# Evidence of Carbon Uptake Associated with Vegetation Greening Trends in Eastern China
## 4 Discussion
### Uncertainty of GM-XCO\({}_{2}\) Trends  
The GM-XCO\({}_{2}\) product involves merging and gap-filling multiple satellite records (Section 2.2.2), therefore, we need to consider the uncertainty associated with this product. Spurious trends could be caused by the gap-filling method, so we checked the amount of available satellite-observed XCO\({}_{2}\) in each grid cell over EC (Figure S11). A greater proportion of observations are distributed in the northern  
Figure 8: Pearson correlation coefficients between corrected GM-XCO\({}_{2}\) and land surface temperature (**(a)**, LST) and precipitation (**(b)**, Precipitation) in the growing period from 2003 to 2016. The corrected method is using CT-XCO\({}_{2}\) fitting. LST and precipitation were from MODIS (MOD11C2 v6) and TRMM (TRMM 3B42), respectively. Color bar shows the correlation coefficients and grid cells with black outline show grid cells with significant correlations (\(p<0.05\)). Observed changes in temperature and precipitation do not effectively explain the observed trends in GM-XCO\({}_{2}\).  
part of China for SCIAMACHY BESD XCO\({}_{2}\) (2003.01-2009.05), GOSAT ACOS XCO\({}_{2}\) (2009.06-2014.08), and OCO-2 XCO\({}_{2}\) (2014.09-2016.03) because of the cloud contamination discussed earlier. Here, we show the temporal change of integrated XCO\({}_{2}\) (the merging of data from the three sensors) and GM-XCO\({}_{2}\) (gap-filled) using available data in EC as shown in Figure 9. GM-XCO\({}_{2}\) agreed well with integrated XCO\({}_{2}\) with a mean difference of 0.25 \(\pm\) 1.33 ppm. Some differences exist during summer for distinct XCO\({}_{2}\) spatial variations shown with mean \(\pm\) 1 STD GM-XCO\({}_{2}\) (red dashed lines). We found no significant trend in the GP residuals between integrated XCO\({}_{2}\) and the gap-filled GM-XCO\({}_{2}\) using the Mann-Kendal trend test. Therefore, we conclude that the XCO\({}_{2}\) decreasing trends are not caused by the gap-filling method.  
In addition, we checked the GM-XCO\({}_{2}\) product against TCCON measurements as shown in Figure S5. The mean absolute difference (bias) between GM-XCO\({}_{2}\) and TCCON observations with 3608 co-located data pairs was 0.90 \(\pm\) 1.16 ppm. Individual grid-cell GM-XCO\({}_{2}\) bias followed a Gaussian distribution, therefore the XCO\({}_{2}\) uncertainty over EC was estimated as the averaged bias divided by the square root of the number of grid cells in EC. As a result, the uncertainty was less than 0.045 ppm, which is significantly less than the XCO\({}_{2}\) total change (0.840-1.008 ppm from 2003 to 2016). XCO\({}_{2}\) differences between GM-XCO\({}_{2}\) and TCCON measurements from 2003 to 2016 are shown in Figure 10. The differences fluctuate around zero and present no trends from 2003 to 2016 for data pairs in the northern hemisphere. This comparison with the TCCON observations showed that there was no evidence of sensor offsets or drift that would create artificial GM-XCO\({}_{2}\) trends over time.  
Figure 10: XCO\({}_{2}\) difference between GM-XCO\({}_{2}\) and TCCON measurements from 2003 to 2016 using available datasets (GM-XCO\({}_{2}\) minus TCCON XCO\({}_{2}\)). All GM-XCO\({}_{2}\) values within 0.5 degrees around the TCCON sites (different markers) were averaged for the difference calculation.  
Figure 9: Integrated multi-satellite XCO\({}_{2}\) records and GM-XCO\({}_{2}\) over Eastern China. Light gray, dark gray. and black points represent the integrated BESC-XCO\({}_{2}\), ACOS-XCO\({}_{2}\), and OCO-XCO\({}_{2}\) points from 2003 to 2016 in EC. Blue points are the median value of integrated XCO\({}_{2}\). Red line is the regional mean (solid) GM-XCO\({}_{2}\)\(\pm\) 1 STD (dashed). Dark green points are the difference between mean GM-XCO\({}_{2}\) and median integrated XCO\({}_{2}\) (+355 ppm).