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