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Published February 29, 2016 | Published
Journal Article Open

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


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_(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 ppm 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 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 by satellites, coincidence criteria, and hemisphere. Biases at individual stations have year-to-year variability of  ∼  0.3 ppm, with biases larger than the TCCON-predicted bias uncertainty of 0.4 ppm at many stations. We find that GOSAT and CT2013b underpredict the seasonal cycle amplitude in the Northern Hemisphere (NH) between 46 and 53° N, MACC overpredicts between 26 and 37° N, and CT2013b underpredicts the seasonal cycle amplitude in the Southern Hemisphere (SH). The seasonal cycle phase indicates whether a data set or model lags another data set in time. We find that the GOSAT measurements improve the seasonal cycle phase substantially over the prior while SCIAMACHY measurements improve the phase significantly for just two of seven sites. The models reproduce the measured seasonal cycle phase well except for at Lauder_125HR (CT2013b) and Darwin (MACC). We compare the variability within 1 day between TCCON and models in JJA; there is correlation between 0.2 and 0.8 in the NH, with models showing 10–50 % the variability of TCCON at different stations and CT2013b showing more variability than MACC. This paper highlights findings that provide inputs to estimate flux errors in model assimilations, and places where models and satellites need further investigation, e.g., the SH for models and 45–67° N for GOSAT and CT2013b.

Additional Information

© Author(s) 2016. This work is distributed under the Creative Commons Attribution 3.0 License. Received: 19 May 2015 – Discussion started: 22 Jun 2015 – Revised: 24 Jan 2016 – Accepted: 29 Jan 2016 – Published: 29 Feb 2016. Funded by NASA Roses ESDR-ERR 10/10-ESDRERR10-0031, "Estimation of biases and errors of CO₂ satellite observations from AIRS, GOSAT, SCIAMACHY, TES, and OCO-2". Maximilian Reuter and Michael Buchwitz received funding from ESA (GHG-CCI project of ESA's Climate Change Initiative) and from the University and state of Bremen. Information about all TCCON sites and their sources of funding can be found on the TCCON website (https://tccon-wiki.caltech.edu/). Manvendra K. Dubey is grateful for the funding for monitoring at Four Corners by LANL-LDRD, 20110081DR. Frédéric Chevallier received funding from the EU H2020 Programme (grant agreement no. 630080, MACC III). NCEP Reanalysis data used in dynamic coincidence criteria were provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their website at http://www.esrl.noaa.gov/psd/. Thanks to Andrew R. Jacobson for help with CarbonTracker. Author contributions. Susan Kulawik set the direction of the research and did much of the analysis. The following authors were involved with discussions of results with specific knowledge in the listed areas: Debra Wunch, TCCON, Christopher O'Dell, ACOS-GOSAT, Christian Frankenberg, ACOS-GOSAT, Maximilian Reuter, BESD-SCIAMACHY, Tomohiro Oda, CarbonTracker, Frederic Chevallier, MACC, Vanessa Sherlock, TCCON, Michael Buchwitz, BESD-SCIAMACHY, Greg Osterman, ACOS-GOSAT, Charles Miller, CO₂ data records. The TCCON data providers, who also provide expertise regarding TCCON sites are Paul Wennberg, David Griffith, Isamu Morino, Manvendra Dubey, Nicholas M. Deutscher, Justus Notholt, Frank Hase, Thorsten Warneke, Ralf Sussmann, John Robinson, Kimberly Strong, and Matthias Schneider. Joyce Wolf is a science programmer and provided technical expertise. Edited by: H. Worden.

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