Predicting regional coastal sea level changes with machine learning

Abstract All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, however, important regional differences, resulting from distinct processes at different timescales (temperature-driven changes being a major contributor on multi-year timescales)....

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Autores principales:	Veronica Nieves, Cristina Radin, Gustau Camps-Valls
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/acbbe65649e14f259055673f95c8d4de
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spelling	oai:doaj.org-article:acbbe65649e14f259055673f95c8d4de2021-12-02T14:17:16ZPredicting regional coastal sea level changes with machine learning10.1038/s41598-021-87460-z2045-2322https://doaj.org/article/acbbe65649e14f259055673f95c8d4de2021-04-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-87460-zhttps://doaj.org/toc/2045-2322Abstract All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, however, important regional differences, resulting from distinct processes at different timescales (temperature-driven changes being a major contributor on multi-year timescales). In view of this complexity, it deems essential to move towards more sophisticated data-driven techniques as well as diagnostic and prognostic prediction models to interpret observations of ocean warming and sea level variations at local or regional sea basins. In this context, we present a machine learning approach that exploits key ocean temperature estimates (as proxies for the regional thermosteric sea level component) to model coastal sea level variability and associated uncertainty across a range of timescales (from months to several years). Our findings also demonstrate the utility of machine learning to estimate the possible tendency of near-future regional sea levels. When compared to actual sea-level records, our models perform particularly well in the coastal areas most influenced by internal climate variability. Yet, the models are widely applicable to evaluate the patterns of rising and falling sea levels across many places around the globe. Thus, our approach is a promising tool to model and anticipate sea level changes in the coming (1–3) years, which is crucial for near-term decision making and strategic planning about coastal protection measures.Veronica NievesCristina RadinGustau Camps-VallsNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-6 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Veronica Nieves Cristina Radin Gustau Camps-Valls Predicting regional coastal sea level changes with machine learning
description	Abstract All ocean basins have been experiencing significant warming and rising sea levels in recent decades. There are, however, important regional differences, resulting from distinct processes at different timescales (temperature-driven changes being a major contributor on multi-year timescales). In view of this complexity, it deems essential to move towards more sophisticated data-driven techniques as well as diagnostic and prognostic prediction models to interpret observations of ocean warming and sea level variations at local or regional sea basins. In this context, we present a machine learning approach that exploits key ocean temperature estimates (as proxies for the regional thermosteric sea level component) to model coastal sea level variability and associated uncertainty across a range of timescales (from months to several years). Our findings also demonstrate the utility of machine learning to estimate the possible tendency of near-future regional sea levels. When compared to actual sea-level records, our models perform particularly well in the coastal areas most influenced by internal climate variability. Yet, the models are widely applicable to evaluate the patterns of rising and falling sea levels across many places around the globe. Thus, our approach is a promising tool to model and anticipate sea level changes in the coming (1–3) years, which is crucial for near-term decision making and strategic planning about coastal protection measures.
format	article
author	Veronica Nieves Cristina Radin Gustau Camps-Valls
author_facet	Veronica Nieves Cristina Radin Gustau Camps-Valls
author_sort	Veronica Nieves
title	Predicting regional coastal sea level changes with machine learning
title_short	Predicting regional coastal sea level changes with machine learning
title_full	Predicting regional coastal sea level changes with machine learning
title_fullStr	Predicting regional coastal sea level changes with machine learning
title_full_unstemmed	Predicting regional coastal sea level changes with machine learning
title_sort	predicting regional coastal sea level changes with machine learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/acbbe65649e14f259055673f95c8d4de
work_keys_str_mv	AT veronicanieves predictingregionalcoastalsealevelchangeswithmachinelearning AT cristinaradin predictingregionalcoastalsealevelchangeswithmachinelearning AT gustaucampsvalls predictingregionalcoastalsealevelchangeswithmachinelearning
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Predicting regional coastal sea level changes with machine learning

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