Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation ...
Recent research on the Atlantic Meridional Overturning Circulation (AMOC) raised concern about its potential collapse through a tipping point due to the climate-change caused increase in the freshwater input into the North Atlantic. The predicted time window of collapse is centered about the middle...
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ftdatacite:10.48550/arxiv.2402.14877 2024-03-31T07:54:17+00:00 Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation ... Panahi, Shirin Kong, Ling-Wei Moradi, Mohammadamin Zhai, Zheng-Meng Glaz, Bryan Haile, Mulugeta Lai, Ying-Cheng 2024 https://dx.doi.org/10.48550/arxiv.2402.14877 https://arxiv.org/abs/2402.14877 unknown arXiv arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ Atmospheric and Oceanic Physics physics.ao-ph Machine Learning cs.LG Dynamical Systems math.DS Data Analysis, Statistics and Probability physics.data-an Popular Physics physics.pop-ph FOS Physical sciences FOS Computer and information sciences FOS Mathematics article Preprint Article CreativeWork 2024 ftdatacite https://doi.org/10.48550/arxiv.2402.14877 2024-03-04T14:10:29Z Recent research on the Atlantic Meridional Overturning Circulation (AMOC) raised concern about its potential collapse through a tipping point due to the climate-change caused increase in the freshwater input into the North Atlantic. The predicted time window of collapse is centered about the middle of the century and the earliest possible start is approximately two years from now. More generally, anticipating a tipping point at which the system transitions from one stable steady state to another is relevant to a broad range of fields. We develop a machine-learning approach to predicting tipping in noisy dynamical systems with a time-varying parameter and test it on a number of systems including the AMOC, ecological networks, an electrical power system, and a climate model. For the AMOC, our prediction based on simulated fingerprint data and real data of the sea surface temperature places the time window of a potential collapse between the years 2040 and 2065. ... : 6 pages, 3 figures ... Report North Atlantic DataCite Metadata Store (German National Library of Science and Technology) |
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DataCite Metadata Store (German National Library of Science and Technology) |
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topic |
Atmospheric and Oceanic Physics physics.ao-ph Machine Learning cs.LG Dynamical Systems math.DS Data Analysis, Statistics and Probability physics.data-an Popular Physics physics.pop-ph FOS Physical sciences FOS Computer and information sciences FOS Mathematics |
spellingShingle |
Atmospheric and Oceanic Physics physics.ao-ph Machine Learning cs.LG Dynamical Systems math.DS Data Analysis, Statistics and Probability physics.data-an Popular Physics physics.pop-ph FOS Physical sciences FOS Computer and information sciences FOS Mathematics Panahi, Shirin Kong, Ling-Wei Moradi, Mohammadamin Zhai, Zheng-Meng Glaz, Bryan Haile, Mulugeta Lai, Ying-Cheng Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation ... |
topic_facet |
Atmospheric and Oceanic Physics physics.ao-ph Machine Learning cs.LG Dynamical Systems math.DS Data Analysis, Statistics and Probability physics.data-an Popular Physics physics.pop-ph FOS Physical sciences FOS Computer and information sciences FOS Mathematics |
description |
Recent research on the Atlantic Meridional Overturning Circulation (AMOC) raised concern about its potential collapse through a tipping point due to the climate-change caused increase in the freshwater input into the North Atlantic. The predicted time window of collapse is centered about the middle of the century and the earliest possible start is approximately two years from now. More generally, anticipating a tipping point at which the system transitions from one stable steady state to another is relevant to a broad range of fields. We develop a machine-learning approach to predicting tipping in noisy dynamical systems with a time-varying parameter and test it on a number of systems including the AMOC, ecological networks, an electrical power system, and a climate model. For the AMOC, our prediction based on simulated fingerprint data and real data of the sea surface temperature places the time window of a potential collapse between the years 2040 and 2065. ... : 6 pages, 3 figures ... |
format |
Report |
author |
Panahi, Shirin Kong, Ling-Wei Moradi, Mohammadamin Zhai, Zheng-Meng Glaz, Bryan Haile, Mulugeta Lai, Ying-Cheng |
author_facet |
Panahi, Shirin Kong, Ling-Wei Moradi, Mohammadamin Zhai, Zheng-Meng Glaz, Bryan Haile, Mulugeta Lai, Ying-Cheng |
author_sort |
Panahi, Shirin |
title |
Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation ... |
title_short |
Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation ... |
title_full |
Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation ... |
title_fullStr |
Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation ... |
title_full_unstemmed |
Machine-learning prediction of tipping and collapse of the Atlantic Meridional Overturning Circulation ... |
title_sort |
machine-learning prediction of tipping and collapse of the atlantic meridional overturning circulation ... |
publisher |
arXiv |
publishDate |
2024 |
url |
https://dx.doi.org/10.48550/arxiv.2402.14877 https://arxiv.org/abs/2402.14877 |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_rights |
arXiv.org perpetual, non-exclusive license http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
op_doi |
https://doi.org/10.48550/arxiv.2402.14877 |
_version_ |
1795035079736557568 |