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| Titel |
Extreme events in total ozone over Arosa – Part 2: Fingerprints of atmospheric dynamics and chemistry and effects on mean values and long-term changes |
| VerfasserIn |
H. E. Rieder, J. Staehelin, J. A. Maeder, T. Peter, M. Ribatet, A. C. Davison, R. Stübi, P. Weihs, F. Holawe |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1680-7316
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Chemistry and Physics ; 10, no. 20 ; Nr. 10, no. 20 (2010-10-25), S.10033-10045 |
| Datensatznummer |
250008852
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| Publikation (Nr.) |
 copernicus.org/acp-10-10033-2010.pdf |
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| Zusammenfassung |
| In this study the frequency of days with extreme low (termed ELOs) and
extreme high (termed EHOs) total ozone values and their influence on mean
values and trends are analyzed for the world's longest total ozone record
(Arosa, Switzerland). The results show (i) an increase in ELOs and (ii) a
decrease in EHOs during the last decades and (iii) that the overall trend
during the 1970s and 1980s in total ozone is strongly dominated by changes
in these extreme events. After removing the extremes, the time series shows
a strongly reduced trend (reduction by a factor of 2.5 for trend in annual
mean). Excursions in the frequency of extreme events reveal "fingerprints"
of dynamical factors such as ENSO or NAO, and chemical factors, such as cold
Arctic vortex ozone losses, as well as major volcanic eruptions of the
20th century (Gunung Agung, El Chichón, Mt. Pinatubo). Furthermore,
atmospheric loading of ozone depleting substances leads to a continuous
modification of column ozone in the Northern Hemisphere also with respect to
extreme values (partly again in connection with polar vortex contributions).
Application of extreme value theory allows the identification of many more
such "fingerprints" than conventional time series analysis of annual and
seasonal mean values. The analysis shows in particular the strong influence
of dynamics, revealing that even moderate ENSO and NAO events have a
discernible effect on total ozone. Overall the approach to extremal
modelling provides new information on time series properties, variability,
trends and the influence of dynamics and chemistry, complementing earlier
analyses focusing only on monthly (or annual) mean values. |
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