Using machine learning to build temperature-based ozone parameterizations for climate sensitivity si...
Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations
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Publisher
Bristol: IOP Publishing
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Language
English
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Publisher
Bristol: IOP Publishing
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Scope and Contents
Contents
A number of studies have demonstrated the importance of ozone in climate change simulations, for example concerning global warming projections and atmospheric dynamics. However, fully interactive atmospheric chemistry schemes needed for calculating changes in ozone are computationally expensive. Climate modelers therefore often use climatological o...
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Full title
Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations
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TN_cdi_doaj_primary_oai_doaj_org_article_7cb178799f4f4e91aff10b40b61fac91
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_7cb178799f4f4e91aff10b40b61fac91
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ISSN
1748-9326
E-ISSN
1748-9326
DOI
10.1088/1748-9326/aae2be
