SCIAMACHY CO over land and oceans: 2003–2007 interannual variability
- 1SRON Netherlands Institute for Space Research, Utrecht, The Netherlands
- 2Royal Netherlands Meteorological Institute (KNMI), de Bilt, The Netherlands
- 3Faculty of Earth and Life Sciences, VU University, Amsterdam, The Netherlands
Abstract. We present a new method to obtain accurate SCIAMACHY CO columns over clouded ocean scenes. Based on an improved version of the Iterative Maximum Likelihood Method (IMLM) retrieval algorithm, we now have retrieved five years of data over both land and clouded ocean scenes between 2003 and 2007. The ocean-cloud method uses the CH4 columns retrieved simultaneously with the CO columns to determine the cloud top height. The CH4 cloud top height is in good agreement with the FRESCO+ cloud top height determined from UV-VIS oxygen-A band measurements, providing confidence that the CH4 cloud top height is a good diagnostic of the cloud top height over (partially) clouded ocean scenes. The CO measurements over clouded ocean scenes have been compared with collocated modeled CO columns over the same clouds and agree well. Using clouded ocean scenes quadruples the number of useful CO measurements compared to land-only measurements.
The five-year CO data set over land and clouded ocean scenes presented here is based on an improved version of the IMLM algorithm which includes a more accurate determination of the random instrument-noise error for CO. This leads to a smaller spread in the differences between single CO measurements and the corresponding model values. The new version, IMLM version 7.4, also uses updated spectroscopic parameters for H2O and CH4 but this has only a minor impact on the retrieved CO columns. The five-year data set shows significant interannual variability over land and over clouded ocean scenes. Three examples are highlighted: the Asian outflow of pollution over the northern Pacific, the biomass-burning outflow over the Indian Ocean originating from Indonesia, and biomass burning in Brazil. In general there is good agreement between observed and modeled seasonal cycles and interannual variability.