Abstract
Abstract. We use statistical models for mean and extreme values of total column ozone to analyze "fingerprints" of atmospheric dynamics and chemistry on long-term ozone changes at northern and southern mid-latitudes on grid cell basis. At each grid cell, the r-largest order statistics method is used for the analysis of extreme events in low and high total ozone (termed ELOs and EHOs, respectively), and an autoregressive moving average (ARMA) model is used for the corresponding mean value analysis. In order to describe the dynamical and chemical state of the atmosphere, the statistical models include important atmospheric covariates: 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 statistical distribution of ozone, respectively described by mean values 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. Column ozone is enhanced over Labrador/Greenland, the North Atlantic sector and over the Norwegian Sea, but is reduced over Europe, Russia and the Eastern United States during the positive NAO phase, and vice-versa during the negative phase. The NAO's southern counterpart, the AAO, strongly influences column ozone at lower southern mid-latitudes, including the southern parts of South America and the Antarctic Peninsula, and the central southern mid-latitudes. Results for both NAO and AAO confirm the importance of atmospheric dynamics for ozone variability and changes from local/regional to global scales.
Highlights
Interest in changes in total ozone is linked to its direct influence on biologically active UV radiation (e.g., Calboet al., 2005), and since the detection of the Antarctic ozone hole (Farman et al, 1985) the development of the Earth’s ozone layer has been a key focus in atmospheric research
Various indices describing the dynamical and chemical state of the atmosphere are used as covariates in this study, namely the 11-yr solar cycle, the Quasi-Biennial Oscillation (QBO), the El Nino/Southern Oscillation (ENSO), the North Atlantic Oscillation (NAO), the Antarctic Oscillation (AAO), ozone depleting substances (ODS) in terms of equivalent effective stratospheric chlorine (EESC) as calculated by Newman et al (2007), and the stratospheric aerosol load after the major volcanic eruptions of El Chichon and Mt
The topic of multiple testing is undergoing rapid development and a definitive treatment in the present context cannot yet be provided, so in order to assess the strength of the conclusions below, we applied four approaches to the z-statistics for the covariate effects on extremes: (i) making no correction for multiple testing; (ii) false discovery rate (FDR); (iii) a conservative version of FDR that allows for general correlation in the zstatistics; and (iv) the ultra-conservative Bonferroni correction
Summary
Interest in changes in total ozone is linked to its direct influence on biologically active UV radiation (e.g., Calboet al., 2005), and since the detection of the Antarctic ozone hole (Farman et al, 1985) the development of the Earth’s ozone layer has been a key focus in atmospheric research. Applications of multiple regression models on large spatial scales and analyses on a grid cell basis, as for satellite data sets, are rare for column ozone, and the results mostly describe the influence of atmospheric dynamics and chemistry on mean column ozone, leaving the extremes unaccountedfor. One possibility is max-stable processes (see Davison et al, 2012, for an application-oriented review), but as current fitting methods are computationally infeasible for massive (gridded) data sets, we fit a model for univariate extremes individually to each grid cell, as if the neighboring cells did not exist This pointwise approach naturally accounts for non-stationarity in space and avoids averaging effects, caused, for example, by averaging over zonal bands. For each grid cell and produce maps of these estimates for interpretation and comparison
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