Harmonisation of the GOME, SCIAMACHY, and GOME-2 total ozone data records for a better understanding of long-term trends and their causes

This thesis addresses the issue of the accurate measurements of ozone distributions in the atmosphere obtained from different satellite borne atmospheric chemistry spectrometers which represent a major need and pre-requisite for determining whether the atmospheric burden of ozone depleting substances (ODS) are reduced in accordance to the Montreal Protocol, and valuable for long-term trend analysis to detect a subsequent ozone recovery. A consolidated and homogeneous long term dataset requires a careful analysis of the relevant parameters used in the retrievals, one important parameter is the absorption cross section. This work presents the procedures followed to correct the ozone cross section data of SCIAMACHY and GOME-2 spectrometers starting from original raw data (optical density spectra). Using the available versions of SCIAMACHY and GOME-2 FM cross sections in the retrieval of total ozone from each satellite leads to an overestimation in the total ozone by 3-5% and 8-9% compared to collocated GOME data, respectively. The quality of the revised temperature-dependent ozone absorption cross sections is investigated over GOME-2 and SCIAMACHY's entire spectral range. The revised data agree well within 3% with other published ozone cross sections and preserve the correct temperature dependence in the Hartley, Huggins, Chappuis and Wulf bands as displayed by the literature data. SCIAMACHY's total ozone columns retrieved using the revised cross section data are shown to be within 1% compared to the ozone amounts retrieved routinely from SCIAMACHY, which uses Bogumil et al. (2003) data but adjusted with a scaling factor of 5.3% and a wavelength shift of 0.08 nm. The total ozone column retrieved from the GOME-2/MetOp-A satellite using the new cross section data is within 1% compared to the ozone amounts retrieved from the standard retrieval performed for GOME-2. The study also presents a long term statistical trend analysis of total ozone datasets obtained from various satellites. A multi-variate linear regression was applied to annual mean zonal mean data using various natural and anthropogenic explanatory variables that represent dynamical and chemical processes which modify global ozone distributions in a changing climate. The study investigated the magnitude and zonal distribution of the different atmospheric chemical and dynamical factors contributing to long-term total ozone changes. The regression model included the Equivalent Effective Stratospheric Chlorine (EESC), the 11 year solar cycle, the Quasi-Biennial Oscillation (QBO), stratospheric aerosol loading describing the effects from major volcanic eruptions, the El Nino/ Southern Oscillation (ENSO), the Arctic and Antarctic Oscillation, and accumulated eddy heat flux (EHF), the latter representing changes due to the Brewer Dobson circulation. The total ozone column dataset used here comprises the SBUV/TOMS/OMI merged data (1979 - 2012) MOD V8.0. The analysis explained most of the ozone variability. The results show that QBO dominates the ozone variability in…