1Mathematics Institute for Analysis and Applications, EPF Lausanne, Lausanne, Switzerland
2Institute for Atmospheric and Climate Science, ETH Zurich, Zurich, Switzerland
3Department of Applied Physics and Applied Mathematics, Columbia University, New York, USA
4Institute of Mathematics and Mathematical Modeling, University Montpellier II, Montpellier, France
5Department for Geography, University of Zurich, Zurich, Switzerland
Abstract. We use models for mean and extreme values of total column ozone on spatial scales to analyze "fingerprints" of atmospheric dynamics and chemistry on long-term ozone changes at northern and southern mid-latitudes. The r-largest order statistics method is used for pointwise analysis of extreme events in low and high total ozone (termed ELOs and EHOs, respectively). For the corresponding mean value analysis a pointwise autoregressive moving average model (ARMA) is used. The statistical models include important atmospheric covariates to describe the dynamical and chemical state of the atmosphere: the solar cycle, the Quasi-Biennial Oscillation (QBO), ozone depleting substances (ODS) in terms of equivalent effective stratospheric chlorine (EESC), the North Atlantic Oscillation (NAO), the Antarctic Oscillation (AAO), the El~Niño/Southern Oscillation (ENSO), and aerosol load after the volcanic eruptions of El Chichón and Mt. Pinatubo. The influence of the individual covariates on mean and extreme levels in total column ozone is derived on a grid cell basis. The results show that "fingerprints", i.e., significant influence, of dynamical and chemical features are captured in both the "bulk" and the tails of the ozone distribution, respectively described by means and EHOs/ELOs. While results for the solar cycle, QBO and EESC are in good agreement with findings of earlier studies, unprecedented spatial fingerprints are retrieved for the dynamical covariates.