## 2 Preprocessing  
During preprocessing, we collect all datasets that are needed to run the retrievals and pre-filter soundings with potentially degraded quality or potential cloud or aerosol contamination. Due to the demanding precision and accuracy requirements for XCO\({}_{2}\) retrievals (e.g., [2; 3; 4]) and the large amount of OCO-2 data, we prioritize quality over quantity in the course of pre-filtering. The primary input data used for this publication are global OCO-2 L1b calibrated radiances (i.e., a single linear component of the polarization of the incoming light) version 7r [9; 24] of the year 2015 in glint (GL), nadir (ND), target (TG), and transition (XS) mode which have been obtained from [[https://daac.gsfc.nasa.gov](https://daac.gsfc.nasa.gov)]([https://daac.gsfc.nasa.gov](https://daac.gsfc.nasa.gov)). Each of OCO-2's three bands consists of 1016 spectral pixels which we group into the four fit windows illustrated in Figure 1 showing a typical OCO-2 measurement fitted with FOCAL. Each L1b orbit file includes information on spectral pixels with potentially reduced quality, e.g., due to radiometric problems. Based on the last nadir orbit in 2015 (oco2_L1 bScND_07974a_151231 _B7200r_160121043229.h5), we generated a dead or bad pixel mask which we use for the O\({}_{2}\) and both CO\({}_{2}\) fit windows. For the SIF fit window, we ignore the dead or bad pixel mask because it is located in a spectral region generally flagged as potentially bad. Figure 1: OCO-2 measurement of June 6, 2015, 12:01 UTC near Hamburg, Germany (sounding ID: 2015060512011938) fitted with FOCAL. (**Top**) Simulated and fitted radiance measurement in gray and red, respectively. (**Bottom**) Adapted measurement noise (see Section 3.1) and fit residual (fit minus measurement) in gray and red, respectively; \(\chi_{j}\) is an estimate of the goodness of fit (relative to the noise) in fit window \(j\) and is computed as defined in part 1 [23]. We reject all soundings flagged to have potentially reduced quality (quality flag \(\
eq\) 0) or failing a data integrity test (e.g., unreasonable sounding ID or time). We filter out potentially \"tricky\" scenes with solar or satellite zenith angles greater than 70\({}^{\circ}\), latitudes beyond \(\pm\)70\({}^{\circ}\), or extreme surface roughnesses (standard deviation of the surface elevation) greater than 1000 m. In Figure 2, this filter is referred to as LAT/SUZ/SAZ/\(\sigma\)ALT. We use the spike EOF analysis provided with the OCO-2 L1b data [24] and accept only soundings with less than 60 spectral pixels with potentially poor quality (referred to as bad colors) in the O\({}_{2}\) and no bad colors in the CO\({}_{2}\) bands. This primarily filters out soundings above South America and the South Atlantic because of contamination by cosmic rays within the South Atlantic Anomaly (SAA) caused by the shape of the inner Van Allen radiation belt (Figure 2). Potentially aerosol contaminated scenes are filtered using OMI (Ozone Monitoring Instrument aboard Aura lagging OCO-2 by 30 min) L3 global daily gridded 1\({}^{\circ}\)\(\times\) 1\({}^{\circ}\) UV aerosol index data (OMAERUVd v003 obtained from [[https://daac.gsfc.nasa.gov](https://daac.gsfc.nasa.gov)]([https://daac.gsfc.nasa.gov](https://daac.gsfc.nasa.gov))) with a filtering threshold of one. As described by [PERSON] [25], the UV aerosol index is derivedby comparing the measured reflectance ratio at two wavelengths (342.5 nm and 388.0 nm) to the calculated reflectance ratio using a Rayleigh atmosphere with an assumed surface albedo. The UV index is relatively insensitive to scattering aerosol layers or clouds, because it is mainly determined by the reduction of Rayleigh multiple scattering due to aerosol absorption. As illustrated in Figure 2, this filter most prominently impacts regions contaminated with desert dust aerosols.