JOURNAL ARTICLE

ENSO‐induced decadal variability in the tropical Pacific subsurface in CMIP6 models.

  • Published In: International Journal of Climatology, 2023, v. 43, n. 9. P. 4033 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Chen, Yue; Huang, Ping 3 of 3

Abstract

The tropical Pacific decadal variability (TPDV) is an important component of the global interdecadal variability. Previous studies have shown that the TPDV in the sea surface temperature (SST) has two clear patterns, the El Niño–Southern Oscillation (ENSO)‐like and ENSO‐induced patterns, but the pattern of TPDV in the ocean subsurface is still a matter of debate in observations and models. The present study analyses the subsurface TPDV in the simulations of 26 CMIP6 models. The ENSO‐like and ENSO‐induced TPDVs in the subsurface are defined by the regression of the interdecadal anomalies of the oceanic subsurface temperature (Tsub) onto the PCs of two leading EOF modes of the interdecadal SST anomalies in the tropical Pacific. The pattern of the ENSO‐like TPDV in the subsurface shows high model consistency, whereas the ENSO‐induced TPDV in the subsurface has two distinct modes among the models, one with a centre in the central Pacific and the other showing a zonal dipole in the equatorial Pacific. The zonal pattern of the ENSO‐induced TPDV in the subsurface is mainly induced by the SST skewness in the equatorial eastern Pacific, which is further related to the surface heat flux feedback during La Niña. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Climatology. 2023/07, Vol. 43, Issue 9, p4033
  • Document Type:Article
  • Subject Area:Earth and Atmospheric Sciences
  • Publication Date:2023
  • ISSN:0899-8418
  • DOI:10.1002/joc.8071
  • Accession Number:164879516
  • Copyright Statement:Copyright of International Journal of Climatology is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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