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This study presents results from the European Centre for Medium-Range WeatherForecasts (ECMWF) carbon dioxide (CO2) analysis system where theatmospheric CO2 is controlled through the assimilation ofcolumn-averaged dry-air mole fractions of CO2 (XCO2) from theGreenhouse gases Observing Satellite (GOSAT). The analysis is compared toa free-run simulation (without assimilation of XCO2), and they areboth evaluated against XCO2 data from the Total Carbon ColumnObserving Network (TCCON). We show that the assimilation of the GOSATXCO2 product from the Bremen Optimal Estimation Differential OpticalAbsorption Spectroscopy (BESD) algorithm during the year 2013 providesXCO2 fields with an improved mean absolute error of 0.6 parts permillion (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 GOSATBESD data (3.3  ppm). We also show that the analysis has skill forsynoptic situations in the vicinity of frontal systems, where the GOSATretrievals are sparse due to cloud contamination. We finally computed the10-day forecast from each analysis at 00:00  UTC, and wedemonstrate that the CO2 forecast shows synoptic skill for thelargest-scale weather patterns (of the order of 1000  km) even up today 5 compared to its own analysis

atmospheric chemistry and physics

Ability of the 4-D-Var analysis of the GOSAT BESD XCO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; retrievals to characterize atmospheric CO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; at large and synoptic scales