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

Yiling Liu (), Markus. G. Donat, Matthew. H. England, Lisa. V. Alexander, Annette L. Hirsch and Carlos Delgado-Torres
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Yiling Liu: Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, UNSW
Markus. G. Donat: Barcelona Supercomputing Center (BSC)
Matthew. H. England: Centre for Marine Science and Innovation and Australian Centre for Excellence in Antarctic Science, UNSW
Lisa. V. Alexander: Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, UNSW
Annette L. Hirsch: Climate Change Research Centre and ARC Centre of Excellence for Climate Extremes, UNSW
Carlos Delgado-Torres: Barcelona Supercomputing Center (BSC)

Nature Communications, 2023, vol. 14, issue 1, 1-9

Abstract: Abstract 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.

Date: 2023
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DOI: 10.1038/s41467-023-42113-9

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