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Enhanced multi-year predictability after El Niño and La Niña events

Nature Communications, ISSN: 2041-1723, Vol: 14, Issue: 1, Page: 6387
2023
  • 10
    Citations
  • 0
    Usage
  • 14
    Captures
  • 1
    Mentions
  • 0
    Social Media
Metric Options:   Counts1 Year3 Year

Metrics Details

  • Citations
    10
  • Captures
    14
  • Mentions
    1
    • News Mentions
      1
      • News
        1

Most Recent News

Research Reports on Science from University of New South Wales (UNSW) Provide New Insights (Enhanced multi-year predictability after El Nino and La Nina events)

2023 NOV 01 (NewsRx) -- By a News Reporter-Staff News Editor at NewsRx Science Daily -- Investigators publish new report on science. According to news

Article Description

Several aspects of regional climate including near-surface temperature and precipitation are predictable on interannual to decadal time scales. Despite indications that some climate states may provide higher predictability than others, previous studies analysing decadal predictions typically sample a variety of initial conditions. Here we assess multi-year predictability conditional on the phase of the El Niño–Southern Oscillation (ENSO) at the time of prediction initialisation. We find that predictions starting with El Niño or La Niña conditions exhibit higher skill in predicting near-surface air temperature and precipitation multiple years in advance, compared to predictions initialised from neutral ENSO conditions. This holds true in idealised prediction experiments with the Community Climate System Model Version 4 and to a lesser extent also real-world predictions using the Community Earth System Model and a multi-model ensemble of hindcasts contributed to the Coupled Model Intercomparison Project Phase 6 Decadal Climate Prediction Project. This enhanced predictability following ENSO events is related to phase transitions as part of the ENSO cycle, and related global teleconnections. Our results indicate that certain initial states provide increased predictability, revealing windows of opportunity for more skillful multi-year predictions.

Bibliographic Details

Yiling Liu; Markus. G. Donat; Matthew. H. England; Lisa. V. Alexander; Annette L. Hirsch; Carlos Delgado-Torres

Springer Science and Business Media LLC

Chemistry; Biochemistry, Genetics and Molecular Biology; Physics and Astronomy

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