1Max-Planck-Institute for Chemistry, Mainz, Germany
2Air Quality Research Division Environment Canada, Downsview, Ontario, Canada
3Cyprus Institute, Nicosia, Cyprus
4Jet Propulsion Laboratory, California Institute of Technology, Pasadena, USA
5Institute for Environmental Physics, University of Heidelberg, Germany
*now at: Institute for Atmospheric Physics, University Mainz, Germany
**now at: Institut für Physik der Atmosphäre, Deutsches Zentrum für Luft- und Raumfahrt (DLR), Germany
Abstract. We developed a new CO vertical column density product from near IR observations of the SCIAMACHY instrument onboard ENVISAT. For the correction of a temporally and spatially variable offset of the CO vertical column densities we apply a normalisation procedure based on coincident MOPITT (version 4) observations over the oceans. The resulting normalised SCIAMACHY CO data are well suited for the investigation of the CO distribution over continents, where important emission sources are located. We use only SCIAMACHY observations for effective cloud fractions below 20%. Since the remaining effects of clouds can still be large (up to 100%), we applied a cloud correction scheme which explicitly considers the cloud fraction, cloud top height and surface albedo of individual observations. We compared our new SCIAMACHY CO data set, and also observations from the MOPITT instrument, to the results from three global atmospheric chemistry models (MATCH, EMAC at low and high resolution, and GEOS-Chem); the focus of this comparison is on regions with strong CO emissions (from biomass burning or anthropogenic sources). The comparison indicates that over most of these regions the seasonal cycle is generally captured well but the simulated CO vertical column densities are systematically smaller than those from the satellite observations, in particular with respect to SCIAMACHY observations. Because of the high sensitivity of the SCIAMACHY observations to the lowest part of the atmosphere, this indicates that especially close to the surface the model simulations systematically underestimate the true atmospheric CO concentrations, probably caused by an underestimation of CO emissions by current emission inventories. For some biomass burning regions, however, such as Central Africa in July–August, model results are also found to be higher than the satellite observations.