Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application
<p>The assessment of forest-based climate change mitigation strategies relies on computationally intensive scenario analyses, particularly when dynamic vegetation models are coupled with socioeconomic models in multi-model frameworks. In this study, we developed surrogate models for the LPJ-GU...
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2025-07-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/18/4317/2025/gmd-18-4317-2025.pdf |
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| author | C. Natel D. M. Belda P. Anthoni N. Haß S. Rabin A. Arneth |
| author_facet | C. Natel D. M. Belda P. Anthoni N. Haß S. Rabin A. Arneth |
| author_sort | C. Natel |
| collection | DOAJ |
| description | <p>The assessment of forest-based climate change mitigation strategies relies on computationally intensive scenario analyses, particularly when dynamic vegetation models are coupled with socioeconomic models in multi-model frameworks. In this study, we developed surrogate models for the LPJ-GUESS dynamic global vegetation model to accelerate the prediction of carbon stocks and fluxes, enabling quicker scenario optimization within a multi-model coupling framework. We trained two machine learning methods: random forest and neural network. We assessed and compared the emulators using performance metrics and Shapley-based explanations. Our emulation approach accurately captured global and biome-specific forest carbon dynamics, closely replicating the outputs of LPJ-GUESS for both historical (1850–2014) and future (2015–2100) periods under various climate scenarios. Among the two trained emulators, the neural network extrapolated better at the end of the century for carbon stocks and fluxes and provided more physically consistent predictions, as verified by Shapley values. Overall, the emulators reduced the simulation execution time by 95 %, bridging the gap between complex process-based models and the need for scalable and fast simulations. This offers a valuable tool for scenario analysis in the context of climate change mitigation, forest management, and policy development.</p> |
| format | Article |
| id | doaj-art-be4d03b9dda047e887af1e6d3e963283 |
| institution | Kabale University |
| issn | 1991-959X 1991-9603 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj-art-be4d03b9dda047e887af1e6d3e9632832025-08-20T03:27:28ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032025-07-01184317433310.5194/gmd-18-4317-2025Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 applicationC. Natel0D. M. Belda1P. Anthoni2N. Haß3S. Rabin4A. Arneth5Karlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, Garmisch-Partenkirchen, GermanyKarlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, Garmisch-Partenkirchen, GermanyKarlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, Garmisch-Partenkirchen, GermanyKarlsruhe Institute of Technology, Institute of Geography and Geoecology, Karlsruhe, GermanyNational Center for Atmospheric Research, Climate and Global Dynamics, Boulder, CO, United StatesKarlsruhe Institute of Technology, Institute of Meteorology and Climate Research/Atmospheric Environmental Research, Garmisch-Partenkirchen, Germany<p>The assessment of forest-based climate change mitigation strategies relies on computationally intensive scenario analyses, particularly when dynamic vegetation models are coupled with socioeconomic models in multi-model frameworks. In this study, we developed surrogate models for the LPJ-GUESS dynamic global vegetation model to accelerate the prediction of carbon stocks and fluxes, enabling quicker scenario optimization within a multi-model coupling framework. We trained two machine learning methods: random forest and neural network. We assessed and compared the emulators using performance metrics and Shapley-based explanations. Our emulation approach accurately captured global and biome-specific forest carbon dynamics, closely replicating the outputs of LPJ-GUESS for both historical (1850–2014) and future (2015–2100) periods under various climate scenarios. Among the two trained emulators, the neural network extrapolated better at the end of the century for carbon stocks and fluxes and provided more physically consistent predictions, as verified by Shapley values. Overall, the emulators reduced the simulation execution time by 95 %, bridging the gap between complex process-based models and the need for scalable and fast simulations. This offers a valuable tool for scenario analysis in the context of climate change mitigation, forest management, and policy development.</p>https://gmd.copernicus.org/articles/18/4317/2025/gmd-18-4317-2025.pdf |
| spellingShingle | C. Natel D. M. Belda P. Anthoni N. Haß S. Rabin A. Arneth Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application Geoscientific Model Development |
| title | Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application |
| title_full | Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application |
| title_fullStr | Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application |
| title_full_unstemmed | Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application |
| title_short | Emulating grid-based forest carbon dynamics using machine learning: an LPJ-GUESS v4.1.1 application |
| title_sort | emulating grid based forest carbon dynamics using machine learning an lpj guess v4 1 1 application |
| url | https://gmd.copernicus.org/articles/18/4317/2025/gmd-18-4317-2025.pdf |
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