Scientists identified a climate signal that helps predict El Nino and La Nina events a year in advance. A study in Science Advances showed that Pacific Ocean conditions determine forecast accuracy. Dr. Mandy Freund said, "Our study identifies valuable additional information for predicting El Nino and La Nina events."
Scientists have identified a crucial climate signal that could help predict El Nino and La Nina events up to a year in advance, potentially giving governments and communities more time to prepare for extreme weather events.
A study published in Science Advances has found that the initial state of the Pacific Ocean plays a critical role in determining how accurately climate models can forecast the El Nino-Southern Oscillation (ENSO) up to 12 months ahead.
ENSO is a recurring climate phenomenon involving fluctuations in sea surface temperatures and atmospheric conditions across the tropical Pacific. It alternates between three phases: El Nino, La Nina and neutral conditions. These phases influence weather patterns worldwide, affecting monsoons, droughts, floods, heatwaves and tropical cyclones.
While ENSO events typically peak around December and can generally be predicted with reasonable confidence about six months in advance, forecasting them a year ahead has remained a major challenge.
LA NINA OFFERS A STRONGER PREDICTION SIGNAL
Researchers from Australia's national science agency CSIRO and the University of Melbourne analysed more than 7,500 historical ENSO forecasts generated by climate models from nine countries.
They discovered that forecasts initiated during La Nina conditions consistently performed well even when predicting ENSO conditions a year later. In contrast, forecasts beginning when the Pacific was in a neutral state were considerably less reliable.
This suggests that the ocean's prevailing conditions at the beginning of a forecast can provide an important indication of how much confidence scientists should place in long-range predictions.
"Our study identifies valuable additional information for predicting El Nino and La Nina events well ahead of time," said Dr Mandy Freund, co-author of the study from the University of Melbourne and researcher at the Australian Research Council Centre of Excellence for 21st Century Weather.
She added that the consistency of one-year forecasts beginning during La Nia across all nine models suggests that these conditions make the tropical Pacific climate system more predictable over the following year.
IT COULD IMPROVE EARLY WARNINGS
Lead author Dr Nandini Ramesh, Senior Research Scientist at CSIRO and Adjunct Research Fellow at UNSW Sydney, 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. Machine learning and statistical forecasting approaches, however, could offer ways to improve predictions in such situations.
The findings could eventually strengthen early-warning systems, allowing countries to prepare earlier for droughts, floods and other weather extremes associated with 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, particularly at a time when improving preparedness for climate extremes is becoming increasingly important.