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Inicio  /  Atmosphere  /  Vol: 9 Núm: 5 Par: May (2018)  /  Artículo
ARTÍCULO
TITULO

Global Atmospheric CO2 Concentrations Simulated by GEOS-Chem: Comparison with GOSAT, Carbon Tracker and Ground-Based Measurements

Yingying Jing    
Tianxing Wang    
Peng Zhang    
Lin Chen    
Na Xu and Ya Ma    

Resumen

Accurate quantification of the distribution and variability of atmospheric CO2 is crucial for a better understanding of global carbon cycle characteristics and climate change. Model simulation and observations are only two ways to globally estimate CO2 concentrations and fluxes. However, large uncertainties still exist. Therefore, quantifying the differences between model and observations is rather helpful for reducing their uncertainties and further improving model estimations of global CO2 sources and sinks. In this paper, the GEOS-Chem model was selected to simulate CO2 concentration and then compared with the Greenhouse Gases Observing Satellite (GOSAT) observations, CarbonTracker (CT) and the Total Carbon Column Observing Network (TCCON) measurements during 2009–2011 for quantitatively evaluating the uncertainties of CO2 simulation. The results revealed that the CO2 simulated from GEOS-Chem is in good agreement with other CO2 data sources, but some discrepancies exist including: (1) compared with GOSAT retrievals, modeled XCO2 from GEOS-Chem is somewhat overestimated, with 0.78 ppm on average; (2) compared with CT, the simulated XCO2 from GEOS-Chem is slightly underestimated at most regions, although their time series and correlation show pretty good consistency; (3) compared with the TCCON sites, modeled XCO2 is also underestimated within 1 ppm at most sites, except at Garmisch, Karlsruhe, Sodankylä and Ny-Ålesund. Overall, the results demonstrate that the modeled XCO2 is underestimated on average, however, obviously overestimated XCO2 from GEOS-Chem were found at high latitudes of the Northern Hemisphere in summer. These results are helpful for understanding the model uncertainties as well as to further improve the CO2 estimation.

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