## 3 Results
### Model Simulations of GPP, Re, and NEP under All Sky Conditions from 2003-2017
#### 3.1.3 Monthly Sum Simulations of GPP, Re, and NEP  
Similarly, the monthly sum simulations of GPP, Re, and NEP were computed and displayed in Figures 4-6. The empirical models of GPP, Re, and NEP can capture their seasonal variations under three atmospheric situations: higher in summer and lower in winter. It was found that (1) much higher carbon was predicated (about two times the observed GPP) under relatively clean atmospheric conditions (low S/Q, monthly mean ratio GPP\({}_{\text{cal1}}\)/GPP\({}_{\text{obs}}\) from 2003-2017 = 1.93), e.g., a clean atmosphere is beneficial to carbon dioxide storage in the plants; (2) this ratio was 1.04 and 1.11 for polluted (high S/Q) and realistic atmospheric conditions, i.e., less GPP was fixed in the ecosystem under polluted atmosphere or high GLP load conditions. Therefore, a cleaner atmosphere and reducing high GLP loads in the atmosphere are synergistically effective and beneficial methods to enhance carbon storage in the ecosystem. A similar result is also reported by [PERSON] et al. [82]. (3) the simulated GPP values (GPP\({}_{\text{cal3}}\)) under realistic atmospheric conditions were in reasonable line with the observations, and most of the GPP\({}_{\text{cal3}}\) were within two times standard deviations of the observed GPPs. (4) similar simulations as GPP\({}_{\text{cal3}}\) were also obtained for GPP\({}_{\text{cal2}}\), revealing that the realistic atmospheric conditions were close to that under high GLPs conditions (i.e., S/Q \(\geq\) 0.5), i.e., the atmospheric and environmental conditions in the Danzhou Region were under high GLP loads.  
Figure 4: Observed and calculated monthly sums of GPP using the 2-factor EMGPP with coefficients at S/Q < 0.5 and S/Q \(\geq\) 0.5, and their combinations (GPP\({}_{\text{obs}}\), GPP\({}_{\text{cal1}}\), GPP\({}_{\text{cal2}}\), GPP\({}_{\text{cal3}}\), respectively) with error bars showing two times standard deviations of the observed GPPs from 2003–2017.  
The EMRe overestimated the measured respirations by 5.52% in the averages from 2003-2017, but with a larger variation range. The EMNEP underestimated the NEPs by 52.71%, 40.09%, and 34.99%, respectively, for the three atmospheric conditions (clean, polluted, and realistic). Large differences also existed in NEP simulations such as GPP. The reasons were (1) more complicated processes happened in daytime and nighttime respirations and then influenced NEP; (2) both GPP and daytime respirations were influenced by PAR [40; 41] and GPP was more sensitive to the changes in PAR than other factors (E, S/Q); (3) the observed PAR from 2003-2017 by the station may have large measurement errors, which may be the main reason. It can be speculated from the results that the simulations of monthly sums of Re, as well as NEP using EMRe and EMNEP and measured PAR by [PERSON]'s group, were in reasonable agreement with their observations from 2013-2016 [40; 41]; (4) extreme positive and negative observed respirations were removed in the quality control, resulting in the small change ranges; (5) there were data gaps in the observations, but this  
Figure 5: Observed and calculated monthly sums of respiration using the 2-factor EMRe (Re\({}_{\text{obs}}\) and Re\({}_{\text{cal}}\), respectively), with error bars showing two times standard deviations of the observed respirations from 2003–2017.  
Figure 6: Observed and calculated monthly sums of NEPs using the 2-factor EMGFP with coefficients at S/Q < 0.5, S/Q \(\geq\) 0.5, and their combinations (NEP\({}_{\text{obs}}\), NEP\({}_{\text{cal}}\), NEP\({}_{\text{cal}}\), and NEP\({}_{\text{cal}}\), respectively), with error bars showing two times standard deviations of the observed NEPs and EMRe from 2003–2017.  
issue did not affect empirical model developments of EMGPP, EMRe, and EMNEP [40; 41]; and (6) estimation errors in empirical models.