper)
Latest update: 11 Aug 2026
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 the emissions and associated source signatures of multiple emission categories, this new system can efficiently differentiate the co-located emission categories and provide estimates of CH4 sources that are consistent with isotopic data.
Estimating CH4 sources by exploiting observations within an inverse modeling framework is a...