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

Ling, Fenghua; Luo, Jing–Jia; Li, Yue; Tang, Tao; Bai, Lei; Ouyang, Wanli; Yamagata, Toshio

doi:10.1038/s41467-022-35412-0

Cited by 21 publications

(14 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…At the 10‐month lead, the predictand made by eight NMME models clearly have large errors in representing the SSTA zonal pattern of ENSO events, although the predicted Niño3.4 index is fairly close to the observation (Figures 1c–1z). On the other hand, most of the CNN‐based models focus on the deterministic prediction of indices, such as the Niño3.4 index for ENSO (Ham et al., 2021; Hu et al., 2021; Patil et al., 2023) and dipole mode index for Indian Ocean Dipole (Ling et al., 2022; Liu et al., 2021); while they neglect the whole spatial distribution of SSTA. The pioneering work of H19 constructed an additional CNN model to predict the El Niño type in the probabilistic sense but without the specific SSTA zonal pattern.…”

Section: Introductionmentioning

confidence: 99%

CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation

Sun,

Chen,

et al. 2023

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

Deep learning (DL) has achieved notable success in El Niño‐Southern Oscillation (ENSO) forecasts. Most DL‐based models focused on forecasting ENSO indices while the zonal distribution of sea surface temperature anomalies (SSTA) over the equatorial Pacific was overlooked. To provide accurate predictions for the SSTA zonal pattern, this study developed a model through leveraging the merits of the cosine distance in constructing the convolutional neural network. This model can skillfully predict the SSTA zonal pattern over the equatorial Pacific 1 year in advance, remarkably outperforming current dynamical models. Moreover, the physical interpretation of the model prediction reveals that the sources for ENSO predictability at different lead times are distinct. For the 10‐month‐lead predictions, the precursors in the north Pacific, south Pacific and tropical Atlantic play critical roles in determining the model behaviors; while for the 16‐month‐lead predictions, the initial signals in the tropical Pacific associated with the discharge‐recharge cycle are essential.

show abstract

Section: Introductionmentioning

confidence: 99%

CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation

Sun,

Chen,

et al. 2023

Geophysical Research Letters

Self Cite

View full text Add to dashboard Cite

show abstract

“…This RC-model has the highest correlation coefficient and lowest RMSE, improving the prediction skill of the IOD at the development and peak seasons from JAS to SON (figure 4). The correlation coefficient (∼0.86) and RMSE (∼0.24 • C) between the RC-model and observations for the culminated IOD at a lead time of 3 months is nearly equivalent to that predicted by the current machine learning methods (Ratnam et al 2020, Liu et al 2021, Ling et al 2022, demonstrating the advantage of the RC-model relative to the SM + N34 + PS model and MME. However, the MME is slightly better than the empirical model in the performance of the decay phase in OND (figure 4).…”

Section: Prediction Of the Iod Based On Empirical And Nmme Modelsmentioning

confidence: 77%

“…The NMME system has been widely used to assess the predictability of the IOD (Zhao et al 2019, Ling et al 2022, Lu et al 2022. Model predictable skills for the IOD are compared for the SM + N34 + PS model and 12 NMME models.…”

Section: Prediction Of the Iod Based On Empirical And Nmme Modelsmentioning

confidence: 99%

Reconciling roles of the South China Sea summer monsoon and ENSO in prediction of the Indian Ocean dipole

Zhang,

Li,

Diao

et al. 2023

Environ. Res. Lett.

View full text Add to dashboard Cite

The Indian Ocean dipole (IOD) is a remarkable interannual variability in the tropical Indian Ocean. The improved prediction of IOD is of a great value because of its large socioeconomic impacts. Previous studies reported that both El Niño-Southern Oscillation (ENSO) and South China Sea summer monsoon (SM) play a dominant role in the western and eastern pole of the IOD, respectively. They can be used as predictors of the IOD at 3 month lead beyond self-persistence. Here, we develop an empirical model of multi-factors in which the western pole is predicted by ENSO and persistence and the eastern pole is predicted by SM and persistence. This new empirical model outperforms largely the average level of the dynamical models from the North American multi-model ensemble (NMME) project in predicting the peak IOD in boreal autumn, with a correlation coefficient of ∼0.86 and a root mean square error of ∼0.24 °C. Furthermore, the hit rate of positive culminated IOD in this new empirical model is equivalent to that in current NMME models (above 65%), much higher than that for negative culminated IOD. This improvement of skill using the empirical model suggests a perspective for better understanding and predicting the IOD.

show abstract

“…By analyzing patterns and trends in historical observational data, statistical methods attempt to establish predictive models to infer the likelihood of future ENSO events. Early studies typically focused on using linear models for ENSO prediction, but over time, more researchers have begun to combine machine learning algorithms and statistical models to improve prediction accuracy [15][16][17]. For example, Wang et al [18] proposed an integrated method based on empirical mode decomposition and Convolutional Long Short-Term Memory (Conv-LSTM) to predict ENSO events, which can achieve an effective forecast period of 12 months in advance.…”

Section: Traditional Methodsmentioning

confidence: 99%

ENSO-Former: Spatiotemporal fusion network based on multivariate and dual-branch Transformer for ENSO prediction

Fang,

Sha

2024

Preprint

View full text Add to dashboard Cite

El Niño Southern Oscillation (ENSO) is a natural climate phenomenon. Mainly characterized by the periodic variation of sea surface temperature anomalies in the eastern equatorial Pacific. It has significant impacts on global climate change. The traditional ENSO prediction methods rely on complex physical models and empirical rules, but these methods still have significant biases and uncertainties. We design a novel multi-head spatiotemporal convolutional attention module, TSCA Block. The module employs a dual-branch structure to fully exploit spatiotemporal features, where the spatiotemporal attention mechanism is used to capture long-term dependencies in time and space. Subsequent convolutional modules are used to locally enhance features. In addition, we also propose a spatiotemporal fusion prediction model based on multivariable and dual-branch Transformers, called ENSO-Former. By repeating basic building blocks at multiple stages and adopting long skip connections between shallow and deep layers, it captures multiscale features and dynamic changes of ENSO more effectively. We train using multiple meteorological factors, initially pre-training with various model-simulated data, followed by fine-tuning using transfer learning on reanalysis datasets. Experimental results show that the model achieves a correlation skill of 0.51 for predicting ENSO 20 months in advance during the period from 1983 to 2021. Compared to the current state-of-the-art deep learning model, the correlation skill is improved by 0.06. The source code will be available at https://github.com/shaxiaoyuyu/ensoformer.git.

show abstract

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

Cited by 21 publications

References 50 publications

CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation

CNN‐Based ENSO Forecasts With a Focus on SSTA Zonal Pattern and Physical Interpretation

Reconciling roles of the South China Sea summer monsoon and ENSO in prediction of the Indian Ocean dipole

ENSO-Former: Spatiotemporal fusion network based on multivariate and dual-branch Transformer for ENSO prediction

Contact Info

Product

Resources

About