Improving Terrestrial Carbon Flux Simulations With Machine Learning and Global Earth Observations

Abstract. The land carbon cycle can act as both a negative and positive climate feedback. Currently, it serves as a negative feedback, absorbing about one-third of anthropogenic CO₂ emissions. However, multi-model studies project a weakening of this sink, with the potential for a future shift to a carbon source. Significant inter-model differences persist, limiting confidence in these projections. Some of these discrepancies may arise from parameter uncertainty. Advances in artificial intelligence, computing, and Earth observations now offer new opportunities to better constrain key model parameters. While previous studies have shown that parameter optimization can substantially improve model performance, they have not explored its impact on the future carbon balance. To address this gap, I use a machine learning algorithm to optimize 28 model parameters based on 13 global Earth observation datasets. The resulting parameter set is then applied in carbon cycle simulations under historical conditions and a high-emissions future scenario. Results show that optimization significantly improves model performance, particularly for gross primary productivity (GPP), leaf area index, and sensible heat flux. Globally, optimized net biome productivity is lower than in the default simulation (33 % lower from 1960 to 2022 and 43 % lower from 2015 to 2100) due to reduced GPP and increased autotrophic respiration. Regionally, optimization tends to weaken both carbon sinks and sources, reducing the contrast between them. In conclusion, parameter tuning can substantially alter historical and future carbon fluxes, with effects comparable to adding new processes. To reduce inter-model spread, modeling groups should integrate advanced parameter optimization frameworks into their model development cycle.