Trends in stratospheric ozone profiles using functional mixed models

• Published in: Atmospheric Chemistry and Physics
• Main Authors: Park, A., Guillas, S., Petropavlovskikh, I.
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2013
• Subjects: article; Verlagsveröffentlichung; Arctic
• Online Access: https://doi.org/10.5194/acp-13-11473-2013; https://noa.gwlb.de/receive/cop_mods_00045282; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00044902/acp-13-11473-2013.pdf; https://acp.copernicus.org/articles/13/11473/2013/acp-13-11473-2013.pdf

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.