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| Titel |
Trends in stratospheric ozone profiles using functional mixed models |
| VerfasserIn |
A. Park, S. Guillas, I. Petropavlovskikh |
| 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 ; 13, no. 22 ; Nr. 13, no. 22 (2013-11-27), S.11473-11501 |
| Datensatznummer |
250085838
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| Publikation (Nr.) |
 copernicus.org/acp-13-11473-2013.pdf |
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| Zusammenfassung |
| This paper is devoted to the modeling of altitude-dependent patterns of ozone
variations over time. Umkehr ozone profiles (quarter of Umkehr layer) from
1978 to 2011 are investigated at two locations: Boulder (USA) and Arosa
(Switzerland). The study consists of two statistical stages. First we
approximate ozone profiles employing an appropriate basis. To capture primary
modes of ozone variations without losing essential information, a functional
principal component analysis is performed. It penalizes roughness of the
function and smooths excessive variations in the shape of the ozone profiles.
As a result, data-driven basis functions (empirical basis functions) are
obtained. The coefficients (principal component scores) corresponding to the
empirical basis functions represent dominant temporal evolution in the shape
of ozone profiles. We use those time series coefficients in the second
statistical step to reveal the important sources of the patterns and
variations in the profiles. We estimate the effects of covariates – month,
year (trend), quasi-biennial oscillation, the solar cycle, the Arctic
oscillation, the El Niño/Southern Oscillation cycle and the Eliassen–Palm
flux – on the principal component scores of ozone profiles using additive
mixed effects models. The effects are represented as smooth functions and the
smooth functions are estimated by penalized regression splines. We also
impose a heteroscedastic error structure that reflects the observed
seasonality in the errors. The more complex error structure enables us to
provide more accurate estimates of influences and trends, together with
enhanced uncertainty quantification. Also, we are able to capture fine
variations in the time evolution of the profiles, such as the semi-annual
oscillation. We conclude by showing the trends by altitude over Boulder and
Arosa, as well as for total column ozone. There are great variations in the
trends across altitudes, which highlights the benefits of modeling ozone
profiles. |
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