Pacific State Determines One-Year ENSO Forecast Reliability

Scientists from the University of Melbourne and the Commonwealth Scientific and Industrial Research Organisation (CSIRO) have identified specific conditions that make it possible to predict some El Niño and La Niña events up to a year in advance. The research, published in Science Advances, reveals that the state of the Pacific Ocean at the beginning of a forecast plays a critical and often overlooked role in determining whether long-range predictions of the El Niño-Southern Oscillation (ENSO) will be accurate. This finding potentially gives communities and decision-makers significantly more time to prepare for weather extremes associated with these climate events.

ENSO events, which include El Niño and La Niña phases, influence weather patterns around the globe. While these events typically peak around December and can usually be forecast with confidence around six months in advance, predicting them earlier has long been a challenge. To address this challenge, Australian scientists at CSIRO and the University of Melbourne analysed more than 7,500 historical ENSO forecasts from climate models developed in nine countries.

This meta-analysis focused on the relationship between the starting state of the Pacific Ocean and the subsequent accuracy of the forecast one year later. The study found that forecasts initiated during La Niña conditions consistently performed well even when predicting ENSO events a year ahead. In contrast, forecasts beginning from neutral conditions were far less reliable.

Co-author Dr. Mandy Freund from the University of Melbourne and researcher with the Australian Research Council Centre of Excellence for 21st Century Weather, stated that the findings provide a new way to assess confidence in long-range climate forecasts. She noted that the consistent performance of one-year forecasts starting from La Niña suggests there is something about these conditions that makes the tropical Pacific system more predictable. This insight allows forecasters to identify which specific predictions deserve greater confidence, potentially enabling earlier warnings for El Niño events.

Lead author Dr. Nandini Ramesh, Senior Research Scientist at CSIRO and Adjunct Research Fellow at UNSW Sydney, explained that while it has been known for some time that starting conditions affect how well model forecasts perform, the study shows how drastic those differences can be. Dr. Ramesh emphasized that the researchers can now use this information to identify which forecasts deserve greater confidence. This capability could help improve how early warnings are communicated to the public, allowing for more effective preparation for climate impacts.

The researchers also found that different forecasting approaches may be suited to different climate conditions. Traditional physics-based models struggled when forecasts began from neutral conditions, whereas machine learning and statistical models may have potential to improve performance in these situations. The findings highlight opportunities to strengthen early-warning systems, although further research is needed before the approach can be incorporated into operational forecasting systems.

Dr. Ramesh noted that they are still a few steps away from applying the findings to the models used in practice by forecasting agencies. However, the results hold promise for being able to communicate early warnings for future events more effectively. The research offers new insights into the drivers of ENSO predictability and could ultimately help improve preparedness for droughts, floods and other climate impacts associated with El Niño and La Niña events.

Subject of Research: Predictability of El Niño-Southern Oscillation based on initial Pacific Ocean conditions

Article Title: Scientists find key to predicting El Niño 12 months ahead

Article References: Scientists find key to predicting El Niño 12 months ahead. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: El Niño, La Niña, ENSO, Climate Forecasting, Pacific Ocean, CSIRO, University of Melbourne, Weather Patterns, Science Advances, Climate Models, Scientists, find