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

By Ramesh, Freund, Irving +6Science AdvancesOct 23, 2026

Score████░░░░░░4.4

Key numbers

70% of the time

VerdictWorth a reader's time today.

Read the original

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.

Nandini Ramesh, Mandy B. Freund, Damien Irving, Dougal T. Squire, Carly R. Tozer, Didier Paolo Monselesan, Tess Parker, Annette Stellema, James S. Risbey

The editor's rubric

Heuristic review

Leverage3 / 5: A method or resource many groups across the field will adopt within a year.; 16% of this desk’s rubric
Magnitude3 / 5: Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.; 20% of this desk’s rubric
Evidence3 / 5: Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.; 20% of this desk’s rubric
Novelty3 / 5: A genuinely new approach to an open problem.; 10% of this desk’s rubric
Trajectory2 / 5: Some room to improve with obvious engineering.; 14% of this desk’s rubric
Stakes3 / 5: Meaningful benefit to many people within a few years.; 20% of this desk’s rubric

Dimension	Level	Weight	What that level means
Leverage	███░░3/5	16%	A method or resource many groups across the field will adopt within a year.
Magnitude	███░░3/5	20%	Large gain: roughly 2x, or a clear new state of the art on a hard, unsaturated problem.
Evidence	███░░3/5	20%	Solid: multiple benchmarks or cohorts, ablations, fair baselines, released code or data.
Novelty	███░░3/5	10%	A genuinely new approach to an open problem.
Trajectory	██░░░2/5	14%	Some room to improve with obvious engineering.
Stakes	███░░3/5	20%	Meaningful benefit to many people within a few years.

Editor’s rationale

Heuristic triage from title and abstract text only, not a reading of the paper. Cues found: method (we propose, new method); gains (outperforms); verification (multiple benchmarks); stakes (global scale, climate).

How the score was computed

rank-2026-10-18

Score████░░░░░░4.4

Score = 10 × (75% × adjusted merit / 10 + 15% × attention + 10% × freshness)

Desks place stronger verdicts first, then use this score to order work within the same verdict.

Merit: 5.7 / 10; Weighted rubric, evidence-gated.
Adjusted merit: 4.7 / 10; Shrunk toward the desk prior by editor confidence (42%).
Attention: 0%; Citations, upvotes, points, mentions.
Freshness: 85%; Half-life decay since publication.

Citations0 (reference 15, via openalex, Oct 25, 2026, 07:29 UTC)

The record

Reviewed by heuristic-v10 on Oct 25, 2026, 07:29 UTC. Paper type: method.
Categories: Climate variability and models, Meteorological Phenomena and Simulations, Geology and Paleoclimatology Research, Global and Planetary Change, Environmental Science
TOP, No.6 in the Front page edition of October 25, 2026.
TOP, No.2 in the Climate & Energy edition of October 25, 2026.