## 3 Results
### Evaluation of SIF Simulations  
#### 3.3.1 Evaluation of SIF Simulations with Fixed FQE  
First, we evaluated the simulating accuracy of \(\mathrm{SIF_{B}}\) and \(\mathrm{SIF_{A}}\) related to both their diurnal cycles and intensities. Figure 9 displays the diurnal cycles of the half-hourly simulated SIF with fixed FQE, compared to the measured SIF on ten fieldwork days in 2015 and 2016. As illustrated, the SCOPE model can well reproduce the diurnal cycles of the SIF variation for each fieldwork day. Many studies have proven that SIF is positively correlated with PAR and GPP at the canopy scale [7; 8; 57; 64]. Similarly, here, the simulated SIF values increase until noon and then decrease over time (with the changing of SZA), which obviously agrees with the diurnal patterns of PAR and GPP observation. Like the diurnal PAR and GPP observations shown in Figures 1 and 7, the diurnal curves of SIF exhibit the same fluctuations due to unstable weather on 25 April 2015, and on 18 April and 4 May 2016. All these phenomena indicate that in the SCOPE model, the TB12 biochemical module can credibly regulate the diurnal variation of SIF emission efficiencies as a function of the dynamic micrometeorological variables. Nevertheless, on several fieldwork days--like for \(\mathrm{SIF_{A}}\) on 24 April 2015 or for \(\mathrm{SIF_{B}}\) on 3 May 2016--the systematic deviation in the diurnal SIF simulations is obvious: the simulated \(\mathrm{SIF_{B}}\) or \(\mathrm{SIF_{A}}\) values during the entire day collectively deviated from their measured points. Figure 8: The correlation and relative root mean square error (RRMSE) value between simulated and measured GPP for all half-hour flux observations during ten experiments in 2015 and 2016. Figure 9: The diurnal cycles of simulated SIF with fixed FQE, compared to the measured SIF at O\({}_{2}\)-B and O\({}_{2}\)-A bands on ten fieldwork days in 2015 and 2016. Table 4 concludes the quantitative assessment of the errors and deviations in \(\mathrm{SIF_{B}}\) and \(\mathrm{SIF_{A}}\) simulations with fixed FQE. This table lists the RRMSE of diurnal simulations for each fieldwork day (hereafter denoted as the daily RRMSE) and the RRMSE for all ten days of \(\mathrm{SIF_{A}}\) and \(\mathrm{SIF_{B}}\) simulations, as well as the \(\mathit{bias_{\mathrm{SIF_{B}}}}\) and \(\mathit{bias_{\mathrm{SIF_{A}}}}\) (as described in Section 2.4). The results show that the SCOPE model can provide acceptable accuracy for the \(\mathrm{SIF_{B}}\) and \(\mathrm{SIF_{A}}\) simulations with fixed FQE: the RRMSE values of \(\mathrm{SIF_{B}}\) and \(\mathrm{SIF_{A}}\) simulations for all 106 spectral measurements were 24.35% and 23.67%, respectively; and the daily RRMSEs were lower than 30% for each fieldwork day, except for the \(\mathrm{SIF_{B}}\) on 3 & 4 May 2016. Nevertheless, the systematic deviations of diurnal \(\mathrm{SIF_{A}}\) and \(\mathrm{SIF_{B}}\) simulations were noteworthy on many fieldwork days, in particular for \(\mathrm{SIF_{B}}\) in 3 April 2015 and 3 & 4 May 2016, and for \(\mathrm{SIF_{A}}\) on 24 April 2015 the absolute bias values were greater than 20%. On these days, the error in diurnal \(\mathrm{SIF}\) simulation was mainly caused by the systematic deviation, not the inconsistency of \(\mathrm{SIF}\) change cycles. Note that the bias variation in \(\mathrm{SIF_{B}}\) simulations shows a clear seasonal pattern that is coincident between 2015 and 2016. Specifically, at the erecting period (i.e., 3 April 2015 and 8 & 9 April 2016), the \(\mathrm{SIF_{B}}\) was underestimated with the negative bias value; but at the booting or flowering period (i.e., 24 & 25 April 2015 and 3 & 4 May 2016), the \(\mathrm{SIF_{B}}\) was largely overestimated with a positive bias value. These results indicated that the fixed FQE was not suitable for unbiased \(\mathrm{SIF_{B}}\) and \(\mathrm{SIF_{A}}\) simulations at all growth stages, and the unsuitable FQE values may be the major factor that caused the systematic deviations in simulated \(\mathrm{SIF}\).