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...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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IOP Publishing
2019-01-01
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| Series: | Environmental Research Letters |
| Subjects: | |
| Online Access: | https://doi.org/10.1088/1748-9326/aaf0cd |
| _version_ | 1797748065590837248 |
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| author | Jeremy Rohmer Gonéri Le Cozannet |
| author_facet | Jeremy Rohmer Gonéri Le Cozannet |
| author_sort | Jeremy Rohmer |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T16:00:38Z |
| format | Article |
| id | doaj.art-6e6c8c13e9934d688213097bde256d2a |
| institution | Directory Open Access Journal |
| issn | 1748-9326 |
| language | English |
| last_indexed | 2024-03-12T16:00:38Z |
| publishDate | 2019-01-01 |
| publisher | IOP Publishing |
| record_format | Article |
| series | Environmental Research Letters |
| spelling | doaj.art-6e6c8c13e9934d688213097bde256d2a2023-08-09T14:39:35ZengIOP PublishingEnvironmental Research Letters1748-93262019-01-0114101400810.1088/1748-9326/aaf0cdDominance of the mean sea level in the high-percentile sea levels time evolution with respect to large-scale climate variability: a Bayesian statistical approachJeremy Rohmer0https://orcid.org/0000-0001-9083-5965Gonéri Le Cozannet1https://orcid.org/0000-0003-2421-3003BRGM, 3 av. C. Guillemin-45060 Orléans Cedex 2 - FranceBRGM, 3 av. C. Guillemin-45060 Orléans Cedex 2 - FranceChanges 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.https://doi.org/10.1088/1748-9326/aaf0cdmean sea levelextremesBayesian structure time series modelKalman-filterclimate indices |
| spellingShingle | 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 Environmental Research Letters mean sea level extremes Bayesian structure time series model Kalman-filter climate indices |
| 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_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_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_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 |
| topic | mean sea level extremes Bayesian structure time series model Kalman-filter climate indices |
| url | https://doi.org/10.1088/1748-9326/aaf0cd |
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