ALBERT

Description: Carbon dioxide (CO2) is the most important greenhouse gas whose atmospheric loading has been significantly increased by anthropogenic activity leading to global warming. Accurate measurements and models are needed in order to reliably predict our future climate. This, however, has challenging requirements. Errors in measurements and models need to be identified and minimised. In this context, we present a comparison between satellite-derived column-averaged dry air mole fractions of CO2, denoted XCO2, retrieved from SCIAMACHY/ENVISAT using the WFM-DOAS algorithm, and output from NOAA's global CO2 modelling and assimilation system CarbonTracker. We investigate to what extent differences between these two data sets are influenced by systematic retrieval errors due to aerosols and unaccounted clouds. We analyse seven years of SCIAMACHY WFM-DOAS version 2.1 retrievals (WFMDv2.1) using the latest version of CarbonTracker (version 2010). We investigate to what extent the difference between SCIAMACHY and CarbonTracker XCO2 are temporally and spatially correlated with global aerosol and cloud data sets. For this purpose, we use a global aerosol data set generated within the European GEMS project, which is based on assimilated MODIS satellite data. For clouds, we use a data set derived from CALIOP/CALIPSO. We find significant correlations of the SCIAMACHY minus CarbonTracker XCO2 difference with thin clouds over the Southern Hemisphere. The maximum temporal correlation we find for Darwin, Australia (r2 = 54%). Large temporal correlations with thin clouds are also observed over other regions of the Southern Hemisphere (e.g. 43% for South America and 31% for South Africa). Over the Northern Hemisphere the temporal correlations are typically much lower. An exception is India, where large temporal correlations with clouds and aerosols have also been found. For all other regions the temporal correlations with aerosol are typically low. For the spatial correlations the picture is less clear. They are typically low for both aerosols and clouds, but dependent on region and season, they may exceed 30% (the maximum value of 46% has been found for Darwin during September to November). Overall we find that the presence of thin clouds can potentially explain a significant fraction of the difference between SCIAMACHY WFMDv2.1 XCO2 and CarbonTracker over the Southern Hemisphere. Aerosols appear to be less of a problem. Our study indicates that the quality of the satellite derived XCO2 will significantly benefit from a reduction of scattering related retrieval errors at least for the Southern Hemisphere.

Description: Global observations of column-averaged dry air mole fractions of carbon dioxide (CO2), denoted by XCO2, retrieved from passive remote sensing instruments on Earth orbiting satellites can provide important and missing global information on the distribution and magnitude of regional CO2 surface fluxes. This application has challenging precision and accuracy requirements. SCIAMACHY on-board ENVISAT is the first satellite instrument, which measures the upwelling electromagnetic radiation in the near and short wave infrared at an adequate spectral and spatial resolution to yield near-surface sensitive XCO2. In a previous publication (Heymann et al., 2012), it has been shown by analysing seven years of SCIAMACHY WFM-DOAS XCO2 (WFMDv2.1) that unaccounted thin cirrus clouds can result in significant errors. In order to enhance the quality of the SCIAMACHY XCO2 data product, we have developed a new version of the retrieval algorithm (WFMDv2.2), which is described in this manuscript. It is based on an improved cloud filtering and correction method using the 1.4 μm strong water vapour absorption and 0.76 μm O2-A bands. The new algorithm has been used to generate a SCIAMACHY XCO2 data set covering the years 2003–2009. The new XCO2 data set has been validated using ground-based observations from the Total Carbon Column Observing Network (TCCON). The validation shows a significant improvement of the new product (v2.2) in comparison to the previous product (v2.1). For example, the standard deviation of the difference to TCCON at Darwin, Australia, has been reduced from 4 ppm to 2 ppm. The monthly regional-scale scatter of the data (defined as the mean inner monthly standard deviation of all quality filtered XCO2 retrievals within a radius of 350 km around various locations) has also been reduced, typically by a factor of about 1.5. Overall, the validation of the new WFMDv2.2 XCO2 data product can be summarised by a single measurement precision of 3.8 ppm, an estimated regional-scale (radius of 500 km) precision of monthly averages of 1.6 ppm and an estimated regional-scale relative accuracy of 0.8 ppm. In addition to the comparison with the limited number of TCCON sites, we also present a comparison with NOAA's global CO2 modelling and assimilation system CarbonTracker. This comparison also shows significant improvements especially over the Southern Hemisphere.

Description: A simple empirical CO2 model (SECM) is presented to estimate column-average dry-air mole fractions of atmospheric CO2 (XCO2) as well as mixing ratio profiles. SECM is based on a simple equation depending on 17 empirical parameters, latitude, and date. The empirical parameters have been determined by least squares fitting to NOAA's (National Oceanic and Atmospheric Administration) assimilation system CarbonTracker version 2010 (CT2010). Comparisons with TCCON (total column carbon observing network) FTS (Fourier transform spectrometer) measurements show that SECM XCO2 agrees quite well with reality. The synthetic XCO2 values have a standard error of 1.39 ppm and systematic station-to-station biases of 0.46 ppm. Typical column averaging kernels of the TCCON FTS, a SCIAMACHY (Scanning Imaging Absorption Spectrometer for Atmospheric CHartographY), and two GOSAT (Greenhouse gases Observing SATellite) XCO2 retrieval algorithms have been used to assess the smoothing error introduced by using SECM profiles instead of CT2010 profiles as a priori. The additional smoothing error amounts to 0.17 ppm for a typical SCIAMACHY averaging kernel and is most times much smaller for the other instruments (e.g. 0.05 ppm for a typical TCCON FTS averaging kernel). Therefore, SECM is well-suited to provide a priori information for state of the art ground-based (FTS) and satellite-based (GOSAT, SCIAMACHY) XCO2 retrievals. Other potential applications are: (i) quick check for obvious retrieval errors (by monitoring the difference to SECM), (ii) near real time processing systems (that cannot make use of models like CT2010 operated in delayed mode), (iii) "CO2 proxy" methods for XCH4 retrievals (as correction for the XCO2 background), (iv) observing system simulation experiments especially for future satellite missions.