Detectability of the Impacts of Ozone Depleting Substances and Greenhouse Gases upon Stratospheric Ozone Accounting for Nonlinearities in Historical Forcings

Abstract

<p><strong>Abstract.</strong> 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 (<q>fingerprints</q>) to combined forcing by ozone depleting substances (ODS) and well-mixed greenhouse gases (GHG), 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 (S/N) ratios for each of the three fingerprints (ODS, GHG, and ODS+GHG). In both the upper stratosphere (defined in this paper as 1 to 10 hPa) and lower stratosphere (40 to 100 hPa), 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). Furthermore, we 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 are used in the trend regression, we find that the S/N ratios obtained with the stratospheric ODS and ODS+GHG fingerprints are enhanced relative to standard linear trend analysis. With this method, the complete observational record can be used in the S/N analysis, without applying piece-wise linear regression and introducing arbitrary break points. The GHG-driven fingerprint of ozone changes was not statistically identifiable in the either the upper or lower stratospheric SWOOSH data, irrespective of the method used. Use of the nonlinear signal method, instead of directly operating on ozone trends, also reduces the detection time – the estimate of the date at which ODS and GHG impacts on ozone can be formally identified. In the WACCM future simulations, the GHG signal is statistically identifiable between 2020–2030. 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.</p>

Radiosondes Show That After Decades of Cooling, the Lower Stratosphere Is Now Warming

“…In the lower stratosphere, changes in ODSs have a clearly detectable influence on ozone. Other influences (such as volcanic effects) are also detectable (Aquila et al, ; Bandoro et al, ; Gillett et al, ; Polvani et al, ).…”

Interactive comment on "Continuous decline in lower stratospheric ozone offsets ozone layer recovery"

“…Whether the difference between the models and observations is a result of model design, incorrect boundary conditions (e.g. aerosol contributions from anthropogenic (Yu et al, 2017) or volcanic (Bandoro et al, 2017) sources may be underestimated), or missing chemistry remains an open question (see below 440 for further discussion).…”