## 3 Results
### Evaluation of the EMGPP Models under All Sky Conditions
#### 3.2.2 Evaluation of the EMGPP Model for S/Q \(\geq\) 0.5  
Similar to the above GPP evaluations for S/Q \(<\) 0.5, hourly GPP was also computed using the 3-factor and 2-factor models and compared against the observations from 22 May 2013 to 31 December 2016 for S/Q \(\geq\) 0.5. Compared to measured GPP, the modeled hourly GPP was overestimated by 41% (range from 28% to 54%) in 2013-2016 (Table 7) when using the 3-factor model and 38% (26% to 50%) (Table 8) when using the 2-factor model. In contrast to the GPP estimates at S/Q \(<\) 0.5, the calculation errors (\(\delta_{\rm avg}\), \(\sigma_{\rm cal}\), MAD, \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**3-F** & \multirow{2}{*}{\(\delta_{\rm avg}\)} & \multirow{2}{*}{\(\delta_{\rm max}\)} & \multirow{2}{*}{\(\text{GPP}_{\text{cal}}\)} & \multirow{2}{*}{\(\text{GPP}_{\text{obs}}\)} & \multirow{2}{*}{**cal/obs**} & \multicolumn{2}{c}{**2-F**} & \multirow{2}{*}{\(\delta_{\rm avg}\)} & \multirow{2}{*}{\(\text{GPP}_{\text{cal}}\)} & \multirow{2}{*}{\(\text{GPP}_{\text{obs}}\)} & \multirow{2}{*}{**cal/obs**} \\
**Time Period** & & & & & & & & & \\ \hline
2013 & 12.17 & 285.17 & 254.23 & 1.12 & 2013 & 13.46 & 288.45 & 254.23 & 1.13 \\
2014 & 9.38 & 254.74 & 281.11 & 0.91 & 2014 & 5.50 & 265.66 & 281.11 & 0.95 \\
2015 & 2.83 & 291.47 & 283.46 & 1.03 & 2015 & 4.00 & 294.79 & 283.46 & 1.04 \\
2016 & 7.20 & 216.94 & 202.36 & 1.07 & 2016 & 10.68 & 223.98 & 202.36 & 1.11 \\
2013–2014 & 0.85 & 539.91 & 535.34 & 1.01 & 2013–2014 & 3.51 & 554.11 & 535.34 & 1.04 \\
2013–2016 & 2.66 & 1048.32 & 1021.16 & 1.03 & 2013–2016 & 5.06 & 1072.87 & 1021.16 & 1.05 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Same as Table 5 but for annual amounts of GPP simulations (mg CO\({}_{2}\) m\({}^{-2}\)) for S/Q \(<\) 0.5. Figure 5: Scatter plot of calculated versus measured monthly average GPP in Qianyanzhou sub-tropical conifer plantation under all sky conditions (S/Q \(<\) 0.5), using the 3-factor (**left**) and 2-factor (**right**) EMGPP models. \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**3-F** & \multirow{2}{*}{\(\delta_{\rm avg}\)} & \multirow{2}{*}{\(\delta_{\rm max}\)} & \multirow{2}{*}{\(\text{GPP}_{\text{cal}}\)} & \multirow{2}{*}{\(\text{GPP}_{\text{obs}}\)} & \multirow{2}{*}{**cal/obs**} & \multicolumn{2}{c}{**2-F**} & \multirow{2}{*}{\(\delta_{\rm avg}\)} & \multirow{2}{*}{\(\text{GPP}_{\text{cal}}\)} & \multirow{2}{*}{\(\text{GPP}_{\text{obs}}\)} & \multirow{2}{*}{**cal/obs**} \\
**Time Period** & & & & & & & & & & \\ \hline
2013 & 51.08 & 137.92 & 40.74 & 36.32 & 0.84 & 2013 & 37.88 & 105.36 & 41.21 & 36.32 & 0.97 \\
2014 & 33.62 & 87.80 & 21.23 & 23.43 & 0.77 & 2014 & 30.09 & 86.06 & 22.14 & 23.43 & 0.83 \\
2015 & 26.82 & 56.51 & 29.15 & 28.35 & 0.95 & 2015 & 22.28 & 54.46 & 29.48 & 28.35 & 0.99 \\
2016 & 36.10 & 139.35 & 19.72 & 18.40 & 1.16 & 2016 & 43.52 & 139.93 & 20.36 & 18.40 & 1.24 \\
2013–2014 & 40.05 & 137.92 & 28.42 & 28.18 & 0.80 & 2013–2014 & 32.96 & 105.36 & 29.16 & 28.18 & 0.88 \\ \hline
2013–2016 & 35.66 & 139.35 & 26.21 & 25.53 & 0.93 & 2013–2016 & 33.19 & 139.93 & 26.82 & 25.53 & 1.01 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Same as Table 3 but for simulations of monthly amounts of GPP (mg CO\({}_{2}\) m\({}^{-2}\)) (S/Q \(<\) 0.5). RMSE) at S/Q \(\geq\) 0.5 increased to different extents, which was mainly caused by the larger and increased observational errors in GPP, solar radiation and meteorological variables. For example, the standard errors (\(\sigma_{\rm obs}\)) were 0.298 and 0.260 mg CO\({}_{2}\) m\({}^{-2}\) for observed GPP at S/Q \(\geq\) 0.5 and S/Q \(<\) 0.5 during 2013-2016, respectively, increasing by 15% at S/Q \(\geq\) 0.5 compared to that at S/Q \(<\) 0.5.