Published in

European Geosciences Union, Atmospheric Chemistry and Physics, 3(16), p. 1653-1671, 2016

DOI: 10.5194/acp-16-1653-2016

European Geosciences Union, Atmospheric Chemistry and Physics Discussions, 18(15), p. 26273-26313

DOI: 10.5194/acpd-15-26273-2015

Links

Tools

Export citation

Search in Google Scholar

Ability of the 4-D-Var analysis of the GOSAT BESD XCO<sub>2</sub> retrievals to characterize atmospheric CO<sub>2</sub> at large and synoptic scales

Journal article published in 2016 by Sébastien Massart ORCID, Anna Agustí-Panareda, Jens Heymann, Michael Buchwitz ORCID, Frédéric Chevallier ORCID, Maximilian Reuter ORCID, Michael Hilker, John P. Burrows ORCID, Nicholas M. Deutscher, Dietrich G. Feist ORCID, Frank Hase, Ralf Sussmann, Filip Desmet, Manvendra K. Dubey ORCID, David W. T. Griffith ORCID and other authors.
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

Full text: Download

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Green circle
Published version: archiving allowed
Upload
Policy details
Data provided by SHERPA/RoMEO

Abstract

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.