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Preprints
https://doi.org/10.5194/essd-2019-126
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-2019-126
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

  07 Aug 2019

07 Aug 2019

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A revised version of this preprint is currently under review for the journal ESSD.

Improved estimate of global gross primary production for reproducing its long-term variation, 1982–2017

Yi Zheng1, Ruoque Shen1, Yawen Wang1,2, Xiangqian Li1, Shuguang Liu3, Shunlin Liang4,5, Jing M. Chen6,7, Weimin Ju7,8, Li Zhang9, and Wenping Yuan1,2 Yi Zheng et al.
  • 1School of Atmospheric Sciences, Sun Yat-sen University, Guangzhou 510245, Guangdong, China
  • 2Southern Laboratory of Ocean Science and Engineering (Guangdong, Zhuhai), Zhuhai 519000, Guangdong, China
  • 3College of Life Science and Technology, Central South University of Forestry and Technology (CSUFT), Changsha, Hunan 410004, China
  • 4Department of Geographical Sciences, University of Maryland, College Park, MD 20742, USA
  • 5School of Remote Sensing Information Engineering, Wuhan University, Wuhan 430072, Hubei, China
  • 6Department of Geography, University of Toronto, M5G 3G3, Canada
  • 7International Institute for Earth System Sciences, Nanjing University, Nanjing, China
  • 8Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, Nanjing, China
  • 9Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China

Abstract. Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at site, regional, or global scales in recent years. However, accurately reproducing the interannual variations in GPP remains a major challenge, and the long-term changes in GPP remain highly uncertain. In this study, we generated a long-term global GPP dataset at 0.05° latitude by 0.05° longitude at 8-day interval by revising a light use efficiency model (i.e. EC-LUE). In the revised EC-LUE model, we integrated the regulations of several major environmental variables: atmospheric CO2 concentration, radiation components, and atmospheric vapor pressure deficit (VPD). These environmental variables showed substantial long-term changes, which could greatly impact the global vegetation productivity. Eddy covariance (EC) measurements at 84 towers from the FLUXNET2015 dataset, covering nine major ecosystem types of the globe, were used to calibrate and validate the model. The revised EC-LUE model could explain 83 % and 68 % of the spatial variations in the annual GPP at 42 calibration and 43 validation sites, respectively. In particular, the revised EC-LUE model could very well reproduce (~ 74 % sites R2 > 0.5; averaged R2 = 0.65) the interannual variations in GPP at 51 sites with observations greater than 5-years. At global scale, sensitivity analysis indicated that the long-term changes of environmental variables could be well reflected in the global GPP dataset. The CO2 fertilization effect on the global GPP (0.14 ± 0.001 Pg C yr−1) could be offset by the increased VPD (−0.16 ± 0.02 Pg C yr−1). The global GPP derived from different datasets exist substantial uncertainty in magnitude and interannual variations. The magnitude of global summed GPP simulated by the revised EC-LUE model was comparable to other global models. While the revised EC-LUE model has a unique superiority in simulating the interannual variations in GPP at both site level and global scales. The revised EC-LUE model provides a reliable long-term estimate of global GPP because of integrating the important environmental variables. The dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v1 (Zheng et al., 2019).

Yi Zheng et al.

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Improved estimate of global gross primary production for reproducing its long-term variation, 1982-2017 Y. Zheng, R. Shen, Y. Wang, X. Li, S. Liu, S. Liang, J. M. Chen, W. Ju, L. Zhang, and W. Yuan https://doi.org/10.6084/m9.figshare.8942336

Yi Zheng et al.

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Short summary
Accurately reproducing the interannual variations in vegetation gross primary production (GPP) is a major challenge, and the long-term changes in GPP remain highly uncertain. A global GPP dataset was generated by integrating the regulations of several major environmental variables with long-term changes. The dataset can effectively reproduce the spatial, seasonal, and particularly interannual variations in global GPP. Our study will contribute to accurate carbon flux estimates at longtime scale.
Accurately reproducing the interannual variations in vegetation gross primary production (GPP)...
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