Underestimation of extremes in sea level surge reconstruction
International audience Statistical models are an alternative to numerical models for reconstructing storm surges at a low computational cost. These models directly link surges to metocean variables, i.e., predictors such as atmospheric pressure, wind and waves. Such reconstructions usually underesti...
Published in: | Scientific Reports |
---|---|
Main Authors: | , , |
Other Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
HAL CCSD
2024
|
Subjects: | |
Online Access: | https://hal.science/hal-04692142 https://hal.science/hal-04692142/document https://hal.science/hal-04692142/file/s41598-024-65718-6.pdf https://doi.org/10.1038/s41598-024-65718-6 |
id |
ftinraparis:oai:HAL:hal-04692142v1 |
---|---|
record_format |
openpolar |
spelling |
ftinraparis:oai:HAL:hal-04692142v1 2024-09-30T14:39:53+00:00 Underestimation of extremes in sea level surge reconstruction Harter, Ludovic Pineau-Guillou, Lucia Chapron, Bertrand Laboratoire d'Océanographie Physique et Spatiale (LOPS) Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) Institut d'écologie et des sciences de l'environnement de Paris (iEES Paris ) Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) ANR-21-CE01-0004,ClimEx,Evolution des niveaux marins extrêmes dans le contexte du changement climatique(2021) European Project: 856408,STUOD(2020) 2024-06-27 https://hal.science/hal-04692142 https://hal.science/hal-04692142/document https://hal.science/hal-04692142/file/s41598-024-65718-6.pdf https://doi.org/10.1038/s41598-024-65718-6 en eng HAL CCSD Nature Publishing Group info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-024-65718-6 info:eu-repo/grantAgreement//856408/EU/Stochastic Transport in Upper Ocean Dynamics/STUOD hal-04692142 https://hal.science/hal-04692142 https://hal.science/hal-04692142/document https://hal.science/hal-04692142/file/s41598-024-65718-6.pdf doi:10.1038/s41598-024-65718-6 info:eu-repo/semantics/OpenAccess ISSN: 2045-2322 EISSN: 2045-2322 Scientific Reports https://hal.science/hal-04692142 Scientific Reports, 2024, 14 (1), pp.14875. ⟨10.1038/s41598-024-65718-6⟩ [SDE]Environmental Sciences [SDU]Sciences of the Universe [physics] info:eu-repo/semantics/article Journal articles 2024 ftinraparis https://doi.org/10.1038/s41598-024-65718-6 2024-09-17T14:54:41Z International audience Statistical models are an alternative to numerical models for reconstructing storm surges at a low computational cost. These models directly link surges to metocean variables, i.e., predictors such as atmospheric pressure, wind and waves. Such reconstructions usually underestimate extreme surges. Here, we explore how to reduce biases on extremes using two methods—multiple linear regressions and neural networks—for surge reconstructions. Models with different configurations are tested at 14 long-term tide gauges in the North-East Atlantic. We found that (1) using the wind stress rather than the wind speed as predictor reduces the bias on extremes. (2) Adding the significant wave height as a predictor can reduce biases on extremes at a few locations tested. (3) Building on these statistical models, we show that atmospheric reanalyses likely underestimate extremes over the 19th century. Finally, it is demonstrated that neural networks can effectively predict extreme surges without wind information, but considering the atmospheric pressure input extracted over a sufficiently large area around a given station. This last point may offer new insights into air-sea interaction studies and wind stress parametrization. Article in Journal/Newspaper North East Atlantic Institut National de la Recherche Agronomique: ProdINRA Scientific Reports 14 1 |
institution |
Open Polar |
collection |
Institut National de la Recherche Agronomique: ProdINRA |
op_collection_id |
ftinraparis |
language |
English |
topic |
[SDE]Environmental Sciences [SDU]Sciences of the Universe [physics] |
spellingShingle |
[SDE]Environmental Sciences [SDU]Sciences of the Universe [physics] Harter, Ludovic Pineau-Guillou, Lucia Chapron, Bertrand Underestimation of extremes in sea level surge reconstruction |
topic_facet |
[SDE]Environmental Sciences [SDU]Sciences of the Universe [physics] |
description |
International audience Statistical models are an alternative to numerical models for reconstructing storm surges at a low computational cost. These models directly link surges to metocean variables, i.e., predictors such as atmospheric pressure, wind and waves. Such reconstructions usually underestimate extreme surges. Here, we explore how to reduce biases on extremes using two methods—multiple linear regressions and neural networks—for surge reconstructions. Models with different configurations are tested at 14 long-term tide gauges in the North-East Atlantic. We found that (1) using the wind stress rather than the wind speed as predictor reduces the bias on extremes. (2) Adding the significant wave height as a predictor can reduce biases on extremes at a few locations tested. (3) Building on these statistical models, we show that atmospheric reanalyses likely underestimate extremes over the 19th century. Finally, it is demonstrated that neural networks can effectively predict extreme surges without wind information, but considering the atmospheric pressure input extracted over a sufficiently large area around a given station. This last point may offer new insights into air-sea interaction studies and wind stress parametrization. |
author2 |
Laboratoire d'Océanographie Physique et Spatiale (LOPS) Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS) Institut d'écologie et des sciences de l'environnement de Paris (iEES Paris ) Institut de Recherche pour le Développement (IRD)-Sorbonne Université (SU)-Université Paris-Est Créteil Val-de-Marne - Paris 12 (UPEC UP12)-Centre National de la Recherche Scientifique (CNRS)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) ANR-21-CE01-0004,ClimEx,Evolution des niveaux marins extrêmes dans le contexte du changement climatique(2021) European Project: 856408,STUOD(2020) |
format |
Article in Journal/Newspaper |
author |
Harter, Ludovic Pineau-Guillou, Lucia Chapron, Bertrand |
author_facet |
Harter, Ludovic Pineau-Guillou, Lucia Chapron, Bertrand |
author_sort |
Harter, Ludovic |
title |
Underestimation of extremes in sea level surge reconstruction |
title_short |
Underestimation of extremes in sea level surge reconstruction |
title_full |
Underestimation of extremes in sea level surge reconstruction |
title_fullStr |
Underestimation of extremes in sea level surge reconstruction |
title_full_unstemmed |
Underestimation of extremes in sea level surge reconstruction |
title_sort |
underestimation of extremes in sea level surge reconstruction |
publisher |
HAL CCSD |
publishDate |
2024 |
url |
https://hal.science/hal-04692142 https://hal.science/hal-04692142/document https://hal.science/hal-04692142/file/s41598-024-65718-6.pdf https://doi.org/10.1038/s41598-024-65718-6 |
genre |
North East Atlantic |
genre_facet |
North East Atlantic |
op_source |
ISSN: 2045-2322 EISSN: 2045-2322 Scientific Reports https://hal.science/hal-04692142 Scientific Reports, 2024, 14 (1), pp.14875. ⟨10.1038/s41598-024-65718-6⟩ |
op_relation |
info:eu-repo/semantics/altIdentifier/doi/10.1038/s41598-024-65718-6 info:eu-repo/grantAgreement//856408/EU/Stochastic Transport in Upper Ocean Dynamics/STUOD hal-04692142 https://hal.science/hal-04692142 https://hal.science/hal-04692142/document https://hal.science/hal-04692142/file/s41598-024-65718-6.pdf doi:10.1038/s41598-024-65718-6 |
op_rights |
info:eu-repo/semantics/OpenAccess |
op_doi |
https://doi.org/10.1038/s41598-024-65718-6 |
container_title |
Scientific Reports |
container_volume |
14 |
container_issue |
1 |
_version_ |
1811642468263788544 |