Articles | Volume 16, issue 3
Atmos. Chem. Phys., 16, 1653–1671, 2016
Atmos. Chem. Phys., 16, 1653–1671, 2016

Research article 12 Feb 2016

Research article | 12 Feb 2016

Ability of the 4-D-Var analysis of the GOSAT BESD XCO2 retrievals to characterize atmospheric CO2 at large and synoptic scales

Sébastien Massart1, Anna Agustí-Panareda1, Jens Heymann2, Michael Buchwitz2, Frédéric Chevallier3, Maximilian Reuter2, Michael Hilker2, John P. Burrows2, Nicholas M. Deutscher2,9, Dietrich G. Feist4, Frank Hase5, Ralf Sussmann6, Filip Desmet7, Manvendra K. Dubey8, David W. T. Griffith9, Rigel Kivi10, Christof Petri2, Matthias Schneider5, and Voltaire A. Velazco9 Sébastien Massart et al.
  • 1European Centre for Medium-Range Weather Forecasts, Reading, UK
  • 2Institute of Environmental Physics, University of Bremen, Bremen, Germany
  • 3Laboratoire des Sciences du Climat et de l'Environnement, CEA-CNRS-UVSQ, IPSL, Gif sur Yvette, France
  • 4Max Planck Institute for Biogeochemistry, Jena, Germany
  • 5Karlsruhe Institute of Technology, IMK-ASF, Karlsruhe, Germany
  • 6Karlsruhe Institute of Technology, IMK-IFU, Garmisch-Partenkirchen, Germany
  • 7Department of Chemistry, University of Antwerp, Antwerp, Belgium
  • 8Earth and Environmental Sciences, Los Alamos National Laboratory, Los Alamos, USA
  • 9Centre for Atmospheric Chemistry, School of Chemistry, University of Wollongong, Wollongong, Australia
  • 10Finnish Meteorological Institute, Arctic Research, Sodankylä, Finland

Abstract. This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO2) analysis system where the atmospheric CO2 is controlled through the assimilation of column-averaged dry-air mole fractions of CO2 (XCO2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO2), and they are both evaluated against XCO2 data from the Total Carbon Column Observing Network (TCCON). We show that the assimilation of the GOSAT XCO2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO2 fields with an improved mean absolute error of 0.6 parts per million (ppm) and an improved station-to-station bias deviation of 0.7  ppm compared to the free run (1.1 and 1.4  ppm, respectively) and an improved estimated precision of 1  ppm compared to the GOSAT BESD data (3.3  ppm). We also show that the analysis has skill for synoptic situations in the vicinity of frontal systems, where the GOSAT retrievals are sparse due to cloud contamination. We finally computed the 10-day forecast from each analysis at 00:00  UTC, and we demonstrate that the CO2 forecast shows synoptic skill for the largest-scale weather patterns (of the order of 1000  km) even up to day 5 compared to its own analysis.

Short summary
This study presents the European Centre for Medium-Range Weather Forecasts (ECMWF) monitoring of atmospheric CO2 using measurements from the Greenhouse gases Observing Satellite (GOSAT). We show that the modelled CO2 has a better precision than standard CO2 satellite products compared to ground-based measurements. We also present the CO2 forecast based on our best knowledge of the atmospheric CO2 distribution. We show that it has skill to forecast the largest scale CO2 patterns up to day 5.
Final-revised paper