Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach
Changes in mean sea level (MSL) are a major, but not the unique, cause of changes in high-percentile sea levels (HSL), e.g. the annual 99.9th quantile of sea level (among other factors, climate variability may also have huge influence). To unravel the respective influence of each contributor, we pro...
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ftdoajarticles:oai:doaj.org/article:6e6c8c13e9934d688213097bde256d2a 2023-09-05T13:21:43+02:00 Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach Jeremy Rohmer Gonéri Le Cozannet 2019-01-01T00:00:00Z https://doi.org/10.1088/1748-9326/aaf0cd https://doaj.org/article/6e6c8c13e9934d688213097bde256d2a EN eng IOP Publishing https://doi.org/10.1088/1748-9326/aaf0cd https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/aaf0cd 1748-9326 https://doaj.org/article/6e6c8c13e9934d688213097bde256d2a Environmental Research Letters, Vol 14, Iss 1, p 014008 (2019) mean sea level extremes Bayesian structure time series model Kalman-filter climate indices Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 article 2019 ftdoajarticles https://doi.org/10.1088/1748-9326/aaf0cd 2023-08-13T00:37:31Z Changes in mean sea level (MSL) are a major, but not the unique, cause of changes in high-percentile sea levels (HSL), e.g. the annual 99.9th quantile of sea level (among other factors, climate variability may also have huge influence). To unravel the respective influence of each contributor, we propose to use structural time series models considering six major climate indices (CI) (Artic Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Southern Oscillation Index, Niño 1 + 2 and Niño 3.4) as well as a reconstruction of MSL. The method is applied to eight century-long tide gauges across the world (Brest (France), Newlyn (UK), Cuxhaven (Germany), Stockholm (Sweden), Gedser (Danemark), Halifax (Canada), San Francisco (US), and Honolulu (US)). The treatment within a Bayesian setting enables to derive an importance indicator, which measures how often the considered driver is included in the model. The application to the eight tide gauges outlines that MSL signal is a strong driver (except for Gedser), but is not unique. In particular, the influence of Artic Oscillation index at Cuxhaven, Stockholm and Halifax, and of Niño Sea Surface Temperature index 1 + 2 at San Francisco appear to be very strong as well. A similar analysis was conducted by restricting the time period of interest to the 1st part of the 20th century. Over this period, we show that the MSL dominance is lower, whereas an ensemble of CI contribute to a large part to HSL time evolution as well. The proposed setting is flexible and could be applied to incorporate any alternative predictive time series such as river discharge, tidal constituents or vertical ground motions where relevant. Article in Journal/Newspaper North Atlantic North Atlantic oscillation Directory of Open Access Journals: DOAJ Articles Canada Environmental Research Letters 14 1 014008 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
mean sea level extremes Bayesian structure time series model Kalman-filter climate indices Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
spellingShingle |
mean sea level extremes Bayesian structure time series model Kalman-filter climate indices Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 Jeremy Rohmer Gonéri Le Cozannet Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach |
topic_facet |
mean sea level extremes Bayesian structure time series model Kalman-filter climate indices Environmental technology. Sanitary engineering TD1-1066 Environmental sciences GE1-350 Science Q Physics QC1-999 |
description |
Changes in mean sea level (MSL) are a major, but not the unique, cause of changes in high-percentile sea levels (HSL), e.g. the annual 99.9th quantile of sea level (among other factors, climate variability may also have huge influence). To unravel the respective influence of each contributor, we propose to use structural time series models considering six major climate indices (CI) (Artic Oscillation, North Atlantic Oscillation, Atlantic Multidecadal Oscillation, Southern Oscillation Index, Niño 1 + 2 and Niño 3.4) as well as a reconstruction of MSL. The method is applied to eight century-long tide gauges across the world (Brest (France), Newlyn (UK), Cuxhaven (Germany), Stockholm (Sweden), Gedser (Danemark), Halifax (Canada), San Francisco (US), and Honolulu (US)). The treatment within a Bayesian setting enables to derive an importance indicator, which measures how often the considered driver is included in the model. The application to the eight tide gauges outlines that MSL signal is a strong driver (except for Gedser), but is not unique. In particular, the influence of Artic Oscillation index at Cuxhaven, Stockholm and Halifax, and of Niño Sea Surface Temperature index 1 + 2 at San Francisco appear to be very strong as well. A similar analysis was conducted by restricting the time period of interest to the 1st part of the 20th century. Over this period, we show that the MSL dominance is lower, whereas an ensemble of CI contribute to a large part to HSL time evolution as well. The proposed setting is flexible and could be applied to incorporate any alternative predictive time series such as river discharge, tidal constituents or vertical ground motions where relevant. |
format |
Article in Journal/Newspaper |
author |
Jeremy Rohmer Gonéri Le Cozannet |
author_facet |
Jeremy Rohmer Gonéri Le Cozannet |
author_sort |
Jeremy Rohmer |
title |
Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach |
title_short |
Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach |
title_full |
Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach |
title_fullStr |
Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach |
title_full_unstemmed |
Dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approach |
title_sort |
dominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a bayesian statistical approach |
publisher |
IOP Publishing |
publishDate |
2019 |
url |
https://doi.org/10.1088/1748-9326/aaf0cd https://doaj.org/article/6e6c8c13e9934d688213097bde256d2a |
geographic |
Canada |
geographic_facet |
Canada |
genre |
North Atlantic North Atlantic oscillation |
genre_facet |
North Atlantic North Atlantic oscillation |
op_source |
Environmental Research Letters, Vol 14, Iss 1, p 014008 (2019) |
op_relation |
https://doi.org/10.1088/1748-9326/aaf0cd https://doaj.org/toc/1748-9326 doi:10.1088/1748-9326/aaf0cd 1748-9326 https://doaj.org/article/6e6c8c13e9934d688213097bde256d2a |
op_doi |
https://doi.org/10.1088/1748-9326/aaf0cd |
container_title |
Environmental Research Letters |
container_volume |
14 |
container_issue |
1 |
container_start_page |
014008 |
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1776202297137692672 |