Unified FLUXes (UFLUX) emulator v2 and Data of Journal Publication

Process-based biosphere models, which formulate biophysical ecosystem processes, provide a mechanistic framework for understanding terrestrial carbon dynamics. In contrast, data-driven approaches, such as upscaling eddy covariance (EC) fluxes using satellite observations and machine learning, offer empirical estimates. These complementary methods often diverge significantly, particularly in estimating global photosynthetic uptake and ecosystem respiration, with discrepancies exceeding 50 Pg C yr−1^{-1}−1, highlighting persistent uncertainties. To bridge this gap, we adopt a hybrid strategy that embeds physiological understanding via semi-empirical models, refines it with EC fluxes constrained by machine learning, and integrates process-based allocation to resolve component fluxes. This process-informed hybrid approach links ecological knowledge with predictive models, enabling generalisation beyond flux tower sites and supporting the development of new insights. We assess global carbon dynamics over the past two decades, applying Bayesian inference to evaluate climate impacts on land carbon processes. Our study delivers the first observational and process-informed hybrid assessment of global carbon flux and stock changes. Notably, while gross carbon uptake is consistent across methods (130 Pg C yr−1^{-1}−1 in 2022, increasing by 0.4 Pg C yr−1^{-1}−1 annually), respiration estimates diverge — from 6 Pg C yr−1^{-1}−1 in process-based models to 26 Pg C yr−1^{-1}−1 in conventional upscaling, and 16 Pg C yr−1^{-1}−1 in our hybrid model — revealing structural limitations in respiration parameterisation. Improved representation of respiratory processes is essential to capture the competing roles of photosynthesis and respiration under climate change. Despite rising global carbon fluxes and biomass stocks, tipping point risks remain: in tropical regions, increased photosynthesis (0.1 Pg C yr−1^{-1}−1) is offset by rising respiration (0.05 Pg C yr−1^{-1}−1).