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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)
Sponsoring Org.:
USDOE; National Science Foundation (NSF); European Union (EU)
OSTI Identifier:
1394452
Alternate Identifier(s):
OSTI ID: 1402146
Grant/Contract Number:
AC05-00OR22725; SC0008270; SC0014085; DE SC0008270; SC00114085; EF 1137293; OIA-1301789
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},
number = 2,
volume = 122,
place = {United States},
year = {Thu Jan 26 00:00:00 EST 2017},
month = {Thu Jan 26 00:00:00 EST 2017}
}

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  • We conducted a model-based assessment of changes in permafrost area and carbon storage for simulations driven by RCP4.5 and RCP8.5 projections between 2010 and 2299 for the northern permafrost region. All models simulating carbon represented soil with depth, a critical structural feature needed to represent the permafrost carbon–climate feedback, but that is not a universal feature of all climate models. Between 2010 and 2299, simulations indicated losses of permafrost between 3 and 5 million km2 for the RCP4.5 climate and between 6 and 16 million km 2 for the RCP8.5 climate. For the RCP4.5 projection, cumulative change in soil carbonmore » varied between 66-Pg C (10 15-g carbon) loss to 70-Pg C gain. For the RCP8.5 projection, losses in soil carbon varied between 74 and 652 Pg C (mean loss, 341 Pg C). For the RCP4.5 projection, gains in vegetation carbon were largely responsible for the overall projected net gains in ecosystem carbon by 2299 (8- to 244-Pg C gains). In contrast, for the RCP8.5 projection, gains in vegetation carbon were not great enough to compensate for the losses of carbon projected by four of the five models; changes in ecosystem carbon ranged from a 641-Pg C loss to a 167-Pg C gain (mean, 208-Pg C loss). The models indicate that substantial net losses of ecosystem carbon would not occur until after 2100. In conclusion, this assessment suggests that effective mitigation efforts during the remainder of this century could attenuate the negative consequences of the permafrost carbon–climate feedback.« less
  • Approximately 1700 Pg of soil carbon (C) are stored in the northern circumpolar permafrost zone, more than twice as much C than in the atmosphere. The overall amount, rate, and form of C released to the atmosphere in a warmer world will influence the strength of the permafrost C feedback to climate change. We used a survey to quantify variability in the perception of the vulnerability of permafrost C to climate change. Experts were asked to provide quantitative estimates of permafrost change in response to four scenarios of warming. For the highest warming scenario (RCP 8.5), experts hypothesized that Cmore » release from permafrost zone soils could be 19–45 Pg C by 2040, 162–288 Pg C by 2100, and 381–616 Pg C by 2300 in CO 2 equivalent using 100-year CH 4 global warming potential (GWP). These values become 50% larger using 20-year CH 4 GWP, with a third to a half of expected climate forcing coming from CH 4 even though CH 4 was only 2.3 % of the expected C release. Experts projected that two-thirds of this release could be avoided under the lowest warming scenario (RCP 2.6). These results highlight the potential risk from permafrost thaw and serve to frame a hypothesis about the magnitude of this feedback to climate change. However, the level of emissions proposed here are unlikely to overshadow the impact of fossil fuel burning, which will continue to be the main source of C emissions and climate forcing.« less
  • This paper presents the results of a project designed to integrate biogeographical and biogeochemical models of terrestrial ecosystem response to climatic change caused by increased emissions of greenhouse gases. Three biogeographical and three biogeochemical models were first compared independently of one another. Simulations were performed for the conterminous United States under conditions of current atmospheric carbon dioxide (CO{sub 2}) and climate, and doubled CO{sub 2} and various climates. For contemporary conditions, the biogeography models appropriately simulated the geographic distribution of major vegetation types and forest area. The results of biogeochemistry models were similar for net primary production and total carbonmore » storage under conditions of current climate. Variable model estimates resulted for input conditions of doubled CO{sub 2} due to differing model sensitivities to temperature and CO{sub 2}. When the biogeochemistry models were run in conjunction with the biogeographical models, variable results were also produced. The variability of model results indicates that ecosystem properties, particularly distribution of major vegetation types, primary productivity, and carbon storage, may be extremely sensitive to the magnitude of climatic change predicted by some models. However, the variation between models in magnitude and direction of change is considerable. Four broad areas of research are identified as deserving immediate attention: modularization of models, reduction of uncertainties regarding key processes, validation of models, and development of models of transient ecological responses. 89 refs., 6 figs., 10 tabs.« less