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

• Published in: Earth system science data Vol. 12; no. 4; pp. 2725 - 2746
• Main Authors: Zheng, Yi, Shen, Ruoque, Wang, Yawen, Li, Xiangqian, Liu, Shuguang, Liang, Shunlin, Chen, Jing M., Ju, Weimin, Zhang, Li, Yuan, Wenping
• Format: Journal Article
• Language: English
• Published: Katlenburg-Lindau Copernicus GmbH 12.11.2020; Copernicus Publications
• Subjects: Annual variations; Atmospheric models; Biological fertilization; Carbon; Carbon cycle; Carbon dioxide; Carbon dioxide atmospheric concentrations; Carbon dioxide concentration; Coefficients; Correlation coefficient; Correlation coefficients; Covariance; Datasets; Eddy covariance; Environmental changes; Environmental regulations; Experiments; Fertilization; Humidity; Interannual variations; Learning algorithms; Long-term changes; Machine learning; Photosynthesis; Primary production; Productivity; Radiation; Remote sensing; Seasonal variation; Seasonal variations; Sensitivity analysis; Simulation; Spatial variations; Terrestrial ecosystems; Towers; Vapor pressure; Vapour pressure; Variables; Vegetation; United States > US; North America; Europe
• ISSN: 1866-3516; 1866-3508; 1866-3516
• DOI: 10.5194/essd-12-2725-2020

Satellite-based models have been widely used to simulate vegetation gross primary production (GPP) at the 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 and 8 d interval by revising a light use efficiency model (i.e., EC-LUE model). 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 95 towers from the FLUXNET2015 dataset, covering nine major ecosystem types around the globe, were used to calibrate and validate the model. In general, the revised EC-LUE model could effectively reproduce the spatial, seasonal, and annual variations in the tower-estimated GPP at most sites. The revised EC-LUE model could explain 71 % of the spatial variations in annual GPP over 95 sites. At more than 95 % of the sites, the correlation coefficients (R2) of seasonal changes between tower-estimated and model-simulated GPP are larger than 0.5. Particularly, the revised EC-LUE model improved the model performance in reproducing the interannual variations in GPP, and the averaged R2 between annual mean tower-estimated and model-simulated GPP is 0.44 over all 55 sites with observations longer than 5 years, which is significantly higher than those of the original EC-LUE model (R2=0.36) and other LUE models (R2 ranged from 0.06 to 0.30 with an average value of 0.16). At the global scale, GPP derived from light use efficiency models, machine learning models, and process-based biophysical models shows substantial differences in magnitude and interannual variations. The revised EC-LUE model quantified the mean global GPP from 1982 to 2017 as 106.2±2.9 Pg C yr−1 with the trend 0.15 Pg C yr−1. Sensitivity analysis indicated that GPP simulated by the revised EC-LUE model was sensitive to atmospheric CO2 concentration, VPD, and radiation. Over the period of 1982–2017, the CO2 fertilization effect on the global GPP (0.22±0.07 Pg C yr−1) could be partly offset by increased VPD (-0.17±0.06 Pg C yr−1). The long-term changes in the environmental variables could be well reflected in global GPP. Overall, the revised EC-LUE model is able to provide a reliable long-term estimate of global GPP. The GPP dataset is available at https://doi.org/10.6084/m9.figshare.8942336.v3 (Zheng et al., 2019).