ML-TOMCAT: machine-learning-based satellite-corrected global stratospheric ozone profile data set from a chemical transport model
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Published:2021-12-10
Issue:12
Volume:13
Page:5711-5729
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ISSN:1866-3516
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Container-title:Earth System Science Data
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language:en
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Short-container-title:Earth Syst. Sci. Data
Author:
Dhomse Sandip S.ORCID, Arosio CarloORCID, Feng WuhuORCID, Rozanov AlexeiORCID, Weber MarkORCID, Chipperfield Martyn P.ORCID
Abstract
Abstract. 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. 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
https://doi.org/10.5281/zenodo.5651194 (Dhomse et al., 2021).
Funder
Natural Environment Research Council
Publisher
Copernicus GmbH
Subject
General Earth and Planetary Sciences
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