Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range...

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Autores principales:	Janni Yuval, Paul A. O’Gorman
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/ecda3df6d70848f78f335430e31079e4
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id	oai:doaj.org-article:ecda3df6d70848f78f335430e31079e4
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spelling	oai:doaj.org-article:ecda3df6d70848f78f335430e31079e42021-12-02T14:34:06ZStable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions10.1038/s41467-020-17142-32041-1723https://doaj.org/article/ecda3df6d70848f78f335430e31079e42020-07-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-17142-3https://doaj.org/toc/2041-1723Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range of grid spacings.Janni YuvalPaul A. O’GormanNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-10 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Janni Yuval Paul A. O’Gorman Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
description	Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range of grid spacings.
format	article
author	Janni Yuval Paul A. O’Gorman
author_facet	Janni Yuval Paul A. O’Gorman
author_sort	Janni Yuval
title	Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
title_short	Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
title_full	Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
title_fullStr	Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
title_full_unstemmed	Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
title_sort	stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/ecda3df6d70848f78f335430e31079e4
work_keys_str_mv	AT janniyuval stablemachinelearningparameterizationofsubgridprocessesforclimatemodelingatarangeofresolutions AT paulaogorman stablemachinelearningparameterizationofsubgridprocessesforclimatemodelingatarangeofresolutions
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Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions

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