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Atmospheric Chemistry and Physics An interactive open-access journal of the European Geosciences Union
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Volume 18, issue 1 | Copyright
Atmos. Chem. Phys., 18, 143-166, 2018
https://doi.org/10.5194/acp-18-143-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.

Research article 05 Jan 2018

Research article | 05 Jan 2018

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

Justin Bandoro1, Susan Solomon1, Benjamin D. Santer2, Douglas E. Kinnison3, and Michael J. Mills3 Justin Bandoro et al.
  • 1Department of Earth, Atmospheric, and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
  • 2Program for Climate Model Diagnosis and Intercomparison (PCMDI), Lawrence Livermore National Laboratory, Livermore, CA 94550, USA
  • 3Atmospheric Chemistry Observations and Modeling Laboratory, National Center for Atmospheric Research, Boulder, CO 80307, USA

Abstract. We perform a formal attribution study of upper- and lower-stratospheric ozone changes using observations together with simulations from the Whole Atmosphere Community Climate Model. Historical model simulations were used to estimate the zonal-mean response patterns (fingerprints) to combined forcing by ozone-depleting substances (ODSs) and well-mixed greenhouse gases (GHGs), as well as to the individual forcing by each factor. Trends in the similarity between the searched-for fingerprints and homogenized observations of stratospheric ozone were compared to trends in pattern similarity between the fingerprints and the internally and naturally generated variability inferred from long control runs. This yields estimated signal-to-noise (SN) ratios for each of the three fingerprints (ODS, GHG, and ODS+GHG). In both the upper stratosphere (defined in this paper as 1 to 10hPa) and lower stratosphere (40 to 100hPa), the spatial fingerprints of the ODS+GHG and ODS-only patterns were consistently detectable not only during the era of maximum ozone depletion but also throughout the observational record (1984–2016). We also develop a fingerprint attribution method to account for forcings whose time evolutions are markedly nonlinear over the observational record. When the nonlinearity of the time evolution of the ODS and ODS+GHG signals is accounted for, we find that the SN ratios obtained with the stratospheric ODS and ODS+GHG fingerprints are enhanced relative to standard linear trend analysis. Use of the nonlinear signal detection method also reduces the detection time – the estimate of the date at which ODS and GHG impacts on ozone can be formally identified. Furthermore, by explicitly considering nonlinear signal evolution, the complete observational record can be used in the SN analysis, without applying piecewise linear regression and introducing arbitrary break points. The GHG-driven fingerprint of ozone changes was not statistically identifiable in either the upper- or lower-stratospheric SWOOSH data, irrespective of the signal detection method used. In the WACCM simulations of future climate change, the GHG signal is statistically identifiable between 2020 and 2030. Our findings demonstrate the importance of continued stratospheric ozone monitoring to improve estimates of the contributions of ODS and GHG forcing to global changes in stratospheric ozone.

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We studied the attribution of stratospheric ozone changes and identified similarities between observations and human fingerprints from both emissions of ozone-depleting substances (ODSs) and greenhouse gases (GHGs). We developed an improvement on the traditional pattern correlation method that accounts for nonlinearities in the climate forcing time evolution. Use of the latter resulted in increased S / N ratios for the ODS fingerprint. The GHG fingerprint was not identifiable.
We studied the attribution of stratospheric ozone changes and identified similarities between...
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