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Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates.
Yang, Hui; Ciais, Philippe; Santoro, Maurizio; Huang, Yuanyuan; Li, Wei; Wang, Yilong; Bastos, Ana; Goll, Daniel; Arneth, Almut; Anthoni, Peter; Arora, Vivek K; Friedlingstein, Pierre; Harverd, Vanessa; Joetzjer, Emilie; Kautz, Markus; Lienert, Sebastian; Nabel, Julia E M S; O'Sullivan, Michael; Sitch, Stephen; Vuichard, Nicolas; Wiltshire, Andy; Zhu, Dan.
Afiliação
  • Yang H; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France.
  • Ciais P; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France.
  • Santoro M; Gamma Remote Sensing, Gümligen, Switzerland.
  • Huang Y; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France.
  • Li W; CSIRO Oceans and Atmosphere, Aspendale, Vic., Australia.
  • Wang Y; Department of Earth System Science, Ministry of Education Key Laboratory for Earth System Modeling, Tsinghua University, Beijing, China.
  • Bastos A; Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, Gif-sur-Yvette, France.
  • Goll D; Department für Geographie, Ludwig-Maximilians-Universität München, Munchen, Germany.
  • Arneth A; Department of Geography, University of Augsburg, Augsburg, Germany.
  • Anthoni P; Institute of Meteorology and Climate Research/Atmospheric Environmental Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany.
  • Arora VK; Institute of Meteorology and Climate Research/Atmospheric Environmental Research, Karlsruhe Institute of Technology, Garmisch-Partenkirchen, Germany.
  • Friedlingstein P; Canadian Centre for Climate Modelling and Analysis, Climate Research Division, Environment and Climate Change Canada, Victoria, BC, Canada.
  • Harverd V; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
  • Joetzjer E; LMD/IPSL, ENS, PSL Université, École Polytechnique, Institut Polytechnique de Paris, Sorbonne Université, CNRS, Paris, France.
  • Kautz M; CSIRO Oceans and Atmosphere, Canberra, ACT, Australia.
  • Lienert S; CNRM, Université de Toulouse, Météo-France, CNRS, Toulouse, France.
  • Nabel JEMS; Department of Forest Health, Forest Research Institute Baden-Württemberg, Freiburg, Germany.
  • O'Sullivan M; Climate and Environmental Physics, Physics Institute, University of Bern, Bern, Switzerland.
  • Sitch S; Oeschger Centre for Climate Change Research, University of Bern, Bern, Switzerland.
  • Vuichard N; Max Planck Institute for Meteorology, Hamburg, Germany.
  • Wiltshire A; College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, UK.
  • Zhu D; College of Life and Environmental Sciences, University of Exeter, Exeter, UK.
Glob Chang Biol ; 26(7): 3997-4012, 2020 07.
Article em En | MEDLINE | ID: mdl-32427397
Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model-data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB95 ) in tropics can be considered as the potential maximum of AGB density which can be reached for a given annual precipitation. GlobBiomass data show local deficits of AGB density compared to the AGB95 , particularly in transitional and/or wet regions in tropics. We hypothesize that local human disturbances cause more AGB density deficits from GlobBiomass than from DGVMs, which rarely represent human disturbances. We then analyse empirical relationships between AGB density deficits and forest cover changes, population density, burned areas and livestock density. Regression analysis indicated that more than 40% of the spatial variance of AGB density deficits in South America and Africa can be explained; in Southeast Asia, these factors explain only ~25%. This result suggests TRENDY v6 DGVMs tend to underestimate biomass loss from diverse and widespread anthropogenic disturbances, and as a result overestimate turnover time in AGB.
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Texto completo: 1 Base de dados: MEDLINE Assunto principal: Árvores / Florestas Idioma: En Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Base de dados: MEDLINE Assunto principal: Árvores / Florestas Idioma: En Ano de publicação: 2020 Tipo de documento: Article