Academic Journal
The convolutional neural network for Pacific decadal oscillation forecast
| Title: | The convolutional neural network for Pacific decadal oscillation forecast |
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| Authors: | Nutta Skanupong, Yongsheng Xu, Lejiang Yu, Zhang Wan, Shuo Wang |
| Source: | Environmental Research Letters, Vol 19, Iss 12, p 124022 (2024) |
| Publisher Information: | IOP Publishing, 2024. |
| Publication Year: | 2024 |
| Collection: | LCC:Environmental technology. Sanitary engineering LCC:Environmental sciences LCC:Science LCC:Physics |
| Subject Terms: | convolutional neural network (CNN), machine learning (ML), pacific decadal oscillation (PDO), sea surface temperature (SST), Environmental technology. Sanitary engineering, TD1-1066, Environmental sciences, GE1-350, Science, Physics, QC1-999 |
| More Details: | The Pacific decadal oscillation (PDO) is often described as a long-lived El Niño-like pattern of Pacific climate variability, and it has widespread climate and ecosystem impacts. PDO forecasts can provide useful information for policymakers on how to handle PDO impacts. Nevertheless, due to the long duration of the PDO cycles and their complex formation mechanisms, it remains a challenge to predict long lead time PDO. In this paper, we propose a transfer-learning-enhanced convolutional neural network (CNN) to tackle complex ocean dynamic forecasting and predict PDO events with up to a one-year lead time. Our method first trains the CNN on historical simulations from Coupled Model Intercomparison Project 6 (CMIP6), covering the period from 1850 to 1972. This prior knowledge is then refined by further training the model with observational data from 1854 to 1972, ensuring robust performance on unseen data. Additionally, k-fold cross-validation is also employed to evaluate the model’s performance across diverse subsets of data, enhancing its reliability. Throughout the testing phase from 1983 to 2022, the CNN model consistently outperforms existing dynamical forecast systems, exhibiting superior correlation skills in predicting annual mean PDO indices and PDO phases, including displaying resilience to seasonal variations. The transferred CNN is thus a powerful method to predict PDO events and is potentially valuable for a wide range of applications. This work directly supports the objectives of the World Climate Research Programme Grand Challenge on Climate Prediction. |
| Document Type: | article |
| File Description: | electronic resource |
| Language: | English |
| ISSN: | 1748-9326 |
| Relation: | https://doaj.org/toc/1748-9326 |
| DOI: | 10.1088/1748-9326/ad8be2 |
| Access URL: | https://doaj.org/article/77b6dc782f164527a59b3700d5734e55 |
| Accession Number: | edsdoj.77b6dc782f164527a59b3700d5734e55 |
| Database: | Directory of Open Access Journals |
| ISSN: | 17489326 |
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| DOI: | 10.1088/1748-9326/ad8be2 |
| Published in: | Environmental Research Letters |
| Language: | English |