Data‐driven estimates of global litter production imply slower vegetation carbon turnover
Yue He, Xuhui Wang, Kai Wang, Shuchang Tang, Hao Xu, Anping Chen, Philippe Ciais, Xiangyi Li, Josep Peñuelas, Shilong Piao
Abstract
Abstract 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.