## 4 Results
### Simulations of GPP\({}_{\text{SIM}}\) with SCOPE Model  
Finally, we evaluated the performance of the SCOPE model by comparing the simulated daily GPP\({}_{\text{SIM}}\) with the daily GPP\({}_{\text{EC}}\) measured during the 2016 vegetation growing season. Figure 5 illustrates the temporal variation in daily GPP\({}_{\text{SIM}}\) for three designed scenarios (Section 3.3), together with the daily GPP\({}_{\text{EC}}\). We also present the scatterplots between daily GPP\({}_{\text{SIM}}\) and GPP\({}_{\text{EC}}\) to show their correlation between them. Under both the Fixed and HLS info scenarios, daily GPP\({}_{\text{SIM}}\) closely followed the daily GPP\({}_{\text{EC}}\) (Figure 5a,c), visually indicating the accuracy of GPP\({}_{\text{SIM}}\). The quantitative description of the GPP\({}_{\text{SIM}}\) accuracy was confirmed with the estimated NSE value (Equation (3)), which was found to be greater than 0.5 for both scenarios. The high NSE value indicated the sufficient quality of the predictive power of the SCOPE model for simulating gross primary production. We, however, observed some  
Figure 5: Left panels show the daily sum of flux-tower-measured (GPP\({}_{\text{EC}}\)) and SCOPE-simulated (GPP\({}_{\text{SIM}}\)) gross primary production for three scenarios: (**a,b**) Fixed scenario, (**c,d**) HLS info scenario, and (**e,f**) HLS info & var Vcmax scenario (detailed in Section 3.3) during the 2016 vegetation growing season. The right panels show the corresponding scatterplots between daily GPP\({}_{\text{EC}}\) and GPP\({}_{\text{SIM}}\) with the statistical evaluations. NSE: Nash–Sutcliffe efficiency, COR: correlation coefficient, RMSE: root mean square error, in g C m\({}^{-2}\) s\({}^{-1}\)). under- and over-estimation in GPP\({}_{\text{SIM}}\) compared to the GPP\({}_{\text{EC}}\). The scatterplots (Figure 5b,d) further explain the positive linear relationship between GPP\({}_{\text{SIM}}\) and GPP\({}_{\text{EC}}\), with the value of COR being high (close to 0.9 for both scenarios). This high value indicated that the variation of GPP\({}_{\text{SIM}}\) between consecutive days were synchronized with that of GPP\({}_{\text{EC}}\). We also concluded that both Fixed and HLS info scenarios led to similar magnitude and variation in daily GPP\({}_{\text{SIM}}\) during the vegetation growing season, and both scenarios quantitatively produced similar NSE values, together with similar RMSE values (Equation (2)). Under the HLS info & var V\({}_{\text{cmax}}\) scenario, we estimated \(V_{\text{cmax}}\) from the time series of \(C_{\text{ab}}\) (Equation (1)) as ranging from 31.8 to 108.5 umol m\({}^{-2}\) s\({}^{-1}\) during the vegetation growing season (Figure 4j). The variation in \(V_{\text{cmax}}\) resulted in the synchronization between daily GPP\({}_{\text{SIM}}\) and GPP\({}_{\text{EC}}\), but substantial differences in their magnitudes (Figure 5e) compared to the other two scenarios. These indicated the decrease in the quality of the predictive power of the SCOPE model for GPP\({}_{\text{SIM}}\). The drop in NSE below 0.5 and the increase in RMSE provided quantitative evidence of this decrease. The linearity in the relationship between GPP\({}_{\text{SIM}}\) and GPP\({}_{\text{EC}}\) also reduced, which resulted in a drop in COR (Figure 5f). Most of the variability in daily GPP\({}_{\text{SIM}}\) can be explained by its response to meteorological variables. We showed that R\({}_{\text{in}}\) was the main driver of GPP\({}_{\text{SIM}}\), with strong dependence especially up to daily mean R\({}_{\text{in}}\) = 200 W m\({}^{-2}\) (Figure 6a). The GPP\({}_{\text{SIM}}\) response to R\({}_{\text{in}}\) was further modified by T\({}_{\text{a}}\) (Figure 6b). The GPP\({}_{\text{SIM}}\) response to T\({}_{\text{a}}\) can be roughly separated to two regimes by a threshold of \(\approx\)13 \({}^{\circ}\)C. The colder regime (\(<\)13 \({}^{\circ}\)C) was associated with generally lower GPP\({}_{\text{SIM}}\) than during the warmer regime (\(>\)13 \({}^{\circ}\)C).