Total ozone trends and variability during 1979–2012 from merged data sets of various satellites
- Institute of Environmental Physics (IUP), University of Bremen, Bremen, Germany
Abstract. The study presents a long-term statistical trend analysis of total ozone data sets 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 Niño–Southern Oscillation (ENSO), the Arctic and Antarctic oscillation (AO/AAO), and accumulated eddy heat flux (EHF), the latter representing changes due to the Brewer–Dobson circulation. The total ozone column data set used here comprises the Solar Backscater Ultraviolet SBUV/SBUV-2 merged ozone data set (MOD) V8.6, the merged data set of the Solar Backscaterr Ultraviolet, the Total Ozone Mapping Spectrometer and the Ozone Monitoring Instrument SBUV/TOMS/OMI (1979–2012) MOD V8.0 and the merged data set of the Global Ozone Monitoring Experiment, the Scanning Imaging Absorption spectroMeter for Atmospheric ChartograpHY and the Global Ozone Monitoring Experiment 2 GOME/SCIAMACHY/GOME-2 (GSG) (1995–2012). The trend analysis was performed for twenty-six 5° wide latitude bands from 65° S to 65° N, and the analysis explained most of the ozone variability to within 70 to 90%. The results show that QBO dominates the ozone variability in the tropics (±7 DU) while at higher latitudes, the dynamical indices, AO/AAO and eddy heat flux, have substantial influence on total ozone variations by up to ±10 DU. The contribution from volcanic aerosols is only prominent during the major eruption periods (El Chichón and Mt. Pinatubo), and together with the ENSO signal, is more evident in the Northern Hemisphere. The signature of the solar cycle covers all latitudes and contributes about 10 DU from solar maximum to solar minimum. EESC is found to be a main contributor to the long-term ozone decline and the trend changes after the end of the 1990s. From the EESC fits, statistically significant upward trends after 1997 were found in the extratropics, which points at the slowing of ozone decline and the onset of ozone recovery. The EESC based trends are compared with the trends obtained from the statistical piecewise linear trend (PWLT) model (known as hockey stick) with a turnaround in 1997 to examine the differences between both approaches. In case of the SBUV merged V8.6 data the EESC and PWLT trends before and after 1997 are in good agreement (within 2 σ), however, the positive post-1997 linear trends from the PWLT regression are not significant within 2 σ.
A sensitivity study is carried out by comparing the regression results, using SBUV/SBUV-2 MOD V8.6 merged time series (1979–2012) and a merged data set combining SBUV/SBUV-2 (1979–June 1995) and GOME/SCIAMACHY/GOME-2 ("GSG") WFDOAS (Weighting Function DOAS) (July 1995–2012) as well as SBUV/TOMS/OMI MOD V8.0 (1979–2012) in the regression analysis in order to investigate the uncertainty in the long-term trends due to different ozone data sets and data versions. Replacing the late SBUV/SBUV-2 merged data record with GSG data (unscaled and adjusted) leads to very similar results demonstrating the high consistency between satellite data sets. However, the comparison of the new SBUV/SBUV-2 MOD V8.6 with the MOD V8.0 and MOD8.6/GSG data showed somewhat smaller sensitivities with regard to several proxies as well as the linear EESC trends. On the other hand, the PWLT trends after 1997 show some differences, however, within the 2 σ error bars the PWLT trends agree with each other for all three data sets.