## 3 Results
### GPP Simulations and Measurements under All Sky Conditions during 2013-2016
(3) Monthly GPP estimates using 3-factor and 2-factor models can also capture seasonal variations and have similar GPP values when S/Q = 0-1, indicating that both models can be applied to calculate GPP under all sky conditions. (4) The annual mean GPP estimates overestimated the observed GPP by 31% (ranging from 15% to 43%) and 29% (17-39%), respectively. Annual amounts of GPP over different periods in 2013-2016 were calculated and compared with the observed values (Table S5, in Supplementary Material). The EMGPP models also displayed reasonable performance; the ratios of the annual amounts of GPP calculated versus observed were 1.41 (ranging from 1.15 to 1.43) and 1.29 (1.17-1.39) when using the 3-factor and 2-factor models, respectively, that is, the empirical models still overestimated the annual amounts of GPP by approximately 41% (15-43%) and 29% (17-39%), respectively, while the 2-factor model presented a slightly better performance. Generally, the model performance for S/Q = 0-1 was better than that for S/Q \(\geq\) 0.5 and worse than that for S/Q \(<\) 0.5. Yale Interactive Terrestrial Biosphere (YIB) estimations reproduce measured GPP seasonality (correlation coefficient R \(>\) 0.9) with low biases (\(-\)21-15%) at 5 out of 10 sites in China. The calculated GPP shows reasonable seasonality in Qianyanzhou Forest, although the YIB model overestimates its magnitude by 71% [75]. The mean RMSE for GPP is approximately 3.21 g C m\({}^{-2}\) day\({}^{-1}\) for 57 evergreen coniferous forest (ENF) sites calculated using the YIB model [76], which is in good agreement with the values of 3.55 and 3.44 g C m\({}^{-2}\) day\({}^{-1}\) in 2013-2016 obtained when using 3-factor and 2-factor models, respectively (\(n\) = 1320). The YIB model yields mean biases of GPP of \(-\)33-50% (equivalent to ratios 0.7-1.5) for 117 out of 195 sites [76]. The ratios are smaller than 1.50 (ranging from 1.15-1.43 and 1.17-1.39 for the 3-factor and 2-factor models, respectively) under different conditions in this study (Table S5, in Supplementary Material). It should be mentioned that the YIB model shows good GPP performance in model evaluations among 17 dynamic global vegetation models (DGVMs) [77]. Specifically, YIB simulations show good performance at flux sites in China [75,76]. The predicted GPP shows reasonable seasonality at an EBF site (CN-Din) in the south, although the model overestimates its magnitude by 71%. For the remaining 4 sites, the model underestimates GPP by 49-90%. The lowest correlations (R \(<\) 0.6) are found at two northern grassland sites (CN-Sw2 and CN-Du3) because simulations yield near-zero values in winter, while observations still show high values. Additionally, the simulated biases of annual GPP using the original Biome-BGC model and those from data assimilation are \(-\)21.76% and \(-\)1.62%, respectively [78]. The RMSE values for estimated GPP at 8 days in the Qianyanzhou coniferous forest were 13.41-20.44 g C m\({}^{-2}\) and 35.35-53.88% between the VPM (vegeration photosynthesis model), EC-LUE (eddy covariance-light use efficiency model), TG (temperature and greenness model), and MODIS-PSN (moderate resolution imaging spectroradiometer-photosynthesis) models compared to the observed GPP [79].