Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data
Abstract
Earth system models (ESMs) have been widely used for projecting global vegetation carbon dynamics, yet how well ESMs performed for simulating vegetation carbon density remains untested. Here we have compiled observational data of vegetation carbon density from literature and existing data sets to evaluate nine ESMs at site, biome, latitude, and global scales. Three variables—root (including fine and coarse roots), total vegetation carbon density, and the root:total vegetation carbon ratios (R/T ratios), were chosen for ESM evaluation. ESM models performed well in simulating the spatial distribution of carbon densities in root (r = 0.71) and total vegetation (r = 0.62). However, ESM models had significant biases in simulating absolute carbon densities in root and total vegetation biomass across the majority of land ecosystems, especially in tropical and arctic ecosystems. Particularly, ESMs significantly overestimated carbon density in root (183%) and total vegetation biomass (167%) in climate zones of 10°S–10°N. Substantial discrepancies between modeled and observed R/T ratios were found: the R/T ratios from ESMs were relatively constant, approximately 0.2 across all ecosystems, along latitudinal gradients, and in tropic, temperate, and arctic climatic zones, which was significantly different from the observed large variations in the R/T ratios (0.1–0.8). There were substantial inconsistenciesmore »
- Authors:
-
- Biology Department San Diego State University San Diego California USA, Biology Department University of Texas at El Paso El Paso Texas USA
- Environmental Sciences Division and Climate Change Science Institute Oak Ridge National Laboratory Oak Ridge Tennessee USA
- Institute of Geographic Sciences and Natural Resources Research Chinese Academy of Sciences Beijing China
- Biology Department San Diego State University San Diego California USA
- Biology Department San Diego State University San Diego California USA, Biology Department University of Texas at El Paso El Paso Texas USA, Northeast Institute of Geography and Agroecology Chinese Academy of Sciences Changchun China
- Publication Date:
- Research Org.:
- Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Biological and Environmental Research (BER); National Science Foundation (NSF)
- OSTI Identifier:
- 1389091
- Alternate Identifier(s):
- OSTI ID: 1389092; OSTI ID: 1393808
- Grant/Contract Number:
- DE‐AC05‐00OR22725; AC05-00OR22725; ACI-1053575
- Resource Type:
- Published Article
- Journal Name:
- Journal of Geophysical Research. Biogeosciences
- Additional Journal Information:
- Journal Name: Journal of Geophysical Research. Biogeosciences Journal Volume: 122 Journal Issue: 9; Journal ID: ISSN 2169-8953
- Publisher:
- American Geophysical Union (AGU)
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; carbon density; root; vegetation; root/vegetation ratio; Earth system models
Citation Formats
Song, Xia, Hoffman, Forrest M., Iversen, Colleen M., Yin, Yunhe, Kumar, Jitendra, Ma, Chun, and Xu, Xiaofeng. Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data. United States: N. p., 2017.
Web. doi:10.1002/2017JG003914.
Song, Xia, Hoffman, Forrest M., Iversen, Colleen M., Yin, Yunhe, Kumar, Jitendra, Ma, Chun, & Xu, Xiaofeng. Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data. United States. https://doi.org/10.1002/2017JG003914
Song, Xia, Hoffman, Forrest M., Iversen, Colleen M., Yin, Yunhe, Kumar, Jitendra, Ma, Chun, and Xu, Xiaofeng. Sat .
"Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data". United States. https://doi.org/10.1002/2017JG003914.
@article{osti_1389091,
title = {Significant inconsistency of vegetation carbon density in CMIP5 Earth system models against observational data},
author = {Song, Xia and Hoffman, Forrest M. and Iversen, Colleen M. and Yin, Yunhe and Kumar, Jitendra and Ma, Chun and Xu, Xiaofeng},
abstractNote = {Earth system models (ESMs) have been widely used for projecting global vegetation carbon dynamics, yet how well ESMs performed for simulating vegetation carbon density remains untested. Here we have compiled observational data of vegetation carbon density from literature and existing data sets to evaluate nine ESMs at site, biome, latitude, and global scales. Three variables—root (including fine and coarse roots), total vegetation carbon density, and the root:total vegetation carbon ratios (R/T ratios), were chosen for ESM evaluation. ESM models performed well in simulating the spatial distribution of carbon densities in root (r = 0.71) and total vegetation (r = 0.62). However, ESM models had significant biases in simulating absolute carbon densities in root and total vegetation biomass across the majority of land ecosystems, especially in tropical and arctic ecosystems. Particularly, ESMs significantly overestimated carbon density in root (183%) and total vegetation biomass (167%) in climate zones of 10°S–10°N. Substantial discrepancies between modeled and observed R/T ratios were found: the R/T ratios from ESMs were relatively constant, approximately 0.2 across all ecosystems, along latitudinal gradients, and in tropic, temperate, and arctic climatic zones, which was significantly different from the observed large variations in the R/T ratios (0.1–0.8). There were substantial inconsistencies between ESM-derived carbon density in root and total vegetation biomass and the R/T ratio at multiple scales, indicating urgent needs for model improvements on carbon allocation algorithms and more intensive field campaigns targeting carbon density in all key vegetation components.},
doi = {10.1002/2017JG003914},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 9,
volume = 122,
place = {United States},
year = {2017},
month = {9}
}
https://doi.org/10.1002/2017JG003914
Web of Science
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