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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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Volume 8, issue 7 | Copyright
Atmos. Meas. Tech., 8, 2961-2980, 2015
https://doi.org/10.5194/amt-8-2961-2015
© Author(s) 2015. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 24 Jul 2015

Research article | 24 Jul 2015

Consistent satellite XCO2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm

J. Heymann1, M. Reuter1, M. Hilker1, M. Buchwitz1, O. Schneising1, H. Bovensmann1, J. P. Burrows1, A. Kuze2, H. Suto2, N. M. Deutscher4,1, M. K. Dubey3, D. W. T. Griffith4, F. Hase5, S. Kawakami2, R. Kivi6, I. Morino7, C. Petri1, C. Roehl8, M. Schneider5, V. Sherlock9,a, R. Sussmann10, V. A. Velazco4, T. Warneke1, and D. Wunch8 J. Heymann et al.
  • 1Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
  • 2Japan Aerospace Exploration Agency (JAXA), Tsukuba, Japan
  • 3Los Alamos National Laboratory, Los Alamos, USA
  • 4Centre for Atmospheric Chemistry, University of Wollongong, Wollongong, Australia
  • 5IMK-ASF, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany
  • 6Finnish Meteorological Institute, Sodankylä, Finland
  • 7National Institute for Environmental Studies (NIES), Tsukuba, Japan
  • 8California Institute of Technology, Pasadena, USA
  • 9National Institute of Water and Atmospheric Research, Wellington, New Zealand
  • 10IMK-IFU, Karlsruhe Institute of Technology (KIT), Garmisch-Partenkirchen, Germany
  • anow at: Laboratoire de Météorologie Dynamique, Palaiseau, France

Abstract. Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO2) are required for carbon cycle and climate-related research. However, global data sets based on satellite observations may suffer from inconsistencies originating from the use of products derived from different satellites as needed to cover a long enough time period. One reason for inconsistencies can be the use of different retrieval algorithms. We address this potential issue by applying the same algorithm, the Bremen Optimal Estimation DOAS (BESD) algorithm, to different satellite instruments, SCIAMACHY on-board ENVISAT (March 2002–April 2012) and TANSO-FTS on-board GOSAT (launched in January 2009), to retrieve XCO2, the column-averaged dry-air mole fraction of CO2. BESD has been initially developed for SCIAMACHY XCO2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO2 product. GOSAT BESD XCO2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System. We describe the modifications of the BESD algorithm needed in order to retrieve XCO2 from GOSAT and present detailed comparisons with ground-based observations of XCO2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between SCIAMACHY and GOSAT XCO2. For example, we found a mean difference for daily averages of −0.60 ± 1.56 ppm (mean difference ± standard deviation) for GOSAT–SCIAMACHY (linear correlation coefficient r=0.82), −0.34 ± 1.37 ppm (r = 0.86) for GOSAT–TCCON and 0.10 ± 1.79 ppm (r = 0.75) for SCIAMACHY–TCCON. The remaining differences between GOSAT and SCIAMACHY are likely due to non-perfect collocation (± 2 h, 10° x 10° around TCCON sites), i.e. the observed air masses are not exactly identical but likely also due to a still non-perfect BESD retrieval algorithm, which will be continuously improved in the future. Our overarching goal is to generate a satellite-derived XCO2 data set appropriate for climate and carbon cycle research covering the longest possible time period. We therefore also plan to extend the existing SCIAMACHY and GOSAT data set discussed here by also using data from other missions (e.g. OCO-2, GOSAT-2, CarbonSat) in the future.

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Long-term data sets of global atmospheric carbon dioxide concentrations based on observations from different satellite instruments may suffer from inconsistencies originating from the use of different retrieval algorithms. This issue has been addressed by applying the Bremen Optimal Estimation DOAS retrieval algorithm to SCIAMACHY and TANSO-FTS observations. Detailed comparisons with TCCON and CarbonTracker show good consistency between the SCIAMACHY and TANSO-FTS data sets.
Long-term data sets of global atmospheric carbon dioxide concentrations based on observations...
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