Machine-learning emulation of DGVM ensembles enables low-latency terrestrial CO2 flux estimates

Timely detection of climate-driven anomalies in terrestrial CO2 exchange is limited by the latency of current bottom-up and top-down flux products. Dynamic global vegetation model (DGVM) ensembles underpin the annual Global Carbon Budget, yet their reliance on forcing datasets updated on annual cycles delays the assessment of emerging extremes. Here we develop a member-wise machine-learning emulation system that reproduces monthly net biome production (NBP) from DGVM ensembles using near-real-time meteorological reanalysis and atmospheric CO2. The emulators learn each DGVM’s spatiotemporal response on a 0.5° grid, including memory effects from antecedent conditions, and can be run as an ensemble to provide both mean behaviour and spread. In strictly forward evaluation, the emulated ensemble preserves the seasonal cycle and interannual variability of global land–atmosphere CO2 exchange and captures the timing and broad spatial structure of deseasonalized anomalies. Skill is reduced in some tropical forest regions and the strongest positive and negative excursions are damped, indicating a conservative response under extremes. By replacing offline DGVM integrations with lightweight surrogates, this framework reduces product latency to approximately one month and delivers DGVM-consistent near-real-time CO2 flux estimates that can serve as operational priors for integrated carbon-cycle monitoring.