Exploring multiyear-to-decadal North Atlantic sea level predictability and prediction using machine learning

Coastal communities face substantial risks from long-term sea level rise and decadal sea level variations, with the North Atlantic and U.S. East Coast being particularly vulnerable under changing climates. Employing a self-organizing map-based framework, we assess the North Atlantic sea level variability and predictability using 5000-year sea level anomalies (SLA) from two preindustrial control model simulations. Preferred transitions among patterns of variability are identified, revealing long-term predictability on decadal timescales related to shifts in Atlantic meridional overturning circulation phases. Combining this framework with model-analog techniques, we demonstrate prediction skill of large-scale SLA patterns and low-frequency coastal SLA variations comparable to that from initialized hindcasts. Moreover, additional short-term predictability is identified after the exclusion of low-frequency signals, which arises from slow gyre circulation adjustment triggered by the North Atlantic Oscillation-like stochastic variability. This study highlights the potential of machine learning to assess sources of predictability and to enable long-term climate prediction. 

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Related Dataset #1 : Sea level daily gridded data from satellite observations for the global ocean from 1993 to present

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Author Gu, Q.
Zhang, Liping
Jia, L.
Delworth, T. L.
Yang, X.
Zeng, F.
Cooke, W. F.
Li, S.
Publisher UCAR/NCAR - Library
Publication Date 2024-12-01T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-10T19:56:30.363059
Metadata Record Identifier edu.ucar.opensky::articles:42405
Metadata Language eng; USA
Suggested Citation Gu, Q., Zhang, Liping, Jia, L., Delworth, T. L., Yang, X., Zeng, F., Cooke, W. F., Li, S.. (2024). Exploring multiyear-to-decadal North Atlantic sea level predictability and prediction using machine learning. UCAR/NCAR - Library. https://n2t.net/ark:/85065/d7g44vkb. Accessed 03 August 2025.

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