Tighten the bolts and nuts on GPP estimations from sites to the globe:
an assessment of LUE models and supporting data fields
Abstract
Gross primary production (GPP) determines the amounts of carbon and
energy that enter terrestrial ecosystems. However, the tremendous
uncertainty of the GPP still hinders the reliability of the GPP
estimates and therefore understanding of the global carbon cycle. In
this study, using observations from global eddy covariance (EC) flux
towers, we appraised the performance of 22 widely used GPP models and
quality of major spatial data layers that drive the models. Results show
that the global GPP products generated by the 22 models varied greatly
in the means (from 92.7 to 178.9 Pg C yr-1), trends (from -0.25 to 0.84
Pg C yr-1). Model structures (i.e., light use efficiency models, machine
learning models, and process-based biophysical models) are an important
aspect contributing to the large uncertainty. In addition, various
biases in currently available spatial datasets have found (e.g., only
57% of the observed variation in photosynthetically active radiation
was explained by the spatial dataset), which contributed greatly affects
global GPP estimates. Our analysis indicates that the model development
did not converge GPP simulations with the advance of time. Moving
forward, research into efficacy of model structures and the precision of
input data may be more important than the development of new models for
global GPP estimation.