5.1. Two-Year Average Maps of Methane Abundances
 Figure 4a depicts the two-year average VMR of methane. A simple cloud filter discarding retrievals with an effective cloud top height of over 1 km was applied. The overall variations are rather small, most deviations from the global mean being well below 5%. The most striking feature is the clear north-south gradient caused by higher natural and anthropogenic methane emissions in the northern hemisphere. The entire Australian continent as well as southern parts of Africa and South America show very low methane abundances compared to the mid-latitudes. Moreover, there is little scatter on smaller scales, i.e., most pixels show a strong spatial correlation to surrounding areas. This is mostly due to the relatively long lifetime of methane, leading to extended areas of enhancement even for point sources. We restrict the long-term average plots to landmasses since a reasonable retrieval over the ocean requires either low lying clouds, sun glint or a very rough ocean surface to exhibit a sufficiently high albedo. Thus, long-term maps over the ocean would be strongly biased to certain seasons. In addition, the statistical uncertainties (1-σ) in the SCIAMACHY averages (as defined in section 3) are shown in Figure 4c. Higher uncertainties are mainly due to low sampling frequency or low surface albedo (e.g., parts of Russia and Canada as well as retrievals at coastlines). Figure 4b shows the corresponding TM4 model output. Both the north-south gradient and the main regions with enhancements predicted by the model are on the whole consistent with the measurements. In northern South America and central Africa, however, the retrievals reflect higher methane abundances than the model. As already mentioned, disparities in the light path due to high-frequency spectral structures in surface or cloud albedo are a possible source of bias. This bias would therefore also depend on the surface type and albedo. However, there is no indication for such behavior in the two-year average: The retrievals vary smoothly over different surface types.
Figure 4. (a) Two-year average of column averaged mixing ratios (in ppb) of methane retrieved from SCIAMACHY from January 2003 through December 2004. The measurements have been gridded with a spatial resolution of 0.5° longitude times 0.5° latitude. (b) Corresponding (i.e., sampled at the exact place and time of the measurements) TM4 model results. (c) Estimated statistical uncertainty associated with the SCIAMACHY retrieval (see section 3.3 for details).
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 Figure 5 depicts the same SCIAMACHY retrievals as in Figure 4a but with a less strict cloud filter (effective cloud top height below 2.5 km) and without discarding retrievals over the ocean. Thereby we achieve full global coverage. The north-south gradient can also be well observed over the ocean. Most methane abundances, surprisingly, are higher than in Figure 4a. However, the absolute values have to be interpreted with care because, as discussed in section 3, clouds are a potential source of bias, especially if no strict cloud filter is applied. At the continent-ocean border, the frequency of reasonable retrievals changes abruptly (less reliable measurements over the ocean), often resulting in a rather steep gradient in the observed mean of methane abundances. We see that clouds do not always hinder a reasonable retrieval, contributing, on the contrary, to meaningful measurements if a suitable proxy for the light-path is applied. In the future, the combination of information on effective cloud top height and cloud fraction as well as surface and cloud albedos will facilitate the quantification of the effect of clouds. At present, we minimize the errors induced by partial cloud cover by confining our analysis to retrievals that comply with the strict cloud filter criteria.
Figure 5. Two-year average of methane VMR as retrieved by SCIAMACHY with less strict cloud filter (effective cloud top height less than 2.5 km) and gridded on 1° longitude times 1° latitude.
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 In Figure 4a, various regions with enhanced methane abundances (on larger spatial scales) can be detected. Retrieved VMRs in Russia show substantial variation and are hard to interpret due to the absence of strong spatial correlation of high methane abundances to surrounding pixels and a relatively high uncertainty in the retrievals (mostly due to low surface albedo, see Figure 4c). Thus, we focus on regions where uncertainties in the average are low and signals from methane emissions strong.
 There are regions with substantially enhanced CH4 VMRs indicating strong regional methane emissions. The most outstanding region is the Red Basin in China showing the highest mean abundances on the entire globe (please note that the highest values are depicted in white). Further, there are high VMRs in northern parts of South America (not in line with the modeled abundances in Figure 4a), central Africa and also slight enhancements in the U.S.A. A zoom into these regions with a narrower color-scale is shown in Figure 6.
Figure 6. Global methane mean column averaged mixing ratios (in ppb). The same time period and spatial resolution as in Figure 4a is used but with a different color scale and with focus on four specific geographical regions, namely, (a) United States, (b) Asia, (c) South America, and (d) Africa. Note that the highest abundances are shown in white whereas pixels with missing data (e.g., over ocean) are shown in gray. Only pixels with a maximum statistical error of 8 ppb are depicted.
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 In the United States (Figure 6a), the highest abundances are observed south of the Great lakes. According to the EDGAR base [Olivier and Berdowski, 2001], emissions from coal mining are very strong in this regions, but other anthropogenic activities also play a significant role. The Rocky mountains show lower VMRs partly due to high surface elevation that results directly in a reduced column averaged VMR (due to the relatively stronger weight of the stratosphere in case of low surface pressure). Also, this region exhibits few sources of methane. Strong variations in coastal areas may be related to an overall higher uncertainty in the averaged mixing ratios in these regions (see Figure 4c) due to lower albedos or a smaller number of measurements within these pixels. The same could hold for the peculiar enhancement in New Mexico at approximately 36.75°N and 108°W, but further investigations are necessary to ascertain the cause of the enhancement.
 In Asia (Figure 6b), the highest methane abundances by far are found, particularly in the Red Basin (major cities: Chengdu and Chongqing) and regions further south-east. Rice paddies, coal mining, domestic ruminants and waste handling can be assumed to constitute the main sources. In India, the entire Gangetic plains (rice paddies, domestic ruminants) show higher methane VMRs than the surrounding areas. Also in Thailand, Laos and Vietnam, substantial enhancements are observed. Inverse modeling is required to attribute the emissions quantitatively to different sources. In Southeast Asia, rice paddies, wetlands, landfills and domestic ruminants are the strongest sources according to Olivier and Berdowski .
 In South America (Figure 6c), the highest abundances are found in Venezuela (close to Lake Maracaibo), Columbia, Ecuador and parts of Peru. Also parts of Brazil in the Amazon basin show high VMRs. Particularly surprising are the high values in Venezuela and Columbia: The observed VMRs are much higher than those modeled by TM4 (based on the emission inventories in Table 2). In Colombia, the major part of emissions is supposed to be attributable to digestive processes of dairy and meat cattle [Gonzalez and Rodriguez, 2000; Olivier and Berdowski, 2001]. However, these sources are not expected to exhibit the strong seasonal variations that we observe in methane abundances over Colombia (see section 5.2). One might argue that high CH4 concentrations may be transported into this region. However, the effect of emissions on atmospheric concentrations would be expected to diminish away from source regions. Since the CH4 abundances are highest in Colombia/Venezuela, they have to be caused mainly by local emissions, while transport can only explain part of the enhancements.
 In Africa (Figure 6d), rather homogenous enhancements between 4°S and 8°N (west of 40°E) are found. Below 8°S, methane is clearly depleted. In Sudan, a region with distinctively high methane emissions from wetlands is apparent in the SCIAMACHY retrievals as predicted by wetland models [Walter et al., 2001]. Surprisingly, on the other hand, there are also enhanced VMRs in Ethiopia.
5.2. Seasonal Variations in Global Methane Abundances
 Seasonal variations in methane abundances may be induced due to several factors apart from emissions with strong seasonal patterns such as rice emissions or biomass burning. Variations in predominant wind fields caused by ITCZ movements, for instance, or changes in OH fields can also cause strong seasonal variations in methane abundances. For a first quantitative analysis of the time dependence of the methane distribution, we developed time series in different geographic locations. For the SCIAMACHY retrieval and the TM4 model results, we applied a 30 day box filter (±15 days) to smooth the data. In Figure 7, the temporal behavior over Asia (20–30°N/80–120°E), tropical Africa (10°S–15°N/20°W–60°E) and tropical South America (10°S–15°N/40–90°W) are shown. In Asia, the main seasonality is caused by rice emissions, which are very intense during a relatively short time period. As can be seen in the upper panel of Figure 7, methane enhancements due to rice emissions (peak months are August through October) seem to occur approximately 1 month earlier than given in the model, although the magnitude is very similar (given 60 Tg yr−1 rice emissions in the model). Higher methane abundances are also seen to decline earlier, resulting in markedly lower SCIAMACHY retrievals in November as compared to the model. These results are consistent with the findings of Chen  who also found a time shift in methane emissions with respect to current emission inventories. The start of the rice emissions is also reflected in an enhanced standard deviation of the SCIAMACHY retrievals since local sources induce large scatter.
Figure 7. Time series of SCIAMACHY measurements in different geographic locations smoothed in time with a 30 day box filter (±15 days). In the upper panels, the SCIAMACHY retrieval (corrected with model CO2 fields) and the TM4 model results (using averaging kernel correction) are depicted. The respective bottom panels show the total number of SCIAMACHY retrievals (in gray) and their standard deviation (black dots, not to be confused with the 1-σ precision error) as used for the calculation of the 30-day running average. For the averaging, only retrievals in areas with less than 1 km surface elevation are considered. The exact geographic locations of the areas are 20–30°N/80–120°E for Asia, 10°S–15°N/20°W–60°E for tropical Africa and 10°S–15°N/40–90°W for tropical South America. Gaps in the time series are caused by missing SCIAMACHY data and the cloud filter (not all SCIAMACHY data were available and due to the ice layer on channels 7 and 8, SCIAMACHY switches to the decontamination mode from time to time).
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 Especially for modeling purposes, the temporal evolution of sources is of primary importance. These emissions largely determine methane abundances in the entire Asian region and are also transported towards Africa [Frankenberg et al., 2005a] or North America. Different regions can be affected depending on predominant wind fields during the time of peak emissions.
 Also in tropical Africa, we observe a phase shift between SCIAMACHY retrievals and model simulations. The absolute abundances are in general on the same order but the measurements show higher mixing ratios starting already in July/August. In tropical South America, measurements are higher than the model throughout the year. However, as in Frankenberg et al. [2005a], the largest discrepancies are found from August through December, especially in 2003. For the rest of the year, the temporal evolution of measurement and model is very similar apart from an offset of ≈15–30 ppb at any given time. Part of the seasonality, such as the decline in methane abundances after April, is due to movements of the ITCZ. August through September is the typical biomass burning season in tropical South America as is evident in observations of increased carbon monoxide abundances [Bremer et al., 2004; Frankenberg et al., 2005c]. Depending on vegetation and fire type, molar emission ratios (CH4/CO2) of biomass burning range from 0.4% to 1.3% [Andreae and Merlet, 2001] with indications of even higher ratios in tropical rainforest areas [Alvala and Kirchhoff, 1998]. If the emission ratio of methane is exactly the same as the ratio of the background concentrations (≈0.5%), the methane emissions would be indiscernible to us if the particular biomass burning event is not correctly included in the CO2 model. Thus, biomass burning can be a source of differences between model and measurements, especially in direct proximity of the fires.
 Figure 8 shows time series for the Sahara and Australia. In general, the retrieval over the Sahara does not show large discrepancies compared to the model. Both capture enhanced methane abundances starting in July 2003 but with a higher magnitude in the measurement. These enhancements are accompanied by a relatively high standard deviation in the measurements. Because of the lack of local sources, this suggests that the enhancements are induced by transport, either from Europe or from Asia.
Figure 8. Time series of SCIAMACHY measurements over the Sahara and Australia. The same criteria as in Figure 4 apply. The exact geographic locations of the areas are 20–30°N/0–20°E for the Sahara and 20°–30°S for the complete Australian continent. The latitudinal bins have been chosen to have comparable solar zenith angles (with a phase shift of 6 months) for both regions. The respective bottom panels depict the total number of SCIAMACHY retrievals and their standard deviation as used for the calculation of the 30-day running average.
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 In Australia, there are systematic differences between model and measurement. The SCIAMACHY retrieval shows seasonal variations that are not predicted by the model. The model simulations show close agreement with NOAA-CMDL surface measurements at Cape Grim, Samoa and Easter Island, and are also consistent with FTIR CH4 column measurements at Wollongong [Dils et al., 2005]. The sinusoidal variations in time have a minimum in June and a maximum in December, albeit with a relatively low amplitude (±1.2%). A retrieval dependence on solar zenith angle cannot explain the variations as this should result in a similar behavior over the Sahara (with a 6-month phase shift), which is not the case (Figure 8). Potential alternative explanations are unaccounted variations in CO2 or a hitherto unknown retrieval bias.
 A global view of the seasonal cycle gives a better overview of the seasonal cycles in different geographic locations. Figure 9 shows seasonal plots of retrieved and simulated global methane VMRs.
Figure 9. Seasonal plots (December/January/February, March/April/May, June/July/August and September/October/November) as retrieved by SCIAMACHY in the the years 2003 and 2004 (averaged over 1° longitude and 1° latitude). For comparison, the TM4 model results for the same time periods are depicted on the right-hand side. Only pixels with an estimated statistical error of less than 15 ppb are depicted. The statistical retrieval uncertainties in the SCIAMACHY averages are shown in the lower panels (Figures 9i–9l).
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 The associated CO2 model fields that were used for the computation of methane column averaged mixing ratios are depicted in Figure 10. It should be noted that the retrievals in the seasonal mean are sometimes only single snapshots of the methane distribution. Especially over the ocean, only few valid measurements are available due to the low ocean albedo in the near infrared, causing, in most cases, an unacceptably reduced signal-to-noise ratio. Hence, care has to be taken in interpreting the seasonal means and, the 1-σ uncertainties given in Figures 9i–9l have to be considered. Enhanced methane VMRs south of Iceland appear in the June/July/August average. From the model simulation we could infer that the enhancement is largely due to a transport event on 18–20 July 2003.
Figure 10. Seasonal plots (December/January/February, March/April/May, June/July/August and September/October/November, derived from TM3-MPI) of the modeled column averaged CO2 mixing ratio as used for the calculation of the mixing ratio of CH4.
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 The most pronounced feature we observe is due to the temporal variation of methane emissions from rice paddies in Southeast Asia with typical maxima from August through October, resulting in higher VMRs in the periods June–August and September–November. The differences between model and measurement in this case partly result from the deviation of the observed from the modeled temporal evolution (see Figure 7). In Africa, the highest methane abundances are situated towards the south in Dec/Jan/Feb, while they are strongest and situated further northward in Sep/Oct/Nov. This, on the whole, corresponds well to the temporal evolution and spatial distribution of wetland emissions [Shindell et al., 2004].
 In South America, the highest abundances are found from January through April, in line with current wetland distributions. However, in northern parts of South America the highest abundances appear from September through November, as also evident in the two-year mean (see Figure 6c). The current emission inventory for Colombia and Venezuela (together 4.9 Tg yr−1 given by the EDGAR (Emission Database for Global Atmospheric Research) database [Olivier and Berdowski, 2001]) can hardly explain such abundances. The seasonality of the signal calls for an exclusion of more or less constant sources such as energy related emissions or cattle breeding. The total rice production in Colombia is only 2% of that in India (see Food and Agriculture Organization of the United Nations, http://apps.fao.org) and thus unlikely to be the source of such high methane abundances. Other possible tropical sources are biomass burning and termites. However, in northern South America no substantial biomass burning was detected in the July–November period (Bremer et al. , Tropospheric Emission Monitoring Internet Service, http://www.temis.nl), while termites are not expected to exhibit strong seasonality in their methane emissions [Zimmermann et al., 1982; Sanderson, 1996]. Thus, the origin of these high abundances still remains unclear.
 A more detailed discussion and rigorous comparison with atmospheric models using different emission inventories will be presented in a subsequent publication (P. Bergamaschi et al., manuscript in preparation, 2006).