A team led by Dr. Dake Chen has recently developed a sophisticated multi-model ensemble (MME) prediction system for ENSO. This system comprises five dynamical coupled models with different complexities, parameterizations, resolutions, initializations, and ensemble strategies to effectively address the uncertainties associated with ENSO prediction.
An ensemble hindcast spanning from 1880 to 2017 has demonstrated the superiority of the MME over individual models, as it exhibits better deterministic and probabilistic skills and is less affected by the spring predictability barrier. Comparison with the North American Multi-Model Ensemble has shown that this MME prediction system can compete with, and even surpass, other leading prediction models in the world.
Since 2020, the MME system has been providing real-time ENSO predictions, accurately capturing the occurrence of successive triple La Niña events up to six months in advance, including a third-year La Niña event. These predictions have been consistently collected by the National Marine Environmental Forecasting Center and used as valuable advice for national operational prediction.
The groundbreaking research detailing this multi-model prediction system for ENSO has been published in the esteemed journal Science China Earth Sciences.
More information: Ting Liu et al, A multi-model prediction system for ENSO, Science China Earth Sciences (2023). DOI: 10.1007/s11430-022-1094-0
Citation: A multi-model prediction system for ENSO (2023, July 7) retrieved 7 July 2023 from https://phys.org/news/2023-07-multi-model-enso.html
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