# Analysis of Four Years of Global XCO\({}_{2}\) Anomalies as
## 1 Introduction
Recently, TROPOMI (TROPOspheric Monitoring Instrument), on board the European Space Agency (ESA) Sentinel-5 Precursor (S5P) satellite, began retrieving estimates of the methane dry air mole fraction from observations of reflected sunlight, but not CO\({}_{2}\). Although the current space-based CO\({}_{2}\) missions have focused primarily on the need to better understand the biospheric carbon fluxes [11], satellite-based CO\({}_{2}\) observations have also been used in anthropogenic CO\({}_{2}\) studies. For example, [PERSON] et al. [12] analyzed the CO\({}_{2}\)-to-NO\({}_{2}\) emission ratios with collocated SCIAMACHY data, [PERSON] et al. [13] studied localized CO\({}_{2}\) enhancements using GOSAT data, [PERSON] et al. [14] studied the CO\({}_{2}\) signal from Los Angeles and Mumbai megacities from GOSAT data, [PERSON] et al. [11] estimated the CO\({}_{2}\) emissions from individual power plants using OCO-2 data, [PERSON] et al. [15] analyzed localized carbon dioxide sources (e.g., Los Angeles) using OCO-2 data, and [PERSON] et al. [16] evaluated the possibility of constraining the fossil fuel CO\({}_{2}\) emissions from urban areas using OCO-2 observations. Recently, [PERSON] et al. [17] used OCO-2 XCO\({}_{2}\) anomalies for distinguishing anthropogenic CO\({}_{2}\) emissions from different energy intensive industrial sources in northern China. Since the Paris Agreement was signed in 2015, the focus has shifted more and more to anthropogenic CO\({}_{2}\) emissions. For example, the European Commission is currently planning a dedicated anthropogenic CO\({}_{2}\) monitoring mission within the Copernicus Programme. One large obstacle limiting the quantification of anthropogenic CO\({}_{2}\) flux from space, is the limited mapping capability of current satellite missions. GOSAT provides single soundings with 10.5 km diameter surface footprints that are separated by \(\sim\)250 km. OCO-2 provides measurements with eight 2.25 km long footprints along a narrow (less than 10 km) swath, but the swaths are separated by \(\sim\)1.5 degrees of longitude. In both cases, the data coverage is quite sparse, and the sampling density is further reduced by optically thick clouds and aerosols, which preclude full-column XCO\({}_{2}\) measurements. Figure 1 illustrates an example of the OCO-2 data coverage on \(1^{\circ}\times 1^{\circ}\) latitude-longitude grid during different periods of time: one day, one week, one month and one year. The OCO-2 XCO\({}_{2}\) anomalies illustrated in Figure 1 are further discussed in Section 3. In addition to the nominal measurements, special target mode measurements are performed in selected measurement locations for calibration and/or validation purposes [18]. Figure 1: Mean Orbiting Carbon Observatory-2 (OCO-2) XCO\({}_{2}\) anomalies on \(1^{\circ}\times 1^{\circ}\) latitude-longitude grid during one day, one week, one month and one year. For the background we calculate the daily medians for each 10-degree latitude bands and linearly interpolate the resulting values to each OCO-2 data point. The added value of satellite data in carbon cycle research has been demonstrated in several model-based studies (e.g., [19; 20]). Our previous study [21] introduced the concept of XCO\({}_{2}\) anomaly and analyzed the first 18 months of OCO-2 measurements for three selected areas in the Northern Hemisphere. The objective of this paper is to describe the spatio-temporal variability of the XCO\({}_{2}\) anomalies (including anthropogenic emission patterns and seasonal variability related to vegetation and biomass burning) solely using satellite observations. The aim is to exploit the intrinsic value of satellite-based XCO\({}_{2}\) observations beyond their application as model input. We analyze four years (2015-2018) of global (60\({}^{\circ}\)S-60\({}^{\circ}\)N) XCO\({}_{2}\) anomalies as seen by OCO-2.