News Release

A multi-model prediction system for ENSO

Peer-Reviewed Publication

Science China Press


image: The sea surface anomalies of positive and negative ENSO phase(from view more 

Credit: ©Science China Press

A multi-model ensemble (MME) prediction system has been recently developed by a team led by Dr. Dake Chen. This prediction system consists of 5 dynamical coupled models with various complexities, parameterizations, resolutions, initializations, and ensemble strategies, to address various possible uncertainties of ENSO prediction. One long term over past 100 year (1880-2017) ensemble hindcast demonstrated the superiority of the MME over individual models, evaluated by both deterministic and probabilistic skills, and suffered less from the spring predictability barrier. Comparison with the North American Multi-Model Ensemble reveals that this MME prediction system can compete with, or even exceed the counterparts of pioneering prediction models in this world. Since 2020, the MME system has been issuing the real-time ENSO prediction, which has successfully captured the latest successive triple La Niña events six months ahead including the occurrence of a third-year La Niña event. This MME prediction has been regularly collected by the National Marine Environmental Forecasting Center, used as a consultant advice for national operational prediction.

See the article:

Liu T, Gao Y, Song X, Gao C, Tao L, Tang Y, Duan W, Zhang R H, Chen D. 2023. A multi-model prediction system for ENSO. Science China Earth Sciences, 66(6): 1231–1240, https://10.1007/s11430-022-1094-0


Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.