Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections

The ensemble spread of climate models is often interpreted as the uncertainty of the projection, but this is not always justified. Applying learning algorithms to an ensemble of climate predictions allows for a significant uncertainty reduction of projected global mean surface temperatures compared...

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Autores principales:	Ehud Strobach, Golan Bel
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/091cdd00c72a4b78b45a73bb4d21935a
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spelling	oai:doaj.org-article:091cdd00c72a4b78b45a73bb4d21935a2021-12-02T15:34:13ZLearning algorithms allow for improved reliability and accuracy of global mean surface temperature projections10.1038/s41467-020-14342-92041-1723https://doaj.org/article/091cdd00c72a4b78b45a73bb4d21935a2020-01-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-14342-9https://doaj.org/toc/2041-1723The ensemble spread of climate models is often interpreted as the uncertainty of the projection, but this is not always justified. Applying learning algorithms to an ensemble of climate predictions allows for a significant uncertainty reduction of projected global mean surface temperatures compared to the ensemble spread.Ehud StrobachGolan BelNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-7 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Ehud Strobach Golan Bel Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections
description	The ensemble spread of climate models is often interpreted as the uncertainty of the projection, but this is not always justified. Applying learning algorithms to an ensemble of climate predictions allows for a significant uncertainty reduction of projected global mean surface temperatures compared to the ensemble spread.
format	article
author	Ehud Strobach Golan Bel
author_facet	Ehud Strobach Golan Bel
author_sort	Ehud Strobach
title	Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections
title_short	Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections
title_full	Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections
title_fullStr	Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections
title_full_unstemmed	Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections
title_sort	learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/091cdd00c72a4b78b45a73bb4d21935a
work_keys_str_mv	AT ehudstrobach learningalgorithmsallowforimprovedreliabilityandaccuracyofglobalmeansurfacetemperatureprojections AT golanbel learningalgorithmsallowforimprovedreliabilityandaccuracyofglobalmeansurfacetemperatureprojections
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Learning algorithms allow for improved reliability and accuracy of global mean surface temperature projections

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