Estimating decadal predictability for the Southern Ocean using the GFDL CM2.1 model

Liping Zhang, Thomas L. Delworth, Xiaosong Yang, Richard G. Gudgel, Liwei Jia, Gabriel Andres Vecchi, Fanrong Zeng

Research output: Contribution to journalArticlepeer-review

10 Scopus citations


This study explores the potential predictability of the Southern Ocean (SO) climate on decadal time scales as represented in the GFDL CM2.1 model using prognostic methods. Perfect model predictability experiments are conducted starting from 10 different initial states, showing potentially predictable variations of Antarctic bottom water (AABW) formation rates on time scales as long as 20 years. The associated Weddell Sea (WS) subsurface temperatures and Antarctic sea ice have potential predictability comparable to that of the AABW cell. The predictability of sea surface temperature (SST) variations over the WS and the SO is somewhat smaller, with predictable scales out to a decade. This reduced predictability is likely associated with stronger damping from air-sea interaction. As a complement to this perfect predictability study, the authors also make hindcasts of SO decadal variability using the GFDL CM2.1 decadal prediction system. Significant predictive skill for SO SST on multiyear time scales is found in the hindcast system. The success of the hindcasts, especially in reproducing observed surface cooling trends, is largely due to initializing the state of theAABWcell. Aweak state of theAABWcell leads to cooler surface conditions and more extensive sea ice. Although there are considerable uncertainties regarding the observational data used to initialize the hindcasts, the consistency between the perfect model experiments and the decadal hindcasts at least gives some indication as to where and to what extent skillful decadal SO forecasts might be possible.

Original languageEnglish (US)
Pages (from-to)5187-5203
Number of pages17
JournalJournal of Climate
Issue number14
StatePublished - Jul 1 2017

All Science Journal Classification (ASJC) codes

  • Atmospheric Science


  • Climate prediction
  • Climate prediction


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