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Title: Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005

Abstract

Here, despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr –1, respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr –1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Ourmore » results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.« less

Authors:
 [1];  [2];  [3];  [4];  [2];  [2];  [4];  [5];  [6];  [5];  [7];  [8];  [9]; ORCiD logo [10];  [5];  [11]; ORCiD logo [12];  [13];  [14];  [15] more »; ORCiD logo [10];  [16];  [17];  [18];  [10];  [19];  [20];  [16] « less
  1. East China Normal Univ., Shanghai (China); Fudan Univ., Shanghai (China)
  2. East China Normal Univ., Shanghai (China)
  3. Univ. of Oklahoma, Norman, OK (United States)
  4. Fudan Univ., Shanghai (China)
  5. Chinese Academy of Sciences, Beijing (China)
  6. California Inst. of Technology (CalTech), Pasadena, CA (United States)
  7. China Meteorological Administration, Beijing (China)
  8. Northern Arizona Univ., Flagstaff, AZ (United States)
  9. Univ. of Illinois at Urbana-Champaign, Urbana, IL (United States)
  10. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
  11. Carnegie Institution for Science, Stanford, CA (United States)
  12. California Inst. of Technology (CalTech), Pasadena, CA (United States); Univ. of California, Los Angeles, CA (United States)
  13. Univ. of Quebec at Montreal, Montreal, QC (Canada); Northwest A&F Univ., Yangling (China)
  14. Montana State Univ., Bozeman, MT (United States)
  15. Woods Hole Research Center, Falmouth, MA (United States)
  16. Beijing Normal Univ., Beijing (China)
  17. Chinese Academy of Sciences, Beijing (China); Natural Resources Institute Finland (Luke), Vantaa (Finland)
  18. Auburn Univ., Auburn, AL (United States)
  19. Univ. of Maryland, College Park, MD (United States)
  20. Northwest A&F Univ., Yangling (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) (SC-23)
OSTI Identifier:
1394602
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Journal of Geophysical Research. Biogeosciences
Additional Journal Information:
Journal Volume: 121; Journal Issue: 5; Journal ID: ISSN 2169-8953
Publisher:
American Geophysical Union
Country of Publication:
United States
Language:
English
Subject:
58 GEOSCIENCES; 54 ENVIRONMENTAL SCIENCES; China; interannual variability; model structure; net primary productivity; net biome productivity; uncertainty

Citation Formats

Shao, Junjiong, Zhou, Xuhui, Luo, Yiqi, Zhang, Guodong, Yan, Wei, Li, Jiaxuan, Li, Bo, Dan, Li, Fisher, Joshua B., Gao, Zhiqiang, He, Yong, Huntzinger, Deborah, Jain, Atul K., Mao, Jiafu, Meng, Jihua, Michalak, Anna M., Parazoo, Nicholas C., Peng, Changhui, Poulter, Benjamin, Schwalm, Christopher R., Shi, Xiaoying, Sun, Rui, Tao, Fulu, Tian, Hanqin, Wei, Yaxing, Zeng, Ning, Zhu, Qiuan, and Zhu, Wenquan. Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005. United States: N. p., 2016. Web. doi:10.1002/2015JG003062.
Shao, Junjiong, Zhou, Xuhui, Luo, Yiqi, Zhang, Guodong, Yan, Wei, Li, Jiaxuan, Li, Bo, Dan, Li, Fisher, Joshua B., Gao, Zhiqiang, He, Yong, Huntzinger, Deborah, Jain, Atul K., Mao, Jiafu, Meng, Jihua, Michalak, Anna M., Parazoo, Nicholas C., Peng, Changhui, Poulter, Benjamin, Schwalm, Christopher R., Shi, Xiaoying, Sun, Rui, Tao, Fulu, Tian, Hanqin, Wei, Yaxing, Zeng, Ning, Zhu, Qiuan, & Zhu, Wenquan. Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005. United States. doi:10.1002/2015JG003062.
Shao, Junjiong, Zhou, Xuhui, Luo, Yiqi, Zhang, Guodong, Yan, Wei, Li, Jiaxuan, Li, Bo, Dan, Li, Fisher, Joshua B., Gao, Zhiqiang, He, Yong, Huntzinger, Deborah, Jain, Atul K., Mao, Jiafu, Meng, Jihua, Michalak, Anna M., Parazoo, Nicholas C., Peng, Changhui, Poulter, Benjamin, Schwalm, Christopher R., Shi, Xiaoying, Sun, Rui, Tao, Fulu, Tian, Hanqin, Wei, Yaxing, Zeng, Ning, Zhu, Qiuan, and Zhu, Wenquan. Thu . "Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005". United States. doi:10.1002/2015JG003062. https://www.osti.gov/servlets/purl/1394602.
@article{osti_1394602,
title = {Uncertainty analysis of terrestrial net primary productivity and net biome productivity in China during 1901-2005},
author = {Shao, Junjiong and Zhou, Xuhui and Luo, Yiqi and Zhang, Guodong and Yan, Wei and Li, Jiaxuan and Li, Bo and Dan, Li and Fisher, Joshua B. and Gao, Zhiqiang and He, Yong and Huntzinger, Deborah and Jain, Atul K. and Mao, Jiafu and Meng, Jihua and Michalak, Anna M. and Parazoo, Nicholas C. and Peng, Changhui and Poulter, Benjamin and Schwalm, Christopher R. and Shi, Xiaoying and Sun, Rui and Tao, Fulu and Tian, Hanqin and Wei, Yaxing and Zeng, Ning and Zhu, Qiuan and Zhu, Wenquan},
abstractNote = {Here, despite the importance of net primary productivity (NPP) and net biome productivity (NBP), estimates of NPP and NBP for China are highly uncertain. To investigate the main sources of uncertainty, we synthesized model estimates of NPP and NBP for China from published literature and the Multi-scale Synthesis and Terrestrial Model Intercomparison Project (MsTMIP). The literature-based results showed that total NPP and NBP in China were 3.35 ± 1.25 and 0.14 ± 0.094 Pg C yr–1, respectively. Classification and regression tree analysis based on literature data showed that model type was the primary source of the uncertainty, explaining 36% and 64% of the variance in NPP and NBP, respectively. Spatiotemporal scales, land cover conditions, inclusion of the N cycle, and effects of N addition also contributed to the overall uncertainty. Results based on the MsTMIP data suggested that model structures were overwhelmingly important (>90%) for the overall uncertainty compared to simulations with different combinations of time-varying global change factors. The interannual pattern of NPP was similar among diverse studies and increased by 0.012 Pg C yr–1 during 1981–2000. In addition, high uncertainty in China's NPP occurred in areas with high productivity, whereas NBP showed the opposite pattern. Our results suggest that to significantly reduce uncertainty in estimated NPP and NBP, model structures should be substantially tested on the basis of empirical results. To this end, coordinated distributed experiments with multiple global change factors might be a practical approach that can validate specific structures of different models.},
doi = {10.1002/2015JG003062},
journal = {Journal of Geophysical Research. Biogeosciences},
number = 5,
volume = 121,
place = {United States},
year = {2016},
month = {4}
}

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Works referenced in this record:

Global, Regional, and National Fossil-Fuel CO2 Emissions (1751 - 2014)
dataset, January 2017

  • Boden, T.; Marland, G.; Andres, R. J.
  • Environmental System Science Data Infrastructure for a Virtual Ecosystem (ESS-DIVE), 27 files
  • DOI: 10.3334/CDIAC/00001_V2013