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-average dry-air mole fractions of CO₂ (XCO₂) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free run simulation 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 DOAS (BESD) algorithm during the year 2013 provides XCO₂ fields with an improved station-to-station bias deviation of 0.7 parts per million (ppm) compared to the free run (1.4 ppm) and an improved estimated precision of ~ 1 ppm compared to the used GOSAT data (3.4 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. Compared to its own analysis the CO₂ forecast shows synoptic skill for the largest scale weather patterns even up to day 5 according to the anomaly correlation coefficient.