Multi-task machine learning improves multi-seasonal prediction of the Indian Ocean Dipole

• Published in: Nature communications Vol. 13; no. 1; pp. 7681 - 9
• Main Authors: Ling, Fenghua, Luo, Jing-Jia, Li, Yue, Tang, Tao, Bai, Lei, Ouyang, Wanli, Yamagata, Toshio
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 12.12.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 704/106/35; 704/106/829; Africa; Asia; Australia; Climate models; Deep learning; Dipoles; Dynamic models; Economic impact; El Nino; Humanities and Social Sciences; Indian Ocean; Laboratories; Machine Learning; Mathematical models; multidisciplinary; Predictions; Science; Science (multidisciplinary); Skills; Statistical analysis; Statistical methods; Wind; Indian Ocean; Australia; Asia; Africa

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...