JOURNAL ARTICLE

A regional and seasonal approach to explain the observed trends in the Antarctic sea ice in recent decades.

  • Published In: International Journal of Climatology, 2023, v. 43, n. 6. P. 2953 1 of 3

  • Database: Academic Search Ultimate 2 of 3

  • Authored By: Yu, Lejiang; Zhong, Shiyuan; Sui, Cuijuan; Sun, Bo 3 of 3

Abstract

In contrast to the declining sea ice extent across most of the Arctic in the past several decades, sea ice extent in the Antarctic has experienced opposite regional trends with regions of sea ice expansion exceeding those of contraction. Various mechanisms have been put forward to explain the Antarctic sea ice trends, but few have been successful at explaining a large portion of the observed trends. Dividing the Southern Ocean into the Pacific, Atlantic, and Indian Ocean sectors and examining the trends over 1979–2018 separately for each sector and for each season of the year, we are able to identify modes of variability that statistically explain more than 42% of the trends in all three sectors and all four seasons. In certain sector and season, up to 94% of the trends can be explained. The leading modes of variability that explain a substantial portion of the trends appear to be related to several known climate variability modes including the Southern Annular Mode (SAM), the Pacific South American (PSA) 1 and 2, and the Zonal Wavenumber 2–6 patterns. In the Atlantic and Indian Ocean sectors, the changes in the occurrences of the main contributing modes to sea ice trends are dominated by local sea‐surface temperature (SST) variations, while in the Pacific sector, they are related to changes in global SST. [ABSTRACT FROM AUTHOR]

Additional Information

  • Source:International Journal of Climatology. 2023/05, Vol. 43, Issue 6, p2953
  • Document Type:Article
  • Subject Area:Environmental Sciences
  • Publication Date:2023
  • ISSN:0899-8418
  • DOI:10.1002/joc.8010
  • Accession Number:163670245
  • 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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