# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 4 Results  
### Annual XCO\({}_{2}\) Anomalies  
Figure 3 illustrates the mean annual OCO-2 XCO\({}_{2}\) anomaly for 2015-2018. The largest anthropogenic anomalies correspond to the areas in China, North-East India, Middle East, central Europe, and eastern USA, as also noted in [21]. In the Southern Hemisphere, the largest anthropogenic emission area, the Highveld region in South Africa, is clearly visible.  
Figure 3: Global XCO\({}_{2}\) anomalies as seen by OCO-2. The first panel corresponds to the four-year average 2015–2018, while the second, third, fourth, and fifth panels include the annual mean anomalies for the years 2015, 2016, 2017, and 2018, respectively.  
The largest positive XCO\({}_{2}\) anomalies are observed in the tropical regions in northern and southern Africa, Indonesia, Indochina, and South America. These correspond to large-scale biomass burning and to positive biospheric fluxes (respiration). These anomalies are most intense during the 2015-2016 El Nino, but persist through the four-year period considered here.  
The largest negative anomalies are observed in the northern middle-latitudes (40\({}^{\circ}\)N-60\({}^{\circ}\)N) over Asia and North America. These are clearly associated with the strong biospheric sink during the growing season. This area is mainly sampled by OCO-2 during summer months and not during winter due to persistent cloudiness and low signal levels at high solar zenith angles (see Figure 2). This temporal sampling bias may introduce biases in the annual-average anomalies (examined in greater detail below). We also observe a large area with negative anomalies in the Southern Cone in South America.  
The large areas with positive and negative anomalies are quite consistent from year to year. Also, smaller areas with large anomalies seem to be consistently visible in all years (e.g., the Highveld region). Some differences in the XCO\({}_{2}\) anomaly patterns between different years are, at least partly, related to the sampling of the instrument and the number of data points available. These are most visible for example at northern and southern mid-latitudes, where we can still identify individual \"satellite tracks\" from the map. In Figure S2 of the supplementary material, we present the maps of the number of data points that were used to calculate the mean in Figure 3. In addition to northern latitudes, the number of data points is lower also in places with high cloud density and/or large aerosol load (Himalaya, Amazonia, central Africa and China). Thus, the anomalies calculated over these areas are less robust. This is further illustrated in Figure S3, which shows the standard error of the mean, i.e., sample standard deviation divided by the square root of the sample size.  
We find that the ocean XCO\({}_{2}\) anomalies are \(\sim\)0.5 ppm lower than those over land. The XCO\({}_{2}\) concentrations are generally continuous across coastlines, clearly revealing outflow regions west of tropical Africa, and east of the east coasts of China and the U.S. The low bias over the ocean is generally consistent with the hypothesis that the ocean is a net sink for CO\({}_{2}\), absorbing about 8.9 GtCO\({}_{2}\)/year [31]. However, it is important to note that the approach for defining the background and anomalies adopted here was optimized for identifying point emission sources. It might not be ideal for quantifying weak, spatially extensive sinks, like those expected over ocean and seen in our anomaly plots. For example, [PERSON] et al. [32] found that the simulations of oceanic CO\({}_{2}\) surface concentrations show about 1 ppm variation in the annual zonal mean. Also, the low XCO\({}_{2}\) values over the tropical and high latitude ocean are among the most difficult to validate given the lack of TCCON stations at those latitudes and very limited in situ measurements. This is therefore active area of research within the scientific community.