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Title: Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region: Modeled Productivity in Permafrost Regions

Abstract

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m -2 yr -1), most models produced higher NPP (309 ± 12 g C m -2 yr -1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m -2 yr -1), which mainly resulted from differences in simulated maximum monthly GPP (GPP max). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation bymore » the enzyme Rubisco at 25°C (Vc max_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO 2 concentration. In conclusion, these results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPP max as well as their sensitivity to climate change.« less

Authors:
 [1]; ORCiD logo [2]; ORCiD logo [3];  [4]; ORCiD logo [5]; ORCiD logo [6]; ORCiD logo [7]; ORCiD logo [8]; ORCiD logo [9]; ORCiD logo [10]; ORCiD logo [11]; ORCiD logo [12];  [13]; ORCiD logo [7]; ORCiD logo [6];  [14];  [7];  [15];  [12]; ORCiD logo [16] more »;  [1]; ORCiD logo [17]; ORCiD logo [18];  [6];  [19]; ORCiD logo [17];  [19]; ORCiD logo [20];  [21];  [1]; ORCiD logo [21];  [21];  [17]; ORCiD logo [22] « less
  1. East China Normal Univ. (ECNU), Shanghai (China). Research Center for Global Change and Ecological Forecasting and Tiantong National Field Observation Station for Forest Ecosystem, School of Ecological and Environmental Sciences
  2. Univ. of Alaska Fairbanks, Fairbanks, AK (United States). US Geological Survey, Alaska Cooperative Fish and Wildlife Research Unit
  3. National Center for Atmospheric Research, Boulder, CO (United States)
  4. Met Office Hadley Centre, Exeter (United Kingdom)
  5. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division
  6. Univ. of Washington, Seattle, WA (United States). Dept. of Civil and Environmental Engineering
  7. Centre National de Recherches Meteorologiques (CNRM), Toulouse (France)
  8. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
  9. Univ. of Victoria, BC (Canada). School of Earth and Ocean Sciences
  10. Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette (France); Centre National de la Recherche Scientifique (CNRS), Grenoble (France). Laboratoire de Glaciologie et Geophysique de l'Environnement (LGGE); Univ. Grenoble Alpes, Grenoble (France). Laboratoire de Glaciologie et Geophysique de l'Environnement (LGGE)
  11. Beijing Normal Univ., Beijing (China). College of Global Change and Earth System Science; Alfred Wegener Inst. Helmholtz Centre for Polar and Marine Research, Potsdam (Germany)
  12. Japan Agency for Marine-Earth, Yokohama (Japan). Dept. of Integrated Climate Change Projection Research
  13. Univ. of Copenhagen (Denmark). Center for Permafrost (CENPERM), Dept. of Geosciences and Natural Resource Management
  14. Laboratoire des Sciences du Climat et de l'Environnement (LSCE), Gif-sur-Yvette (France)
  15. Centre National de la Recherche Scientifique (CNRS), Grenoble (France). Laboratoire de Glaciologie et Geophysique de l'Environnement (LGGE); Univ. Grenoble Alpes, Grenoble (France). Laboratoire de Glaciologie et Geophysique de l'Environnement (LGGE); Irstea, Villeurbanne (France)
  16. Univ. of Maine, Orono, ME (United States). School of Forest Resources
  17. Beijing Normal Univ., Beijing (China). College of Global Change and Earth System Science
  18. Centre National de la Recherche Scientifique (CNRS), Grenoble (France). Laboratoire de Glaciologie et Geophysique de l'Environnement (LGGE); Univ. Grenoble Alpes, Grenoble (France). Laboratoire de Glaciologie et Geophysique de l'Environnement (LGGE)
  19. Lund Univ. (Sweden). Dept. of Physical Geography and Ecosystem Science
  20. National Inst. of Polar Research, Tachikawa (Japan); Japan Agency for Marine-Earth Science and Technology, Yokohama (Japan)
  21. Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology
  22. Univ. of Oklahoma, Norman, OK (United States). Dept. of Microbiology and Plant Biology; Tsinghua Univ., Beijing (China). Dept. for Earth System Science
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States); Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
National Science Foundation (NSF); European Union (EU); USDOE Office of Science (SC)
OSTI Identifier:
1394452
Alternate Identifier(s):
OSTI ID: 1402146; OSTI ID: 1476459
Grant/Contract Number:  
AC05-00OR22725; SC0008270; SC0014085; SC00114085; EF 1137293; OIA-1301789; AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Volume: 122; Journal Issue: 2; Journal ID: ISSN 2169-8953
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; arctic; carbon use efficiency; climate warming; CO2 elevation; high latitudes; model intercomparison

Citation Formats

Xia, Jianyang, McGuire, A. David, Lawrence, David, Burke, Eleanor, Chen, Guangsheng, Chen, Xiaodong, Delire, Christine, Koven, Charles, MacDougall, Andrew, Peng, Shushi, Rinke, Annette, Saito, Kazuyuki, Zhang, Wenxin, Alkama, Ramdane, Bohn, Theodore J., Ciais, Philippe, Decharme, Bertrand, Gouttevin, Isabelle, Hajima, Tomohiro, Hayes, Daniel J., Huang, Kun, Ji, Duoying, Krinner, Gerhard, Lettenmaier, Dennis P., Miller, Paul A., Moore, John C., Smith, Benjamin, Sueyoshi, Tetsuo, Shi, Zheng, Yan, Liming, Liang, Junyi, Jiang, Lifen, Zhang, Qian, and Luo, Yiqi. Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region: Modeled Productivity in Permafrost Regions. United States: N. p., 2017. Web. doi:10.1002/2016JG003384.
Xia, Jianyang, McGuire, A. David, Lawrence, David, Burke, Eleanor, Chen, Guangsheng, Chen, Xiaodong, Delire, Christine, Koven, Charles, MacDougall, Andrew, Peng, Shushi, Rinke, Annette, Saito, Kazuyuki, Zhang, Wenxin, Alkama, Ramdane, Bohn, Theodore J., Ciais, Philippe, Decharme, Bertrand, Gouttevin, Isabelle, Hajima, Tomohiro, Hayes, Daniel J., Huang, Kun, Ji, Duoying, Krinner, Gerhard, Lettenmaier, Dennis P., Miller, Paul A., Moore, John C., Smith, Benjamin, Sueyoshi, Tetsuo, Shi, Zheng, Yan, Liming, Liang, Junyi, Jiang, Lifen, Zhang, Qian, & Luo, Yiqi. Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region: Modeled Productivity in Permafrost Regions. United States. doi:10.1002/2016JG003384.
Xia, Jianyang, McGuire, A. David, Lawrence, David, Burke, Eleanor, Chen, Guangsheng, Chen, Xiaodong, Delire, Christine, Koven, Charles, MacDougall, Andrew, Peng, Shushi, Rinke, Annette, Saito, Kazuyuki, Zhang, Wenxin, Alkama, Ramdane, Bohn, Theodore J., Ciais, Philippe, Decharme, Bertrand, Gouttevin, Isabelle, Hajima, Tomohiro, Hayes, Daniel J., Huang, Kun, Ji, Duoying, Krinner, Gerhard, Lettenmaier, Dennis P., Miller, Paul A., Moore, John C., Smith, Benjamin, Sueyoshi, Tetsuo, Shi, Zheng, Yan, Liming, Liang, Junyi, Jiang, Lifen, Zhang, Qian, and Luo, Yiqi. Thu . "Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region: Modeled Productivity in Permafrost Regions". United States. doi:10.1002/2016JG003384. https://www.osti.gov/servlets/purl/1394452.
@article{osti_1394452,
title = {Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region: Modeled Productivity in Permafrost Regions},
author = {Xia, Jianyang and McGuire, A. David and Lawrence, David and Burke, Eleanor and Chen, Guangsheng and Chen, Xiaodong and Delire, Christine and Koven, Charles and MacDougall, Andrew and Peng, Shushi and Rinke, Annette and Saito, Kazuyuki and Zhang, Wenxin and Alkama, Ramdane and Bohn, Theodore J. and Ciais, Philippe and Decharme, Bertrand and Gouttevin, Isabelle and Hajima, Tomohiro and Hayes, Daniel J. and Huang, Kun and Ji, Duoying and Krinner, Gerhard and Lettenmaier, Dennis P. and Miller, Paul A. and Moore, John C. and Smith, Benjamin and Sueyoshi, Tetsuo and Shi, Zheng and Yan, Liming and Liang, Junyi and Jiang, Lifen and Zhang, Qian and Luo, Yiqi},
abstractNote = {Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m-2 yr-1), most models produced higher NPP (309 ± 12 g C m-2 yr-1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m-2 yr-1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. In conclusion, these results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.},
doi = {10.1002/2016JG003384},
journal = {Journal of Geophysical Research. Biogeosciences},
issn = {2169-8953},
number = 2,
volume = 122,
place = {United States},
year = {2017},
month = {1}
}

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