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

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.