Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning
International audience 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 transp...
Published in: | Journal of Advances in Modeling Earth Systems |
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Online Access: | https://hal-insu.archives-ouvertes.fr/insu-03721909 https://hal-insu.archives-ouvertes.fr/insu-03721909/document https://hal-insu.archives-ouvertes.fr/insu-03721909/file/J%20Adv%20Model%20Earth%20Syst%20-%202021%20-%20Sonnewald%20-%20Revealing%20the%20Impact%20of%20Global%20Heating%20on%20North%20Atlantic%20Circulation%20Using.pdf https://doi.org/10.1029/2021MS002496 |
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ftunivnantes:oai:HAL:insu-03721909v1 2023-05-15T17:25:17+02:00 Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning Sonnewald, Maike Lguensat, Redouane Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) 2021 https://hal-insu.archives-ouvertes.fr/insu-03721909 https://hal-insu.archives-ouvertes.fr/insu-03721909/document https://hal-insu.archives-ouvertes.fr/insu-03721909/file/J%20Adv%20Model%20Earth%20Syst%20-%202021%20-%20Sonnewald%20-%20Revealing%20the%20Impact%20of%20Global%20Heating%20on%20North%20Atlantic%20Circulation%20Using.pdf https://doi.org/10.1029/2021MS002496 en eng HAL CCSD info:eu-repo/semantics/altIdentifier/doi/10.1029/2021MS002496 insu-03721909 https://hal-insu.archives-ouvertes.fr/insu-03721909 https://hal-insu.archives-ouvertes.fr/insu-03721909/document https://hal-insu.archives-ouvertes.fr/insu-03721909/file/J%20Adv%20Model%20Earth%20Syst%20-%202021%20-%20Sonnewald%20-%20Revealing%20the%20Impact%20of%20Global%20Heating%20on%20North%20Atlantic%20Circulation%20Using.pdf BIBCODE: 2021JAMES.1302496S doi:10.1029/2021MS002496 http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess Journal of Advances in Modeling Earth Systems https://hal-insu.archives-ouvertes.fr/insu-03721909 Journal of Advances in Modeling Earth Systems, 2021, 13, ⟨10.1029/2021MS002496⟩ oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean [SDU]Sciences of the Universe [physics] [SDU.STU]Sciences of the Universe [physics]/Earth Sciences info:eu-repo/semantics/article Journal articles 2021 ftunivnantes https://doi.org/10.1029/2021MS002496 2022-10-18T23:15:09Z International audience 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 CO 2 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 CO 2 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. Article in Journal/Newspaper north atlantic current North Atlantic Université de Nantes: HAL-UNIV-NANTES Journal of Advances in Modeling Earth Systems 13 8 |
institution |
Open Polar |
collection |
Université de Nantes: HAL-UNIV-NANTES |
op_collection_id |
ftunivnantes |
language |
English |
topic |
oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean [SDU]Sciences of the Universe [physics] [SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
spellingShingle |
oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean [SDU]Sciences of the Universe [physics] [SDU.STU]Sciences of the Universe [physics]/Earth Sciences Sonnewald, Maike Lguensat, Redouane Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning |
topic_facet |
oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean [SDU]Sciences of the Universe [physics] [SDU.STU]Sciences of the Universe [physics]/Earth Sciences |
description |
International audience 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 CO 2 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 CO 2 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. |
author2 |
Laboratoire des Sciences du Climat et de l'Environnement Gif-sur-Yvette (LSCE) Université de Versailles Saint-Quentin-en-Yvelines (UVSQ)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Institut national des sciences de l'Univers (INSU - CNRS)-Université Paris-Saclay-Centre National de la Recherche Scientifique (CNRS) |
format |
Article in Journal/Newspaper |
author |
Sonnewald, Maike Lguensat, Redouane |
author_facet |
Sonnewald, Maike Lguensat, Redouane |
author_sort |
Sonnewald, Maike |
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 |
HAL CCSD |
publishDate |
2021 |
url |
https://hal-insu.archives-ouvertes.fr/insu-03721909 https://hal-insu.archives-ouvertes.fr/insu-03721909/document https://hal-insu.archives-ouvertes.fr/insu-03721909/file/J%20Adv%20Model%20Earth%20Syst%20-%202021%20-%20Sonnewald%20-%20Revealing%20the%20Impact%20of%20Global%20Heating%20on%20North%20Atlantic%20Circulation%20Using.pdf https://doi.org/10.1029/2021MS002496 |
genre |
north atlantic current North Atlantic |
genre_facet |
north atlantic current North Atlantic |
op_source |
Journal of Advances in Modeling Earth Systems https://hal-insu.archives-ouvertes.fr/insu-03721909 Journal of Advances in Modeling Earth Systems, 2021, 13, ⟨10.1029/2021MS002496⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1029/2021MS002496 insu-03721909 https://hal-insu.archives-ouvertes.fr/insu-03721909 https://hal-insu.archives-ouvertes.fr/insu-03721909/document https://hal-insu.archives-ouvertes.fr/insu-03721909/file/J%20Adv%20Model%20Earth%20Syst%20-%202021%20-%20Sonnewald%20-%20Revealing%20the%20Impact%20of%20Global%20Heating%20on%20North%20Atlantic%20Circulation%20Using.pdf BIBCODE: 2021JAMES.1302496S doi:10.1029/2021MS002496 |
op_rights |
http://creativecommons.org/licenses/by/ info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1029/2021MS002496 |
container_title |
Journal of Advances in Modeling Earth Systems |
container_volume |
13 |
container_issue |
8 |
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