Comparison of forest above‐ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation‐based estimates. (19th May 2020)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Global change biology. Volume 26:Number 7(2020)
• Page Start: 3997
• Page End: 4012
• Publication Date: 2020-05-19
• Subjects: AGB density deficits -- carbon cycle -- forest ecosystems -- human disturbances -- model evaluation -- remote sensing‐based biomass; Climatic changes -- Environmental aspects -- Periodicals; Troposphere -- Environmental aspects -- Periodicals; Biodiversity conservation -- Periodicals; Eutrophication -- Periodicals; 551.5
• DOI: 10.1111/gcb.15117
• Languages: English
• ISSNs: 1354-1013

Abstract

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. Abstract : Biomass is strongly coupled with climate. Growing trees remove CO2 from the atmosphere and store it mainly as biomass, whereas adverse climate change can negate this service, causing massive losses during drought and fires. Carbon cycle models struggle to simulate biomass and need to benchmarked by the latest datasets. Here we use a global high‐resolution biomass map derived from remote sensing known as GlobBiomass to evaluate the TRENDY‐Version6 dynamic global vegetation models used to assess each year the global carbon budget, and show that human disturbance reducing biomass in the tropics is a critical process missing in models.