## 4 Discussion
### Uncertainty of XCO\({}_{2}\) Global Land Mapping Dataset
#### 4.1.2 Verification with TCCON  
TCCON has been widely applied in verification of XCO\({}_{2}\) from satellite observations [14; 20; 36]. In this study, we perform a comparison between TCCON XCO\({}_{2}\) data for 12 sites and the XCO\({}_{2}\) time series reconstructed from the global land mapping XCO\({}_{2}\) data. For each TCCON site, we use all available data within the period from June 2009 to May 2014. The averaging kernel, as described in [67], for comparing ACOS-GOSAT and TCCON data is used for this comparison. Time series of TCCON XCO\({}_{2}\), GM-XCO\({}_{2}\), and ACOS-GOSAT XCO\({}_{2}\) within 500 km of every TCCON site are shown in Figure S2. Table 3 shows the statistical results in terms of the number of coincident data pairs, bias, and SCA difference between the mapping dataset and TCCON. The SCA of TCCON XCO\({}_{2}\) shown in Table 3 is derived in the same way as SCA of GM-XCO\({}_{2}\), as shown in Figure S2 which depicts the seasonal variations of XCO\({}_{2}\) from ACOS-GOSAT retrievals, mapping data, and TCCON measurements. Figure S2 compares the seasonal variation of observed and mapping XCO\({}_{2}\) and TCCON measurements. We can see that the mapping XCO\({}_{2}\) data go through the original satellite observations and the mapping data well capture the seasonal variations of the observations. From Table 3, we can see that the overall absolute difference of SCA between GM-XCO\({}_{2}\) and TCCON is 0.4 ppm. The SCAs from GM-XCO\({}_{2}\) are systematically shallower than that from TCCON for all sites in continental Europe, which is consistent with conclusions from previous studies [14; 15] of XCO\({}_{2}\) SCA using original GOSAT-ACOS retrievals, due to retrieval biases that were evaluated in previous studies [14; 15]. The SCAs of GM-XCO\({}_{2}\) are well consistent to that of TCCON (SCA difference < 0.7 ppm) at Park Falls, Lamont and Wollongong with long-term measurement that covers all the five years as shown in Figure S1. The largest absolute different SCA are 1.15 ppm in Darwin, 1.75 ppm at Orleans, and 1.38 ppm at Karlsruhe. It should be noted that Darwin is a coastal site of SH, whose SCA is small and has a combined contribution from local, oceanic, and adjacent land regions. From the comparison results, we can see that the mapping dataset can generally capture the seasonal characteristics of XCO\({}_{2}\). However, the mapping result and its data uncertainty from this data-driven gap-filling method rely heavily on the original XCO\({}_{2}\) retrievals. As a result, the retrieval artifacts on original XCO\({}_{2}\) retrievals are propagated into the mapping dataset [19], primarily leading to the differences between the seasonal cycles of GM-XCO\({}_{2}\) and TCCON. As shown in [PERSON] et al. [68], evidence of systematic retrieval errors can be found over dark surfaces of the high-latitude lands, particularly in the boreal region. These retrieval errors, which propagate into the mapping data, may play a role in the difference when compared to TCCON as shown in Table 3 and may lead to underestimation of XCO\({}_{2}\) SCA in the boreal region as shown in Figure 3. However, these possible errors (about 1 ppm for Continental Europe) are  
Figure 8: The relationship between predicted XCO\({}_{2}\) and observed XCO\({}_{2}\) values in cross-validation of global land mapping of XCO\({}_{2}\). The color grids represent the density of data distribution. The dotted line is derived from linear regression of predicted values of XCO\({}_{2}\) (Y) and the observed values of XCO\({}_{2}\) (X), which shows a significant linear relationship with R\({}^{2}\) equals 0.91 (\(p\)-value < 0.01) and good consistency of observed XCO\({}_{2}\) and predicted XCO\({}_{2}\) with MAPE equal to 0.85. The solid line shows the one-to-one line.