Premium
Data‐driven estimates of global litter production imply slower vegetation carbon turnover
Author(s) -
He Yue,
Wang Xuhui,
Wang Kai,
Tang Shuchang,
Xu Hao,
Chen Anping,
Ciais Philippe,
Li Xiangyi,
Peñuelas Josep,
Piao Shilong
Publication year - 2021
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.15515
Subject(s) - primary production , environmental science , vegetation (pathology) , carbon cycle , litter , ecosystem , productivity , production (economics) , latitude , plant litter , global change , physical geography , atmospheric sciences , climate change , ecology , geography , biology , geology , medicine , macroeconomics , economics , geodesy , pathology
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.