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Consistent validation of satellite CO2 estimates is a prerequisite forusing multiple satellite CO2 measurements for joint flux inversion, andfor establishing an accurate long-term atmospheric CO2 data record.Harmonizing satellite CO2 measurements is particularly important sincethe differences in instruments, observing geometries, sampling strategies,etc. imbue different measurement characteristics in the various satelliteCO2 data products. We focus on validating model and satelliteobservation attributes that impact flux estimates and CO2 assimilation,including accurate error estimates, correlated and random errors, overallbiases, biases by season and latitude, the impact of coincidence criteria,validation of seasonal cycle phase and amplitude, yearly growth, and dailyvariability. We evaluate dry-air mole fraction (XCO2) for Greenhouse gases Observing SATellite (GOSAT) (AtmosphericCO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeterfor Atmospheric CHartographY (SCIAMACHY) (Bremen Optimal Estimation DOAS, BESD v2.00.08) as well as the CarbonTracker (CT2013b)simulated CO2 mole fraction fields and the Monitoring Atmospheric Composition and Climate (MACC) CO2 inversionsystem (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). Wefind standard deviations of 0.9, 0.9, 1.7, and 2.1 ppm vs. TCCON forCT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the singleobservation errors 1.9 and 0.9 times the predicted errors for GOSAT andSCIAMACHY, respectively. We quantify how satellite error drops with dataaveraging by interpreting according to error2 = a2 + b2/n(with n being the number of observations averaged, a the systematic (correlated)errors, and b the random (uncorrelated) errors). a and b are estimated bysatellites, coincidence criteria, and hemisphere. Biases at individualstations have year-to-year variability of  ∼  0.3 ppm, withbiases larger than the TCCON-predicted bias uncertainty of 0.4 ppm at manystations. We find that GOSAT and CT2013b underpredict the seasonal cycleamplitude in the Northern Hemisphere (NH) between 46 and 53° N, MACC overpredicts between26 and 37° N, and CT2013b underpredicts the seasonal cycle amplitude in theSouthern Hemisphere (SH). The seasonal cycle phase indicates whether a data set ormodel lags another data set in time. We find that the GOSAT measurementsimprove the seasonal cycle phase substantially over the prior whileSCIAMACHY measurements improve the phase significantly for just two of sevensites. The models reproduce the measured seasonal cycle phase well exceptfor at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variabilitywithin 1 day between TCCON and models in JJA; there is correlation between0.2 and 0.8 in the NH, with models showing 10–50 % the variability ofTCCON at different stations and CT2013b showing more variability than MACC.This paper highlights findings that provide inputs to estimate flux errorsin model assimilations, and places where models and satellites need furtherinvestigation, e.g., the SH for models and 45–67° N for GOSAT and CT2013b

Consistent evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC through comparisons to TCCON