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.
Highlights
Carbon in the atmosphere is present mostly in the form of carbon dioxide (CO2)
In the case of atmospheric CO2, model errors are related to potentially inaccurate surface fluxes or transport
This article demonstrates the benefit of the assimilation of XCO2 data derived from the Greenhouse gases Observing Satellite (GOSAT) by intermediate versions of the Bremen Optimal Estimation DOAS (BESD) algorithm of the University of Bremen (UoB)
Summary
Carbon in the atmosphere is present mostly in the form of carbon dioxide (CO2). Its amount is relatively small compared to the amount of carbon present in other reservoirs like the ocean (Ciais et al, 2013). Atmospheric CO2 is easier to monitor by measurements than other carbon reservoirs. To improve the monitoring of atmospheric CO2, one can combine atmospheric CO2 measurements with a numerical model. This paper describes such a system, which has been developed for the Copernicus Atmosphere Monitoring Service (CAMS)
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