# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 5 Discussion  
Current CO\({}_{2}\) missions have been primarily designed to extend the spatial coverage of the ground-based atmospheric observation networks and to improve the model estimates of biospheric fluxes on regional scale. The COP 21 Paris agreement emphasizes the need to monitor anthropogenic CO\({}_{2}\) emissions over a range of scales [35]. The anomaly approach presented here exploits the intrinsic value and capabilities of satellite-based CO\({}_{2}\) observations for mapping anthropogenic and natural emission patterns, beyond their application as model input information. The observed consistency between satellite-based XCO\({}_{2}\) anomalies and model outputs (distinguishing fossil fuel and biospheric contributions) demonstrates the capability of satellite observations for describing the CO\({}_{2}\) spatio-temporal variability and, in particular, for detecting anthropogenic CO\({}_{2}\) emission patterns. A feature of the anomaly approach is that the XCO\({}_{2}\) anomalies (given in ppms) are not directly convertible to fluxes (given in ktons/day). The approach often taken with short-lived air pollutants is to use a statistical model that describes their spatial distribution near the emission sources as a function of wind speed and direction (e.g., [36; 37]). In the future, a similar approach might also be possible with XCO\({}_{2}\) anomalies, for example using CO\({}_{2}\) measuring satellites with wider swaths, such as the anthropogenic CO\({}_{2}\) monitoring satellite constellation planned by the European Copernicus Programme. In this context, the results presented here highlight the need for satellite observations with high precision, good accuracy and dense sampling covering a wider swath (e.g., few hundred km) than OCO-2, in order to separate individual emission sources (e.g., cities or large power plants) from the background signal. Here we showed how overlapping OCO-2 XCO\({}_{2}\) data and TROPOMI NO\({}_{2}\) observations (covering a large swath) can be used to identify small scale anthropogenic CO\({}_{2}\) signatures. The anomaly approach is also a useful tool for identifying CO\({}_{2}\) fluxes associated with the natural carbon cycle. For example, the XCO\({}_{2}\) anomaly maps show persistent positive XCO\({}_{2}\) anomalies (1-2 ppm) over the Amazon, Congo, and tropical Oceania. The intensity and spatial extent of the positive XCO\({}_{2}\) anomaly is largest during the 2015-2016 El Nino, but it spanned every season of every year included in this study. This appears to support the conclusions of [PERSON] et al. [38] who assert that the tropical deforestation and degradation have compromised these forests to the extent that they are now net sources instead of sinks of carbon, even if this conclusion is still under debate [39]. A recent study by [PERSON] et al. [40] also indicates that contrary to previous findings, the sink is predominantly in mid-high latitude, rather than tropical, forests. Figure 12: OCO-2 XCO\({}_{2}\) values (black dots) as a function of latitude on 11 July 2018 and 20 July 2018. The enhancement corresponds to plumes originating from Medupi and Matimba coal-fired power plants. The red dots indicate the XCO\({}_{2}\) values obtained by converting TROPOMI NO\({}_{2}\) tropospheric columns (collocated to the OCO-2 measurements) into XCO\({}_{2}\) using the results of the linear regression between the two data sets. The slope (CO\({}_{2}\)-to-NO\({}_{2}\) ratio) is in both cases \(\sim\)1.1 \(\times 10^{-16}\) ppm/(molec./cm\({}^{2}\)). The method presented here can also be applied to future planned XCO\({}_{2}\) missions, such as OCO-3, GOSAT-2, MicroCarb, GeoCarb, and the Chinese and European wide-swath constellations.