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...
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ftdoajarticles:oai:doaj.org/article:fafb76437f244bffa9b51e31db03a57f 2023-05-15T17:25:17+02:00 Revealing the Impact of Global Heating on North Atlantic Circulation Using Transparent Machine Learning Maike Sonnewald Redouane Lguensat 2021-08-01T00:00:00Z https://doi.org/10.1029/2021MS002496 https://doaj.org/article/fafb76437f244bffa9b51e31db03a57f EN eng American Geophysical Union (AGU) https://doi.org/10.1029/2021MS002496 https://doaj.org/toc/1942-2466 1942-2466 doi:10.1029/2021MS002496 https://doaj.org/article/fafb76437f244bffa9b51e31db03a57f Journal of Advances in Modeling Earth Systems, Vol 13, Iss 8, Pp n/a-n/a (2021) oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean Physical geography GB3-5030 GC1-1581 article 2021 ftdoajarticles https://doi.org/10.1029/2021MS002496 2022-12-31T14:51:55Z 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. Article in Journal/Newspaper north atlantic current North Atlantic Directory of Open Access Journals: DOAJ Articles Journal of Advances in Modeling Earth Systems 13 8 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean Physical geography GB3-5030 GC1-1581 |
spellingShingle |
oceanography transparent machine learning climate modeling explainable and interpretable AI global heating North Atlantic Ocean Physical geography GB3-5030 GC1-1581 Maike Sonnewald Redouane Lguensat 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 Physical geography GB3-5030 GC1-1581 |
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 in Journal/Newspaper |
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://doi.org/10.1029/2021MS002496 https://doaj.org/article/fafb76437f244bffa9b51e31db03a57f |
genre |
north atlantic current North Atlantic |
genre_facet |
north atlantic current North Atlantic |
op_source |
Journal of Advances in Modeling Earth Systems, Vol 13, Iss 8, Pp n/a-n/a (2021) |
op_relation |
https://doi.org/10.1029/2021MS002496 https://doaj.org/toc/1942-2466 1942-2466 doi:10.1029/2021MS002496 https://doaj.org/article/fafb76437f244bffa9b51e31db03a57f |
op_doi |
https://doi.org/10.1029/2021MS002496 |
container_title |
Journal of Advances in Modeling Earth Systems |
container_volume |
13 |
container_issue |
8 |
_version_ |
1766116668053913600 |