Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks
North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable locations in the world’s oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks...
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2023
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Online Access: | http://dx.doi.org/10.22541/essoar.169504625.54140776/v1 |
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crwinnower:10.22541/essoar.169504625.54140776/v1 2024-06-02T08:10:53+00:00 Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks Liu, Glenn Yu-zu Wang, Peidong Kwon, Young-Oh 2023 http://dx.doi.org/10.22541/essoar.169504625.54140776/v1 unknown Authorea, Inc. posted-content 2023 crwinnower https://doi.org/10.22541/essoar.169504625.54140776/v1 2024-05-07T14:19:23Z North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable locations in the world’s oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25-year leadtimes. Layer-wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream-North Atlantic Current region for accurate predictions. Additionally, CESM1-trained NNs do not need additional transfer learning to successfully predict the phasing of multidecadal variability in an observational dataset, suggesting consistency in physical processes driving NASST variability between CESM1 and observations. Other/Unknown Material north atlantic current North Atlantic The Winnower |
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description |
North Atlantic sea surface temperatures (NASST), particularly in the subpolar region, are among the most predictable locations in the world’s oceans. However, the relative importance of atmospheric and oceanic controls on their variability at multidecadal timescales remain uncertain. Neural networks (NNs) are trained to examine the relative importance of oceanic and atmospheric predictors in predicting the NASST state in the Community Earth System Model 1 (CESM1). In the presence of external forcings, oceanic predictors outperform atmospheric predictors, persistence, and random chance baselines out to 25-year leadtimes. Layer-wise relevance propagation is used to unveil the sources of predictability, and reveal that NNs consistently rely upon the Gulf Stream-North Atlantic Current region for accurate predictions. Additionally, CESM1-trained NNs do not need additional transfer learning to successfully predict the phasing of multidecadal variability in an observational dataset, suggesting consistency in physical processes driving NASST variability between CESM1 and observations. |
format |
Other/Unknown Material |
author |
Liu, Glenn Yu-zu Wang, Peidong Kwon, Young-Oh |
spellingShingle |
Liu, Glenn Yu-zu Wang, Peidong Kwon, Young-Oh Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks |
author_facet |
Liu, Glenn Yu-zu Wang, Peidong Kwon, Young-Oh |
author_sort |
Liu, Glenn Yu-zu |
title |
Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks |
title_short |
Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks |
title_full |
Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks |
title_fullStr |
Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks |
title_full_unstemmed |
Physical Insights from the Multidecadal Prediction of North Atlantic Sea Surface Temperature Variability Using Explainable Neural Networks |
title_sort |
physical insights from the multidecadal prediction of north atlantic sea surface temperature variability using explainable neural networks |
publisher |
Authorea, Inc. |
publishDate |
2023 |
url |
http://dx.doi.org/10.22541/essoar.169504625.54140776/v1 |
genre |
north atlantic current North Atlantic |
genre_facet |
north atlantic current North Atlantic |
op_doi |
https://doi.org/10.22541/essoar.169504625.54140776/v1 |
_version_ |
1800756809972056064 |