Diagnosing Two-Way Coupling in Decadal North Atlantic SST Variability Using Time-Evolving Self-Organizing Maps

Decadal variability in the North Atlantic plays a critical role in modulating regional and global climate. To identify the complex spatiotemporal patterns associated with decadal variability and diagnose mechanisms responsible spatially and over time simultaneously, we debut a novel application of a machine learning method—evolution self-organizing maps. This time-evolving framework is applied to a Community Earth System Model pre-industrial simulation to identify 10-year consecutive spatiotemporal evolutions of winter sea surface temperature (SST). Here we focus on a single evolution that transitions from SST patterns typically associated with a positive North Atlantic Oscillation (NAO) to a positive Atlantic Multidecadal Variability to a weak negative NAO and find that it can occur over just a 10-year period. This method facilitates a new examination of buoyancy-driven and wind-driven ocean circulations as well as ocean-atmosphere transient-eddy feedbacks that confirms the importance of coupled atmosphere-ocean dynamics in producing this decadal variability.

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Work Title Diagnosing Two-Way Coupling in Decadal North Atlantic SST Variability Using Time-Evolving Self-Organizing Maps
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Open Access
Creators
  1. Qinxue Gu
  2. Melissa Gervais
License In Copyright (Rights Reserved)
Work Type Article
Publisher
  1. Geophysical Research Letters
Publication Date March 30, 2022
Publisher Identifier (DOI)
  1. https://doi.org/10.1029/2021GL096560
Deposited July 21, 2022

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  • Created
  • Added Gu_and_Gervais___2021___GRL___3_Figures___For_third_submission_____Marks_Cleaned_Up_for_submission_and_track_changes_.pdf
  • Added Creator Qinxue Gu
  • Added Creator Melissa Gervais
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    Publication Date
    • 2022-04-28
    • 2022-03-30
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