An intercomparison of multidecadal observational and reanalysis data sets for global total ozone trends and variability analysis

A four-step adaptive ozone trend estimation scheme is proposed by integrating multivariate linear regression (MLR) and ensemble empirical mode decomposition (EEMD) to analyze the long-term variability of total column ozone from a set of four observational and reanalysis total ozone data sets, including the rarely explored ERA-Interim total ozone reanalysis, from 1979 to 2009. Consistency among the four data sets was first assessed, indicating a mean relative difference of 1% and root-mean-square error around 2% on average, with respect to collocated ground-based total ozone observations. Nevertheless, large drifts with significant spatiotemporal inhomogeneity were diagnosed in ERA-Interim after 1995. To emphasize long-term trends, natural ozone variations associated with the solar cycle, quasi-biennial oscillation, volcanic aerosols, and El Nino-Southern Oscillation were modeled with MLR and then removed from each total ozone record, respectively, before performing EEMD analyses. The resulting rates of change estimated from the proposed scheme captured the long-term ozone variability well, with an inflection time of 2000 clearly detected. The positive rates of change after 2000 suggest that the ozone layer seems to be on a healing path, but the results are still inadequate to conclude an actual recovery of the ozone layer, and more observational evidence is needed. Further investigations suggest that biases embedded in total ozone records may significantly impact ozone trend estimations by resulting in large uncertainty or even negative rates of change after 2000.