CuiHuangArainEtAl2019

Référence

Cui, E., Huang, K., Arain, M.A., Fisher, J.B., Huntzinger, D.N., Ito, A., Luo, Y., Jain, A.K., Mao, J., Michalak, A.M., Niu, S., Parazoo, N.C., Peng, C., Peng, S., Poulter, B., Ricciuto, D.M., Schaefer, K.M., Schwalm, C.R., Shi, X., Tian, H., Wang, W., Wang, J., Wei, Y., Yan, E., Yan, L., Zeng, N., Zhu, Q., Xia, J. (2019) Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region. Global Biogeochemical Cycles, 33(6):668-689. (Scopus )

Résumé

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. 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. ©2019. American Geophysical Union. All Rights Reserved.

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@ARTICLE { CuiHuangArainEtAl2019,
    AUTHOR = { Cui, E. and Huang, K. and Arain, M.A. and Fisher, J.B. and Huntzinger, D.N. and Ito, A. and Luo, Y. and Jain, A.K. and Mao, J. and Michalak, A.M. and Niu, S. and Parazoo, N.C. and Peng, C. and Peng, S. and Poulter, B. and Ricciuto, D.M. and Schaefer, K.M. and Schwalm, C.R. and Shi, X. and Tian, H. and Wang, W. and Wang, J. and Wei, Y. and Yan, E. and Yan, L. and Zeng, N. and Zhu, Q. and Xia, J. },
    TITLE = { Vegetation Functional Properties Determine Uncertainty of Simulated Ecosystem Productivity: A Traceability Analysis in the East Asian Monsoon Region },
    JOURNAL = { Global Biogeochemical Cycles },
    YEAR = { 2019 },
    VOLUME = { 33 },
    NUMBER = { 6 },
    PAGES = { 668-689 },
    NOTE = { cited By 0 },
    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%) than observations, leading to the overestimation of simulated GPP across the East Asian monsoon region. 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. ©2019. American Geophysical Union. All Rights Reserved. },
    AFFILIATION = { Zhejiang Tiantong National Forest Ecosystem Observation and Research Station, Shanghai Key Lab for Urban Ecological Processes and Eco-Restoration, Center for Global Change and Ecological Forecasting, School of Ecological and Environmental Sciences, East China Normal University, Shanghai, China; Institute of Eco-Chongming, Shanghai, China; School of Geography and Earth Sciences and McMaster Centre for Climate Change, McMaster University, Hamilton, ON, Canada; Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, United States; School of Earth Sciences and Environmental Sustainability, Northern Arizona University, Flagstaff, AZ, United States; National Institute for Environmental Studies, Tsukuba, Japan; Center for Ecosystem Science and Society, Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, United States; Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Environmental Sciences Division and Climate Change Science Institute, Oak Ridge National Laboratory, Oak Ridge, TN, United States; Department of Global Ecology, Carnegie Institution for Science, Stanford, CA, United States; Department of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China; Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China; Department of Biology Sciences, Institute of Environment Sciences, University of Quebec at MontrealQC, Canada; Center for Ecological Forecasting and Global Change, College of Forestry, Northwest A&F University, Yangling, China; Sino-French Institute for Earth System Science, College of Urban and Environmental Sciences, Peking University, Beijing, China; Department of Ecology, Montana State University, Bozeman, MT, United States; National Snow and Ice Data Center, Cooperative Institute for Research in Environmental Sciences, University of Colorado Boulder, Boulder, CO, United States; Woods Hole Research Center, Falmouth, MA, United States; International Center for Climate and Global Change Research and School of Forestry and Wildlife Sciences, Auburn University, Auburn, AL, United States; Ames Research Center, National Aeronautics and Space Administration, Moffett Field, CA, United States; Department of Atmospheric and Oceanic Science, University of Maryland, College Park, MD, United States },
    AUTHOR_KEYWORDS = { environmental drivers; initial conditions; model uncertainty; MsTMIP; relative importance; vegetation functional property },
    DOCUMENT_TYPE = { Article },
    DOI = { 10.1029/2018GB005909 },
    SOURCE = { Scopus },
    URL = { https://www.scopus.com/inward/record.uri?eid=2-s2.0-85067405401&doi=10.1029%2f2018GB005909&partnerID=40&md5=ce91b573836d8379c5bcf9a3796e84cd },
}

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