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Abstract. Carbon dioxide (CO 2 ) is the most important anthropogenic greenhouse gas (GHG) causing global warming. The atmospheric CO 2 concentration increased by more than 30% since pre-industrial times -primarily due to burning of fossil fuels -and still continues to increase. Reporting of CO 2 emissions is required by the Kyoto protocol. Independent verification of reported emissions, which are typially not directly measured, by methods such as inverse modeling of measured atmospheric CO 2 concentrations is currently not possible globally due to lack of appropriate observations. Existing satellite instruments such as SCIAMACHY/ENVISAT and TANSO/GOSAT focus on advancing our understanding of natural CO 2 sources and sinks. The obvious next step for future generation satellites is to also constrain anthropogenic CO 2 emissions. Here we present a promising satellite remote sensing concept based on spectroscopic measurements of reflected solar radiation and show, using power plants as an example, that strong localized CO 2 point sources can be detected and their emissions quantified. This requires mapping the atmospheric CO 2 column distribution at a spatial resolution of 2×2 km 2 with a precision of 0.5% (2 ppm) or better. We indicate that this can be achieved with existing technology. For a single satellite in sun-synchronous orbit with a swath width of 500 km, each power plant (PP) is overflown every 6 days or more frequent. Based on the MODIS cloud mask data product we conservatively estimate that typically 20 sufficiently cloud free overpasses per PP can be achieved every year. We found that for typical wind speeds in the range of 2-6 m/s the statistical uncertainty of the retrieved Correspondence to: M. Buchwitz (michael.buchwitz@iup.physik.unibremen.de) PP CO 2 emission due to instrument noise is in the range 1.6-4.8 MtCO 2 /yr for single overpasses. This corresponds to 12-36% of the emission of a mid-size PP (13 MtCO 2 /yr). We have also determined the sensitivity to parameters which may result in systematic errors such as atmospheric transport and aerosol related parameters. We found that the emission error depends linearly on wind speed, i.e., a 10% wind speed error results in a 10% emission error, and that neglecting enhanced aerosol concentrations in the PP plume may result in errors in the range 0.2-2.5 MtCO 2 /yr, depending on PP aerosol emission. The discussed concept has the potential to contribute to an independent verification of reported anthropogenic CO 2 emissions and therefore could be an important component of a future global anthropogenic GHG emission monitoring system. This is of relevance in the context of Kyoto protocol follow-on agreements but also allows detection and monitoring of a variety of other strong natural and anthropogenic CO 2 and CH 4 emitters. The investigated instrument is not limited to these applications as it has been specified to also deliver the data needed for global regionalscale CO 2 and CH 4 surface flux inverse modeling.

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.

Abstract.A globally integrated carbon observation and analysis system is needed to improve the fundamental understanding of the global carbon cycle, to improve our ability to project future changes, and to verify the effectiveness of policies aiming to reduce greenhouse gas emissions and increase carbon sequestration. Building an integrated carbon observation system requires transformational advances from the existing sparse, exploratory framework towards a dense, robust, and sustained system in all components: anthropogenic emissions, the atmosphere, the ocean, and the terrestrial biosphere. The paper is addressed to scientists, policymakers, and funding agencies who need to have a global picture of the current state of the (diverse) carbon observations. We identify the current state of carbon observations, and the needs and notional requirements for a global integrated carbon observation system that can be built in the next decade. A key conclusion is the substantial expansion of the ground-based observation networks required to reach the high spatial resolution for CO 2 and CH 4 fluxes, and for carbon stocks for addressing policy-relevant objectives, and attributing flux changes to underlying processes in each region. In order to establish flux and stock diagnostics over areas such as the southern oceans, tropical forests, and the Arctic, in situ observations will have to be complemented with remote-sensing measurements. Remote sensing offers the advantage of dense spatial coverage and frequent revisit. A key challenge is to bring remote-sensing measurements to a level of long-term consistency and accuracy so that they can be efficiently combined in models to reduce uncertainties, in synergy with groundbased data. Bringing tight observational constraints on fossil fuel and land use change emissions will be the biggest challenge for deployment of a policy-relevant integrated carbon observation system. This will require in situ and remotely sensed data at much higher resolution and density than currently achieved for natural fluxes, although over a small land area (cities, industrial sites, power plants), as well as the inclusion of fossil fuel CO 2 proxy measurements such as radiocarbon in CO 2 and carbon-fuel combustion tracers. Additionally, a policy-relevant carbon monitoring system should also provide mechanisms for reconciling regional top-down (atmosphere-based) and bottom-up (surface-based) flux estimates across the range of spatial and temporal scales relevant to mitigation policies. In addition, uncertainties for each observation data-stream should be assessed. The success of the system will rely on long-term commitments to monitoring, on improved international collaboration to fill gaps in the current observations, on sustained efforts to improve access to the different data streams and make databases interoperable, and on the calibration of each component of the system to agreed-upon international scales.

[1] The Bremen Optimal Estimation differential optical absorption spectroscopy (DOAS) (BESD) algorithm for satellite based retrievals of XCO 2 (the column-average dry-air mole fraction of atmospheric CO 2 ) has been applied to Scanning Imaging Absorption Spectrometer for Atmospheric Cartography (SCIAMACHY) data. It uses measurements in the O 2 -A absorption band to correct for scattering of undetected clouds and aerosols. Comparisons with precise and accurate ground-based Fourier transform spectrometer (FTS) measurements at four Total Carbon Column Observing Network (TCCON) sites have been used to quantify the quality of the new SCIAMACHY XCO 2 data set. Additionally, the results have been compared to NOAA's assimilation system CarbonTracker. The comparisons show that the new retrieval meets the expectations from earlier theoretical studies. We find no statistically significant regional XCO 2 biases between SCIAMACHY and the FTS instruments. However, the standard error of the systematic differences is in the range of 0.2 ppm and 0.8 ppm. The XCO 2 single-measurement precision of 2.5 ppm is similar to theoretical estimates driven by instrumental noise. There are no significant differences found for the year-to-year increase as well as for the average seasonal amplitude between SCIAMACHY XCO 2 and the collocated FTS measurements. Comparison of the year-to-year increase and also of the seasonal amplitude of CarbonTracker exhibit significant differences with the corresponding FTS values at Darwin. Here the differences between SCIAMACHY and CarbonTracker are larger than the standard error of the SCIAMACHY values. The difference of the seasonal amplitude exceeds the significance level of 2 standard errors. Therefore, our results suggest that SCIAMACHY may provide valuable additional information about XCO 2 , at least in regions with a low density of in situ measurements.