A method for evaluating bias in global measurements of CO<sub>2</sub> total columns from space

Abstract

Abstract. We describe a method of evaluating systematic errors in measurements of total column dry-air mole fractions of CO 2 (X CO 2 ) from space, and we illustrate the method by applying it to the v2.8 Atmospheric CO 2 Observations from Space retrievals of the Greenhouse Gases Observing Satellite (ACOS-GOSAT) measurements over land. The approach exploits the lack of large gradients in X CO 2 south of 25 • S to identify large-scale offsets and other biases in the ACOS-GOSAT data with several retrieval parameters and errors in instrument calibration. We demonstrate the effectiveness of the method by comparing the ACOS-GOSAT data in the Northern Hemisphere with ground truth provided by the Total Carbon Column Observing Network (TCCON). We use Correspondence to: D. Wunch (dwunch@gps.caltech.edu) the observed correlation between free-tropospheric potential temperature and X CO 2 in the Northern Hemisphere to define a dynamically informed coincidence criterion between the ground-based TCCON measurements and the ACOS-GOSAT measurements. We illustrate that this approach provides larger sample sizes, hence giving a more robust comparison than one that simply uses time, latitude and longitude criteria. Our results show that the agreement with the TC-CON data improves after accounting for the systematic errors, but that extrapolation to conditions found outside the region south of 25 • S may be problematic (e.g., high airmasses, large surface pressure biases, M-gain, measurements made over ocean). A preliminary evaluation of the improved v2.9 ACOS-GOSAT data is also discussed.

Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data

“…Therefore, several sophisticated methods were devised to obtain a sufficient number of co-located data. Following Keppel-Aleks et al (2011) who implied a relationship between meridional gradients of free-tropospheric potential temperature and CO 2 concentrations in mid-latitudes over the Northern Hemisphere, Wunch et al (2011b) used the distribution of potential temperature at 700 hPa when defining the co-location criteria in the Northern Hemisphere. Expansively, Nguyen et al (2014) utilized a modified Euclidean distance weighted average of distance, time, and temperature at 700 hPa.…”

“…These biases can lead to large errors in the estimations of regional fluxes of CO 2 and CH 4 from inversion analyses (Takagi et al, 2011;Maksyutov et al, 2013;Deng et al, 2014;Ishizawa et al, 2016b). Consequently, several studies have described bias corrections of the satellite retrieval data by using linear regression (e.g., Wunch et al, 2011b;Cogan et al, 2012;Guerlet et al, 2013;Schneising et al, 2013;Nguyen et al, 2014). Wunch et al (2011b) have attempted to correct spatially and temporally varying biases in the Atmospheric CO 2 Observations from Space retrievals of the GOSAT (ACOS-GOSAT; O'Dell et al, 2012;Crisp et al, 2012) data obtained over land using an empirical linear regression model with which they correlated variabilities in XCO 2 retrievals with surface albedo, the difference between the retrieved and a priori surface pressure, airmass, and the oxygen A-band spectral radiance.…”

“…Consequently, several studies have described bias corrections of the satellite retrieval data by using linear regression (e.g., Wunch et al, 2011b;Cogan et al, 2012;Guerlet et al, 2013;Schneising et al, 2013;Nguyen et al, 2014). Wunch et al (2011b) have attempted to correct spatially and temporally varying biases in the Atmospheric CO 2 Observations from Space retrievals of the GOSAT (ACOS-GOSAT; O'Dell et al, 2012;Crisp et al, 2012) data obtained over land using an empirical linear regression model with which they correlated variabilities in XCO 2 retrievals with surface albedo, the difference between the retrieved and a priori surface pressure, airmass, and the oxygen A-band spectral radiance. They used the GOSAT data in the Southern Hemisphere as the reference values for the linear regression and evaluated the bias correction against the Total Carbon Column Observing Network (TCCON) data from the Northern Hemisphere.…”

“…They used the GOSAT data in the Southern Hemisphere as the reference values for the linear regression and evaluated the bias correction against the Total Carbon Column Observing Network (TCCON) data from the Northern Hemisphere. Following Wunch et al (2011b), Cogan et al (2012) performed bias correction of GOSAT XCO 2 data retrieved from the University of Leicester full physics (UoL-FP) retrieval algorithm using pseudo observations based on GEOS-Chem model calculations. Guerlet et al (2013) used XCO 2 measurements from 12 TCCON sites around the world as a reference for correction of GOSAT XCO 2 data retrieved from the Netherlands Institute for Space Research/Karlsruhe Institute of Technology (SRON/KIT) full physics retrieval algorithm.…”

Simulated retrievals for the remote sensing of CO<sub>2</sub>, CH<sub>4</sub>, CO, and H<sub>2</sub>O from geostationary orbit

“…It has been assumed that the spectroscopic parameters and the ILS are known with certainty and the surface is Lambertian. In reality, uncertainties in these variables would lead to retrieval errors (e.g., Wunch et al, 2011;Fu et al, 2014). In addition, no micro-windows are used in this study.…”