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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 9, issue 2 | Copyright
Atmos. Meas. Tech., 9, 683-709, 2016
https://doi.org/10.5194/amt-9-683-2016
© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 29 Feb 2016

Research article | 29 Feb 2016

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

Susan Kulawik1, Debra Wunch2, Christopher O'Dell3, Christian Frankenberg2,4, Maximilian Reuter5, Tomohiro Oda6,7, Frederic Chevallier8, Vanessa Sherlock9,8, Michael Buchwitz5, Greg Osterman4, Charles E. Miller4, Paul O. Wennberg2, David Griffith10, Isamu Morino11, Manvendra K. Dubey12, Nicholas M. Deutscher5,10, Justus Notholt5, Frank Hase13, Thorsten Warneke5, Ralf Sussmann13, John Robinson9, Kimberly Strong14, Matthias Schneider12, Martine De Mazière15, Kei Shiomi16, Dietrich G. Feist17, Laura T. Iraci18, and Joyce Wolf4,* Susan Kulawik et al.
  • 1Bay Area Environm. Res. Inst., Sonoma, CA 95476, USA
  • 2California Institute of Technology, Pasadena, CA, USA
  • 3Colorado State University, Fort Collins, CO, USA
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 5University of Bremen, Institute of Environmental Physics, Bremen, Germany
  • 6Goddard Earth Sciences Technology and Research, Universities Space Research Association, Columbia, MD, USA
  • 7Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, MD, USA
  • 8Laboratoire de Meteorologie Dynamique, Palaiseau, France
  • 9The National Institute of Water and Atmospheric Research, Wellington and Lauder, New Zealand
  • 10University of Wollongong, Wollongong, New South Wales, Australia
  • 11Center for Global Environmental Research, National Institute for Environmental Studies (NIES), Tsukuba, Ibaraki, Japan
  • 12Earth and Environmental Science, Los Alamos National Laboratory, Los Alamos, NM 87545, USA
  • 13Karlsruhe Institute of Technology, Institute for Meteorology and Climate Research (IMK-ASF), Karlsruhe, Germany
  • 14Department of Physics, University of Toronto, Toronto, Ontario, Canada
  • 15Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
  • 16Earth Observation Research Center, Japan Aerospace Exploration Agency, Tsukuba, Ibaraki, Japan
  • 17Max Planck Institute for Biogeochemistry, Jena, Germany
  • 18NASA Ames Research Center, Atmospheric Science Branch, Moffett Field, CA, USA
  • *retired

Abstract. 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 (XCO2) 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.

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To accurately estimate source and sink locations of carbon dioxide, systematic errors in satellite measurements and models must be characterized. This paper examines two satellite data sets (GOSAT, launched 2009, and SCIAMACHY, launched 2002), and two models (CarbonTracker and MACC) vs. the TCCON CO2 validation data set. We assess biases and errors by season and latitude, satellite performance under averaging, and diurnal variability. Our findings are useful for assimilation of satellite data.
To accurately estimate source and sink locations of carbon dioxide, systematic errors in...
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