{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T05:45:06Z","timestamp":1781243106165,"version":"3.54.1"},"reference-count":68,"publisher":"MDPI AG","issue":"19","license":[{"start":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T00:00:00Z","timestamp":1632355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42006021, 41806039, 4276233, 41806004"],"award-info":[{"award-number":["42006021, 41806039, 4276233, 41806004"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The satellite altimeter data, temperature and salinity data, and 1.5-layer reduced gravity model are used to quantitatively evaluate the contributions of the steric effect and the dynamic process to sea level variations in the Tropical Pacific Ocean (TPO) on different time scales. Concurrently, it also analyses the influence of wind forcing over the different regions of the Pacific Ocean on the sea level variations in the TPO. Seasonal sea level variations in the TPO were the most important in the middle and eastern regions of the 5\u00b0\u201315\u00b0N latitude zone, explaining 40\u201360% of the monthly mean sea level variations. Both the steric effect and dynamic process jointly affected the seasonal sea level variations. Among them, the steric effect was dominant, contributing over 70% in most regions of the TPO, while the dynamic process primarily acted near the equator and southwest regions, contributing approximately 55\u201385%. At the same time, the seasonal dynamic sea level variations were caused by the combined actions of primarily local wind forcing, alongside subtropical north Pacific wind forcing. On the interannual to decadal time scale, the sea level interannual variations were significant in the northwestern, southwestern, and middle eastern regions of the TPO and explained 45\u201360% of the monthly mean sea level variations. The decadal sea level variations were the most intense in the eastern Philippine Sea, contributing 25\u201345% to the monthly mean sea level variations. The steric effect and the dynamic process can explain 100% of the interannual to decadal sea level variations. The contribution of the steric effect was generally high, accounting for more than 85% in the regions near the equator. The impact of the dynamic process was mainly concentrated in the northwest, northeast, and southern regions of the TPO, contributing approximately 55\u201380%. Local wind forcing is the leading role of interannual to decadal sea level variations. The combined actions of El Ni\u00f1o\u2013Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO) can explain 90% of the interannual to decadal sea level variations in the northwestern and eastern of the TPO.<\/jats:p>","DOI":"10.3390\/rs13193809","type":"journal-article","created":{"date-parts":[[2021,9,27]],"date-time":"2021-09-27T22:16:38Z","timestamp":1632780998000},"page":"3809","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Sea Level Seasonal, Interannual and Decadal Variability in the Tropical Pacific Ocean"],"prefix":"10.3390","volume":"13","author":[{"given":"Jianhu","family":"Wang","sequence":"first","affiliation":[{"name":"College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyuan","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"},{"name":"Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuchen","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,9,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"802","DOI":"10.1038\/nclimate1979","article-title":"Future flood losses in major coastal cities","volume":"3","author":"Hallegatte","year":"2013","journal-title":"Nat. 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