ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-13-1771-2013 A joint effort to deliver satellite retrieved atmospheric CO<sub>2</sub> concentrations for surface flux inversions: the ensemble median algorithm EMMA Reuter M. 1 Bösch H. 2 Bovensmann H. 1 Bril A. 3 Buchwitz M. 1 Butz A. 4 Burrows J. P. 1 O'Dell C. W. 5 Guerlet S. 6 Hasekamp O. 6 Heymann J. 1 Kikuchi N. 3 Oshchepkov S. 3 Parker R. 2 Pfeifer S. 7 Schneising O. 1 Yokota T. 3 Yoshida Y. 3 Institute of Environmental Physics, University of Bremen, Bremen, Germany University of Leicester, Department of Physics and Astronomy, Leicester, UK National Institute for Environmental Studies, Tsukuba, Japan IMK-ASF, Karlsruhe Institute of Technology, Karlsruhe, Germany Colorado State University, Fort Collins, CO, USA Netherlands Institute for Space Research, Utrecht, The Netherlands Climate Service Center, Helmholtz Zentrum Geesthacht, Hamburg, Germany 15 02 2013 13 4 1771 1780 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/13/1771/2013/acp-13-1771-2013.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/13/1771/2013/acp-13-1771-2013.pdf

We analyze an ensemble of seven XCO<sub>2</sub> retrieval algorithms for SCIAMACHY (scanning imaging absorption spectrometer of atmospheric chartography) and GOSAT (greenhouse gases observing satellite). The ensemble spread can be interpreted as regional uncertainty and can help to identify locations for new TCCON (total carbon column observing network) validation sites. Additionally, we introduce the ensemble median algorithm EMMA combining individual soundings of the seven algorithms into one new data set. The ensemble takes advantage of the algorithms' independent developments. We find ensemble spreads being often < 1 ppm but rising up to 2 ppm especially in the tropics and East Asia. On the basis of gridded monthly averages, we compare EMMA and all individual algorithms with TCCON and CarbonTracker model results (potential outliers, north/south gradient, seasonal (peak-to-peak) amplitude, standard deviation of the difference). Our findings show that EMMA is a promising candidate for inverse modeling studies. Compared to CarbonTracker, the satellite retrievals find consistently larger north/south gradients (by 0.3–0.9 ppm) and seasonal amplitudes (by 1.5–2.0 ppm).

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