Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates

Hui Yang; Philippe Ciais; Maurizio Santoro; Yuanyuan Huang; Wei Li; Yilong Wang; Ana Bastos; Daniel S. Goll; Almuth Arneth; Peter M. Anthoni; Vivek K. Arora; Pierre Friedlingstein; Vanessa Harverd; Emilie Joetzjer; Markus Kautz; Sebastian Lienert; Julia E.M.S. Nabel; Michael O'Sullivan; Stephen A. Sitch; Nicolas Vuichard; Andy J. Wiltshire; Dan Zhu

doi:10.1111/gcb.15117

Journal articles

Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates

Hui Yang ^{1, *} Philippe Ciais ^{1, 2} Maurizio Santoro ³ Yuanyuan Huang ^{1, 4} Wei Li ⁵ Yilong Wang ^{1, 6} Ana Bastos ⁷ Daniel S. Goll ⁸ Almuth Arneth ⁹ Peter M. Anthoni ⁹ Vivek K. Arora ¹⁰ Pierre Friedlingstein ¹¹ Vanessa Harverd ⁴ Emilie Joetzjer ¹² Markus Kautz ¹³ Sebastian Lienert ^{14, 15} Julia E.M.S. Nabel ¹⁶ Michael O'Sullivan ¹¹ Stephen A. Sitch ¹⁷ Nicolas Vuichard ^{1, 18} Andy J. Wiltshire ¹⁹ Dan Zhu ¹

* Corresponding author

1 LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette]

2 ICOS-ATC - ICOS-ATC

LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] : DRF/LSCE

3 Gamma Remote Sensing Research and Consulting AG

4 CSIRO - Commonwealth Scientific and Industrial Research Organisation [Canberra]

5 Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science, Tsinghua University

6 SATINV - Modélisation INVerse pour les mesures atmosphériques et SATellitaires

LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] : DRF/LSCE

7 Department für Geo-und Umweltwissenschaften [München]

8 Department of Geography, University of Augsburg

9 Institute of Meteorology and Climate Research

10 Climate Research Division [Toronto]

11 EMPS - College of Engineering, Mathematics and Physical Sciences [Exeter]

12 CNRM - Centre national de recherches météorologiques

13 Forest Research Institute Baden-Württemberg - Forstliche Versuchs- und Forschungsanstalt Baden-Württemberg

14 Physics Institute [Bern]

15 OCCR - Oeschger Centre for Climate Change Research

16 MPI-M - Max Planck Institute for Meteorology

17 College of Life and Environmental Sciences [Exeter]

18 MOSAIC - Modélisation des Surfaces et Interfaces Continentales

LSCE - Laboratoire des Sciences du Climat et de l'Environnement [Gif-sur-Yvette] : DRF/LSCE

19 MOHC - Met Office Hadley Centre for Climate Change

Abstract : 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.

Keywords : AGB density deficits carbon cycle forest ecosystems human disturbances model evaluation remote sensing-based biomass

Document type :

Journal articles

Domain :

Environmental Sciences / Global Changes

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Submitted on : Tuesday, September 1, 2020 - 4:17:19 PM
Last modification on : Monday, May 16, 2022 - 8:20:29 AM

Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates

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Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates

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