Prediction of the Indian Ocean Dipole using Deep Learning Method
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摘要: 印度洋偶极子（IOD）是热带印度洋秋季最强的年际变率，它会通过大气遥相关来影响世界许多地区的气候。目前耦合气候模式对IOD预报技巧仍非常有限，远低于热带太平洋的厄尔尼诺事件的预报技巧。鉴于深度学习具备高效的数据处理能力，本文使用深度学习中的卷积神经网络（CNN）与人工神经网络中的多层感知机（MLP）处理再分析观测资料，从而进行IOD预报。由于当预报初始时刻为北半球冬春季时，对IOD事件的预报技巧较低。因此，为探索CNN的预报能力，本文仅使用三种（1～3月、2～4月、3～5月）初始时刻的海表温度异常（SSTA）作为CNN的输入数据，来预报其后续七个月的印度洋偶极子指数（DMI）、东极子指数（EIOI）和西极子指数（WIOI）。结果表明：CNN对DMI、EIOI和WIOI的有效预测时效均超过了6个月。与现在耦合动力模式相比，CNN模型能够显著提升DMI和EIOI的预报技巧，但对WIOI的预报技巧提升有限。当预报提前时间为7个月时，CNN模型能够比较准确地预报1994、1997与2019年的IOD事件。由于CNN模型能够更好地抓住印度洋海温的空间结构特征，它对IOD事件的预报技巧比传统神经网络MLP高。Abstract: In autumn, the Indian Ocean Dipole (IOD) has the strongest interannual variability in the tropical Indian Ocean. It will influence the climate in many parts of the world due to atmospheric teleconnection. The current coupled climate model has very limited IOD forecasting skills, which are far inferior to those of El Niño events in the tropical Pacific. The authors used the convolutional neural network (CNN) of the deep learning and the multi-layer perceptron (MLP) of the artificial neural network, respectively, to perform IOD prediction due to the super capability of deep learning in processing data. In order to explore the forecasting capabilities of CNN, this article only uses three initial conditions in the boreal spring with low prediction skill to forecast the Indian Ocean Dipole Mode Index (DMI), East Pole Index (EIOI), and West Pole Index (WIOI) for the next seven months. The results show that the CNN model can make useful predictions for the DMI, EIOI, and WIOI at least six months in advance. When compared with the current state-of-the-art general coupled model, the CNN model significantly improves the prediction skills of the DMI and EIOI while only slightly improving WIOI prediction skills. The CNN model could predict the strong IOD events in 1994, 1997, and 2019 well for the lead time longer than seven months. In general, CNN outperforms the traditional neural network MLP for the IOD prediction due to its strong capability to capture the spatial structure characteristics of the Indian Ocean SST.
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Key words:
- Indian Ocean Dipole /
- Deep learning /
- Convolutional neural network /
- Climate prediction
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图 2 1990～2019年以JFM、FMA、MAM为起始态，分别使用CNN模型（实线）与MLP模型（虚线）预报的DMI与观测值的（a）相关系数和（b）均方根误差（RMSE）
Figure 2. (a) Correlation coefficients and (b) RMSE (root mean square errors) between the forecasted and observed DMI during 1990–2019 using CNN (solid lines) and MLP (dashed lines) models, respectively, with JFM (January–March), FMA (February–April), and MAM (March–May) as the initial conditions
图 3 1990～2019年观测和CNN模式预报的北半球秋季（9月、10月）平均的DMI（标准化的）
Figure 3. Normalized DMI observed and forecasted by CNN model averaged in boreal autumn (September and October) during 1990–2019
图 4 1990～2019年以JFM、FMA、MAM为起始态，分别使用CNN模型（实线）与MLP模型（虚线）预报的EIO指数与观测值的（a）相关系数和（b）RMSE
Figure 4. (a) Correlation coefficients and (b) RMSE between the forecasted and observed EIOI (East Pole Index for Indian Ocean) during 1990–2019 using CNN (solid lines) and MLP (dashed lines) models, respectively, with JFM, FMA, and MAM as the initial conditions
图 5 1990～2019年观测和CNN模式预报的北半球秋季（9、10月）平均EIO指数（标准化的）
Figure 5. Normalized EIOI index observed and forecasted by CNN mode averaged in boreal autumn (September and October) during 1990–2019
图 6 1990～2019年以JFM、FMA、MAM为起始态，分别使用CNN模型（实线）与MLP模型（虚线）预报的WIO指数与观测值的（a）相关系数和（b）RMSE
Figure 6. (a) Correlation coefficients and (b) RMSE between the forecasted and observed WIOI (West Pole Index for Indian Ocean) during 1990–2019 using CNN and MLP models, respectively, with JFM, FMA, and MAM as the initial conditions
图 7 1990～2019年观测和CNN模式预报的北半球秋季（9、10月）平均WIO指数（标准化的）
Figure 7. Normalized WIOI observed and forecasted by CNN mode averaged in boreal autumn (September and October) during 1990–2019
表 1 CNN模型、MLP模型的输入（SSTA）与输出（DMI）
Table 1. Input (Sea Surface Temperature Anomaly, SSTA) and output (Indian Ocean Dipole Mode Index, DMI) of CNN model and MLP (multi-layer perceptron) model
输入（SSTA） 输出 提前1个月 提前2个月 提前3个月 提前4个月 提前5个月 提前6个月 提前7个月 JFM 4月DMI 5月DMI 6月DMI 7月DMI 8月DMI 9月DMI 10月DMI FMA 5月DMI 6月DMI 7月DMI 8月DMI 9月DMI 10月DMI 11月DMI MAM 6月DMI 7月DMI 8月DMI 9月DMI 10月DMI 11月DMI 12月DMI 
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