{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,14]],"date-time":"2026-02-14T10:33:54Z","timestamp":1771065234740,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["2022YFC2803901"],"award-info":[{"award-number":["2022YFC2803901"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["330000210130313013006"],"award-info":[{"award-number":["330000210130313013006"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["41806032"],"award-info":[{"award-number":["41806032"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key R&amp;D Program of China","doi-asserted-by":"publisher","award":["42176005"],"award-info":[{"award-number":["42176005"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Fund of Zhejiang Province","award":["2022YFC2803901"],"award-info":[{"award-number":["2022YFC2803901"]}]},{"name":"Research Fund of Zhejiang Province","award":["330000210130313013006"],"award-info":[{"award-number":["330000210130313013006"]}]},{"name":"Research Fund of Zhejiang Province","award":["41806032"],"award-info":[{"award-number":["41806032"]}]},{"name":"Research Fund of Zhejiang Province","award":["42176005"],"award-info":[{"award-number":["42176005"]}]},{"name":"National Natural Science Foundation of China","award":["2022YFC2803901"],"award-info":[{"award-number":["2022YFC2803901"]}]},{"name":"National Natural Science Foundation of China","award":["330000210130313013006"],"award-info":[{"award-number":["330000210130313013006"]}]},{"name":"National Natural Science Foundation of China","award":["41806032"],"award-info":[{"award-number":["41806032"]}]},{"name":"National Natural Science Foundation of China","award":["42176005"],"award-info":[{"award-number":["42176005"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The Pacific Decadal Oscillation (PDO), the dominant pattern of sea surface temperature anomalies in the North Pacific basin, is an important low-frequency climate phenomenon. Leveraging data spanning from 1871 to 2010, we employed machine learning models to predict the PDO based on variations in several climatic indices: the Ni\u00f1o3.4, North Pacific index (NPI), sea surface height (SSH), and thermocline depth over the Kuroshio\u2013Oyashio Extension (KOE) region (SSH_KOE and Ther_KOE), as well as the Arctic Oscillation (AO) and Atlantic Multi-decadal Oscillation (AMO). A comparative analysis of the temporal and spatial performance of six machine learning models was conducted, revealing that the Gated Recurrent Unit model demonstrated superior predictive capabilities compared to its counterparts, through the temporal and spatial analysis. To better understand the inner workings of the machine learning models, SHapley Additive exPlanations (SHAP) was adopted to present the drivers behind the model\u2019s predictions and dynamics for modeling the PDO. Our findings indicated that the Ni\u00f1o3.4, North Pacific index, and SSH_KOE were the three most pivotal features in predicting the PDO. Furthermore, our analysis also revealed that the Ni\u00f1o3.4, AMO, and Ther_KOE indices were positively associated with the PDO, whereas the NPI, SSH_KOE, and AO indices exhibited negative correlations.<\/jats:p>","DOI":"10.3390\/rs16132261","type":"journal-article","created":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T05:33:28Z","timestamp":1718948008000},"page":"2261","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Predicting and Understanding the Pacific Decadal Oscillation Using Machine Learning"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0586-5831","authenticated-orcid":false,"given":"Zhixiong","family":"Yao","sequence":"first","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Key Laboratory of Ocean Space Resource Management Technology, Ministry of Natural Resources, Marine Academy of Zhejiang Province, Hangzhou 310012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7120-9491","authenticated-orcid":false,"given":"Dongfeng","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Key Laboratory of Ocean Space Resource Management Technology, Ministry of Natural Resources, Marine Academy of Zhejiang Province, Hangzhou 310012, China"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Key Laboratory of Ocean Space Resource Management Technology, Ministry of Natural Resources, Marine Academy of Zhejiang Province, Hangzhou 310012, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1889-5661","authenticated-orcid":false,"given":"Jian","family":"Ren","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Key Laboratory of Marine Ecosystem Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]},{"given":"Zhenlong","family":"Yu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]},{"given":"Chenghao","family":"Yang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Key Laboratory of Ocean Space Resource Management Technology, Ministry of Natural Resources, Marine Academy of Zhejiang Province, Hangzhou 310012, China"}]},{"given":"Mingquan","family":"Xu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"},{"name":"Key Laboratory of Ocean Space Resource Management Technology, Ministry of Natural Resources, Marine Academy of Zhejiang Province, Hangzhou 310012, China"}]},{"given":"Huiqun","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China"}]},{"given":"Xiaoxiao","family":"Tan","sequence":"additional","affiliation":[{"name":"Key Laboratory of Marine Hazards Forecasting, Ministry of Natural Resources, Hohai University, Nanjing 210098, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1069","DOI":"10.1175\/1520-0477(1997)078<1069:APICOW>2.0.CO;2","article-title":"A Pacific interdecadal climate oscillation with impacts on salmon production","volume":"78","author":"Mantua","year":"1997","journal-title":"Bull. 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