[1] We extend the analysis of a global CH 4 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 CH 4 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 CH 4 distribution are consistent between SCIAMACHY observations and model simulations. However, the analysis suggests that SCIAMACHY CH 4 retrievals may have some bias that depends on latitude and season (up to $30 ppb). Large enhancements of column averaged CH 4 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°Â 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.

Abstract. The remote sensing of the atmospheric greenhouse gases methane (CH 4 ) and carbon dioxide (CO 2 ) in the troposphere from instrumentation aboard satellites is a new area of research. In this manuscript, results obtained from observations of the up-welling radiation in the near-infrared by SCIAMACHY on board ENVISAT are presented. Vertical columns of CH 4 , CO 2 and oxygen (O 2 ) have been retrieved and the (air or) O 2 -normalised CH 4 and CO 2 column amounts, the dry air column averaged mixing ratios XCH 4 and XCO 2 derived. In this manuscript the first results, obtained by using the version 0.4 of the Weighting Function Modified (WFM) DOAS retrieval algorithm applied to SCIAMACHY data, are described and compared with global models. For the set of individual cloud free measurements over land the standard deviation of the difference with respect to the models is in the range ∼100-200 ppbv (5-10%) for XCH 4 and ∼14-32 ppmv (4-9%) for XCO 2 . The interhemispheric difference of the methane mixing ratio, as determined from single day data, is in the range 30-110 ppbv and in reasonable agreement with the corresponding model data (48-71 ppbv). The weak inter-hemispheric difference of the CO 2 mixing ratio can also be detected with single day data. The spatiotemporal pattern of the measured and the modelled XCO 2 are in reasonable agreement. However, the amplitude of the difference between the maximum and the minimum for SCIAMACHY XCO 2 is about ±20 ppmv which is about a factor of four larger than the variability of the model data which is about ±5 ppmv. More studies are needed to explain the observed differences. The XCO 2 model field shows low CO 2 concentrations beginning of January 2003 over a spatially extended CO 2 sink region located Correspondence to: M. Buchwitz (michael.buchwitz@iup.physik.uni-bremen.de) in southern tropical/sub-tropical Africa. The SCIAMACHY data also show low CO 2 mixing ratios over this area. According to the model the sink region becomes a source region about six months later and exhibits higher mixing ratios. The SCIAMACHY and the model data over this region show a similar time dependence over the period from January to October 2003. These results indicate that for the first time a regional CO 2 surface source/sink region has been detected by measurements from space. The interpretation of the SCIAMACHY CO 2 and CH 4 measurements is difficult, e.g., because the error analysis of the currently implemented retrieval algorithm indicates that the retrieval errors are on the same order as the small greenhouse gas mixing ratio changes that are to be detected.

[1] The UV/Vis/near infrared spectrometer SCIAMACHY on board the European ENVISAT satellite enables total column retrieval of atmospheric methane with high sensitivity to the lower troposphere. The vertical column density of methane is converted to column averaged mixing ratio by using carbon dioxide retrievals as proxy for the probed atmospheric column. For this purpose, we apply concurrent total column measurements of CO 2 in combination with modeled column-averaged CO 2 mixing ratios. Possible systematic errors are discussed in detail while the precision error is 1.8% on average. This paper focuses on methane retrievals from January 2003 through December 2004. The measurements with global coverage over continents are compared with model results from the chemistry-transport model TM4. In the retrievals, the north-south gradient as well as regions with enhanced methane levels can be clearly identified. The highest abundances are found in the Red Basin of China, followed by northern South America, the Gangetic plains of India and central parts of Africa. Especially the abundances in northern South America and the Red Basin are generally higher than modeled. Further, we present the seasonal variations within the investigated time period. Peak values in Asia due to rice emissions are observed from August through October. We expand earlier investigations that suggest underestimated emissions in the tropics. It is shown that these underestimations show a seasonal behavior that peaks from August through December. The global measurements may be used for inverse modeling and are thus an important step towards better quantification of the methane budget.

Abstract. Imperfect representation of vertical mixing near the surface in atmospheric transport models leads to uncertainties in modelled tracer mixing ratios. When using the atmosphere as an integrator to derive surface-atmosphere exchange from mixing ratio observations made in the atmospheric boundary layer, this uncertainty has to be quantified and taken into account. A comparison between radiosondederived mixing heights and mixing heights derived from ECMWF meteorological data during May-June 2005 in Europe revealed random discrepancies of about 40% for the daytime with insignificant bias errors, and much larger values approaching 100% for nocturnal mixing layers with bias errors also exceeding 50%. The Stochastic Time Inverted Lagrangian Transport (STILT) model was used to propagate this uncertainty into CO 2 mixing ratio uncertainties, accounting for spatial and temporal error covariance. Average values of 3 ppm were found for the 2 month period, indicating that this represents a large fraction of the overall uncertainty. A pseudo data experiment shows that the error propagation with STILT avoids biases in flux retrievals when applied in inversions. The results indicate that flux inversions employing transport models based on current generation meteorological products have misrepresented an important part of the model error structure likely leading to biases in the estimated mean and uncertainties. We strongly recommend including the solution presented in this work: better, higher resolution atmospheric models, a proper description of correlated random errors, and a modification of the overall sampling strategy.