## 3 Results
### Model-Data Agreement on Monthly and Seasonal Time Scales  
We compared monthly and seasonal GPP variation among the simulations to explore the model's responses to varying weather conditions during different seasons. Table 3 shows that uncertainties of four estimates were large in the warm season. Although the MODIS product had relatively small biases from April to September compared with the other simulations, its standard deviation was 18.6(\(\pm\)15.9) times greater than the value of bias on average, and the RMSE ranged from 1.53 to 2.70 \(\mathrm{gC}\mathrm{\cdot m}^{-2}\mathrm{\cdot day}^{-1}\). Thus the low bias of MOD17A2 was caused by terms with opposite signs cancelling anthers. The standard deviations of the biases and the RMSE were relatively small in the other three simulations during the warm season, indicating that the biases could be considered as a measure of modeling uncertainties. LUE underestimated GPP for all seasons, especially in summer, when the bias was \(-\)1.28(\(\pm\)1.00) \(\mathrm{gC}\mathrm{\cdot m}^{-2}\mathrm{\cdot day}^{-1}\). The negative bias in summer was also apparent for LUE-SW, but it was slightly better. The bias in summer was canceled using the two-leaf Farquhar  
Figure 1: Performances of the GPP models for the 21 selected tower sites (Table 1). The statistics in the Taylor diagram were derived from the simulated and observed GPP of the representative year for each site: (**a**) half-hourly values and (**b**) 8-day average values. An ideal model has a standard deviation ratio (\(\sigma_{norm}\)) of 1.0 and a correlation coefficient of 1.0 (REF, the reference point). model with reduced RMSE. However, there were both overestimation in spring and underestimation in autumn for the Farquhar model overall, even though the leaf maximum carboxylation rate has been optimized for each site. Photosynthesis in winter accounts for \(\sim\)7% of yearly carbon assimilation in the selected 11 evergreen forests, and the contribution is as high as 16.6% at the ES-ES1 site. Thus, we further compared the seasonal GPP simulations of this biome type in Figure 3a. Ignoring the MODIS product, the site-level model-data agreement exhibited a low degree of variability from spring to fall, but the Taylor skill ranged from zero to unity in winter, indicating that photosynthesis dynamic could not be captured well during the cold season. The MODIS product for the evergreen forests had a relatively large Taylor skill, but agreed well with observations decreased for the deciduous forests and shrubs (Figure 3b) and the grasslands (Figure 3c) in all four seasons, especially summer, which contributed to the large RMSE indicated in Table 3.  
Figure 2: Comparisons of the observed GPP and the MOD17A2: the GPP simulated by the LUE-SW and by the Farquhar model for different plant functional types: (**a**,**b**) needleleaf evergreen forests in temperate and boreal zones; (**c**) needleleaf deciduous forests in boreal zone; (**d**) broadleaf evergreen forests in temperate zone; (**e**,**f**) broadleaf deciduous forests in temperate and boreal zones; (**g**,**h**) broadleaf deciduous shrubs in temperate and boreal zones; and (**i**) grasslands. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Months/** & \multicolumn{2}{c}{**MOD17A2**} & \multicolumn{2}{c}{**LUE**} & \multicolumn{2}{c}{**LUE-SW**} & \multicolumn{2}{c}{**Farquhar**} \\
**Seasons** & **Bias** & **RMSE** & **Bias** & **RMSE** & **Bias** & **RMSE** & **Bias** & **RMSE** \\ \hline Jan. & \(-\)0.28(\(\pm\)0.43) \({}^{\mathrm{a}}\) & **0.42**(\(\pm\)0.39) & \(-\)0.36(\(\pm\)0.56) & 0.49(\(\pm  
We compared the biases and RMSE of seasonally composite diurnal variations estimated by the LUE-SW and the Farquhar model to find further discrepancies between the simulations (Figure 4). Overall with site-specific parameters (Table 2), both models had similar half-hourly biases and RMSE in all four seasons, excluding obvious underestimations by LUE-SW during the morning and afternoon of summer.