Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition
The Pacific Decadal Oscillation (PDO), as the dominant mode of decadal sea surface temperature variability in the North Pacific, exhibits both interannual and decadal fluctuations that significantly influence global climate. The complexity associated with PDO changes poses challenges for accurate pr...
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MDPI AG
2025-07-01
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| author | Hang Yu Junbo Lei Pengfei Lin Tao Zhang Hailong Liu Huilin Lai Lindong Lai Bowen Zhao Bo Wu |
| author_facet | Hang Yu Junbo Lei Pengfei Lin Tao Zhang Hailong Liu Huilin Lai Lindong Lai Bowen Zhao Bo Wu |
| author_sort | Hang Yu |
| collection | DOAJ |
| description | The Pacific Decadal Oscillation (PDO), as the dominant mode of decadal sea surface temperature variability in the North Pacific, exhibits both interannual and decadal fluctuations that significantly influence global climate. The complexity associated with PDO changes poses challenges for accurate predictions. This study develops a BiLSTM-WOA-MMD (BWM) model, which integrates a bidirectional long short-term memory network with a whale optimization algorithm (WOA) and multiple modal decomposition (MMD), to forecast PDO at both interannual and decadal time scales. The model successfully predicts monthly/annual average PDO index of up to 15 months/5 years in advance, achieving a correlation coefficient of 0.56/0.55. By utilizing the WOA to effectively optimize hyperparameters, the model enhances the PDO prediction skill compared to existing deep learning PDO prediction models, improving the correlation coefficient from 0.47 to 0.68 at a 6-month lead time. The combination of MMD and WOA further minimizes prediction errors and extends the forecasting effective time to 15 months by capturing essential modes. The BWM model can be employed for future PDO prediction and the predicted PDO will remain in its cool phase in the next year both using the PDO index from NECI and derived from near-time satellite data. This proposed model offers an effective way to advance the prediction skill of climate variability on multiple time scales by utilizing all kinds of data available including satellite data, and provides a large-scale background to monitor marine heatwaves. |
| format | Article |
| id | doaj-art-dd92d7fef8cd430684b1a01c9d5b7706 |
| institution | Kabale University |
| issn | 2072-4292 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj-art-dd92d7fef8cd430684b1a01c9d5b77062025-08-20T04:00:51ZengMDPI AGRemote Sensing2072-42922025-07-011715253710.3390/rs17152537Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal DecompositionHang Yu0Junbo Lei1Pengfei Lin2Tao Zhang3Hailong Liu4Huilin Lai5Lindong Lai6Bowen Zhao7Bo Wu8State Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics (IAP), Beijing 100029, ChinaFujian Key Laboratory of Analytical Mathematics and Applications (FJKLAMA) Center for Applied Mathematics, School of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, ChinaState Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics (IAP), Beijing 100029, ChinaState Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics (IAP), Beijing 100029, ChinaLaoshan Laboratory, Qingdao 266237, ChinaFujian Key Laboratory of Analytical Mathematics and Applications (FJKLAMA) Center for Applied Mathematics, School of Mathematics and Statistics, Fujian Normal University, Fuzhou 350117, ChinaCenter for Philippine Studies, International College of Chinese Studies, Fujian Normal University, Fuzhou 350117, ChinaShanghai Typhoon Institute, China Meteorological Administration, Shanghai 200030, ChinaState Key Laboratory of Earth System Numerical Modeling and Application, Institute of Atmospheric Physics (IAP), Beijing 100029, ChinaThe Pacific Decadal Oscillation (PDO), as the dominant mode of decadal sea surface temperature variability in the North Pacific, exhibits both interannual and decadal fluctuations that significantly influence global climate. The complexity associated with PDO changes poses challenges for accurate predictions. This study develops a BiLSTM-WOA-MMD (BWM) model, which integrates a bidirectional long short-term memory network with a whale optimization algorithm (WOA) and multiple modal decomposition (MMD), to forecast PDO at both interannual and decadal time scales. The model successfully predicts monthly/annual average PDO index of up to 15 months/5 years in advance, achieving a correlation coefficient of 0.56/0.55. By utilizing the WOA to effectively optimize hyperparameters, the model enhances the PDO prediction skill compared to existing deep learning PDO prediction models, improving the correlation coefficient from 0.47 to 0.68 at a 6-month lead time. The combination of MMD and WOA further minimizes prediction errors and extends the forecasting effective time to 15 months by capturing essential modes. The BWM model can be employed for future PDO prediction and the predicted PDO will remain in its cool phase in the next year both using the PDO index from NECI and derived from near-time satellite data. This proposed model offers an effective way to advance the prediction skill of climate variability on multiple time scales by utilizing all kinds of data available including satellite data, and provides a large-scale background to monitor marine heatwaves.https://www.mdpi.com/2072-4292/17/15/2537Pacific Decadal Oscillationinterannual-decadal variabilityprediction skilloptimizing algorithmhyperparameter |
| spellingShingle | Hang Yu Junbo Lei Pengfei Lin Tao Zhang Hailong Liu Huilin Lai Lindong Lai Bowen Zhao Bo Wu Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition Remote Sensing Pacific Decadal Oscillation interannual-decadal variability prediction skill optimizing algorithm hyperparameter |
| title | Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition |
| title_full | Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition |
| title_fullStr | Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition |
| title_full_unstemmed | Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition |
| title_short | Improved Pacific Decadal Oscillation Prediction by an Optimizing Model Combined Bidirectional Long Short-Term Memory and Multiple Modal Decomposition |
| title_sort | improved pacific decadal oscillation prediction by an optimizing model combined bidirectional long short term memory and multiple modal decomposition |
| topic | Pacific Decadal Oscillation interannual-decadal variability prediction skill optimizing algorithm hyperparameter |
| url | https://www.mdpi.com/2072-4292/17/15/2537 |
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