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In Global change biology

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 y-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 y) 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.

He Yue, Wang Xuhui, Wang Kai, Tang Shuchang, Xu Hao, Chen Anping, Ciais Philippe, Li Xiangyi, PeƱuelas Josep, Piao Shilong


boosted regression trees, land-surface models, litter production, vegetation carbon stock, vegetation carbon turnover time