Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 20
Articles | Volume 18, issue 20
https://doi.org/10.5194/gmd-18-7501-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-7501-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 20
Development and technical paper
 | 
21 Oct 2025
Development and technical paper |  | 21 Oct 2025

Towards the assimilation of atmospheric CO2 concentration data in a land surface model using adjoint-free variational methods

Simon Beylat, Nina Raoult, Cédric Bacour, Natalie Douglas, Tristan Quaife, Vladislav Bastrikov, Peter J. Rayner, and Philippe Peylin

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Short summary
Land surface models are important tools for understanding and predicting the land components of the carbon cycle. Atmospheric CO2 concentration data are a valuable source of information that can be used to improve the accuracy of these models. In this study, we present a statistical ensemble-variational data assimilation method named EnVarDA to calibrate parameters of a land surface model using these data. We show that this method is easy to implement and more efficient and accurate than traditional methods.
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