Geoscientific Model Development
Geoscientific Model Development
GMD
Articles | Volume 18, issue 14
Articles | Volume 18, issue 14
https://doi.org/10.5194/gmd-18-4317-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-18-4317-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
Articles | Volume 18, issue 14
Development and technical paper
 | 
18 Jul 2025
Development and technical paper |  | 18 Jul 2025

Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application

Carolina Natel, David Martín Belda, Peter Anthoni, Neele Haß, Sam Rabin, and Almut Arneth

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Short summary
We developed fast machine learning models to predict forest regrowth and carbon dynamics under climate change. These models mimic the outputs of a complex vegetation model but run 95 % faster, enabling global analyses and supporting climate solutions in large modeling frameworks such as LandSyMM.
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