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

Abstract

Abstract. 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).

Tighten the Bolts and Nuts on GPP Estimations from Sites to the Globe: An Assessment of Remote Sensing Based LUE Models and Supporting Data Fields

“…Finally, we evaluated a total of 24 global GPP products: Two from this study (LUE-NDWI with uncorrected and corrected spatial datasets, respectively), and 22 products from TRENDY (http://dgvm.ceh.ac.uk/) [44] and other studies [3]. Specifically, the 22 global GPP products were EC-LUE, revised EC-LUE [6], MODIS [25], MOD17 C6 [45], PR [46], VPM [11], FLUXCOM-ANN, FLUXCOM-MARS, and FLUXCOM-RF from FLUXCOM [47], SVR [48], BESS [49], BEPS [50], and 10 TRENDY products.…”

“…Terrestrial gross primary production (GPP) or the total photosynthetic uptake of carbon by plants plays a critical role in maintaining the global carbon balance between the biosphere and atmosphere. However, the estimation of terrestrial GPP by existing models remains highly uncertain, with global estimates ranging widely between 92.7 to 168.7 Pg C yr −1 [1][2][3]. This large uncertainty poses a serious obstacle to quantifying and understanding the global carbon cycle [4].…”

Integration of Sentinel-3 OLCI Land Products and MERRA2 Meteorology Data into Light Use Efficiency and Vegetation Index-Driven Models for Modeling Gross Primary Production

“…Because of the weak simulation of sensible and latent heat fluxes on a large spatial scale, adopting the Bowen ratio for presenting water stress factors (W s ) will hinder EC-LUE model application on a large scale [75]. In this study, VPD was used to replace sensible and latent heat fluxes to calculate W s [21]. The GPP in EC-LUE is estimated as follows:…”

“…where VPD 0 is the half-saturation coefficient of the Equation (2c), obtained from previous research (Table 4) [21].…”

Seasonal and Spatial Changes of Primary Production in the Baltic Sea (Europe) Based on in situ Measurements in the Period of 1993–2018

“…The presented analyses are substantial for understanding of the marine environment functioning, verification of biogeochemical or ecohydrodynamical models, and mathematical formulas which describe the dependence of photosynthesis in the sea on the environmental factors (Schernewski and Neumann, 2002;Kowalewski, 2005;Wozniak et al, 2011;Murray et al, 2019;Kratzer et al, 2020). The results can also be used to validate the satellite algorithms dedicated to the monitoring of the Baltic Sea state as well as to validate many environment characteristics using remote sensing techniques (Darecki et al, 2008;Siegel and Gerth, 2008;Zheng et al, 2020).…”