Articles | Volume 15, issue 12
https://doi.org/10.5194/gmd-15-4831-2022
https://doi.org/10.5194/gmd-15-4831-2022
Development and technical paper
 | 
27 Jun 2022
Development and technical paper |  | 27 Jun 2022

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, Marielle Saunois, Antoine Berchet, Isabelle Pison, Bruce H. Vaughn, Sylvia Englund Michel, and Philippe Bousquet

Related authors

Investigation of the post-2007 methane renewed growth with high-resolution 3-D variational inverse modelling and isotopic constraints
Joël Thanwerdas, Marielle Saunois, Antoine Berchet, Isabelle Pison, and Philippe Bousquet
EGUsphere, https://doi.org/10.5194/egusphere-2023-1326,https://doi.org/10.5194/egusphere-2023-1326, 2023
Short summary
Estimating methane emissions in the Arctic nations using surface observations from 2008 to 2019
Sophie Wittig, Antoine Berchet, Isabelle Pison, Marielle Saunois, Joël Thanwerdas, Adrien Martinez, Jean-Daniel Paris, Toshinobu Machida, Motoki Sasakawa, Douglas E. J. Worthy, Xin Lan, Rona L. Thompson, Espen Sollum, and Mikhail Arshinov
Atmos. Chem. Phys., 23, 6457–6485, https://doi.org/10.5194/acp-23-6457-2023,https://doi.org/10.5194/acp-23-6457-2023, 2023
Short summary
How do Cl concentrations matter for the simulation of CH4 and δ13C(CH4) and estimation of the CH4 budget through atmospheric inversions?
Joël Thanwerdas, Marielle Saunois, Isabelle Pison, Didier Hauglustaine, Antoine Berchet, Bianca Baier, Colm Sweeney, and Philippe Bousquet
Atmos. Chem. Phys., 22, 15489–15508, https://doi.org/10.5194/acp-22-15489-2022,https://doi.org/10.5194/acp-22-15489-2022, 2022
Short summary
The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies
Antoine Berchet, Espen Sollum, Rona L. Thompson, Isabelle Pison, Joël Thanwerdas, Grégoire Broquet, Frédéric Chevallier, Tuula Aalto, Adrien Berchet, Peter Bergamaschi, Dominik Brunner, Richard Engelen, Audrey Fortems-Cheiney, Christoph Gerbig, Christine D. Groot Zwaaftink, Jean-Matthieu Haussaire, Stephan Henne, Sander Houweling, Ute Karstens, Werner L. Kutsch, Ingrid T. Luijkx, Guillaume Monteil, Paul I. Palmer, Jacob C. A. van Peet, Wouter Peters, Philippe Peylin, Elise Potier, Christian Rödenbeck, Marielle Saunois, Marko Scholze, Aki Tsuruta, and Yuanhong Zhao
Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021,https://doi.org/10.5194/gmd-14-5331-2021, 2021
Short summary
Using ship-borne observations of methane isotopic ratio in the Arctic Ocean to understand methane sources in the Arctic
Antoine Berchet, Isabelle Pison, Patrick M. Crill, Brett Thornton, Philippe Bousquet, Thibaud Thonat, Thomas Hocking, Joël Thanwerdas, Jean-Daniel Paris, and Marielle Saunois
Atmos. Chem. Phys., 20, 3987–3998, https://doi.org/10.5194/acp-20-3987-2020,https://doi.org/10.5194/acp-20-3987-2020, 2020
Short summary

Related subject area

Atmospheric sciences
An optimisation method to improve modelling of wet deposition in atmospheric transport models: applied to FLEXPART v10.4
Stijn Van Leuven, Pieter De Meutter, Johan Camps, Piet Termonia, and Andy Delcloo
Geosci. Model Dev., 16, 5323–5338, https://doi.org/10.5194/gmd-16-5323-2023,https://doi.org/10.5194/gmd-16-5323-2023, 2023
Short summary
Modelling concentration heterogeneities in streets using the street-network model MUNICH
Thibaud Sarica, Alice Maison, Yelva Roustan, Matthias Ketzel, Steen Solvang Jensen, Youngseob Kim, Christophe Chaillou, and Karine Sartelet
Geosci. Model Dev., 16, 5281–5303, https://doi.org/10.5194/gmd-16-5281-2023,https://doi.org/10.5194/gmd-16-5281-2023, 2023
Short summary
Simulation model of Reactive Nitrogen Species in an Urban Atmosphere using a Deep Neural Network: RNDv1.0
Junsu Gil, Meehye Lee, Jeonghwan Kim, Gangwoong Lee, Joonyoung Ahn, and Cheol-Hee Kim
Geosci. Model Dev., 16, 5251–5263, https://doi.org/10.5194/gmd-16-5251-2023,https://doi.org/10.5194/gmd-16-5251-2023, 2023
Short summary
J-GAIN v1.1: a flexible tool to incorporate aerosol formation rates obtained by molecular models into large-scale models
Daniel Yazgi and Tinja Olenius
Geosci. Model Dev., 16, 5237–5249, https://doi.org/10.5194/gmd-16-5237-2023,https://doi.org/10.5194/gmd-16-5237-2023, 2023
Short summary
Metrics for evaluating the quality in linear atmospheric inverse problems: a case study of a trace gas inversion
Vineet Yadav, Subhomoy Ghosh, and Charles E. Miller
Geosci. Model Dev., 16, 5219–5236, https://doi.org/10.5194/gmd-16-5219-2023,https://doi.org/10.5194/gmd-16-5219-2023, 2023
Short summary

Cited articles

Berchet, A., Sollum, E., Thompson, R. L., Pison, I., Thanwerdas, J., Broquet, G., Chevallier, F., Aalto, T., Berchet, A., Bergamaschi, P., Brunner, D., Engelen, R., Fortems-Cheiney, A., Gerbig, C., Groot Zwaaftink, C. D., Haussaire, J.-M., Henne, S., Houweling, S., Karstens, U., Kutsch, W. L., Luijkx, I. T., Monteil, G., Palmer, P. I., van Peet, J. C. A., Peters, W., Peylin, P., Potier, E., Rödenbeck, C., Saunois, M., Scholze, M., Tsuruta, A., and Zhao, Y.: The Community Inversion Framework v1.0: a unified system for atmospheric inversion studies, Geosci. Model Dev., 14, 5331–5354, https://doi.org/10.5194/gmd-14-5331-2021, 2021. a, b
Berchet, A., Sollum, E., Pison, I., Thompson, R. L., Thanwerdas, J., Fortems-Cheiney, A., van Peet, J. C. A., Potier, E., Chevallier, F., Broquet, G., and Berchet, A.: The Community Inversion Framework: codes and documentation (v1.1), Zenodo [code], https://doi.org/10.5281/zenodo.6304912, 2022. a
Bergamaschi, P., Lubina, C., Königstedt, R., Fischer, H., Veltkamp, A. C., and Zwaagstra, O.: Stable isotopic signatures (δ13C, δD) of methane from European landfill sites, J. Geophys. Res.-Atmos., 103, 8251–8265, https://doi.org/10.1029/98JD00105, 1998. a
Bergamaschi, P., Krol, M., Meirink, J. F., Dentener, F., Segers, A., van Aardenne, J., Monni, S., Vermeulen, A. T., Schmidt, M., Ramonet, M., Yver, C., Meinhardt, F., Nisbet, E. G., Fisher, R. E., O'Doherty, S., and Dlugokencky, E. J.: Inverse modeling of European CH4 emissions 2001–2006, J. Geophys. Res.-Atmos., 115, D22309, https://doi.org/10.1029/2010JD014180, 2010. a
Bergamaschi, P., Houweling, S., Segers, A., Krol, M., Frankenberg, C., Scheepmaker, R. A., Dlugokencky, E., Wofsy, S. C., Kort, E. A., Sweeney, C., Schuck, T., Brenninkmeijer, C., Chen, H., Beck, V., and Gerbig, C.: Atmospheric CH4 in the first decade of the 21st century: Inverse modeling analysis using SCIAMACHY satellite retrievals and NOAA surface measurements, J. Geophys. Res.-Atmos., 118, 7350–7369, https://doi.org/10.1002/jgrd.50480, 2013. a, b
Download
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.