1Department of Atmospheric and Oceanic Science, University of Maryland, 3433 Computer and Space Sciences Bldg, College Park, MD 20742, USA
2CNRM-GAME, Météo-France and CNRS, UMR3589, Toulouse, France
3Laboratoire d'Aérologie, CNRS, UMR5560, Toulouse, France
4Université de Toulouse, Toulouse, France
5Norwegian Institute of Air Research, Instituttveien 18, Kjeller 2027, Norway
Abstract. We study the Carbon Monoxide (CO) variability in the last decade measured by NASA's Atmospheric InfraRed Sounder (AIRS) on the Earth Observing Systems (EOS)/Aqua satellite and Europe's Infrared Atmospheric Sounder Interferometer (IASI) on MetOp platform. The focus of this study is to analyze CO variability and short-term trends separately for background CO and new emissions based on a new statistical approach. The AIRS Level 2 (L2) retrieval algorithm, as well as the IASI products from NOAA, utilizes cloud clearing to treat cloud contaminations in the signals; and this increases the data coverage significantly to a yield of more than 50% of the total measurements (Susskind et al., 2003). We first study if the cloud clearing affects CO retrievals and the subsequent trend studies by using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) (Ackerman et al., 1998) cloud mask to identify AIRS clear sky scenes. We then separate AIRS CO data into clear and cloud-cleared scenes and into background and new emissions, respectively. Furthermore, we carry out a similar study for the IASI CO and discuss the consistency with AIRS. We validate the CO variability of the emissions developed from AIRS against other emission inventory databases (i.e., Global Fire Emissions Database – GFED3 and the MACC/CityZEN UE – MACCity) and calculate that the correlation coefficients between the AIRS CO emissions and the emission inventory databases are 0.726 for the Northern Hemisphere (NH) and 0.915 for the Southern Hemisphere (SH).