Reconciling differences in stratospheric ozone composites
Observations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composit...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2017-10-01
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| Series: | Atmospheric Chemistry and Physics |
| Online Access: | https://www.atmos-chem-phys.net/17/12269/2017/acp-17-12269-2017.pdf |
| _version_ | 1818427040085311488 |
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| author | W. T. Ball W. T. Ball J. Alsing J. Alsing D. J. Mortlock D. J. Mortlock D. J. Mortlock E. V. Rozanov E. V. Rozanov F. Tummon J. D. Haigh J. D. Haigh |
| author_facet | W. T. Ball W. T. Ball J. Alsing J. Alsing D. J. Mortlock D. J. Mortlock D. J. Mortlock E. V. Rozanov E. V. Rozanov F. Tummon J. D. Haigh J. D. Haigh |
| author_sort | W. T. Ball |
| collection | DOAJ |
| description | Observations of stratospheric ozone from multiple instruments now span three
decades; combining these into composite datasets allows long-term ozone
trends to be estimated. Recently, several ozone composites have been
published, but trends disagree by latitude and altitude, even between
composites built upon the same instrument data. We confirm that the main
causes of differences in decadal trend estimates lie in (i) steps in the
composite time series when the instrument source data changes and (ii) artificial
sub-decadal trends in the underlying instrument data. These
artefacts introduce features that can alias with regressors in multiple
linear regression (MLR) analysis; both can lead to inaccurate trend estimates.
Here, we aim to remove these artefacts using Bayesian methods to
infer the underlying ozone time series from a set of composites by building a
joint-likelihood function using a Gaussian-mixture
density to model outliers introduced by data artefacts, together with a
data-driven prior on ozone variability that incorporates knowledge of problems
during instrument operation. We apply this Bayesian self-calibration approach
to stratospheric ozone in 10° bands from 60° S to 60° N and from 46 to 1 hPa (∼ 21–48 km) for
1985–2012. There are two main outcomes: (i) we independently identify and
confirm many of the data problems previously identified, but which remain
unaccounted for in existing composites; (ii) we construct an ozone composite,
with uncertainties, that is free from most of these problems – we call this
the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use
dynamical linear modelling (DLM), which provides a more robust estimate of
long-term changes through Bayesian inference than MLR. BASIC and DLM,
together, provide a step forward in improving estimates of decadal trends.
Our results indicate a significant recovery of ozone since 1998 in the upper
stratosphere, of both northern and southern midlatitudes, in all four
composites analysed, and particularly in the BASIC composite. The BASIC
results also show no hemispheric difference in the recovery at midlatitudes,
in contrast to an apparent feature that is present, but not consistent, in
the four composites. Our overall conclusion is that it is possible to
effectively combine different ozone composites and account for artefacts and
drifts, and that this leads to a clear and significant result that upper
stratospheric ozone levels have increased since 1998, following an earlier
decline. |
| first_indexed | 2024-12-14T14:39:24Z |
| format | Article |
| id | doaj.art-7f7df7ec57d74cfbb02b71b2c75c9672 |
| institution | Directory Open Access Journal |
| issn | 1680-7316 1680-7324 |
| language | English |
| last_indexed | 2024-12-14T14:39:24Z |
| publishDate | 2017-10-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Chemistry and Physics |
| spelling | doaj.art-7f7df7ec57d74cfbb02b71b2c75c96722022-12-21T22:57:28ZengCopernicus PublicationsAtmospheric Chemistry and Physics1680-73161680-73242017-10-0117122691230210.5194/acp-17-12269-2017Reconciling differences in stratospheric ozone compositesW. T. Ball0W. T. Ball1J. Alsing2J. Alsing3D. J. Mortlock4D. J. Mortlock5D. J. Mortlock6E. V. Rozanov7E. V. Rozanov8F. Tummon9J. D. Haigh10J. D. Haigh11Institute for Atmospheric and Climate Science, Swiss Federal Institute of Technology Zurich, Universitaetstrasse 16, CHN, 8092 Zurich, SwitzerlandPhysikalisch-Meteorologisches Observatorium Davos World Radiation Centre, Dorfstrasse 33, 7260 Davos Dorf, SwitzerlandCenter for Computational Astrophysics, Flatiron Institute, 162 5th Ave, New York, NY 10010, USAPhysics Department, Blackett Laboratory, Imperial College London, SW7 2AZ London, UKPhysics Department, Blackett Laboratory, Imperial College London, SW7 2AZ London, UKDepartment of Mathematics, Imperial College London, SW7 2AZ London, UKDepartment of Astronomy, Stockholms universitet, 106 91 Stockholm, SwedenInstitute for Atmospheric and Climate Science, Swiss Federal Institute of Technology Zurich, Universitaetstrasse 16, CHN, 8092 Zurich, SwitzerlandPhysikalisch-Meteorologisches Observatorium Davos World Radiation Centre, Dorfstrasse 33, 7260 Davos Dorf, SwitzerlandInstitute for Atmospheric and Climate Science, Swiss Federal Institute of Technology Zurich, Universitaetstrasse 16, CHN, 8092 Zurich, SwitzerlandPhysics Department, Blackett Laboratory, Imperial College London, SW7 2AZ London, UKGrantham Institute – Climate Change and the Environment, Imperial College London, SW7 2AZ London, UKObservations of stratospheric ozone from multiple instruments now span three decades; combining these into composite datasets allows long-term ozone trends to be estimated. Recently, several ozone composites have been published, but trends disagree by latitude and altitude, even between composites built upon the same instrument data. We confirm that the main causes of differences in decadal trend estimates lie in (i) steps in the composite time series when the instrument source data changes and (ii) artificial sub-decadal trends in the underlying instrument data. These artefacts introduce features that can alias with regressors in multiple linear regression (MLR) analysis; both can lead to inaccurate trend estimates. Here, we aim to remove these artefacts using Bayesian methods to infer the underlying ozone time series from a set of composites by building a joint-likelihood function using a Gaussian-mixture density to model outliers introduced by data artefacts, together with a data-driven prior on ozone variability that incorporates knowledge of problems during instrument operation. We apply this Bayesian self-calibration approach to stratospheric ozone in 10° bands from 60° S to 60° N and from 46 to 1 hPa (∼ 21–48 km) for 1985–2012. There are two main outcomes: (i) we independently identify and confirm many of the data problems previously identified, but which remain unaccounted for in existing composites; (ii) we construct an ozone composite, with uncertainties, that is free from most of these problems – we call this the BAyeSian Integrated and Consolidated (BASIC) composite. To analyse the new BASIC composite, we use dynamical linear modelling (DLM), which provides a more robust estimate of long-term changes through Bayesian inference than MLR. BASIC and DLM, together, provide a step forward in improving estimates of decadal trends. Our results indicate a significant recovery of ozone since 1998 in the upper stratosphere, of both northern and southern midlatitudes, in all four composites analysed, and particularly in the BASIC composite. The BASIC results also show no hemispheric difference in the recovery at midlatitudes, in contrast to an apparent feature that is present, but not consistent, in the four composites. Our overall conclusion is that it is possible to effectively combine different ozone composites and account for artefacts and drifts, and that this leads to a clear and significant result that upper stratospheric ozone levels have increased since 1998, following an earlier decline.https://www.atmos-chem-phys.net/17/12269/2017/acp-17-12269-2017.pdf |
| spellingShingle | W. T. Ball W. T. Ball J. Alsing J. Alsing D. J. Mortlock D. J. Mortlock D. J. Mortlock E. V. Rozanov E. V. Rozanov F. Tummon J. D. Haigh J. D. Haigh Reconciling differences in stratospheric ozone composites Atmospheric Chemistry and Physics |
| title | Reconciling differences in stratospheric ozone composites |
| title_full | Reconciling differences in stratospheric ozone composites |
| title_fullStr | Reconciling differences in stratospheric ozone composites |
| title_full_unstemmed | Reconciling differences in stratospheric ozone composites |
| title_short | Reconciling differences in stratospheric ozone composites |
| title_sort | reconciling differences in stratospheric ozone composites |
| url | https://www.atmos-chem-phys.net/17/12269/2017/acp-17-12269-2017.pdf |
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