A global-scale multidecadal variability driven by Atlantic multidecadal oscillation

Young Min Yang, Soon Il An, Bin Wang, Jae Heung Park

Research output: Contribution to journalArticlepeer-review

9 Citations (Scopus)

Abstract

Observational analysis shows that there is a predominant global-scale multidecadal variability (GMV) of sea-surface temperature (SST). Its horizontal pattern resembles that of the interdecadal Pacific oscillation (IPO) in the Pacific and the Atlantic multidecadal oscillation (AMO) in the Atlantic Ocean, which could affect global precipitation and temperature over the globe. Here, we demonstrate that the GMV could be driven by the AMOthrough atmospheric teleconnections and atmosphere-ocean coupling processes. Observations reveal a strong negative correlation when AMO leads GMV by approximately 4-8 years. Pacemaker experiments using a climate model driven by observed AMOsignals reveal that the tropical Atlantic warm SST anomalies of AMO initiate anomalous cooling in the equatorial central-eastern Pacific through atmospheric teleconnections. Anticyclonic anomalies in the North and South Pacific induce equatorward winds along the coasts of North and South America, contributing to further cooling.The upper-ocean dynamics plays a minor role in GMV formation but contributes to a delayed response of the IPO to the AMOforcing.The possible impact of the GMV on AMOwas also tested by prescribing only Pacific SST in the model; however, the model could not reproduce the observed phase relationship between the AMOand the GMV.These results support the hypothesis that the Atlantic Ocean plays a key role in the multidecadal variability of global SST.

Original languageEnglish
Pages (from-to)1190-1197
Number of pages8
JournalNational Science Review
Volume7
Issue number7
DOIs
Publication statusPublished - 2020

Bibliographical note

Funding Information:
This work was supported directly by the National Research Foundation of Korea (NRF) (NRF-2018R1A5A1024958) and the Initial Research Foundation of Nanjing University of Information Science and Technology (1091011901001). This work was also supported by the National Natural Science Foundation of China (41420104002) and the National Key Research and Development Program of China (2016YFA0600401), as well as the National Science Foundation (Climate Dynamics Division) (AGS-1540783) and NOAA/CVP (#NA15OAR4310177). This is Publication No. 1419 of the SOEST, Publication No. YYYY of IPRC and Publication No. 292 of the Earth System Modeling Center.

Publisher Copyright:
© TheAuthor(s) 2019.

All Science Journal Classification (ASJC) codes

  • General

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