## 3 Results and Discussion
### Monthly Scale
#### 3.2.2 Gross Primary Productivity (GPP)  
Figure 5 shows the multi-year average seasonal cycle of simulated and observed monthly GPP and its standard deviation across sites. Simulated and observed GPP generally show good agreement in seasonal variations for most vegetation types, exhibiting single-peak or double-peak structures. Peaks typically occur during the growing season (summer, June-August), and troughs during the non-growing season (winter, December-February). For example, in ENF, both the simulated and observed GPP remain high from May to September, peaking in July, reflecting the model's ability to capture the year-round photosynthetic activity of needleleaf forests.  
The bar charts in Figure 5 indicate that the seasonal distribution of multi-site standard deviation varies among vegetation types. For GRA, the standard deviation is significantly higher in summer (June-August) than in winter (December-February), suggesting greater variability in simulation results among different grassland sites during the peak growing season. Conversely, for CRO, the standard deviation is relatively low throughout the year, indicating high consistency in GPP simulation across cropland sites, possibly related to simplified representations of management practices like irrigation and fertilization.  
Figure 6 displays scatter plots comparing simulated and observed multi-year monthly mean GPP across sites. The model shows high correlation between simulated and observed GPP for different vegetation types (R values range from 0.71 to 0.97, all \(p<0.001\)), indicating that simulations effectively capture the GPP flux trends. Except for EBF and GRA, R values are above 0.8 for all other types. For WET, the scatter points cluster closely around the 1:1 line, with a high correlation (R = 0.95). Forest ecosystems (ENF, DNF, DBF, etc.) generally show high correlations (R \(\geq\) 0.89), while GRA and WSA exhibit weaker performance (R = 0.79, 0.82), potentially underestimating the dynamic response of herbaceous vegetation to environmental forcing like water limitation.  
Figure 4: Scatter plots of monthly ET flux between simulation and observation for sites in different vegetation types: (**a**) ENF; (**b**) EBF; (**c**) DNF; (**d**) DBF; (**e**) MF; (**f**) CSH; (**g**) OSH; (**h**) WSA; (**i**) SAV; (**j**) GRA; (**k**) WET; (**l**) CRO. Dashed lines indicate a 1:1 relationship, and solid lines show linear regression fits, with correlation coefficients (R) displayed.  
Figure 5: Multiyear averaged seasonal variations (solid lines) in the simulated and observed GPP flux across 12 vegetation types and the corresponding standard deviation (bar charts) across sites: (**a**) ENF; (**b**) EBF; (**c**) DNF; (**d**) DBF; (**e**) MF; (**f**) CSH; (**g**) OSH; (**h**) WSA; (**i**) SAV; (**j**) GRA; (**k**) WET; (**l**) CRO.  
Figure 6: Scatter plots of monthly GPP flux between simulation and observation for sites in different vegetation types: (**a**) ENF; (**b**) EBF; (**c**) DNF; (**d**) DBF; (**e**) MF; (**f**) CSH; (**g**) OSH; (**h**) WSA; (**i**) SAV; (**j**) GRA; (**k**) WET; (**l**) CRO. Dashed lines indicate a 1:1 relationship, and solid lines show linear regression fits, with correlation coefficients (R) displayed.