ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model
<p>High-quality stratospheric ozone profile data sets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments provide stratospheric ozone profile measurements over typical mission durations of 5–15 years. Various...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-12-01
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| Series: | Earth System Science Data |
| Online Access: | https://essd.copernicus.org/articles/13/5711/2021/essd-13-5711-2021.pdf |
| _version_ | 1818459357202874368 |
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| author | S. S. Dhomse S. S. Dhomse C. Arosio W. Feng W. Feng A. Rozanov M. Weber M. P. Chipperfield M. P. Chipperfield |
| author_facet | S. S. Dhomse S. S. Dhomse C. Arosio W. Feng W. Feng A. Rozanov M. Weber M. P. Chipperfield M. P. Chipperfield |
| author_sort | S. S. Dhomse |
| collection | DOAJ |
| description | <p>High-quality stratospheric ozone profile data sets are a key
requirement for accurate quantification and attribution of long-term
ozone changes. Satellite instruments provide stratospheric ozone
profile measurements over typical mission durations of 5–15 years.
Various methodologies have then been applied to merge and homogenise
the different satellite data in order to create long-term
observation-based ozone profile data sets with minimal data gaps.
However, individual satellite instruments use different measurement
methods, sampling patterns and retrieval algorithms which complicate
the merging of these different data sets. In contrast, atmospheric
chemical models can produce chemically consistent long-term ozone
simulations based on specified changes in external forcings, but they
are subject to the deficiencies associated with incomplete
understanding of complex atmospheric processes and uncertain
photochemical parameters.</p>
<p>Here, we use chemically self-consistent output from the TOMCAT 3-D
chemical transport model (CTM) and a random-forest (RF) ensemble
learning method to create a merged 42-year (1979–2020) stratospheric
ozone profile data set (ML-TOMCAT V1.0). The underlying CTM
simulation was forced by meteorological reanalyses, specified trends
in long-lived source gases, solar flux and aerosol variations. The RF
is trained using the Stratospheric Water and OzOne Satellite
Homogenized (SWOOSH) data set over the time periods of the Microwave
Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS)
(1991–1998) and Aura (2005–2016) missions. We find that ML-TOMCAT
shows excellent agreement with available independent satellite-based
data sets which use pressure as a vertical coordinate (e.g. GOZCARDS,
SWOOSH for non-MLS periods) but weaker agreement with the data sets
which are altitude-based (e.g. SAGE-CCI-OMPS, SCIAMACHY-OMPS). We
find that at almost all stratospheric levels ML-TOMCAT ozone
concentrations are well within uncertainties of the observational data
sets. The ML-TOMCAT (V1.0) data set is ideally suited for the
evaluation of chemical model ozone profiles from the tropopause to 0.1 hPa and is freely available via
<a href="https://doi.org/10.5281/zenodo.5651194">https://doi.org/10.5281/zenodo.5651194</a> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx29">Dhomse et al.</a>, <a href="#bib1.bibx29">2021</a>)</span>.</p> |
| first_indexed | 2024-12-14T23:13:04Z |
| format | Article |
| id | doaj.art-a5c9b5df79e648c6925ba212d6b1ab24 |
| institution | Directory Open Access Journal |
| issn | 1866-3508 1866-3516 |
| language | English |
| last_indexed | 2024-12-14T23:13:04Z |
| publishDate | 2021-12-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Earth System Science Data |
| spelling | doaj.art-a5c9b5df79e648c6925ba212d6b1ab242022-12-21T22:44:09ZengCopernicus PublicationsEarth System Science Data1866-35081866-35162021-12-01135711572910.5194/essd-13-5711-2021ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport modelS. S. Dhomse0S. S. Dhomse1C. Arosio2W. Feng3W. Feng4A. Rozanov5M. Weber6M. P. Chipperfield7M. P. Chipperfield8School of Earth and Environment, University of Leeds, Leeds, UKNational Centre for Earth Observation, University of Leeds, Leeds, UKInstitute for Environmental Physics, University of Bremen, Bremen, GermanySchool of Earth and Environment, University of Leeds, Leeds, UKNational Centre for Atmospheric Science, University of Leeds, Leeds, UKInstitute for Environmental Physics, University of Bremen, Bremen, GermanyInstitute for Environmental Physics, University of Bremen, Bremen, GermanySchool of Earth and Environment, University of Leeds, Leeds, UKNational Centre for Earth Observation, University of Leeds, Leeds, UK<p>High-quality stratospheric ozone profile data sets are a key requirement for accurate quantification and attribution of long-term ozone changes. Satellite instruments provide stratospheric ozone profile measurements over typical mission durations of 5–15 years. Various methodologies have then been applied to merge and homogenise the different satellite data in order to create long-term observation-based ozone profile data sets with minimal data gaps. However, individual satellite instruments use different measurement methods, sampling patterns and retrieval algorithms which complicate the merging of these different data sets. In contrast, atmospheric chemical models can produce chemically consistent long-term ozone simulations based on specified changes in external forcings, but they are subject to the deficiencies associated with incomplete understanding of complex atmospheric processes and uncertain photochemical parameters.</p> <p>Here, we use chemically self-consistent output from the TOMCAT 3-D chemical transport model (CTM) and a random-forest (RF) ensemble learning method to create a merged 42-year (1979–2020) stratospheric ozone profile data set (ML-TOMCAT V1.0). The underlying CTM simulation was forced by meteorological reanalyses, specified trends in long-lived source gases, solar flux and aerosol variations. The RF is trained using the Stratospheric Water and OzOne Satellite Homogenized (SWOOSH) data set over the time periods of the Microwave Limb Sounder (MLS) from the Upper Atmosphere Research Satellite (UARS) (1991–1998) and Aura (2005–2016) missions. We find that ML-TOMCAT shows excellent agreement with available independent satellite-based data sets which use pressure as a vertical coordinate (e.g. GOZCARDS, SWOOSH for non-MLS periods) but weaker agreement with the data sets which are altitude-based (e.g. SAGE-CCI-OMPS, SCIAMACHY-OMPS). We find that at almost all stratospheric levels ML-TOMCAT ozone concentrations are well within uncertainties of the observational data sets. The ML-TOMCAT (V1.0) data set is ideally suited for the evaluation of chemical model ozone profiles from the tropopause to 0.1 hPa and is freely available via <a href="https://doi.org/10.5281/zenodo.5651194">https://doi.org/10.5281/zenodo.5651194</a> <span class="cit" id="xref_paren.1">(<a href="#bib1.bibx29">Dhomse et al.</a>, <a href="#bib1.bibx29">2021</a>)</span>.</p>https://essd.copernicus.org/articles/13/5711/2021/essd-13-5711-2021.pdf |
| spellingShingle | S. S. Dhomse S. S. Dhomse C. Arosio W. Feng W. Feng A. Rozanov M. Weber M. P. Chipperfield M. P. Chipperfield ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model Earth System Science Data |
| title | ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model |
| title_full | ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model |
| title_fullStr | ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model |
| title_full_unstemmed | ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model |
| title_short | ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model |
| title_sort | ml tomcat machine learning based satellite corrected global stratospheric ozone profile data set from a chemical transport model |
| url | https://essd.copernicus.org/articles/13/5711/2021/essd-13-5711-2021.pdf |
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