In:
Atmospheric Chemistry and Physics, Copernicus GmbH, Vol. 18, No. 1 ( 2018-01-05), p. 143-166
Abstract:
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
(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). 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 S∕N 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 S∕N 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.
Type of Medium:
Online Resource
ISSN:
1680-7324
DOI:
10.5194/acp-18-143-2018
DOI:
10.5194/acp-18-143-2018-supplement
Language:
English
Publisher:
Copernicus GmbH
Publication Date:
2018
detail.hit.zdb_id:
2092549-9
detail.hit.zdb_id:
2069847-1
Permalink
