Abstract
Abstract. The total ozone variations over Europe (~50° N) in the period 1964–2004 are analyzed for detection of signals of ozone recovery. The ozone deviations from the long-term monthly means (1964–1980) for selected European stations, where the ozone observations (by the Dobson spectrophotometers) have been carried out continuously for at least 3–4 decades, are averaged and examined by a regression model. A new method is proposed to disclose both the ozone trend variations and date of the trend turnaround. The regression model contains a piecewise linear trend component and the terms describing the ozone response to forcing by "natural" changes in the atmosphere. Standard proxies for the dynamically driven ozone variations are used. The Multivariate Adaptive Regression Splines (MARS) methodology and principal component analysis are used to find an optimal set of the explanatory variables and the trend pattern. The turnaround of the ozone trend in 1994 is suggested from the pattern of the piecewise linear trend component. Thus, the changes in the ozone mean level are calculated over the periods 1970–1994 and 1994–2003, for both the original time series and the time series having "natural" variations removed. Statistical significance of the changes are derived by bootstrapping. A first stage of recovery (according to the definition of the International Ozone Commission), i.e. lessening of a negative trend, is found over Europe. It seems possible that the increase in the ozone mean level since 1994 of about 1–2% is due to superposition of the "natural" processes. Comparison of the total ozone ground-based network (the Dobson and Brewer spectrophotometers) and the satellite (TOMS, version 8) data over Europe shows the small bias in the mean values for the period 1996–2004, but the differences between the daily ozone values from these instruments are not trendless, and this may hamper an identification of the next stage of the ozone recovery over Europe. Keywords. Atmospheric composition and structure (Middle atmosphere-composition and chemistry) – Meteorology and atmospheric dynamics (Climatology, Middle atmosphere dynamics)
Highlights
In the period 1964–2004 are analyzed for detection of signals of ozone recovery
Multivariate Adaptive Regression Splines (MARS) methodology and principal component analysis are used to find an optimal set of the explanatory variables and the trend pattern
Comparison of the total ozone ground-based network and the satellite (TOMS, version 8) data over Europe shows the small bias in the mean values for the period 1996–2004, but the differences between the daily ozone values from these instruments are not trendless, and this may hamper an identification of the stage of the ozone recovery over Europe
Summary
Stratospheric ozone depletion has been one of the major global scientific and environmental issues of the last century. In all models the trend term has a piecewise linear form representing the sum of the basis functions of time t This assumption corresponds to Reinsel et al (2002) description of a trend as two straight lines (the first one describing ozone depletion and the second one describing an increasing ozone tendency after the turnaround in the ozone trend). A first step is a delineation of the initial trend pattern and random noise based on the above-mentioned regression models, taking into account the piecewise linear trend term and selected proxies explaining “natural” ozone variability. To estimate changes in the mean ozone level and the ozone trend in selected time intervals we calculate the mean value and standard deviation from a sample of smooth curves that are fitted to the bootstrap representatives of the original data, and data having “natural” variations removed. The section contains the results of the trend analyses applied to the combined total ozone data from five European stations (located along ∼50◦ N), having many decades the ozone observations by the Dobson spectrophotometers
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