Using chemistry transport modeling in statistical analysis of stratospheric ozone trends from observations

Since the implementation of the international controls on ozone-depleting chemicals, an important focus in studies of stratospheric ozone has been on the detection of a turnaround in the downward trend. The usual statistical assumption of a piecewise linear representation of the trend does not account for the chemistry involved. Because the actual change is not expected to be piecewise linear, we model the trend using results from a University of Illinois at Urbana-Champaign two-dimensional (UIUC 2-D) chemical transport model of the global atmosphere. As part of the analysis, ozone observations are considered in the spectral domain using a cohesive data set from the SBUV-SBUV/2 satellite system at northern midlatitudes. In this study we find that the new model is better at capturing the long-range correlation of the data than assuming a linear trend. We also compare several statistical trend models, based either on a regression on a linear trend or on the Effective Equivalent Stratospheric Chlorine (EESC). Including a constant halocarbon emissions run of the UIUC 2-D model in the regression, the controlled EESC approach shows the best fit. The smallest future data length necessary to detect a recovery with a certain probability is obtained in this latter case.