Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole
Ling, F.; Luo, J.-J.; Li, Y.; Tang, T.; Bai, L.; Ouyang, W.; Yamagata, T. (2022). Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole. Nature Comm. 13(1): 7681. https://dx.doi.org/10.1038/s41467-022-35412-0
In: Nature Communications. Nature Publishing Group: London. ISSN 2041-1723; e-ISSN 2041-1723, more
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| Authors | | Top |
- Ling, F.
- Luo, J.-J.
- Li, Y.
- Tang, T.
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- Bai, L.
- Ouyang, W.
- Yamagata, T.
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| Abstract |
As one of the most predominant interannual variabilities, the Indian Ocean Dipole (IOD) exerts great socio-economic impacts globally, especially on Asia, Africa, and Australia. While enormous efforts have been made since its discovery to improve both climate models and statistical methods for better prediction, current skills in IOD predictions are mostly limited up to three months ahead. Here, we challenge this long-standing problem using a multi-task deep learning model that we name MTL-NET. Hindcasts of the IOD events during the past four decades indicate that the MTL-NET can predict the IOD well up to 7-month ahead, outperforming most of world-class dynamical models used for comparison in this study. Moreover, the MTL-NET can help assess the importance of different predictors and correctly capture the nonlinear relationships between the IOD and predictors. Given its merits, the MTL-NET is demonstrated to be an efficient model for improved IOD prediction. |
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