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
Received: 23 Jul 2015 – Discussion started: 28 Sep 2015
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.
Revised: 17 Dec 2015 – Accepted: 16 Jan 2016 – Published: 12 Feb 2016
Massart, S., Agustí-Panareda, A., Heymann, J., Buchwitz, M., Chevallier, F., Reuter, M., Hilker, M., Burrows, J. P., Deutscher, N. M., Feist, D. G., Hase, F., Sussmann, R., Desmet, F., Dubey, M. K., Griffith, D. W. T., Kivi, R., Petri, C., Schneider, M., and Velazco, V. A.: Ability of the 4-D-Var analysis of the GOSAT BESD XCO2 retrievals to characterize atmospheric CO2 at large and synoptic scales, Atmos. Chem. Phys., 16, 1653-1671, doi:10.5194/acp-16-1653-2016, 2016.