# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 6 Conclusions  
In this paper, we analyzed four years (2015-2018) of Orbiting Carbon Observatory-2 (OCO-2) data to derive global (60\({}^{\circ}\)S-60\({}^{\circ}\)N) XCO\({}_{2}\) anomalies. We calculated regional-scale XCO\({}_{2}\) anomalies and identified the main anthropogenic emission areas, biomass burning regions, and biospheric sinks. We find that the patterns observed in OCO-2 annual XCO\({}_{2}\) anomalies are robust and consistent from year-to-year. From the seasonal XCO\({}_{2}\) anomalies, we identified the patterns in the Northern Hemispheric growing season and also the different fire seasons in Africa. In addition to large-scale features, we also visualized different local \"case studies\" with high anomalies associated with anthropogenic emissions in both the Northern and Southern Hemispheres. Finally, we combined daily XCO\({}_{2}\) anomalies with TROPOSpheric Monitoring Instrument (TROPOMI) NO\({}_{2}\) tropospheric columns, used as an indicator of anthropogenic pollution plumes, over the Highvelt industrial region in South Africa. We find corresponding gradients and positive correlation between XCO\({}_{2}\) anomalies and NO\({}_{2}\) enhancements. From a methodological point of view, instrument sampling, background choice, and bias correction do have an impact on the OCO-2 XCO\({}_{2}\) anomaly patterns. Thus, the results are most significant over areas where more data are available throughout the year. The consistency of the spatial features from year to year (or for the same season during different years) is an indicator of the robustness of the results. The FLEXible PARTicle dispersion model (FLEXPART) simulations also confirm the observed spatial patterns. On the other hand, the error associated with the anomaly is not purely statistical and tends to be more systematic due to errors in assumed source location, intensity, persistent wind patterns, and uneven geographic distribution of OCO-2 observations. For example, OCO-2 data in the northern high- and middle-latitudes are available predominantly during summer months so the annual maps are likely to be biased towards the anomalies derived during this period. We showed that using a piecewise linear background for the calculation of the anomalies provides a robust tool for global analysis. We have tested different background choices, as well as different OCO-2 data versions (V7r, V8r). We conclude that the structure and amplitude of the anomalies is relatively insensitive to these choices, although, for example, using narrow latitude bands without interpolation would yield sharp latitudinal boundaries in the maps. When analyzing local case studies on regional scales, a different background region can be selected, as shown for example in the South Africa's case study (Section 4.4) or by [PERSON] et al. [17] in China. The following are available online at [[http://www.mdpi.com/2072-4292/11/7/850/s1](http://www.mdpi.com/2072-4292/11/7/850/s1)]([http://www.mdpi.com/2072-4292/11/7/850/s1](http://www.mdpi.com/2072-4292/11/7/850/s1)), Figure S1: OCO-2 seasonal cycle at 10 degree latitude bands and latitudinal gradient at selected days between September 2015 and September 2016, Figure S2: Number of data points on each \(1^{\circ}\times 1^{\circ}\) grid cell, Figure S3: Standard error of the mean on each \(1^{\circ}\times 1^{\circ}\) grid cell, Figure S4: Number of data points on each \(1^{\circ}\times 1^{\circ}\) grid cell at each season from September 2014 to August 2018, Figure S5: Standard error of the mean for the seasonal XCO\({}_{2}\) anomalies 2014-2018, Figure S6: Histograms of XCO\({}_{2}\) anomalies inside and outside the NO\({}_{2}\) plumes. **Author Contributions:** [PERSON] and [PERSON] analyzed the data and wrote the first draft of the manuscript. [PERSON] and [PERSON] provided data and expertise on FLEXPART and OCO-2, respectively, and helped in finalizing the paper.