Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis
Non-stationary extreme value analysis is a powerful framework to address the problem of time evolution of extremes and its link to climate variability as measured by different climate indices CI (like North Atlantic Oscillation NAO index). To model extreme sea levels (ESLs), a widely-used tool is th...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2021-09-01
|
Series: | Weather and Climate Extremes |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2212094721000451 |
id |
doaj-eb74301fcdc74060b405092b01f19afd |
---|---|
record_format |
Article |
spelling |
doaj-eb74301fcdc74060b405092b01f19afd2021-08-26T04:33:47ZengElsevierWeather and Climate Extremes2212-09472021-09-0133100352Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysisJérémy Rohmer0Rémi Thieblemont1Gonéri Le Cozannet2Corresponding author.; BRGM, 3 Av. C. Guillemin, 45060, Orléans Cedex 2, FranceBRGM, 3 Av. C. Guillemin, 45060, Orléans Cedex 2, FranceBRGM, 3 Av. C. Guillemin, 45060, Orléans Cedex 2, FranceNon-stationary extreme value analysis is a powerful framework to address the problem of time evolution of extremes and its link to climate variability as measured by different climate indices CI (like North Atlantic Oscillation NAO index). To model extreme sea levels (ESLs), a widely-used tool is the non-stationary Generalized Extreme Value distribution (GEV) where the parameters (location, scale and shape) are allowed to vary as a function of some covariates like the month-of-year or some CIs. A commonly used assumption is that only a few CIs impact the GEV parameters by using a linear model, and most of the time by focusing on two GEV parameters (location or/and the scale parameter). In the present study, these assumptions are revisited by relying on a data-driven spline-based GEV fitting approach combined with a penalization procedure. This allows identifying the type (non- or linear) of the CI influence for any of the three GEV parameters directly from the data, and evaluating the significance of this relation, i.e. without making any a priori assumptions as it is traditionally done. This approach is applied to the monthly maxima of sea levels derived from eight of the longest (quasi century-long) tide gauge dataset (Brest, France; Cuxhaven, Germany; Gedser, Denmark; Halifax, Canada; Honolulu, US; Newlyn, UK; San Francisco, US; Stockholm, Sweden) and by accounting for four major CIs (the North Atlantic Oscillation, the Atlantic Multidecadal Oscillation, the Niño 1 + 2 and the Southern Oscillation indices). From this analysis, we show that: (1) the links between CIs and different parameters of a GEV distribution fitted to ESL data are most of the time linear, but some of them present significant non-linear shapes; (2) multiple CIs should be considered to predict ESLs, and (3) the CI influence of the GEV distribution is not limited to the location parameter. These results are useful to understand current modes of variability of ESLs, and ultimately to improve coastal resilience through more precise extreme water level assessments.http://www.sciencedirect.com/science/article/pii/S2212094721000451ExtremesClimate indicesNon-stationary generalized extreme value distribution |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Jérémy Rohmer Rémi Thieblemont Gonéri Le Cozannet |
spellingShingle |
Jérémy Rohmer Rémi Thieblemont Gonéri Le Cozannet Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis Weather and Climate Extremes Extremes Climate indices Non-stationary generalized extreme value distribution |
author_facet |
Jérémy Rohmer Rémi Thieblemont Gonéri Le Cozannet |
author_sort |
Jérémy Rohmer |
title |
Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis |
title_short |
Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis |
title_full |
Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis |
title_fullStr |
Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis |
title_full_unstemmed |
Revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis |
title_sort |
revisiting the link between extreme sea levels and climate variability using a spline-based non-stationary extreme value analysis |
publisher |
Elsevier |
series |
Weather and Climate Extremes |
issn |
2212-0947 |
publishDate |
2021-09-01 |
description |
Non-stationary extreme value analysis is a powerful framework to address the problem of time evolution of extremes and its link to climate variability as measured by different climate indices CI (like North Atlantic Oscillation NAO index). To model extreme sea levels (ESLs), a widely-used tool is the non-stationary Generalized Extreme Value distribution (GEV) where the parameters (location, scale and shape) are allowed to vary as a function of some covariates like the month-of-year or some CIs. A commonly used assumption is that only a few CIs impact the GEV parameters by using a linear model, and most of the time by focusing on two GEV parameters (location or/and the scale parameter). In the present study, these assumptions are revisited by relying on a data-driven spline-based GEV fitting approach combined with a penalization procedure. This allows identifying the type (non- or linear) of the CI influence for any of the three GEV parameters directly from the data, and evaluating the significance of this relation, i.e. without making any a priori assumptions as it is traditionally done. This approach is applied to the monthly maxima of sea levels derived from eight of the longest (quasi century-long) tide gauge dataset (Brest, France; Cuxhaven, Germany; Gedser, Denmark; Halifax, Canada; Honolulu, US; Newlyn, UK; San Francisco, US; Stockholm, Sweden) and by accounting for four major CIs (the North Atlantic Oscillation, the Atlantic Multidecadal Oscillation, the Niño 1 + 2 and the Southern Oscillation indices). From this analysis, we show that: (1) the links between CIs and different parameters of a GEV distribution fitted to ESL data are most of the time linear, but some of them present significant non-linear shapes; (2) multiple CIs should be considered to predict ESLs, and (3) the CI influence of the GEV distribution is not limited to the location parameter. These results are useful to understand current modes of variability of ESLs, and ultimately to improve coastal resilience through more precise extreme water level assessments. |
topic |
Extremes Climate indices Non-stationary generalized extreme value distribution |
url |
http://www.sciencedirect.com/science/article/pii/S2212094721000451 |
work_keys_str_mv |
AT jeremyrohmer revisitingthelinkbetweenextremesealevelsandclimatevariabilityusingasplinebasednonstationaryextremevalueanalysis AT remithieblemont revisitingthelinkbetweenextremesealevelsandclimatevariabilityusingasplinebasednonstationaryextremevalueanalysis AT gonerilecozannet revisitingthelinkbetweenextremesealevelsandclimatevariabilityusingasplinebasednonstationaryextremevalueanalysis |
_version_ |
1721196164459528192 |