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Peer-reviewedCC BY-NCScience Advances

Using initial states to identify successful El Niño–Southern Oscillation forecasts 1 year in advance

  • Nandini Ramesh1,2
  • Mandy B. Freund3,4
  • Damien B. Irving5
  • Dougal T. Squire6
  • Carly R. Tozer5
  • Didier P. Monselesan5
  • Tess Parker5
  • Annette Stellema5
  • James S. Risbey5
6 affiliations
  1. 1Natural Systems Modelling Group, Technology, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Sydney 2145, Australia.
  2. 2School of Mathematics and Statistics, University of New South Wales, Sydney 2052, Australia.
  3. 3School of Geography, Earth and Atmospheric Sciences, University of Melbourne, Melbourne 3053, Australia.
  4. 4ARC Centre of Excellence for the Weather of the 21st Century, University of Melbourne, Melbourne 3053, Australia.
  5. 5Climate Variability and Hazards, Environment, CSIRO, Hobart 7004, Australia.
  6. 6ACCESS-NRI, Australian National University, Canberra 2601, Australia.
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Abstract

El Niño-Southern Oscillation (ENSO) is the primary source of climate predictability on seasonal and longer timescales over much of the globe, but predicting its phase beyond 9 months has remained challenging. Here, we leverage an underappreciated factor in ENSO forecasting: the substantial variation in forecast skill based on the ENSO phase at the time a forecast is issued. Our analysis of transitions between ENSO phases in a dynamical hindcast dataset demonstrates that this variation markedly exceeds intermodel differences in predictive capability. Forecasts initialized during La Niña conditions perform best, with three models correctly predicting transitions to El Niño 1 year in advance over 70% of the time. We present our findings in a simple framework that allows decision-makers to assess the likelihood of a forecast being correct on the basis of the current ENSO state. Applied effectively, these findings could improve long-lead ENSO forecasting strategies, enhancing global risk management.

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Cite

BibTeXRIS
BibTeX
@article{ramesh2026using,
  title = {{Using initial states to identify successful El Niño–Southern Oscillation forecasts 1 year in advance}},
  author = {Nandini Ramesh and Mandy B. Freund and Damien B. Irving and Dougal T. Squire and Carly R. Tozer and Didier P. Monselesan and Tess Parker and Annette Stellema and James S. Risbey},
  journal = {Science Advances},
  year = {2026},
  month = oct,
  doi = {10.1126/sciadv.aeg8654},
  url = {https://www.science.org/doi/10.1126/sciadv.aeg8654},
  note = {CC BY-NC},
}
RIS
TY  - JOUR
TI  - Using initial states to identify successful El Niño–Southern Oscillation forecasts 1 year in advance
AU  - Nandini Ramesh
AU  - Mandy B. Freund
AU  - Damien B. Irving
AU  - Dougal T. Squire
AU  - Carly R. Tozer
AU  - Didier P. Monselesan
AU  - Tess Parker
AU  - Annette Stellema
AU  - James S. Risbey
PY  - 2026
DA  - 2026/10/23
JO  - Science Advances
DO  - 10.1126/sciadv.aeg8654
UR  - https://www.science.org/doi/10.1126/sciadv.aeg8654
AB  - El Niño-Southern Oscillation (ENSO) is the primary source of climate predictability on seasonal and longer timescales over much of the globe, but predicting its phase beyond 9 months has remained challenging. Here, we leverage an underappreciated factor in ENSO forecasting: the substantial variation in forecast skill based on the ENSO phase at the time a forecast is issued. Our analysis of transitions between ENSO phases in a dynamical hindcast dataset demonstrates that this variation markedly exceeds intermodel differences in predictive capability. Forecasts initialized during La Niña conditions perform best, with three models correctly predicting transitions to El Niño 1 year in advance over 70% of the time. We present our findings in a simple framework that allows decision-makers to assess the likelihood of a forecast being correct on the basis of the current ENSO state. Applied effectively, these findings could improve long-lead ENSO forecasting strategies, enhancing global risk management.
SN  - 2375-2548
ER  -