## 3 Results
### Model Simulations of GPP, Re, and NEP under All Sky Conditions from 2003-2017
#### 3.1.2 Daily Sum Simulations of GPP, Re, and NEP  
The daily sums of GPP, Re, and NEP from 2013-2017 were obtained from their sums of hourly values. The results are shown in Table 2. The RMSE and MAD for daily sums and hourly average of Re were much larger than that of GPP and NEP, and it was mainly caused by the lower Re values compared to that of GPP and NEP. Generally, the daily sums of GPP, Re, and NEP agreed with the observations under realistic atmospheric conditions. For example, the empirical models overestimated the observed (mean) GPP and Re by 11.4% and 5.6%, respectively, and they underestimated the (mean) observed NEP by 35.0%. In contrast, the empirical models overestimated the (mean) observed GPP and Re by a factor of 1.93 and 3.61 for relatively clean atmospheric conditions (low GLP loads) and 1.04 and 0.59 for more polluted atmospheric conditions (high GLP loads), respectively. This indicates that GPP would increase evidently under a relatively clean atmosphere and decrease under a more polluted atmosphere. In other words, more CO\({}_{2}\) (e.g., GPP) would be produced by increased PAR utilization through vegetation photosynthesis and microorganism activity (respiration, e.g., bacterial decomposition of a litter) and then stored in the ecosystem under a clean atmosphere. The empirical models exhibited similar performances for polluted atmospheric conditions as for realistic atmospheric conditions, and it was attributed to the closer GLP loads, e.g., the atmosphere was in high GLP levels in Qianyanzhou coniferous plantation from 2003-2017. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline \multicolumn{2}{c}{**RMSE**} & \multicolumn{3}{c}{**MAD**} & & & & & & & & & & & & & \\ \hline \multirow{2}{*}{**(\%)**} & **(mgCO\({}_{2}\)** & \multirow{2}{*}{**(\%)**} & **(mgCO\({}_{2}\)** & \multirow{2}{*}{**(\sigma_{\rm obs}\)**} & **\(\sigma_{\rm cal}\)** & **NMSE** & **R\({}_{3}\)** & **R\({}_{2}\)** & **R\({}_{1}\)** & **obs** & **cal3** & **cal2** & **cal1** & **Model** \\  & & & & & & & & & & & & & & \\ \hline
69.51 & 0.301 & 52.75 & 0.228 & 0.299 & 0.333 & 0.434 & 1.114 & 1.035 & 1.931 & 0.433 & 0.482 & 0.447 & 0.835 & EMGPP \\ \hline
174.98 & 0.367 & 141.04 & 0.215 & 0.073 & 0.320 & 2.888 & 1.060 & 0.592 & 3.611 & 0.153 & 0.162 & 0.090 & 0.551 & EMRe \\ \hline
41.58 & 0.247 & 35.85 & 0.213 & 0.259 & 0.155 & 0.265 & 0.651 & 0.598 & 0.472 & 0.998 & 0.761 & 0.727 & 0.761 & EMNEP \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Hourly mean estimates of GPP, respiration (Re), and net ecosystem productivity (NEP) (shown in lines 2, 3, and 4) under relatively clean, polluted, and realistic atmospheric conditions (cal1, cal2, cal3), respectively, and their observed values (obs) in a subtropical coniferous plantation from 2003–2017, statistical metrics, i.e., the averages of the calculated and observed GPP, the corresponding ratios of the calculated to observed GPP (cal/obs, R\({}_{1}\), R\({}_{2}\), R\({}_{3}\)), normalized mean-square error (NMSE), standard deviations of calculated, and observed GPP (\(\sigma_{\rm cal}\) and \(\sigma_{\rm obs}\)), together with mean bias errors (MAD, mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), and %) and root mean-square errors (RMSE, mgCO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), and %).