Data-driven estimates of global litter production imply slower vegetation carbon turnover

Accurate quantification of vegetation carbon turnover time (τ_veg ) is critical for reducing uncertainties in terrestrial vegetation response to future climate change. However, in the absence of global information of litter production, τ_veg could only be estimated based on net primary productivity under the steady-state assumption. Here, we applied a machine-learning approach to derive a global dataset of litter production by linking 2401 field observations and global environmental drivers. Results suggested that the observation-based estimate of global natural ecosystem litter production was 44.3 ± 0.4 Pg C year^-1 . By contrast, land-surface models (LSMs) overestimated the global litter production by about 27%. With this new global litter production dataset, we estimated global τ_veg (mean value 10.3 ± 1.4 years) and its spatial distribution. Compared to our observation-based τ_veg , modelled τ_veg tended to underestimate τ_veg at high latitudes. Our empirically derived gridded datasets of litter production and τ_veg will help constrain global vegetation models and improve the prediction of global carbon cycle.