1Global Modeling and Assimilation Office, Goddard Space Flight Center, Greenbelt, MD, USA
2Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA
3Dept of Physics, University of Maryland Baltimore County, Baltimore, MD, USA
4Núcleo de Meio Ambiente, Universidade Federal do Pará, Belém, PA, Brazil
*now at: Joint Center for Earth Systems Technology, University of Maryland Baltimore County, Baltimore, MD, USA
Abstract. Atmospheric CO2 retrievals with peak sensitivity in the mid- to lower troposphere from the Atmospheric Infrared Sounder (AIRS) have been assimilated into the Global Modeling and Assimilation Office (GMAO) constituent assimilation system for the period 1 January 2005 to 31 December 2006. A corresponding model simulation, using identical initial conditions, circulation, and CO2 boundary fluxes was also completed. The analyzed and simulated CO2 fields are compared with surface measurements globally and aircraft measurements over North America. Surface level monthly mean CO2 values show a marked improvement due to the assimilation in the Southern Hemisphere, while less consistent improvements are seen in the Northern Hemisphere. Mean differences with aircraft observations are reduced at all levels, with the largest decrease occurring in the mid-troposphere. The difference standard deviations are reduced slightly at all levels over the ocean, and all levels except the surface layer over land. These initial experiments indicate that the retrieved channel contains useful information on CO2 in the middle to lower troposphere. However, the benefits of assimilating these data are reduced over the land surface, where concentrations are dominated by uncertain local fluxes and where the observation density is quite low. Away from these regions, the study demonstrates the power of the data assimilation technique for evaluating data that are not co-located, in that the improvements in mid-tropospheric CO2 by the sparsely distributed partial-column retrievals are transported by the model to the fixed in-situ surface observation locations in more remote areas.