Abstract and subjects
This study presents results from the European Centre for Medium-Range Weather Forecasts (ECMWF) carbon dioxide (CO 2) analysis system where the atmospheric CO 2 is controlled through the assimilation of column-averaged dry-air mole fractions of CO 2 (XCO 2) from the Greenhouse gases Observing Satellite (GOSAT). The analysis is compared to a free-run simulation (without assimilation of XCO 2), and they are both evaluated against XCO 2 data from the Total Carbon Column Observing Network (TC-CON). We show that the assimilation of the GOSAT XCO 2 product from the Bremen Optimal Estimation Differential Optical Absorption Spectroscopy (BESD) algorithm during the year 2013 provides XCO 2 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 CO 2 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.