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We perform a formal attribution study of upper- and lower-stratospheric ozonechanges using observations together with simulations from the WholeAtmosphere Community Climate Model. Historical model simulations were used toestimate the zonal-mean response patterns (<q>fingerprints</q>) to combinedforcing by ozone-depleting substances (ODSs) and well-mixed greenhouse gases(GHGs), as well as to the individual forcing by each factor. Trends in thesimilarity between the searched-for fingerprints and homogenized observationsof stratospheric ozone were compared to trends in pattern similarity betweenthe fingerprints and the internally and naturally generated variabilityinferred from long control runs. This yields estimated signal-to-noise(S∕N) ratios for each of the three fingerprints (ODS, GHG, andODS + GHG). In both the upper stratosphere (defined in this paper as 1 to10 hPa) and lower stratosphere (40 to 100 hPa), the spatialfingerprints of the ODS + GHG and ODS-only patterns were consistentlydetectable not only during the era of maximum ozone depletion but alsothroughout the observational record (1984–2016). We also developa fingerprint attribution method to account for forcings whose timeevolutions are markedly nonlinear over the observational record. When thenonlinearity of the time evolution of the ODS and ODS + GHG signals isaccounted for, we find that the S∕N ratios obtained with the stratosphericODS and ODS + GHG fingerprints are enhanced relative to standard lineartrend analysis. Use of the nonlinear signal detection method also reduces thedetection time – the estimate of the date at which ODS and GHG impacts onozone can be formally identified. Furthermore, by explicitly consideringnonlinear signal evolution, the complete observational record can be used inthe S∕N analysis, without applying piecewise linear regression andintroducing arbitrary break points. The GHG-driven fingerprint of ozonechanges was not statistically identifiable in either the upper- orlower-stratospheric SWOOSH data, irrespective of the signal detection methodused. In the WACCM simulations of future climate change, the GHG signal isstatistically identifiable between 2020 and 2030. Our findings demonstratethe importance of continued stratospheric ozone monitoring to improveestimates of the contributions of ODS and GHG forcing to global changes instratospheric ozone

atmospheric chemistry and physics

Detectability of the impacts of ozone-depleting substances and greenhouse gases upon stratospheric ozone accounting for nonlinearities in historical forcings