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

Gu, Qinxue; Zhang, Liping; Jia, Liwei; Delworth, Thomas L.; Yang, Xiaosong; Zeng, Fanrong; Cooke, William F.; Li, Shouwei

doi:10.1038/s41612-024-00802-2

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

2024
By Gu, Qinxue ; Zhang, Liping ; Jia, Liwei ; ...
Source: npj Climate and Atmospheric Science, 7(1)

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Journal Title:

npj Climate and Atmospheric Science
Personal Author:

Gu, Qinxue ; Zhang, Liping ; Jia, Liwei ; Delworth, Thomas L. ; Yang, Xiaosong ; ... More +

Gu, Qinxue ; Zhang, Liping ; Jia, Liwei ; Delworth, Thomas L. ; Yang, Xiaosong ; Zeng, Fanrong ; Cooke, William F. ; Li, Shouwei Less -
NOAA Program & Office:

OAR (Oceanic and Atmospheric Research) ; GFDL (Geophysical Fluid Dynamics Laboratory)

OAR (Oceanic and Atmospheric Research) ; GFDL (Geophysical Fluid Dynamics Laboratory) Less -
Description:

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.
Source:

npj Climate and Atmospheric Science, 7(1)
DOI:

https://doi.org/10.1038/s41612-024-00802-2
ISSN:

2397-3722
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pdf
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Springer Science and Business Media LLC
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Journal Article
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https://creativecommons.org/licenses/by-nc-nd/4.0
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CC BY-NC-ND
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Submitted
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urn:sha-512:4637ea3e50ac88ce9b60edb0a862f9022f691a4af1af437a14678b683c7b389b8eb822fde0aa292f2cb4b0be29e07fbdd051b7f8218208e904fa4d18cc1d4b17
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Exploring multiyear-to-decadal North Atlantic sea level predictability and prediction using machine learning

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