Google Scholar: cites
Data-driven estimates of global litter production imply slower vegetation carbon turnover
He, Yue (Sino-French Institute for Earth System Science)
Wang, Xuhui (Sino-French Institute for Earth System Science)
Wang, Kai (Sino-French Institute for Earth System Science)
Tang, Shuchang (Sino-French Institute for Earth System Science)
Xu, Hao (Sino-French Institute for Earth System Science)
Chen, Anping (Colorado State University. Department of Biology)
Ciais, Philippe (Université Paris-Saclay. Laboratoire des Sciences du Climat et de l'Environnement)
Li, Xiangyi (Sino-French Institute for Earth System Science)
Peñuelas, Josep (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Piao, Shilong (Sino-French Institute for Earth System Science)

Data: 2021
Resum: 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.
Drets: Aquest material està protegit per drets d'autor i/o drets afins. Podeu utilitzar aquest material en funció del que permet la legislació de drets d'autor i drets afins d'aplicació al vostre cas. Per a d'altres usos heu d'obtenir permís del(s) titular(s) de drets.
Llengua: Anglès
Document: Article ; recerca ; Versió acceptada per publicar
Matèria: Boosted regression trees ; Land-surface models ; Litter production ; Vegetation carbon stock ; Vegetation carbon turnover time
Publicat a: Global change biology, Vol. 27, issue 8 (April 2021) , p. 1678-1688, ISSN 1365-2486

DOI: 10.1111/gcb.15515


Postprint
32 p, 1.3 MB

El registre apareix a les col·leccions:
Documents de recerca > Documents dels grups de recerca de la UAB > Centres i grups de recerca (producció científica) > Ciències > CREAF (Centre de Recerca Ecològica i d'Aplicacions Forestals)
Articles > Articles de recerca
Articles > Articles publicats

 Registre creat el 2024-08-20, darrera modificació el 2026-01-19



   Favorit i Compartir