Incorrect computation of Madden-Julian oscillation prediction skill

The Madden–Julian oscillation (MJO) is a major tropical weather system and one of the largest sources of predictability for subseasonal-to-seasonal weather forecasts. Skillful prediction of the MJO has been a highly active area of research due to its large socio-economic impacts. Silini et al., here...

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Bibliographic Details
Published in:NPJ climate and atmospheric science 2024-06, Vol.7 (1), p.134-3, Article 134
Main Authors: Suematsu, Tamaki, Martin, Zane K., Barnes, Elizabeth A., DeMott, Charlotte A., Hagos, Samson, Ham, Yoo-Geun, Kim, Daehyun, Kim, Hyemi, Koh, Tieh-Yong, Maloney, Eric D.
Format: Article
Language:English
Subjects:
639/705
704/106/35
Atmospheric Protection/Air Quality Control/Air Pollution
atmospheric science
Atmospheric Sciences
Climate Change/Climate Change Impacts
Climatology
Comment
Earth and Environmental Science
Earth Sciences
Economic impact
Learning algorithms
Machine learning
Madden-Julian oscillation
mathematics and computing
Neural networks
Numerical weather forecasting
Prediction models
Predictions
Skills
Weather
Weather forecasting
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Summary:The Madden–Julian oscillation (MJO) is a major tropical weather system and one of the largest sources of predictability for subseasonal-to-seasonal weather forecasts. Skillful prediction of the MJO has been a highly active area of research due to its large socio-economic impacts. Silini et al., herein S21, developed a machine learning model to predict the MJO, which they claimed to have an MJO prediction skill of 26–27 days over all seasons and 45 days for December–February (DJF) winter. If true, this would make the skill of their model competitive with that of the state-of-the-art dynamical MJO prediction systems at 20–35 days. However, here we show that the MJO prediction was calculated incorrectly in S21, which spuriously increased the performance of their model. Correctly computed skill of their model was substantially lower than that reported in S21; the skill for all seasons drops to 11–12 days and the skill for forecasts initialized during DJF drops to 15 days. Our findings clarify that the S21 machine learning model is not competitive with state-of-the-art numerical weather prediction models in predicting the MJO.
ISSN:2397-3722
2397-3722
DOI:10.1038/s41612-024-00687-1