Using machine learning to build temperature-based ozone parameterizations for climate sensitivity simulations

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. Cli...

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Detalhes bibliográficos

Principais autores:	Peer Nowack, Peter Braesicke, Joanna Haigh, Nathan Luke Abraham, John Pyle, Apostolos Voulgarakis
Formato:	Artigo
Idioma:	Inglês
Publicado em:	IOP Publishing 2018-01-01
coleção:	Environmental Research Letters
Assuntos:	climate change climate sensitivity ozone parameterization machine learning big data
Acesso em linha:	https://doi.org/10.1088/1748-9326/aae2be
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