Sea Level Variability in the Red Sea: A Persistent East–West Pattern
Based on 26 years of satellite altimetry, this study reveals the presence of a persistent east–west pattern in the sea level of the Red Sea, which is visible throughout the years when considering the east–west difference in sea level. This eastern–western (EW) difference is positive during winter wh...
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ftdoajarticles:oai:doaj.org/article:85950cb0b557496e9de9f819bf2b0c37 2023-05-15T17:31:22+02:00 Sea Level Variability in the Red Sea: A Persistent East–West Pattern Cheriyeri P. Abdulla Abdullah M. Al-Subhi 2020-06-01T00:00:00Z https://doi.org/10.3390/rs12132090 https://doaj.org/article/85950cb0b557496e9de9f819bf2b0c37 EN eng MDPI AG https://www.mdpi.com/2072-4292/12/13/2090 https://doaj.org/toc/2072-4292 doi:10.3390/rs12132090 2072-4292 https://doaj.org/article/85950cb0b557496e9de9f819bf2b0c37 Remote Sensing, Vol 12, Iss 2090, p 2090 (2020) satellite altimetry sea level anomaly El-Nino Southern Oscillation Indian Ocean Dipole North Atlantic Oscillation Red Sea Science Q article 2020 ftdoajarticles https://doi.org/10.3390/rs12132090 2022-12-30T20:32:29Z Based on 26 years of satellite altimetry, this study reveals the presence of a persistent east–west pattern in the sea level of the Red Sea, which is visible throughout the years when considering the east–west difference in sea level. This eastern–western (EW) difference is positive during winter when a higher sea level is observed at the eastern coast of the Red Sea and the opposite occurs during summer. May and October are transition months that show a mixed pattern in the sea level difference. The EW difference in the southern Red Sea has a slightly higher range compared to that of the northern region during summer, by an average of 0.2 cm. Wavelet analysis shows a significant annual cycle along with other signals of lower magnitude for both the northern and southern Red Sea. Removing the annual cycle reveals two energy peaks with periodicities of <12 months and 3–7 years, representing the intraseasonal and El Nino—Southern Oscillation (ENSO) signals, respectively. Empirical Orthogonal Function (EOF) analysis shows that EOF1 corresponds to 98% of total variability, EOF2 to 1.3%, and EOF3 to 0.4%. The remote response of ENSO is evident in the variability in the atmospheric bridge, while that of the Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO) is weak. Three physical mechanisms are responsible for the occurrence of this EW difference phenomenon, namely wind, buoyancy, and the polarity of eddies. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Indian Remote Sensing 12 13 2090 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
satellite altimetry sea level anomaly El-Nino Southern Oscillation Indian Ocean Dipole North Atlantic Oscillation Red Sea Science Q |
spellingShingle |
satellite altimetry sea level anomaly El-Nino Southern Oscillation Indian Ocean Dipole North Atlantic Oscillation Red Sea Science Q Cheriyeri P. Abdulla Abdullah M. Al-Subhi Sea Level Variability in the Red Sea: A Persistent East–West Pattern |
topic_facet |
satellite altimetry sea level anomaly El-Nino Southern Oscillation Indian Ocean Dipole North Atlantic Oscillation Red Sea Science Q |
description |
Based on 26 years of satellite altimetry, this study reveals the presence of a persistent east–west pattern in the sea level of the Red Sea, which is visible throughout the years when considering the east–west difference in sea level. This eastern–western (EW) difference is positive during winter when a higher sea level is observed at the eastern coast of the Red Sea and the opposite occurs during summer. May and October are transition months that show a mixed pattern in the sea level difference. The EW difference in the southern Red Sea has a slightly higher range compared to that of the northern region during summer, by an average of 0.2 cm. Wavelet analysis shows a significant annual cycle along with other signals of lower magnitude for both the northern and southern Red Sea. Removing the annual cycle reveals two energy peaks with periodicities of <12 months and 3–7 years, representing the intraseasonal and El Nino—Southern Oscillation (ENSO) signals, respectively. Empirical Orthogonal Function (EOF) analysis shows that EOF1 corresponds to 98% of total variability, EOF2 to 1.3%, and EOF3 to 0.4%. The remote response of ENSO is evident in the variability in the atmospheric bridge, while that of the Indian Ocean Dipole (IOD) and North Atlantic Oscillation (NAO) is weak. Three physical mechanisms are responsible for the occurrence of this EW difference phenomenon, namely wind, buoyancy, and the polarity of eddies. |
format |
Article in Journal/Newspaper |
author |
Cheriyeri P. Abdulla Abdullah M. Al-Subhi |
author_facet |
Cheriyeri P. Abdulla Abdullah M. Al-Subhi |
author_sort |
Cheriyeri P. Abdulla |
title |
Sea Level Variability in the Red Sea: A Persistent East–West Pattern |
title_short |
Sea Level Variability in the Red Sea: A Persistent East–West Pattern |
title_full |
Sea Level Variability in the Red Sea: A Persistent East–West Pattern |
title_fullStr |
Sea Level Variability in the Red Sea: A Persistent East–West Pattern |
title_full_unstemmed |
Sea Level Variability in the Red Sea: A Persistent East–West Pattern |
title_sort |
sea level variability in the red sea: a persistent east–west pattern |
publisher |
MDPI AG |
publishDate |
2020 |
url |
https://doi.org/10.3390/rs12132090 https://doaj.org/article/85950cb0b557496e9de9f819bf2b0c37 |
geographic |
Indian |
geographic_facet |
Indian |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Remote Sensing, Vol 12, Iss 2090, p 2090 (2020) |
op_relation |
https://www.mdpi.com/2072-4292/12/13/2090 https://doaj.org/toc/2072-4292 doi:10.3390/rs12132090 2072-4292 https://doaj.org/article/85950cb0b557496e9de9f819bf2b0c37 |
op_doi |
https://doi.org/10.3390/rs12132090 |
container_title |
Remote Sensing |
container_volume |
12 |
container_issue |
13 |
container_start_page |
2090 |
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1766128892854140928 |