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

Qinxue Gu, Melissa Gervais

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

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.

Original languageEnglish (US)
Article numbere2021GL096560
JournalGeophysical Research Letters
Volume49
Issue number8
DOIs
StatePublished - Apr 28 2022

All Science Journal Classification (ASJC) codes

  • Geophysics
  • General Earth and Planetary Sciences

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