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Tytuł pozycji:

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

Tytuł :
Data-driven estimates of global litter production imply slower vegetation carbon turnover.
Autorzy :
He Y; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Wang X; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Wang K; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Tang S; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Xu H; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Chen A; Department of Biology, Colorado State University, Fort Collins, CO, USA.
Ciais P; Laboratoire des Sciences du Climat et de l'Environnement (LSCE), CEA CNRS UVSQ, Gif Sur Yvette, France.
Li X; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.
Peñuelas J; CREAF, Barcelona, Spain.; Global Ecology Unit CREAF-CSIC-UAB, CSIC, Barcelona, Spain.
Piao S; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China.; Key Laboratory of Alpine Ecology and Biodiversity, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, China.; Center for Excellence in Tibetan Earth Science, Chinese Academy of Sciences, Beijing, China.
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Źródło :
Global change biology [Glob Chang Biol] 2021 Apr; Vol. 27 (8), pp. 1678-1688. Date of Electronic Publication: 2021 Feb 01.
Typ publikacji :
Journal Article
Język :
English
Imprint Name(s) :
Publication: : Oxford : Blackwell Pub.
Original Publication: Oxford, UK : Blackwell Science, 1995-
MeSH Terms :
Carbon*
Ecosystem*
Carbon Cycle ; Climate Change
References :
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Grant Information :
2019YFA0607304 National Key Research & Development Program of China; 41988101 BCTES
Contributed Indexing :
Keywords: boosted regression trees*; land-surface models*; litter production*; vegetation carbon stock*; vegetation carbon turnover time*
Substance Nomenclature :
7440-44-0 (Carbon)
Entry Date(s) :
Date Created: 20210110 Date Completed: 20210423 Latest Revision: 20210423
Update Code :
20210424
DOI :
10.1111/gcb.15515
PMID :
33423389
Czasopismo naukowe
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.
(© 2021 John Wiley & Sons Ltd.)

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