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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
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© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.

Submitted as: research article 30 Oct 2019

Submitted as: research article | 30 Oct 2019

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This discussion paper is a preprint. It is a manuscript under review for the journal Atmospheric Measurement Techniques (AMT).

Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003-2018) for carbon and climate applications

Maximilian Reuter1, Michael Buchwitz1, Oliver Schneising1, Stefan Noel1, Heinrich Bovensmann1, John P. Burrows1, Hartmut Boesch2,3, Antonio Di Noia2,3, Jasdeep Anand2,3, Robert J. Parker2,3, Peter Somkuti2,3,8, Lianghai Wu4, Otto P. Hasekamp4, Ilse Aben4, Akihiko Kuze5, Hiroshi Suto5, Kei Shiomi5, Yukio Yoshida6, Isamu Morino6, David Crisp7, Christopher O'Dell8, Justus Notholt1, Christof Petri1, Thorsten Warneke1, Voltaire Velazco9, Nicholas M. Deutscher9, David W. T. Griffith9, Rigel Kivi10, Dave Pollard11, Frank Hase12, Ralf Sussmann13, Yao V. Te14, Kimberly Strong15, Sebastien Roche15, Mahesh K. Sha16, Martine De Maziere16, Dietrich G. Feist17,18,19, Laura T. Iraci20, Coleen Roehl21, Christian Retscher22, and Dinand Schepers23 Maximilian Reuter et al.
  • 1Institute of Environmental Physics (IUP), University of Bremen, 28334 Bremen, Germany
  • 2Earth Observation Science, University of Leicester, LE1 7RH, Leicester, UK
  • 3NERC National Centre for Earth Observation, LE1 7RH, Leicester, UK
  • 4SRON Netherlands Institute for Space Research, 3584 CA Utrecht, The Netherlands
  • 5Japan Aerospace Exploration Agency (JAXA), 305-8505, Tsukuba, Japan
  • 6National Institute for Environmental Studies (NIES), 305-8506, Tsukuba, Japan
  • 7Jet Propulsion Laboratory (JPL), Pasadena, CA 91109, USA
  • 8Cooperative Institute for Research in the Atmosphere, Colorado State University (CSU), Fort Collins, CO 80523, USA
  • 9Centre for Atmospheric Chemistry, School of Earth, Atmospheric and Life Sciences, University of Wollongong, NSW, 2522, Australia
  • 10Finnish Meteorological Institute (FMI), 99600 Sodankylä, Finland
  • 11National Institute of Water and Atmospheric Research (NIWA), Lauder, New Zealand
  • 12Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK), IMK-ASF, 76021 Karlsruhe, Germany
  • 13Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK), IMK-IFU, 82467 Garmisch-Partenkirchen, Germany
  • 14Laboratoire d'Etudes du Rayonnement et de la Matière en Astrophysique (LERMA-IPSL), Sorbonne Université, CNRS, Observatoire de Paris, PSL Université, 75005 Paris, France
  • 15Department of Physics, University of Toronto, Toronto, ON, M5S 1A7, Canada
  • 16Royal Belgian Institute for Space Aeronomy (BIRA-IASB), 1180 Uccle, Belgium
  • 17Max Planck Institute for Biogeochemistry, 07745 Jena, Germany
  • 18Lehrstuhl für Physik der Atmosphäre, Ludwig-Maximilians-Universität München, 80333 München, Germany
  • 19Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt Oberpfaffenhofen, 82234 Weßling, Germany
  • 20Atmospheric Science Branch, National Aeronautics and Space Administration (NASA), Moffett Field, CA 94035, USA
  • 21California Institute of Technology, Pasadena, CA 91125, USA
  • 22European Space Agency (ESA), ESRIN, 00044 Frascati, Italy
  • 23European Centre for Medium-Range Weather Forecasts (ECMWF), Reading RG2 9AX, UK

Abstract. Satellite retrievals of column-averaged dry-air mole fractions of carbon dioxide (CO2) and methane (CH4), denoted XCO2 and XCH4, respectively, have been used in recent years to obtain information on natural and anthropogenic sources and sinks and for other applications such as comparisons with climate models. Here we present new data sets based on merging several individual satellite data products in order to generate consistent long-term Climate Data Records (CDRs) of these two Essential Climate Variables (ECVs). These ECV CDRs, which cover the time period 2003-2018, have been generated using an ensemble of data products from the satellite sensors SCIAMACHY/ENVISAT, TANSO-FTS/GOSAT and (for XCO2) for the first time also including data from the Orbiting Carbon Observatory-2 (OCO-2) satellite. Two types of products have been generated: (i) Level 2 (L2) products generated with the latest version of the “ensemble median algorithm” (EMMA) and (ii) Level 3 (L3) products obtained by gridding the corresponding L2 EMMA products to obtain a monthly 5ox5o data product in Obs4MIPs (Observations for Model Intercomparisons Project) format. The L2 products consists of daily NetCDF (Network Common Data Form) files, which contain in addition to the main parameters, i.e., XCO2 or XCH4, corresponding uncertainty estimates for random and potential systematic uncertainties and the averaging kernel for each single (quality-filtered) satellite observation. We describe the algorithms used to generate these data products and present quality assessment results based on comparisons with Total Carbon Column Observing Network (TCCON) ground-based retrievals. We found that the XCO2 Level 2 data set at the TCCON validation sites can be characterized by the following figures of merit (the corresponding values for the Level 3 product are listed in brackets): single observation random error (1-sigma): 1.29 ppm (monthly: 1.18 ppm); global bias: 0.20 ppm (0.18 ppm), spatio-temporal bias or “relative accuracy” (1-sigma): 0.66 ppm (0.70 ppm). The corresponding values for the XCH4 products are: single observation random error (1-sigma): 17.4 ppb (monthly: 8.7 ppb); global bias: −2.0 ppb (−2.9 ppb), spatio-temporal bias (1-sigma): 5.0 ppb (4.9 ppb). It has also been found that the data products exhibit very good long-term stability as no significant long-term bias trend has been identified. The new data sets have also been used to derive annual XCO2 and XCH4 growth rates, which are in reasonable to good agreement with growth rates from the National Oceanic and Atmospheric Administration (NOAA) based on marine surface observations. The presented ECV data sets are available (from December 2019 onwards) via the Climate Data Store (CDS, of the Copernicus Climate Change Service (C3S,

Maximilian Reuter et al.
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Short summary
We present new satellite-derived data sets of atmospheric carbon dioxide (CO2) and methane (CH4). The data products are column-averaged dry-air mole fractions of CO2 and CH4, denoted XCO2 and XCH4. The products cover the years 2003-2018 and are merged Level 2 (satellite footprints) and merged Level 3 (gridded at monthly time and 5 deg x 5 deg spatial resolution) products obtained from combining several individual sensor products. We present the merging algorithms and product validation results.
We present new satellite-derived data sets of atmospheric carbon dioxide (CO2) and methane...