Scientists Find Climate Signal That Could Predict El Niño and La Niña a Year Ahead

Scientists Find Climate Signal That Could Predict El Niño and La Niña a Year Ahead

Scientists have identified a crucial climate signal that could help predict El Niño and La Niña events up to a year in advance. This finding may give governments and communities more time to prepare for extreme weather. A study published in Science Advances shows that the initial state of the Pacific Ocean plays a critical role in how accurately climate models forecast the El Niño-Southern Oscillation (ENSO) up to 12 months ahead.

ENSO is a recurring climate phenomenon. It involves fluctuations in sea surface temperatures and atmospheric conditions across the tropical Pacific. It alternates between three phases: El Niño, La Niña, and neutral conditions. These phases influence weather patterns worldwide and affect monsoons, droughts, floods, heatwaves, and tropical cyclones. While ENSO events typically peak around December and can usually be predicted about six months in advance, forecasting them a full year ahead has remained difficult.

Researchers from Australia’s national science agency CSIRO and the University of Melbourne analysed more than 7,500 historical ENSO forecasts. These forecasts came from climate models developed by nine countries. They discovered that forecasts started during La Niña conditions consistently performed well even when predicting ENSO conditions a year later. In contrast, forecasts that began when the Pacific was in a neutral state proved considerably less reliable. This suggests that the ocean’s prevailing conditions at the start of a forecast can indicate how much confidence scientists should place in long-range predictions.

“Our study identifies valuable additional information for predicting El Niño and La Niña events well ahead of time,” said Dr Mandy Freund, co-author of the study from the University of Melbourne. She added that the consistency of one-year forecasts beginning during La Niña across all nine models shows that these conditions make the tropical Pacific climate system more predictable over the following year.

Lead author Dr Nandini Ramesh, Senior Research Scientist at CSIRO, said the findings could help scientists identify which long-range forecasts deserve greater confidence. Researchers also found that traditional physics-based climate models struggled when forecasts began during neutral Pacific conditions. However, machine learning and statistical forecasting approaches could offer ways to improve predictions in such situations.

The findings could eventually strengthen early-warning systems. Countries may prepare earlier for droughts, floods, and other weather extremes linked to ENSO. However, researchers cautioned that the approach is not yet ready for operational forecasting systems and requires further testing. The study offers a potentially important advance in climate prediction at a time when improving preparedness for climate extremes is becoming increasingly important.