Abstract
Abstract. 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 (weighting function modified differential optical absorption spectroscopy) 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 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 depending 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.
Highlights
Since pre-industrial times, the concentration of the atmospheric greenhouse gas carbon dioxide (CO2) has increased by about 36 %, mainly as a result of anthropogenic activities such as fossil fuel combustion, land use change and cement production (Solomon et al, 2007)
We present a comparison between satellitederived 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
Aerosol variability is taken into account as follows: (i) by using O2 as proxy for the light path; (ii) by the low-order polynomial included in the WFMD spectral fits, which makes the retrieval insensitive to spectrally broadband radiance modifications resulting from, for example, aerosols; and (iii) by filtering out scenes contaminated by high loads of aerosols as identified using the SCIAMACHY Absorbing Aerosol Index (AAI) (Tilstra et al, 2007) data product, which is sensitive to
Summary
Since pre-industrial times, the concentration of the atmospheric greenhouse gas carbon dioxide (CO2) has increased by about 36 %, mainly as a result of anthropogenic activities such as fossil fuel combustion, land use change and cement production (Solomon et al, 2007). Previous inverse modelling studies have shown that satellite observations of the vertical column of CO2 or of its columnaveraged dry air mole-fraction, XCO2, can deliver important information on regional CO2 surface fluxes, which currently cannot be provided by the sparse surface networks of very accurate ground based measurements (Rayner and O’Brien, 2001; Houweling et al, 2004; Miller et al, 2007; Chevallier et al, 2007) They concluded that aerosol related XCO2 errors are typically below 1 %
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