Consistent Evaluation of ACOS-GOSAT, BESD-SCIAMACHY, CarbonTracker, and MACC Through Comparisons to TCCON

• Published in: Atmospheric measurement techniques 2016-02, Vol.9 (2), p.683-709
• Main Authors: Kulawik, Susan, Wunch, Debra, O’Dell, Christopher, Frankenberg, Christian, Reuter, Maximilian, Chevallier, Frederic, Oda, Tomohiro, Sherlock, Vanessa, Buchwitz, Michael, Osterman, Greg, Miller, Charles E., Iraci, Laura T., Wolf, Joyce
• Format: Article
• Language: English
• Subjects: Amplitude; Amplitudes; Atmospheric absorption; Atmospheric chemistry; Atmospheric composition; Atmospheric models; Atmospheric monitoring; Bias; Carbon dioxide; Carbon dioxide atmospheric concentrations; Carbon dioxide estimation; Carbon dioxide measurements; Computer simulation; Continental interfaces, environment; Correlation; Criteria; Data; Datasets; Errors; Estimates; Evaluation; Fluctuations; Flux; Gases; Greenhouse effect; Greenhouse gases; Imaging techniques; Instruments; Meteorology And Climatology; Northern Hemisphere; Ocean, Atmosphere; Power plants; Random errors; Remote sensing; Satellite observation; Satellite-borne instruments; Satellites; Sciences of the Universe; Seasonal variation; Seasons; Southern Hemisphere; Spaceborne remote sensing; Stations; Variability
• ISSN: 1867-8548; 1867-1381; 1867-8548
• DOI: 10.5194/amt-9-683-2016

Consistent validation of satellite CO2 estimates is a prerequisite for using multiple satellite CO2 measurements for joint flux inversion, and for establishing an accurate long-term atmospheric CO2 data record. Harmonizing satellite CO2 measurements is particularly important since the differences in instruments, observing geometries, sampling strategies, etc. imbue different measurement characteristics in the various satellite CO2 data products. We focus on validating model and satellite observation attributes that impact flux estimates and CO2 assimilation, including accurate error estimates, correlated and random errors, overall biases, biases by season and latitude, the impact of coincidence criteria, validation of seasonal cycle phase and amplitude, yearly growth, and daily variability. We evaluate dry-air mole fraction (X(sub CO2)) for Greenhouse gases Observing SATellite (GOSAT) (Atmospheric CO2 Observations from Space, ACOS b3.5) and SCanning Imaging Absorption spectroMeter for 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 inversion system (v13.1) and compare these to Total Carbon Column Observing Network (TCCON) observations (GGG2012/2014). We find standard deviations of 0.9, 0.9, 1.7, and 2.1 parts per million vs. TCCON for CT2013b, MACC, GOSAT, and SCIAMACHY, respectively, with the single observation errors 1.9 and 0.9 times the predicted errors for GOSAT and SCIAMACHY, respectively. We quantify how satellite error drops with data averaging by interpreting according to (error(sup 2) equals a(sup 2) plus b(sup 2) divided by 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 by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of 0.3 parts per million, with biases larger than the TCCON predicted bias uncertainty of 0.4 parts per million at many stations. We find that GOSAT and CT2013b under-predict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53 degrees North latitude, MACC over-predicts between 26 and 37 degrees North latitude, and CT2013b under-predicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags