## 4 Discussion
### Uncertainty of XCO\({}_{2}\) Global Land Mapping Dataset
#### 4.1.3 Comparison of Seasonal Cycle between GM-XCO\({}_{2}\) and Model Simulations  
As suggested by previous studies [11; 27; 53; 58], model simulations can well reproduce the large-scale features of atmospheric CO\({}_{2}\). On the other hand, satellite observations make direct measurements of XCO\({}_{2}\) and, therefore, be able to capture the local variability and a more detailed characteristic of XCO\({}_{2}\) variation compared to model simulations. Therefore, we compare the SCA of GM-XCO\({}_{2}\) to that of simulated XCO\({}_{2}\) from CarbonTracker in order to evaluate the satellite measurements and the mapping dataset. Figure 9 shows the global distribution of XCO\({}_{2}\) SCA derived from the CarbonTracker (CT2015) data, overlapped with the contour lines of GM-XCO\({}_{2}\) amplitudes shown in Figure 3. Comparing Figure 3 with Figure 9, it can be clearly seen that the spatial pattern and latitudinal gradient of GM-XCO\({}_{2}\) SCA is in good agreement with CT-XCO\({}_{2}\), especially in the high-latitude NH areas where the contour lines from GM-XCO\({}_{2}\) amplitude match well with the changes of SCA from CT-XCO\({}_{2}\). Moreover, the phenomenon of higher SCA over India and Central Africa than surrounding areas can be seen in SCA maps from both GM-XCO\({}_{2}\) and CT-XCO\({}_{2}\). The SCA difference map of GM-XCO\({}_{2}\) and CT-XCO\({}_{2}\) is shown in Figure S3. Over the NH high-latitude area, the GM-XCO\({}_{2}\) SCA is generally lower than that of CT-XCO\({}_{2}\), which agrees with the results, as shown in Table 3, that GM-XCO\({}_{2}\) SCA is generally lower than TCCON sites at the same area (Bremen, Karlsruhe, Orleans, Garmisch, and Bialystok). Over the low latitude area, GM-XCO\({}_{2}\) SCA is generally higher than that of CT-XCO\({}_{2}\), especially over the areas within the overlapped GM-XCO\({}_{2}\) contour lines of 8 ppm and 4 ppm (Such as India and Southern Africa). There are two possibilities for these local inconsistencies: (1) the retrieve error of satellite observations may be large over these areas because of the impacts of thin clouds and disturbance of wildfire [57; 69]; (2) the uncertainty of simulations by current models [8; 70] may be large over these areas because of the sparseness of the ground-based measures and airborne observations used for assimilations to constrain the local CO\({}_{2}\) fluxes [20; 66]. This comparison shows that satellite observations of XCO\({}_{2}\) with appropriate analysis can be used to assess the earth system modeling coupled with carbon flux estimates, as also suggested by [PERSON] et al. [71]. We can conclude that the GM-XCO\({}_{2}\) SCA is generally consistent with that of CT-XCO\({}_{2}\) on a large scale, even though the discrepancies over some local areas need further investigation.  
Figure 9: Global distribution of XCO2 SCA derived from CarbonTracker (CT2015) CT-XCO2 dataset from June 2009 to May 2014. Similar to Figure 4, the SCA for each grid is calculated by fitting its XCO2 time series using Equation (1) and obtaining the annual mean of differences between peaks and troughs after subtracting the linear trend. The two overlapped contour lines are from the XCO2 SCA (8.0 ppm in the solid black line and 4.0 ppm in the dotted black line) as shown in Figure 3.