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

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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Bibliographic Details
Main Authors: Peer Nowack, Peter Braesicke, Joanna Haigh, Nathan Luke Abraham, John Pyle, Apostolos Voulgarakis
Format: Article
Language:English
Published: IOP Publishing 2018-01-01
Series:Environmental Research Letters
Subjects:
climate change
climate sensitivity
ozone
parameterization
machine learning
big data
Online Access:https://doi.org/10.1088/1748-9326/aae2be

Internet

https://doi.org/10.1088/1748-9326/aae2be

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