Chen Yipeng, Jin Yishuai, Liu Zhengyu, Shen Xingchen, Chen Xianyao, Lin Xiaopei, Zhang Rong-Hua, Luo Jing-Jia, Zhang Wenjun, Duan Wansuo, Zheng Fei, McPhaden Michael J, Zhou Lu
Frontier Science Center for Deep Ocean Multispheres and Earth System (FDOMES) and Physical Oceanography Laboratory, Ocean University of China, Qingdao, China.
SANYA Oceanographic Laboratory, Sanya, China.
Nat Commun. 2025 Apr 24;16(1):3845. doi: 10.1038/s41467-025-59173-8.
Improving the prediction skill of El Niño-Southern Oscillation (ENSO) is of critical importance for society. Over the past half-century, significant improvements have been made in ENSO prediction. Recent studies have shown that deep learning (DL) models can substantially improve the prediction skill of ENSO compared to individual dynamical models. However, effectively integrating the strengths of both DL and dynamical models to further improve ENSO prediction skill remains a critical topic for in-depth investigations. Here, we show that these DL forecasts, including those using the Convolutional Neural Networks and 3D-Geoformer, offer comparable ENSO forecast skill to dynamical forecasts that are based on the dynamic-model mean. More importantly, we introduce a combined dynamical-DL forecast, an approach that integrates DL forecasts with dynamical model forecasts. Two distinct combined dynamical-DL strategies are proposed, both of which significantly outperform individual DL or dynamical forecasts. Our findings suggest the skill of ENSO prediction can be further improved for a range of lead times, with potentially far-reaching implications for climate forecasting.
提高厄尔尼诺-南方涛动（ENSO）的预测能力对社会至关重要。在过去的半个世纪里，ENSO预测取得了显著进展。近期研究表明，与单个动力模型相比，深度学习（DL）模型能大幅提高ENSO的预测能力。然而，有效整合DL和动力模型的优势以进一步提高ENSO预测能力仍是一个亟待深入研究的关键课题。在此，我们表明，这些DL预测，包括使用卷积神经网络和3D地理former的预测，与基于动力模型均值的动力预测具有相当的ENSO预测能力。更重要的是，我们引入了一种动力-DL组合预测方法，即将DL预测与动力模型预测相结合。我们提出了两种不同的动力-DL组合策略，这两种策略均显著优于单个DL或动力预测。我们的研究结果表明，在一系列提前期内，ENSO预测能力都可以进一步提高，这可能对气候预测产生深远影响。
