Analysis and interpretation of satellite measurements in the near-infrared spectral region: Atmospheric carbon dioxide and methane
|Other Titles:||Auswertung und Interpretation von Satellitenmessungen im nahinfraroten Spektralbereich: Atmosphärisches Kohlenstoffdioxid und Methan||Authors:||Schneising, Oliver||Supervisor:||Burrows, John P.||1. Expert:||Burrows, John P.||2. Expert:||Notholt, Justus||Abstract:||
Carbon dioxide (CO2) and methane (CH4) are the two most important anthropogenic greenhouse gases. SCIAMACHY on ENVISAT is the first satellite instrument whose measurements are sensitive to concentration changes of the two gases at all altitude levels down to the Earth's surface where the source/sink signals are largest. Three years (2003-2005) of SCIAMACHY near-infrared nadir measurements have been processed to simultaneously retrieve vertical columns of CO2, CH4, and oxygen using the scientific retrieval algorithm WFM-DOAS. The latest version of WFM-DOAS, version 1.0, which was developed within the scope of this thesis, has been significantly improved with respect to its accuracy compared to the previous versions while essentially maintaining its high processing speed. The greenhouse gas columns are converted to column-averaged dry air mole fractions, denoted XCO2 and XCH4, by dividing the greenhouse gas columns by simultaneously retrieved dry air columns. For XCO2 dry air columns are obtained from the retrieved O2 columns. For XCH4 dry air columns are obtained from the retrieved CO2 columns because of better cancellation of light path related errors compared to using O2 columns retrieved from the spectrally distant O2 A-band.The satellite CO2 data set is compared with ground based Fourier Transform Spectroscopy (FTS) measurements and results from the global assimilation system CarbonTracker showing good agreement concerning the annual increase as well as the seasonal cycle over the northern hemisphere. However, for the southern hemisphere, where significantly less data are available for averaging, the amplitude and phase of the seasonal cycle show systematic differences arising partially from the O2 normalisation most likely caused by unconsidered scattering effects due to subvisual cirrus clouds. Based on the error analysis and on the comparison with the reference data it can be concluded that the XCO2 data set can be characterised by a single measurement retrieval precision (random error) of 1-2%, a systematic low bias of about 1.5%, and by a relative accuracy of about 1-2% for monthly averages at a spatial resolution of about 7°x7°. Averaging the retrieved XCO2 over all three years provides elevated CO2 over densely populated and industrialised source regions indicating that strong regional anthropogenic CO2 emissions can be potentially detected from space.The satellite CH4 data set is compared with global model simulations based on the TM5 model optimised versus high-accuracy surface measurements from the NOAA/ESRL network. After accounting for a systematic low bias of circa 2% agreement with TM5 is typically within 1-2%. The single measurement retrieval precision of XCH4 is estimated to be 1.5-1.7%. It is investigated to what extent the SCIAMACHY XCH4 is influenced by the variability of atmospheric CO2 using global CO2 fields from CarbonTracker showing that agreement with TM5 is better for the CarbonTracker CO2 corrected XCH4. In line with other studies higher methane over the tropics is found compared to the model. Tropical methane is also higher when normalising the CH4 columns with retrieved O2 columns instead of CO2. However, the magnitude of the retrieved tropical methane enhancement is sensitive to changes in spectroscopy and possible inaccuracies in the spectroscopic parameters can thus contribute to a potential overestimation of the tropical methane. First inverse modelling results for methane surface fluxes are presented for the year 2004 performed at the European Commission's Joint Research Centre (EC-JRC) by Peter Bergamaschi.
|Keywords:||greenhouse gases, carbon dioxide, methane, remote sensing, SCIAMACHY, WFM-DOAS, inversion algorithm, retrieval, atmospheric physics, climate change||Issue Date:||24-Nov-2008||URN:||urn:nbn:de:gbv:46-diss000113277||Institution:||Universität Bremen||Faculty:||FB1 Physik/Elektrotechnik|
|Appears in Collections:||Dissertationen|
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