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Title: Global vegetation biomass production efficiency constrained by models and observations

Abstract

Typically, plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. In this work, we introduce a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon–nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnovermore » times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).« less

Authors:
 [1]; ORCiD logo [1]; ORCiD logo [2];  [1];  [1];  [3];  [4];  [5];  [6];  [3];  [7];  [8];  [9]; ORCiD logo [10];  [11];  [12];  [11];  [5];  [13];  [14] more »;  [15];  [16];  [12];  [17];  [18];  [19];  [20]; ORCiD logo [11];  [12];  [21]; ORCiD logo [13] « less
  1. Peking Univ., Beijing (China)
  2. Peking Univ., Beijing (China); Chinese Academy of Sciences (CAS), Beijing (China)
  3. Centre National de la Recherche Scientifique (CNRS), Paris (France). Laboratoire des Sciences du Climat et de l'Environnement
  4. McMaster Univ., Hamilton, ON (Canada)
  5. Carnegie Inst. of Science, Stanford, CA (United States)
  6. California Inst. of Technology (CalTech), La Canada Flintridge, CA (United States). Jet Propulsion Lab.
  7. Univ.of Maine, Orono, ME (United States)
  8. Northern Arizona Univ., Flagstaff, AZ (United States)
  9. National Inst. for Environmental Studies (NIMS), Tsukuba (Japan); Japan Agency for Marine-Earth Science and Technology, Yokohama (Japan)
  10. Univ. of Illinois, Urbana, IL (United States)
  11. Univ. of Antwerp, Wilrijk (Belgium)
  12. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States). Environmental Sciences Division and Climate Change Science Inst.
  13. Univ. of Montreal, Quebec (Canada); Northwest A & F Univ., Yangling (China)
  14. Consejo Superior de Investigaciones Cientificas (CSIC), Barcelona (Spain). Centre de Recerca Ecològica i Aplicacions Forestals (CREAF)
  15. Montana State Univ., Bozeman, MT (United States)
  16. Chinese Academy of Sciences (CAS), Lanzhou (China); National Climate Center, Beijing (China)
  17. Univ. of Colorado, Boulder, CO (United States)
  18. Woods Hole Research Center, Falmouth, MA (United States); Northern Arizona Univ., Flagstaff, AZ (United States)
  19. Univ. of Montreal, Quebec (Canada)
  20. Auburn Univ., AL (United States)
  21. Univ. of Maryland, College Park, MD (United States)
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE; National Natural Science Foundation of China (NNSFC); National Key Research and Development Program of China; European Research Council (ERC)
OSTI Identifier:
1566963
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Accepted Manuscript
Journal Name:
Global Change Biology
Additional Journal Information:
Journal Name: Global Change Biology; Journal ID: ISSN 1354-1013
Publisher:
Wiley
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; biomass production; BPE; carbon sink; emergent constraint; terrestrial biosphere model

Citation Formats

He, Yue, Peng, Shushi, Liu, Yongwen, Li, Xiangyi, Wang, Kai, Ciais, Philippe, Arain, M. Altaf, Fang, Yuanyuan, Fisher, Joshua B., Goll, Daniel, Hayes, Daniel, Huntzinger, Deborah N., Ito, Akihiko, Jain, Atul K., Janssens, Ivan A., Mao, Jiafu, Matteo, Campioli, Michalak, Anna M., Peng, Changhui, Peñuelas, Josep, Poulter, Benjamin, Qin, Dahe, Ricciuto, Daniel M., Schaefer, Kevin, Schwalm, Christopher R., Shi, Xiaoying, Tian, Hanqin, Vicca, Sara, Wei, Yaxing, Zeng, Ning, and Zhu, Qiuan. Global vegetation biomass production efficiency constrained by models and observations. United States: N. p., 2019. Web. doi:10.1111/gcb.14816.
He, Yue, Peng, Shushi, Liu, Yongwen, Li, Xiangyi, Wang, Kai, Ciais, Philippe, Arain, M. Altaf, Fang, Yuanyuan, Fisher, Joshua B., Goll, Daniel, Hayes, Daniel, Huntzinger, Deborah N., Ito, Akihiko, Jain, Atul K., Janssens, Ivan A., Mao, Jiafu, Matteo, Campioli, Michalak, Anna M., Peng, Changhui, Peñuelas, Josep, Poulter, Benjamin, Qin, Dahe, Ricciuto, Daniel M., Schaefer, Kevin, Schwalm, Christopher R., Shi, Xiaoying, Tian, Hanqin, Vicca, Sara, Wei, Yaxing, Zeng, Ning, & Zhu, Qiuan. Global vegetation biomass production efficiency constrained by models and observations. United States. doi:10.1111/gcb.14816.
He, Yue, Peng, Shushi, Liu, Yongwen, Li, Xiangyi, Wang, Kai, Ciais, Philippe, Arain, M. Altaf, Fang, Yuanyuan, Fisher, Joshua B., Goll, Daniel, Hayes, Daniel, Huntzinger, Deborah N., Ito, Akihiko, Jain, Atul K., Janssens, Ivan A., Mao, Jiafu, Matteo, Campioli, Michalak, Anna M., Peng, Changhui, Peñuelas, Josep, Poulter, Benjamin, Qin, Dahe, Ricciuto, Daniel M., Schaefer, Kevin, Schwalm, Christopher R., Shi, Xiaoying, Tian, Hanqin, Vicca, Sara, Wei, Yaxing, Zeng, Ning, and Zhu, Qiuan. Fri . "Global vegetation biomass production efficiency constrained by models and observations". United States. doi:10.1111/gcb.14816.
@article{osti_1566963,
title = {Global vegetation biomass production efficiency constrained by models and observations},
author = {He, Yue and Peng, Shushi and Liu, Yongwen and Li, Xiangyi and Wang, Kai and Ciais, Philippe and Arain, M. Altaf and Fang, Yuanyuan and Fisher, Joshua B. and Goll, Daniel and Hayes, Daniel and Huntzinger, Deborah N. and Ito, Akihiko and Jain, Atul K. and Janssens, Ivan A. and Mao, Jiafu and Matteo, Campioli and Michalak, Anna M. and Peng, Changhui and Peñuelas, Josep and Poulter, Benjamin and Qin, Dahe and Ricciuto, Daniel M. and Schaefer, Kevin and Schwalm, Christopher R. and Shi, Xiaoying and Tian, Hanqin and Vicca, Sara and Wei, Yaxing and Zeng, Ning and Zhu, Qiuan},
abstractNote = {Typically, plants use only a fraction of their photosynthetically derived carbon for biomass production (BP). The biomass production efficiency (BPE), defined as the ratio of BP to photosynthesis, and its variation across and within vegetation types is poorly understood, which hinders our capacity to accurately estimate carbon turnover times and carbon sinks. In this work, we introduce a new global estimation of BPE obtained by combining field measurements from 113 sites with 14 carbon cycle models. Our best estimate of global BPE is 0.41 ± 0.05, excluding cropland. The largest BPE is found in boreal forests (0.48 ± 0.06) and the lowest in tropical forests (0.40 ± 0.04). Carbon cycle models overestimate BPE, although models with carbon–nitrogen interactions tend to be more realistic. Using observation-based estimates of global photosynthesis, we quantify the global BP of non-cropland ecosystems of 41 ± 6 Pg C/year. This flux is less than net primary production as it does not contain carbon allocated to symbionts, used for exudates or volatile carbon compound emissions to the atmosphere. Our study reveals a positive bias of 24 ± 11% in the model-estimated BP (10 of 14 models). When correcting models for this bias while leaving modeled carbon turnover times unchanged, we found that the global ecosystem carbon storage change during the last century is decreased by 67% (or 58 Pg C).},
doi = {10.1111/gcb.14816},
journal = {Global Change Biology},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {9}
}

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