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

Accurate quantification of vegetation carbon turnover time (τveg) is critical for reducing uncertainties in terrestrial vegetation response to future climate change. However, in the absence of global information of litter production, τveg 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 τveg (mean value 10.3 ± 1.4 years) and its spatial distribution. Compared to our observation‐based τveg, modelled τveg tended to underestimate τveg at high latitudes. Our empirically derived gridded datasets of litter production and τveg will help constrain global vegetation models and improve the prediction of global carbon cycle. Vegetation carbon turnover time (τveg) dominates uncertainties in terrestrial vegetation response to future climate change. Due to the lack of global litter production, previous studies commonly use NPP as a proxy based on steady‐state assumption. We applied a machine‐learning approach to derive global litter production by linking 2401 observations and environmental drivers. Our global estimate was 44.3 ± 0.4 Pg C year−1. We then provided a direct quantification of global τveg without relying on any assumptions, which was 10.3 ± 1.4 years. LSMs generally tended to underestimate τveg at high latitudes and consequently the carbon sequestration potential of high‐latitude ecosystems.