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Multiple-regression analyses have been performed on 32 years of total ozonecolumn data that was spatially gridded with a 1 × 1.5°resolution. The total ozone data consist of the MSR (Multi SensorReanalysis; 1979–2008) and 2 years of assimilated SCIAMACHY  (SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY) ozone data(2009–2010). The two-dimensionality in this data set allows us to performthe regressions locally and investigate spatial patterns of regressioncoefficients and their explanatory power. Seasonal dependencies of ozone onregressors are included in the analysis.  A new physically oriented model is developed to parameterize stratosphericozone. Ozone variations on nonseasonal timescales are parameterized byexplanatory variables describing the solar cycle, stratospheric aerosols,the quasi-biennial oscillation (QBO), El Niño–Southern Oscillation (ENSO) and stratosphericalternative halogens which are parameterized by the effective equivalent stratospheric chlorine (EESC). For several explanatory variables, seasonallyadjusted versions of these explanatory variables are constructed to accountfor the difference in their effect on ozone throughout the year. To accountfor seasonal variation in ozone, explanatory variables describing the polarvortex, geopotential height, potential vorticity and average day length areincluded. Results of this regression model are compared to that of a similaranalysis based on a more commonly applied statistically oriented model.  The physically oriented model provides spatial patterns in the regressionresults for each explanatory variable. The EESC has a significant depletingeffect on ozone at mid- and high latitudes, the solar cycle affects ozonepositively mostly in the Southern Hemisphere, stratospheric aerosols affectozone negatively at high northern latitudes, the effect of QBO is positiveand negative in the tropics and mid- to high latitudes, respectively, and ENSOaffects ozone negatively between 30° N and 30° S,particularly over the Pacific. The contribution of explanatory variablesdescribing seasonal ozone variation is generally large at mid- to highlatitudes. We observe ozone increases with potential vorticity and daylength and ozone decreases with geopotential height and variable ozone effectsdue to the polar vortex in regions to the north and south of the polarvortices.  Recovery of ozone is identified globally. However, recovery rates anduncertainties strongly depend on choices that can be made in defining theexplanatory variables. The application of several trend models, each with theirown pros and cons, yields a large range of recovery rate estimates. Overallthese results suggest that care has to be taken in determining ozonerecovery rates, in particular for the Antarctic ozone hole

Spatial regression analysis on 32 years of total column ozone data