Journal cover Journal topic
Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union
Journal topic

Journal metrics

Journal metrics

  • IF value: 3.400 IF 3.400
  • IF 5-year value: 3.841 IF 5-year
  • CiteScore value: 3.71 CiteScore
  • SNIP value: 1.472 SNIP 1.472
  • IPP value: 3.57 IPP 3.57
  • SJR value: 1.770 SJR 1.770
  • Scimago H <br class='hide-on-tablet hide-on-mobile'>index value: 70 Scimago H
    index 70
  • h5-index value: 49 h5-index 49
Discussion papers
© 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 28 Oct 2019

Submitted as: research article | 28 Oct 2019

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

Characterization of OCO-2 and ACOS-GOSAT biases and errors for CO2 flux estimates

Susan S. Kulawik1, Sean Crowell2, David Baker3, Junjie Liu4, Kathryn McKain5,6, Colm Sweeney5, Sebastien C. Biraud7, Steve Wofsy8, Christopher W. O'Dell3, Paul O. Wennberg9, Debra Wunch10, Coleen M. Roehl9, Nicholas M. Deutscher11,12, Matthäus Kiel4, David W. T. Griffith11, Voltaire A. Velazco11,13, Justus Notholt12, Thorsten Warneke12, Christof Petri12, Martine De Mazière14, Mahesh K. Sha14, Ralf Sussmann15, Markus Rettinger16, Dave F. Pollard17, Isamu Morino18, Osamu Uchino18, Frank Hase19, Dietrich G. Feist20,21,22, Sébastien Roche10, Kimberly Strong10, Rigel Kivi23, Laura Iraci24, Kei Shiomi25, Manvendra K. Dubey26, Eliezer Sepulveda27, Omaira Elena Garcia Rodriguez27, Yao Té28, Pascal Jeseck28, Pauli Heikkinen23, Edward J. Dlugokencky5, Michael R. Gunson4, Annmarie Eldering4, David Crisp4, Brendan Fisher4, and Gregory B. Osterman4 Susan S. Kulawik et al.
  • 1BAER Institute, 625 2nd Street, Suite 209, Petaluma, CA, USA
  • 2GeoCarb Mission, University of Oklahoma, Norman, OK, USA
  • 3Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA
  • 4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA, USA
  • 5National Oceanic and Atmospheric Administration, Earth System Research Laboratory, Boulder, CO, USA
  • 6University of Colorado, Cooperative Institute for Research in Environmental Sciences, Boulder, CO, USA
  • 7Lawrence Berkeley National Laboratory, Earth Science Division, Berkeley, CA, USA
  • 8Harvard University, Cambridge, MA, USA
  • 9Division of Geological and Planetary Sciences, California Institute of Technology, Pasadena, CA, USA
  • 10Department of Physics, University of Toronto, Toronto, Canada
  • 11Centre for Atmospheric Chemistry, School of Earth, Atmosphere and Life Sciences, University of Wollongong, Wollongong, NSW, Australia
  • 12University of Bremen, Bremen, Germany
  • 13Oscar M. Lopez Center for Climate Change Adaptation and Disaster Risk Mgmt. Foundation Inc., Manila, Philippines
  • 14Royal Belgian Institute for Space Aeronomy, Brussels, Belgium
  • 15Karlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, Germany
  • 16Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK-IFU), Garmisch-Partenkirchen, Germany
  • 17National Institute of Water and Atmospheric Research, Lauder, New Zealand
  • 18National Institute for Environmental Studies (NIES), Tsukuba, Japan
  • 19Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, Germany
  • 20Lehrstuhl für Physik der Atmosphäre, Ludwig-Maximilians-Universität München, Munich, Germany
  • 21Deutsches Zentrum für Luft und Raumfahrt (DLR), Institut für Physik der Atmosphäre, Oberpfaffenhofen, Germany
  • 22Max Planck Institute for Biogeochemistry, Jena, Germany
  • 23Finnish Meteorological Institute, Sodankylä, Finland
  • 24NASA Ames Research Center, Moffett Field, CA, USA
  • 25EORC Earth Observation Research Center, JAXA Japan Aerospace Exploration Agency
  • 26Los Alamos National Laboratory, Los Alamos, NM, USA
  • 27Izaña Atmospheric Research Center, Meteorological State Agency of Spain (AEMet), Tenerife, Spain
  • 28LERMA-IPSL, Sorbonne Université, CNRS, Observatoire de Paris, Université PSL, Paris, France

Abstract. We characterize the magnitude of seasonally and spatially varying biases in the National Aeronautics and Space Administration (NASA) Orbiting Carbon Observatory-2 (OCO-2) Version 8 (v8) and the Atmospheric CO2 Observations from Space (ACOS) Greenhouse Gas Observing SATellite (GOSAT) version 7.3 (v7.3) satellite CO2 retrievals by comparisons to measurements collected by the Total Carbon Column Observing Network (TCCON), Atmospheric Tomography (ATom) experiment, and National Oceanic and Atmospheric Administration (NOAA) Earth System Research Laboratory (ESRL) and U. S. Department of Energy (DOE) aircraft, and surface stations. Although the ACOS-GOSAT estimates of the column averaged carbon dioxide (CO2) dry air mole fraction (XCO2) have larger random errors than the OCO-2 XCO2 estimates, and the space-based estimates over land have larger random errors than those over ocean, the systematic errors are similar across both satellites and surface types, 0.6 ± 0.1 ppm. We find similar estimates of systematic error whether dynamic versus geometric coincidences or ESRL/DOE aircraft versus TCCON are used for validation (over land), once validation and co-location errors are accounted for. We also find that areas with sparse throughput of good quality data (due to quality flags and preprocessor selection) over land have ~double the error of regions of high-throughput of good quality data. We characterize both raw and bias-corrected results, finding that bias correction improves systematic errors by a factor of 2 for land observations and improves errors by ~ 0.2 ppm for ocean. We validate the lowermost tropospheric (LMT) product for OCO-2 and ACOS-GOSAT by comparison to aircraft and surface sites, finding systematic errors of ~ 1.1 ppm, while having 2–3 times the variability of XCO2. We characterize the time and distance scales of correlations for OCO-2 XCO2 errors, and find error correlations on scales of 0.3 degrees, 5–10 degrees, and 60 days. We find comparable scale lengths for the bias correction term. Assimilation of the OCO-2 bias correction term is used to estimate flux errors resulting from OCO-2 seasonal biases, finding annual flux errors on the order of 0.3 and 0.4 PgC/yr for Transcom-3 ocean and land regions, respectively.

Susan S. Kulawik et al.
Interactive discussion
Status: final response (author comments only)
Status: final response (author comments only)
AC: Author comment | RC: Referee comment | SC: Short comment | EC: Editor comment
Susan S. Kulawik et al.
Susan S. Kulawik et al.
Total article views: 261 (including HTML, PDF, and XML)
HTML PDF XML Total BibTeX EndNote
176 79 6 261 5 7
  • HTML: 176
  • PDF: 79
  • XML: 6
  • Total: 261
  • BibTeX: 5
  • EndNote: 7
Views and downloads (calculated since 28 Oct 2019)
Cumulative views and downloads (calculated since 28 Oct 2019)
Viewed (geographical distribution)  
Total article views: 233 (including HTML, PDF, and XML) Thereof 232 with geography defined and 1 with unknown origin.
Country # Views %
  • 1
No saved metrics found.
No discussed metrics found.
Latest update: 23 Jan 2020
Publications Copernicus
Short summary
This paper provides a benchmark of OCO-2 v8 and ACOS-GOSAT v7.3 XCO2 and lowermost tropospheric (LMT) errors. The paper focuses on the systematic errors and subtracts out validation, co-location, and random errors, looks at the correlation scale-length (spatially and temporally) of systematic errors, finding that the scale lengths are similar to bias correction scale-lengths. The assimilates of the bias correction term is used to place an error on fluxes estimates.
This paper provides a benchmark of OCO-2 v8 and ACOS-GOSAT v7.3 XCO2 and lowermost tropospheric...