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Tighten the bolts and nuts on GPP estimations from sites to the globe: an assessment of LUE models and supporting data fields

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. Significance Statement Gross primary production (GPP), the amount of carbon fixed during photosynthesis in a given length of time, is the fuel of life and one of the key components of the global carbon cycle. Although numerous efforts have been made to estimate GPP at the global scale, its uncertainty has remained high for decades. It is necessary to evaluate the performance of relevant models and supporting datasets against high-quality field measurements to find out where the loose parts are, and then tighten the loose ends. Our study reveals that large biases exist in some of the models and driving spatial data products, and addressing these biases is a high priority in modeling GPP from site to global scales.