Institute for Climate and Application Research (ICAR)/CIC-FEMD/KLME/ILCEC, Nanjing University of Information Science and Technology, Nanjing, China.
Shanghai AI Laboratory, Shanghai, China.
Nat Commun. 2022 Dec 12;13(1):7681. doi: 10.1038/s41467-022-35412-0.
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.
作为最主要的年际变化之一，印度洋偶极子（IOD）在全球范围内产生了巨大的社会经济影响，尤其是对亚洲、非洲和澳大利亚。自发现以来，人们已经做出了巨大的努力来改进气候模型和统计方法，以提高预测能力，但目前IOD 预测的技能大多只能提前三个月。在这里，我们使用一种名为 MTL-NET 的多任务深度学习模型来挑战这一长期存在的问题。过去四十年IOD 事件的回溯结果表明，MTL-NET 可以提前 7 个月很好地预测 IOD，优于本研究中用于比较的大多数世界级动力模型。此外，MTL-NET 可以帮助评估不同预测因子的重要性，并正确捕捉 IOD 与预测因子之间的非线性关系。鉴于其优点，MTL-NET 被证明是改进 IOD 预测的有效模型。
