Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning

Abstract The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Maike Sonnewald, Redouane Lguensat
Formato:	article
Lenguaje:	EN
Publicado:	American Geophysical Union (AGU) 2021
Materias:	oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean Physical geography GB3-5030 Oceanography GC1-1581
Acceso en línea:	https://doaj.org/article/fafb76437f244bffa9b51e31db03a57f
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:fafb76437f244bffa9b51e31db03a57f
record_format	dspace
spelling	oai:doaj.org-article:fafb76437f244bffa9b51e31db03a57f2021-11-12T07:13:23ZRevealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning1942-246610.1029/2021MS002496https://doaj.org/article/fafb76437f244bffa9b51e31db03a57f2021-08-01T00:00:00Zhttps://doi.org/10.1029/2021MS002496https://doaj.org/toc/1942-2466Abstract The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning (ML) method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of the existence of dynamical coherent regions, THOR identifies these and their link to distinct currents and mechanisms such as the formation regions of deep water masses, and the location of the Gulf Stream and North Atlantic Current. Beyond a black box approach, THOR is engineered to elucidate its source of predictive skill rooted in physical understanding. A labeled data set is engineered using an explicitly interpretable equation transform and k‐means application to model data, allowing theoretical inference. A multilayer perceptron is then trained, explaining its skill using a combination of layerwise relevance propagation and theory. With abrupt CO2 quadrupling, the circulation weakens due to a shift in deep water formation regions, a northward shift of the Gulf Stream and an eastward shift in the North Atlantic Current. If CO2 is increased 1% yearly, similar but weaker patterns emerge influenced by natural variability. THOR is scalable and applicable to a range of models using only the ocean depth, dynamic sea level and wind stress, and could accelerate the analysis and dissemination of climate model data. THOR constitutes a step toward trustworthy ML called for within oceanography and beyond, as its predictions are physically tractable.Maike SonnewaldRedouane LguensatAmerican Geophysical Union (AGU)articleoceanographytransparent machine learningclimate modelingexplainable and interpretable AIglobal heatingNorth Atlantic OceanPhysical geographyGB3-5030OceanographyGC1-1581ENJournal of Advances in Modeling Earth Systems, Vol 13, Iss 8, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean Physical geography GB3-5030 Oceanography GC1-1581
spellingShingle	oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean Physical geography GB3-5030 Oceanography GC1-1581 Maike Sonnewald Redouane Lguensat Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
description	Abstract The North Atlantic ocean is key to climate through its role in heat transport and storage. Climate models suggest that the circulation is weakening but the physical drivers of this change are poorly constrained. Here, the root mechanisms are revealed with the explicitly transparent machine learning (ML) method Tracking global Heating with Ocean Regimes (THOR). Addressing the fundamental question of the existence of dynamical coherent regions, THOR identifies these and their link to distinct currents and mechanisms such as the formation regions of deep water masses, and the location of the Gulf Stream and North Atlantic Current. Beyond a black box approach, THOR is engineered to elucidate its source of predictive skill rooted in physical understanding. A labeled data set is engineered using an explicitly interpretable equation transform and k‐means application to model data, allowing theoretical inference. A multilayer perceptron is then trained, explaining its skill using a combination of layerwise relevance propagation and theory. With abrupt CO2 quadrupling, the circulation weakens due to a shift in deep water formation regions, a northward shift of the Gulf Stream and an eastward shift in the North Atlantic Current. If CO2 is increased 1% yearly, similar but weaker patterns emerge influenced by natural variability. THOR is scalable and applicable to a range of models using only the ocean depth, dynamic sea level and wind stress, and could accelerate the analysis and dissemination of climate model data. THOR constitutes a step toward trustworthy ML called for within oceanography and beyond, as its predictions are physically tractable.
format	article
author	Maike Sonnewald Redouane Lguensat
author_facet	Maike Sonnewald Redouane Lguensat
author_sort	Maike Sonnewald
title	Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
title_short	Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
title_full	Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
title_fullStr	Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
title_full_unstemmed	Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
title_sort	revealing the impact of global heating on north atlantic circulation using transparent machine learning
publisher	American Geophysical Union (AGU)
publishDate	2021
url	https://doaj.org/article/fafb76437f244bffa9b51e31db03a57f
work_keys_str_mv	AT maikesonnewald revealingtheimpactofglobalheatingonnorthatlanticcirculationusingtransparentmachinelearning AT redouanelguensat revealingtheimpactofglobalheatingonnorthatlanticcirculationusingtransparentmachinelearning
_version_	1718431146774102016

Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning

Ejemplares similares