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Published July 24, 2015 | Published
Journal Article Open

Consistent satellite XCO_2 retrievals from SCIAMACHY and GOSAT using the BESD algorithm


Consistent and accurate long-term data sets of global atmospheric concentrations of carbon dioxide (CO_2) 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 onboard ENVISAT (March 2002–April 2012) and TANSO-FTS onboard GOSAT (launched in January 2009), to retrieve XCO_2, the column-averaged dry-air mole fraction of CO_2. BESD has been initially developed for SCIAMACHY XCO_2 retrievals. Here, we present the first detailed assessment of the new GOSAT BESD XCO_2 product. GOSAT BESD XCO_2 is a product generated and delivered to the MACC project for assimilation into ECMWF's Integrated Forecasting System (IFS). We describe the modifications of the BESD algorithm needed in order to retrieve XCO_2 from GOSAT and present detailed comparisons with ground-based observations of XCO_2 from the Total Carbon Column Observing Network (TCCON). We discuss detailed comparison results between all three XCO_2 data sets (SCIAMACHY, GOSAT and TCCON). The comparison results demonstrate the good consistency between the SCIAMACHY and the GOSAT XCO_2. 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° × 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 XCO_2 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 using also data from other missions (e.g., OCO-2, GOSAT-2, CarbonSat) in the future.

Additional Information

© 2015 Author(s). This work is distributed under the Creative Commons Attribution 3.0 License. Received: 19 December 2014. Published in Atmos. Meas. Tech. Discuss.: 13 February 2015. Revised: 22 June 2015. Accepted: 4 July 2015. Published: 24 July 2015. We thank JAXA, NIES and ESA for providing us with the GOSAT L1B and L2 IDS data. We are also grateful to Jonathan de Ferranti for the development of the digital elevation model, which we used for our evaluations. We thank TCCON for providing FTS XCO2 data obtained from the TCCON Data Archive, operated by the California Institute of Technology, from the website at http://tccon.ipac.caltech.edu/. The CarbonTracker CT2013B results has been provided by NOAA ESRL, Boulder, Colorado, USA, from the website at http://carbontracker.noaa.gov. We thank NASA for providing us with the ABSCOv4 tables and ECMWF for the meteorological data. This work has been funded by the EU FP7 (MACC-II), EU Horizon 2020 (MACC-III), ESA (GHG-CCI project and Living Planet Fellowship project CARBOFIRES) and the state and the University of Bremen.

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August 20, 2023
August 20, 2023