This is part of DOI:10.1029/2005JD006235.
Composition and Chemistry
Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on inverse model simulations
Article first published online: 24 JAN 2007
Copyright 2007 by the American Geophysical Union.
Journal of Geophysical Research: Atmospheres (1984–2012)
Volume 112, Issue D2, 27 January 2007
How to Cite
2007), Satellite chartography of atmospheric methane from SCIAMACHY on board ENVISAT: 2. Evaluation based on inverse model simulations, J. Geophys. Res., 112, D02304, doi:10.1029/2006JD007268., et al. (
- Issue published online: 24 JAN 2007
- Article first published online: 24 JAN 2007
- Manuscript Accepted: 18 AUG 2006
- Manuscript Revised: 24 MAY 2006
- Manuscript Received: 7 MAR 2006
- atmospheric CH4;
- inverse modeling;
 We extend the analysis of a global CH4 data set retrieved from SCIAMACHY (Frankenberg et al., 2006) by making a detailed comparison with inverse TM5 model simulations for 2003 that are optimized versus high accuracy CH4 surface measurements from the NOAA ESRL network. The comparison of column averaged mixing ratios over remote continental and oceanic regions shows that major features of the atmospheric CH4 distribution are consistent between SCIAMACHY observations and model simulations. However, the analysis suggests that SCIAMACHY CH4 retrievals may have some bias that depends on latitude and season (up to ∼30 ppb). Large enhancements of column averaged CH4 mixing ratios (∼50–100 ppb) are observed and modeled over India, Southeast Asia, and the tropical regions of South America, and Africa. We present a detailed comparison of observed spatial patterns and their seasonal evolution with TM5 1° × 1° zoom simulations over these regions. Application of a new wetland inventory leads to a significant improvement in the agreement between SCIAMACHY retrievals and model simulations over the Amazon basin during the first half of the year. Furthermore, we present an initial coupled inversion that simultaneously uses the surface and satellite observations and that allows the inverse system to compensate for the potential systematic bias. The results suggest significantly greater tropical emissions compared to either the a priori estimates or the inversion based on the surface measurements only. Emissions from rice paddies in India and Southeast Asia are relatively well constrained by the SCIAMACHY data and are slightly reduced by the inversion.