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

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).