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Title: Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region

Abstract

Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe–Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon–use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)–level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg (18.2 ± 26.9%), and SLA (27.4±36.5%)more » than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. Furthermore, these results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems.« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]; ORCiD logo [4]; ORCiD logo [5]; ORCiD logo [6]; ORCiD logo [7]; ORCiD logo [8]; ORCiD logo [9]; ORCiD logo [10]; ORCiD logo [3]; ORCiD logo [11]; ORCiD logo [12]; ORCiD logo [13]; ORCiD logo [8]; ORCiD logo [14];  [15]; ORCiD logo [8];  [16] more »; ORCiD logo [17];  [18]; ORCiD logo [8];  [19]; ORCiD logo [19]; ORCiD logo [20];  [21]; ORCiD logo [1] « less
  1. East China Normal Univ., Shanghai (China); Institute of Eco‐Chongming, Shanghai (China)
  2. McMaster Univ., Hamilton, ON (Canada)
  3. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  4. Northern Arizona Univ. Flagstaff, AZ (United States)
  5. National Institute for Environmental Studies, Tsukuba (Japan)
  6. Northern Arizona Univ., Flagstaff, AZ (United States)
  7. Univ. of Illinois at Urbana‐Champaign, Urbana, IL (United States)
  8. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  9. Carnegie Institution for Science, Stanford, CA (United States)
  10. Univ. of Chinese Academy of Sciences, Beijing (China); Chinese Academy of Sciences (CAS), Beijing (China)
  11. Univ. of Quebec at Montreal, QC (Canada); Northwest A&F Univ., Yangling (China)
  12. Peking Univ., Beijing (China)
  13. Montana State Univ. Bozeman, MT (United States)
  14. Univ. of Colorado Boulder, Boulder, CO (United States)
  15. Northern Arizona Univ., Flagstaff, AZ (United States); Woods Hole Research Center, Falmouth, MA (United States)
  16. Auburn Univ., Auburn, AL (United States)
  17. National Aeronautics and Space Administration, Moffett Field, CA (United States)
  18. Univ. of Chinese Academy of Sciences, Beijing (China)
  19. East China Normal Univ., Shanghai (China)
  20. Univ. of Maryland, College Park, MD (United States)
  21. Northwest A&F Univ., Yangling (China)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1542239
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Global Biogeochemical Cycles
Additional Journal Information:
Journal Volume: 33; Journal Issue: 6; Journal ID: ISSN 0886-6236
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; environmental drivers; initial conditions; model uncertainty; MsTMIP; relative importance; vegetation functional property

Citation Formats

Cui, Erqian, Huang, Kun, Arain, Muhammad Altaf, Fisher, Joshua B., Huntzinger, Deborah N., Ito, Akihiko, Luo, Yiqi, Jain, Atul K., Mao, Jiafu, Michalak, Anna M., Niu, Shuli, Parazoo, Nicholas C., Peng, Changhui, Peng, Shushi, Poulter, Benjamin, Ricciuto, Daniel M., Schaefer, Kevin M., Schwalm, Christopher R., Shi, Xiaoying, Tian, Hanqin, Wang, Weile, Wang, Jinsong, Wei, Yaxing, Yan, Enrong, Yan, Liming, Zeng, Ning, Zhu, Qiuan, and Xia, Jianyang. Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region. United States: N. p., 2019. Web. doi:10.1029/2018GB005909.
Cui, Erqian, Huang, Kun, Arain, Muhammad Altaf, Fisher, Joshua B., Huntzinger, Deborah N., Ito, Akihiko, Luo, Yiqi, Jain, Atul K., Mao, Jiafu, Michalak, Anna M., Niu, Shuli, Parazoo, Nicholas C., Peng, Changhui, Peng, Shushi, Poulter, Benjamin, Ricciuto, Daniel M., Schaefer, Kevin M., Schwalm, Christopher R., Shi, Xiaoying, Tian, Hanqin, Wang, Weile, Wang, Jinsong, Wei, Yaxing, Yan, Enrong, Yan, Liming, Zeng, Ning, Zhu, Qiuan, & Xia, Jianyang. Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region. United States. https://doi.org/10.1029/2018GB005909
Cui, Erqian, Huang, Kun, Arain, Muhammad Altaf, Fisher, Joshua B., Huntzinger, Deborah N., Ito, Akihiko, Luo, Yiqi, Jain, Atul K., Mao, Jiafu, Michalak, Anna M., Niu, Shuli, Parazoo, Nicholas C., Peng, Changhui, Peng, Shushi, Poulter, Benjamin, Ricciuto, Daniel M., Schaefer, Kevin M., Schwalm, Christopher R., Shi, Xiaoying, Tian, Hanqin, Wang, Weile, Wang, Jinsong, Wei, Yaxing, Yan, Enrong, Yan, Liming, Zeng, Ning, Zhu, Qiuan, and Xia, Jianyang. Sun . "Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region". United States. https://doi.org/10.1029/2018GB005909. https://www.osti.gov/servlets/purl/1542239.
@article{osti_1542239,
title = {Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region},
author = {Cui, Erqian and Huang, Kun and Arain, Muhammad Altaf and Fisher, Joshua B. and Huntzinger, Deborah N. and Ito, Akihiko and Luo, Yiqi and Jain, Atul K. and Mao, Jiafu and Michalak, Anna M. and Niu, Shuli and Parazoo, Nicholas C. and Peng, Changhui and Peng, Shushi and Poulter, Benjamin and Ricciuto, Daniel M. and Schaefer, Kevin M. and Schwalm, Christopher R. and Shi, Xiaoying and Tian, Hanqin and Wang, Weile and Wang, Jinsong and Wei, Yaxing and Yan, Enrong and Yan, Liming and Zeng, Ning and Zhu, Qiuan and Xia, Jianyang},
abstractNote = {Global and regional projections of climate change by Earth system models are limited by their uncertain estimates of terrestrial ecosystem productivity. At the middle to low latitudes, the East Asian monsoon region has higher productivity than forests in Europe–Africa and North America, but its estimate by current generation of terrestrial biosphere models (TBMs) has seldom been systematically evaluated. Here, we developed a traceability framework to evaluate the simulated gross primary productivity (GPP) by 15 TBMs in the East Asian monsoon region. The framework links GPP to net primary productivity, biomass, leaf area and back to GPP via incorporating multiple vegetation functional properties of carbon–use efficiency (CUE), vegetation C turnover time (τveg), leaf C fraction (Fleaf), specific leaf area (SLA), and leaf area index (LAI)–level photosynthesis (PLAI), respectively. We then applied a relative importance algorithm to attribute intermodel variation at each node. The results showed that large intermodel variation in GPP over 1901–2010 were mainly propagated from their different representation of vegetation functional properties. For example, SLA explained 77% of the intermodel difference in leaf area, which contributed 90% to the simulated GPP differences. In addition, the models simulated higher CUE (18.1 ± 21.3%), τveg (18.2 ± 26.9%), and SLA (27.4±36.5%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. Furthermore, these results suggest the large uncertainty of current TBMs in simulating GPP is largely propagated from their poor representation of the vegetation functional properties and call for a better understanding of the covariations between plant functional properties in terrestrial ecosystems.},
doi = {10.1029/2018GB005909},
journal = {Global Biogeochemical Cycles},
number = 6,
volume = 33,
place = {United States},
year = {Sun May 12 00:00:00 EDT 2019},
month = {Sun May 12 00:00:00 EDT 2019}
}

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Evaluation of climate-related carbon turnover processes in global vegetation models for boreal and temperate forests.
text, January 2017

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Works referencing / citing this record:

Robust leaf trait relationships across species under global environmental changes
journal, June 2020


High ecosystem stability of evergreen broadleaf forests under severe droughts
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Assessing the generality of global leaf trait relationships
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Robust leaf trait relationships across species under global environmental changes
text, January 2020