Sea levels are projected to rise in France under various Shared Socioeconomic Pathways (SSP). Under the SSP1-2.6 low emission scenario, it is expected that sea levels in France will rise ***** centimeters (cm) during the next decades, and ***** cm by the mid-century, relative to the historic baseline. Sea levels will continue rising to reach **** cm by 2099, under the same scenario.

Land impacted by inundation due to sea level rise is projected to rise in France under various Shared Socioeconomic Pathways (SSP). Under the SSP1-*** low emission scenario, the mean amount of land that falls below local mean sea level due to climate change impact on sea level rise in Mexico is projected to reach *** square kilometers at the end of the century relative to the historic baseline.

Sea levels are projected to rise in Marseille under various Shared Socioeconomic Pathways (SSP). In 2020, there was a sea level rise of ***** centimeters under the SSP1-1.6 scenario, relative to a ********* baseline. It is expected that sea levels in Marseille will rise **** meters (m) under the same scenario by 2150 and *** m under the SSP1-2.6 scenario by the same year. The highest projected sea level rise in Marseille is under the SSP5-8.5 scenario, reaching **** m by 2150.

French Tide gauges data archived at SISMER.

1. Description

This repository contains the data, notebooks, and documents covering the archeology and rescue of tide gauge observation at Socoa (Saint Jean-de-Luz, Southwestern France), and is complementary to the paper (henceforth "data paper") titled "Extension of high temporal resolution sea level time series at Socoa (Saint Jean-de-Luz, France) back to 1875".

Authors: Md Jamal Uddin Khan, Inge Van Den Beld, Guy Woppelmann, Laurent Testut, Alexa Latapy, Nicolas Pouvreau

For up-to-date sea level timeseries, visit the Shom data distribution portal linked in the Related identifier section below. At the time of writing, corrected and valid data over 1875 to date at original sampling is identified as 'Validées temps différé'.

The data and source-codes presented here is open-access. Please consider citing the data paper if you use the data. More information is given in the Data-use section below.

A brief structure of the files contained in this repository is shown below, and a brief description is given in the following sections -

|- data |- auxiliary |- brest_data.shom.fr/ |- santander_marcos_etal.dat |- socoa |- data.shom.fr/ |- corrections.csv |- corrections_marigram.csv |- corrections_registry.csv |- data_inventory.csv |- socoa_L0.csv |- socoa_L1.csv |- socoa_L2.csv |- socoa_L2_nominal.csv |- socoa_L3.csv |- socoa_L4.csv |- socoa_raw.txt |- documents |- archive_records |- AD64_Pau_4S 33.docx |- Archives_Shom_plusieursCotes.docx |- SHD_Brest_MB3W.docx |- SHD_Rochefort_7JJ418-7JJ1551.docx |- SHD_Vincennes_plusieurCotes.docx |- tidegauge_journal |- _Socoa_Notes_Registres.docx |- inventory.xlsx |- figures |- figure01 |- ... |- figure 06 |- supp |- notebooks |- 01_data_processing.ipynb |- 02_buddy_checking.ipynb |- 03_trend_analysis.ipynb |- README.md

1. Data and codes

2.1 Data (/data)

Data directory (/data) contains two folder - auxiliary, and socoa.

/data/socoa contains raw data (socoa_raw.txt) as well as processed dataset (socoa_L*.csv). The directory also contains information regarding the data inventory (data_inventory.csv), and the corrections applied during the processing (corrections_*.csv). A snapshot of data from https://data.shom.fr (April 2022) is also available in the /data/socoa/data.shom.fr directory.

Data files corresponding to various level of dataset is following -

Level 0: No corrections applied (/data/socoa/socoa_L0.csv)

Level 1: Time corrections applied, including conversion to UTC time (/data/socoa/socoa_L1.csv)

Level 2: Time and height corrections applied (/data/socoa/socoa_L2.csv)

Level 3: Hourly interpolated and merged with existing data (/data/socoa/socoa_L3.csv)

Level 4: Hourly data additionally flagged for siltation from tidal analysis (/data/socoa/socoa_L4.csv)

All datafiles are provided as standard comma-separated text files encoded in utf-8. The final Level 4 data has three columns with column header titled - Datetime, Value, and Flag, where,

Datetime: The UTC timestamp of the data

Value: Sea level with respect to Hydrographic Zero

Flag: Data flag as described in the data paper

In addition, Level 4 data contains the following information header -

Station: SAINT-JEAN-DE-LUZ_SOCOA

Longitude: -1.68162

Latitude: 43.39524

Organization: Shom, Pyrénées-Atlantiques

Timesystem: UTC

VerticalReference: Hydrographic Zero

Unit: m

Flag: 4-bit flags yes(1)/no(0) - [siltation][uncertain_correction][height_correction][time_correction]

DOI_DataPaper: Data: 10.5281/zenodo.7438469, Article: 10.5194/essd-2022-443

DOI_SHOM: 10.17183/REFMAR#95

/data/auxiliary contains the auxiliary dataset used for analysis and comparison in the data paper. Brest dataset, /data/auxiliary/brest_data.shom.fr, is retrieved from https://data.shom.fr. Monthly sea level at Santander, /data/auxiliary/santander_marcos_etal.dat, comes from the dataset published by Marcos et al. (2020).

2.2 Documents (/documents)

The /documents folder contains supplimentary documents related to the dataset.

/documents/tidegauge_journal contains the extraction from the tide gauge journal year by year. The files are named as – YEAR_Socoa_Notes_Registres.docx where YEAR is the 4-digit year.

/documents/archive_records contains the excerpts of metadata documents from the various archives. For 5 archives, 5 separate files are provided. These archives are and file names are –

Service historique de la Défense (SHD) at Brest (SHD-Brest): SHD_Brest_MB3W.docx

Service historique de la Défense (SHD) at Rochefort (SHD-Rochefort): SHD_Rochefort_7JJ418-7JJ1551.docx

Service historique de la Défense (SHD) at Vincennes (SHD-Vincennes): SHD_Vincennes_plusieurCotes.docx

Archives des Pyrénées-Atlantiques (AD64): AD64_Pau_4S 33.docx

SHOM archive: Archives_Shom_plusieursCotes.docx

/documents/inventory.xlsx gives an overall inventory of the historic documents catalogued.

2.3 Figures (/figures)

The figures directory (/figures) contains the figures presented in the accompanying data paper organized into individual directories for each figure. The most of the figures related to the analysis is generated using python and their source-code is provided in the /notebooks directory. In case where external graphical software has been used for generating the final image is provided inside the directory for each individual figure.

2.4 Codes (/notebooks)

The codes used in processing this data is provided in the /notebooks directory. The full code is segmented into three self-contained python/jupyter notebooks -

Data processing (01_data_processing.ipynb)

Buddy checking (02_buddy_checking.ipynb)

Trend analysis (03_trend_analysis.ipynb)

The python environment necessary for running the notebooks can be created using conda (https://www.anaconda.com/products/distribution) with the following commands -

conda create -n socoa python=3.7 numpy scipy statsmodels cartopy netcdf4 pandas xarray jupyter conda activate socoa pip install utide==0.2.6 jupyter-notebook

1. Data-use information

Citation: Khan, M. J. U., Van Den Beld, I., Wöppelmann, G., Testut, L., Latapy, A., and Pouvreau, N.: Extension of high temporal resolution sea level time series at Socoa (Saint Jean-de-Luz, France) back to 1875, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2022-443, in review, 2023.

Use rights: The data and source-code is freely available to anyone. Please consider citing the data paper using the citation above.

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

1. Related identifiers

SHOM historical dataset portal: http://refmar.shom.fr/dataRescue/HTML/FR/SOCOA%20(SAINT-JEAN-DE-LUZ).html

SHOM data distribution portal: http://dx.doi.org/10.17183/REFMAR#95

Satellite altimetry missions provide a quasi-global synoptic view of sea level over more than 25 years. The satellite altimetry constellation is used to build sea level maps and regional sea level indicators such as trends and accelerations. Estimating realistic uncertainties on these quantities is crucial to address some current climate science questions such as climate change detection and attribution or regional sea level budget closure for example. Previous studies have estimated the uncertainty for the global mean sea level (GMSL), but no uncertainty information is available at regional scales. In this study we estimate a regional satellite altimetry error budget and use it to derive maps of confidence intervals for local sea rise rates and accelerations. We analyze 27 years of satellite altimetry maps and derive the satellite altimetry error variance-covariance matrix at each grid point, prior to the estimation of confidence intervals on local trends and accelerations at the 90% confidence level using extended least squares estimators. Over 1993–2019, we find that the average local sea level trend uncertainty is 0.83 mm.yr-1 with local values ranging from 0.78 to 1.22 mm.yr-1. For accelerations, uncertainties range from 0.057 to 0.12 mm.yr-2, with a mean value of 0.063 mm.yr-2.

Change history: - 2020/07/08: initial dataset submission over 1993-2018 - 2020/10/21: 1993-2019 update and addition of error levels

The dataset contains 39148 years of sea level data from 1355 station records, with some stations having alternative versions of the records provided from different sources. GESLA-2 data may be obtained from www.gesla.org. The site also contains the file format description and other information. The text files contain headers with lines of metadata followed by the data itself in a simple column format. All the tide gauge data in GESLA-2 have hourly or more frequent sampling. The basic data from the US National Atmospheric and Oceanic Administration (NOAA) are 6-minute values but for GESLA-2 purposes we instead settled on their readily-available 'verified hourly values'. Most UK records are also hourly values up to the 1990s, and 15-minute values thereafter. Records from some other sources may have different sampling, and records should be inspected individually if sampling considerations are considered critical to an analysis. The GESLA-2 dataset has global coverage and better geographical coverage that the GESLA-1 with stations in new regions (defined by stations in the new dataset located more than 50 km from any station in GESLA-1). For example, major improvements can be seen to have been made for the Mediterranean and Baltic Seas, Japan, New Zealand and the African coastline south of the Equator. The earliest measurements are from Brest, France (04/01/1846) and the latest from Cuxhaven, Germany and Esbjerg, Denmark (01/05/2015). There are 29 years in an average record, although the actual number of years varies from only 1 at short-lived sites, to 167 in the case of Brest, France. Most of the measurements in GESLA-2 were made during the second half of the twentieth century. The most globally-representative analyses of sea level variability with GESLA-2 will be those that focus on the period since about 1970. Historically, delayed-mode data comprised spot values of sea level every hour, obtained from inspection of the ink trace on a tide gauge chart. Nowadays tide gauge data loggers provide data electronically. Data can be either spot values, integrated (averaged) values over specified periods (e.g. 6 minutes), or integrated over a specified period within a longer sampling period (e.g. averaged over 3 minutes every 6 minutes). The construction of this dataset is fundamental to research in sea level variability and also to practical aspects of coastal engineering. One component is concerned with encouraging countries to install tide gauges at locations where none exist, to operate them to internationally agreed standards, and to make the data available to interested users. A second component is concerned with the collection of data from the global set of tide gauges, whether gauges have originated through the GLOSS programme or not, and to make the data available. The records in GESLA-2 will have had some form of quality control undertaken by the data providers. However, the extent to which that control will have been undertaken will inevitably vary between providers and with time. In most cases, no further quality control has been made beyond that already undertaken by the data providers. Although there are many individual contributions, over a quarter of the station-years are provided by the research quality dataset of UHSLC. Contributors include: British Oceanographic Data Centre; University of Hawaii Sea Level Center; Japan Meteorological Agency; US National Oceanic and Atmospheric Administration; Puertos del Estado, Spain; Marine Environmental Data Service, Canada; Instituto Espanol de Oceanografica, Spain; idromare, Italy; Swedish Meteorological and Hydrological Institute; Federal Maritime and Hydrographic Agency, Germany; Finnish Meteorological Institute; Service hydrographique et oceanographique de la Marine, France; Rijkswaterstaat, Netherlands; Danish Meteorological Institute; Norwegian Hydrographic Service; Icelandic Coastguard Service; Istituto Talassographico di Trieste; Venice Commune, Italy;

Until recently, classical radar altimetry could not provide reliable sea level data within 10 km to the coast. However dedicated reprocessing of radar waveform together with geophysical corrections adapted for the coastal regions now allows to fill this gap at a large number of coastal sites. In the context of the Climate Change Initiative Sea Level project of the European Space Agency, we have recently performed a complete reprocessing of high resolution (20 Hz, i.e., 350m) along-track altimetry data of the Jason-1, Jason-2 and Jason-3 missions over January 2002 to June 2021 along the coastal zones of Northeast Atlantic, Mediterranean Sea, whole African continent, North Indian Ocean, Southeast Asia, Australia and North and South America. This reprocessing has provided valid sea level data in the 0-20 km band from the coast. More than 1000 altimetry-based virtual coastal stations have been selected and sea level anomalies time series together with associated coastal sea level trends have been computed over the study time span. In the coastal regions devoid from tide gauges (e.g., African coastlines), these virtual stations offer a unique tool for estimating sea level change close to the coast (typically up to 3 km to the coast but in many instances up to 1 km or even closer). Results show that at most of the virtual stations, the rate of sea level rise at the coast is similar to the rate offshore (15 km away from the coast). However, at some stations, the sea level rate in the last 3-4 km to the coast is either faster or slower than offshore.

Global mean sea level is an integral of changes occurring in the climate system in response to unforced climate variability as well as natural and anthropogenic forcing factors. Its temporal evolution allows detecting changes (e.g., acceleration) in one or more components. Study of the sea level budget provides constraints on missing or poorly known contributions, such as the unsurveyed deep ocean or the still uncertain land water component. In the context of the World Climate Research Programme Grand Challenge entitled “Regional Sea Level and Coastal Impacts”, an international effort involving the sea level community worldwide has been recently initiated with the objective of assessing the various data sets used to estimate components of the sea level budget during the altimetry era (1993 to present). These data sets are based on the combination of a broad range of space-based and in situ observations, model estimates and algorithms. Evaluating their quality, quantifying uncertainties and identifying sources of discrepancies between component estimates is extremely useful for various applications in climate research. This effort involves several tens of scientists from about sixty research teams/institutions worldwide (www.wcrp-climate.org/grand-challenges/gc-sea-level). The results presented in this paper are a synthesis of the first assessment performed during 2017-2018. We present estimates of the altimetry-based global mean sea level (average rate of 3.1 +/- 0.3 mm/yr and acceleration of 0.1 mm/yr2 over 1993-present), as well as of the different components of the sea level budget. We further examine closure of the sea level budget, comparing the observed global mean sea level with the sum of components. Ocean thermal expansion, glaciers, Greenland and Antarctica contribute by 42%, 21%, 15% and 8% to the global mean sea level over the 1993-present. We also study the sea level budget over 2005-present, using GRACE-based ocean mass estimates instead of sum of individual mass components. Results show closure of the sea level budget within 0.3 mm/yr. Substantial uncertainty remains for the land water storage component, as shown in examining individual mass contributions to sea level.

This collection contains the complete holdings of the Joint Archive for Sea Level (JASL) for sea level data that have been quality controlled, assessed, and documented. As of October 2015, the holdings consisted of 689 series with 17,369 station-years of quality-assured data. The series are of variable lengths with the greatest concentration between 5 and 30 years, although 61 sites have over 60 years.

JASL is a collaboration between the University of Hawaii Sea Level Center/Joint Institute for Marine and Atmospheric Research and the National Centers for Environmental Information (NOAA/NESDIS/NCEI) continues to acquire, quality control, manage, and distribute sea level data as initiated by the Tropical Ocean Global Atmosphere Program, which ended in 1994. The JASL is a Global Sea Level Observing System archive center and is the largest global collection of quality-controlled hourly sea level. The JASL receives hourly data from regional and national sea level networks. The data are inspected and obvious errors such as data spikes and time shifts are corrected. Gaps less than 25 hours are interpolated. Reference level problems are referred back to the originator. If the originators can not resolve the reference level shift,comparisons with neighboring sites or examination of the hourly residuals may warrant an adjustment. Descriptive station information and quality assessments are prepared. The objective is to assemble a scientifically valid, well-documented archive of hourly and daily sea level values in standardized formats. The JASL set is bi-annually updated and submitted to the World Data Service for Oceanography via NCEI, the Permanent Service for Mean Sea Level, and the British Oceanographic Data Centre.

As part of the FOAM project, the GEOCEAN NC 2019 campaign was focused on the sea level question, with geodetic measurements using both bottom pressure sensors (deployment of 5 tide gauge) and GPS systems (deployment of various floating platforms). The data collected has several objectives: 1. Quantifying the setup and its variations in the Lagoon over a profile ranging from the coral reef to the Noumea tide gauge. 2. Acquiring in situ sea level data under altimetry satellite tracks for the purposes of satellite calibration/validation studies. 3. Acquiring and improving the GPS data processing procedure for sea level mapping. This campaign took place between 3 and 15 October 2019, on board the R/V ALIS in the Noumea lagoon, with the support and assistance of the IRD, Shom, DITTT and the US IMAGO team from the IRD in Noumea.

the dataset consists of raw pressure data and sea level data from 27 low-cost bottom pressure sensors (mastodon probes) sampling at 2 hz. the low-cost sensors were deployed at blancs sablons (le conquet, france) during more than 4 months, from 14 december 2023 to 25 april 2024.the coordinate files (coord_sensors.txt) contains the longitude and latitude of the 27 sensors.the raw_pressure_data directory contains 27 files in text format (csv). the pressure files (climex_blancs_sablons_m*_p.csv) contain a header (10 lines), and then one line per time record (every 0.5 s), with the date, the temperature (°c), and the pressure (bar).the sea_level_data directory contains 27 files in netcdf format (climex_blancs_sablons_m*_sea_level.nc). the ’sea_level’ variable is expressed in meters and related to chart datum. the ’time’ variable is expressed in seconds and represents the number of elapsed days since 1970-01-01 00:00:00 utc.

This data file provides a validated and correctly time-stamped version of the 2009-2021 sea-level records of the five following tide stations in French Polynesia: Vairao (Tahiti-Iti), Mangareva (Gambier Archipelago), Tubuai (Austral Archipelago), Makemo and Rangiroa (Tuamotu Archipelago). The time sampling is one or two minutes, depending on the tide gauge. These data can be found in other databases, including IOC database, but we found that 16.7 % of the timestamps in these databases are incorrect. In addition, outliers found in the raw data were flagged by comparison with a preliminary tide model.

This dataset contains absolute dynamic topography (similar to sea level but with respect to the geoid) binned and averaged monthly on 1 degree grids. The coverage is from October 1992 to December 2010. These data were provided by AVISO (French space agency data provider) to support the CMIP5 (Coupled Model Intercomparison Project Phase 5) under the World Climate Research Program (WCRP) and was first made available via the JPL Earth System Grid. The dynamic topography are derived from sea surface height measured by several satellites including Envisat, TOPEX/Poseidon, Jason-1 and OSTM/Jason-2, and referenced to the geoid. Along with this dataset, two additional ancillary data files are included in the same directory which contain the number of observations and standard error co-located on the same 1 degree grids.

definitionthe omi_extreme_sl_medsea_slev_mean_and_anomaly_obs indicator is based on the computation of the 99th and the 1st percentiles from in situ data (observations). it is computed for the variable sea level measured by tide gauges along the coast. the use of percentiles instead of annual maximum and minimum values, makes this extremes study less affected by individual data measurement errors. the annual percentiles referred to annual mean sea level are temporally averaged and their spatial evolution is displayed in the dataset medsea_omi_sl_extreme_var_slev_mean_and_anomaly_obs, jointly with the anomaly in the target year. this study of extreme variability was first applied to sea level variable (pérez gómez et al 2016) and then extended to other essential variables, sea surface temperature and significant wave height (pérez gómez et al 2018).contextsea level (slev) is one of the essential ocean variables most affected by climate change. global mean sea level rise has accelerated since the 1990’s (abram et al., 2019, legeais et al., 2020), due to the increase of ocean temperature and mass volume caused by land ice melting (wcrp, 2018). basin scale oceanographic and meteorological features lead to regional variations of this trend that combined with changes in the frequency and intensity of storms could also rise extreme sea levels up to one metre by the end of the century (vousdoukas et al., 2020, tebaldi et al., 2021). this will significantly increase coastal vulnerability to storms, with important consequences on the extent of flooding events, coastal erosion and damage to infrastructures caused by waves (boumis et al., 2023). the increase in extreme sea levels over recent decades is, therefore, primarily due to the rise in mean sea level. note, however, that the methodology used to compute this omi removes the annual 50th percentile, thereby discarding the mean sea level trend to isolate changes in storminess. the mediterranean sea shows statistically significant positive sea level trends over the whole basin. however, at sub-basin scale sea level trends show spatial variability arising from local circulation (calafat et al., 2022; meli et al., 2023).copernicus marine service key findingsthe completeness index criteria is fulfilled in this region by 38 stations, 26 more than in 2021, significantly increasing spatial coverage with new in situ data in the central mediterranean sea, primarily from italian stations. the mean 99th percentiles reflect the spatial variability of the tide, a microtidal regime, along the spanish, french and italian coasts, ranging from around 0.20 m above mean sea level in sicily and the balearic islands (e.g.: 0.22 m in porto empedocle, 0.23 m in ibiza)) to around 0.60 m above mean sea level in the northern adriatic sea (e.g.: 0.63 m in trieste, 0.61 m in venice). . the annual 99th percentiles standard deviation ranges between 2 cm in málaga and motril (south of spain) to 8 cm in marseille. . the 2022 99th percentile anomalies present negative values mainly along the spanish coast (as in 2021) and in the islands of corsica and sardinia (western part of the region), while positive values are observed along the eastern french mediterranean coast and at most of the italian stations (closer to the central part of the region), with values ranging from -4 cm in málaga and motril (spain) to +5 cm in ancona (italy).references:abram, n., gattuso, j.-p., prakash, a., cheng, l., chidichimo, m. p., crate, s., enomoto, h., garschagen, m., gruber, n., harper, s., holland, e., kudela, r. m., rice, j., steffen, k., & von schuckmann, k. (2019). framing and context of the report. in h. o. pörtner, d. c. roberts, v. masson-delmotte, p. zhai, m. tignor, e. poloczanska, k. mintenbeck, a. alegría, m. nicolai, a. okem, j. petzold, b. rama, & n. m. weyer (eds.), ipcc special report on the ocean and cryosphere in a changing climate (pp. 73–129). in press. https://www.ipcc.ch/srocc/.boumis, g., moftakhari, h. r., & moradkhani, h. 2023. coevolution of extreme sea levels and sea-level rise under global warming. earth's future, 11, e2023ef003649. https://doi.org/10.1029/2023ef003649. calafat, f. m., frederikse, t., and horsburgh, k.: the sources of sea-level changes in the mediterranean sea since 1960, j geophys res oceans, 127, e2022jc019061, https://doi.org/10.1029/2022jc019061, 2022.legeais j-f, llovel w, melet a, and meyssignac b. 2020. evidence of the topex-a altimeter instrumental anomaly and acceleration of the global mean sea level, in: copernicus marine service ocean state report, issue 4, journal of operational oceanography, s77–s82, https://doi.org/10.1080/1755876x.2020.1785097.meli m, camargo cml, olivieri m, slangen aba, and romagnoli c. 2023. sea-level trend variability in the mediterranean during the 1993–2019 period, front mar sci, 10, 1150488,[...]

The Global Sea Level Observing System (GLOSS) 'Delayed-mode' Data Assembly Centre at the British Oceanographic Data Centre (BODC) quality controls and archives high frequency (i.e. hourly or more frequent) global sea level data and any ancillary measurements (e.g. temperature, wind speed/direction, atmospheric pressure) that are included with the data. The tide gauges are situated on most coastlines, and data cover the Arctic to the Antarctic, and the Atlantic, Indian and Pacific oceans. The main component of GLOSS is the 'Global Core Network' (GCN) of 290 sea level stations around the world for long term climate change and oceanographic sea level monitoring. The Core Network is designed to provide an approximately evenly-distributed sampling. The GLOSS Long Term Trends (LTT) set of gauge sites (some, but not all, of which are in the GCN) are used for monitoring long term trends and accelerations in global sea level. The GLOSS altimeter calibration (ALT) set consists mostly of island stations, and provides a facility for mission intercalibrations. A GLOSS ocean circulation (OC) set, including in particular gauge pairs at straits and in polar area, complements altimetric coverage of the open deep ocean. Data exist from the mid 1800s up to the present day, with particularly long records from Newlyn, U.K.; Brest, France; Prince Rupert, Canada and Honolulu, San Diego and San Francisco, U.S.A.; Sea level has been measured by a variety of different instruments with the historical data mainly coming from mechanical float gauges. More recent technologies include acoustic, pressure, and radar instruments. GLOSS aims at the establishment of high quality global and regional sea level networks to create long‐term sea level records. These records, as well as being used in climate studies (sea level rise), are also used in oceanography (ocean currents, tides, surges), geodesy (national datum), geophysics and geology (coastal land movements) as well as various other disciplines. The programme became known as GLOSS as it provides data for deriving the 'Global Level of the Sea Surface'. GLOSS is an international programme conducted under the auspices of the Joint Technical Commission for Oceanography and Marine Meteorology (JCOMM) of the World Meteorological Organisation (WMO) and the Intergovernmental Oceanographic Commission (IOC). Data are collected by local agencies such as port authorities, universities and navies and sent to or downloaded by the data centre.

---- The bulletin is coded as GRIB code form:FM 92 (GRIB, General regularly-distributed information in binary form) .(Refer to WMO No.306 - Manual on Codes for the definition of WMO international codes)---- The HPNA89 TTAAii Data Designators decode (2) as:T1 (H): Grid point information (GRIB).T2 (P): Pressure.A1 (N): Northern hemisphere.A2 (A): Analysis (00 hour).ii (89): Any parameter reduced to sea level (eg MSLP).(2: Refer to WMO No.386 - Manual on the GTS - Attachment II.5)---- WMO No.9 - Volume C1 'Content' field:(180W-180E)(90N-20S) AND ( 20N-90S) GRID (25x25)---- Grid size: 2.5 X 2.5---- Forecast Offset: 00 (hours)

The bathymetric MNT of the facade of Tahiti and Moorea at a resolution of 0.001° (~ 100 m) was realised for Météo-France (Inter-regional Directorate in French Polynesia). The NTM covers a 10 km band around the islands of Tahiti and Moorea to meet the need for modelling marine submersions in French Polynesia. This product is available with the lowest astronomical sea level (PBMA) or mean sea level (NM) as a vertical reference.

The land-sea limit corresponds to the Highest Astronomical Tide (HAT) in the case of a tide of coefficient 120 and under normal meteorological conditions (no offshore wind and mean atmospheric pressure of 1013 hPa). This geometric line is naturally defined as the intersection of a HAT model (extended to the coast and the top end of the upper estuary) based on the available Bathyelli surfaces, and a high-resolution digital terrain model of the coastal area. It corresponds to the highest limit of the intertidal area and takes into account, when they exist, the 3 administrative maritime limits which are: the transversal limit of the sea (LTM), the salt water limit (LSE) and the limit of maritime jurisdiction (LAM). This limit is measured (quoted) in relation to the local hydrographic chart datum and to the levels of reference for France (IGN69 and IGN78 for Corsica). Relations to other levels of reference (mean sea level and ellipsoid) are known. The land-sea limit product, mainly results from the exploitation of the two products Litto3D®/RGEALTI® and Bathyelli, and represents, with a metric resolution (1 to 5 metres), this theoretical entity by a set of 2D polylines describing the nature of the coast (artificial or natural, rocky or sandy, steep or flat, etc.). The method used for calculating the land-sea limit makes it possible to produce a continuous, homogeneous and spatially coherent limit on the entire French metropolitan coastal area.

Along most of the coastal areas, detailed coastal flood observations (e.g. inland water depths) are scarce, and when they are available, this for a limited number of events. Given recent scientific advances, coastal flooding events can be properly modelled, even in complex environments and under the action of wave overtopping, and thus provide detailed information. However, such models are computationally expensive, which prevents their use for instance for forecasting and warning. At the same time, metamodelling techniques have been explored for coastal hydrodynamics and have shown promising results. Metamodels are functions that aim to reproduce the behaviour of a “true” model (e.g., a numerical hydrodynamic model) for given input variables (for instance, offshore conditions). Within the RISCOPE research project (https://perso.math.univ-toulouse.fr/riscope/) aiming at exploring to which extent such metamodelling techniques may allow to forecast coastal floods with a good accuracy, a simulated flood database has been built for the site of Gâvres (France), characterised by a significant effect of wave overtopping processes.

The CFMDG dataset compiles a set of post-processed coastal flood simulations on the site of Gâvres. The dataset includes 250 scenarios. Each scenarios is defined by 6h time series centered on high tide, with one time series per forcing variables. The forcing variables (called X) are: local relative mean sea-level, tide, atmospheric storm surge, the offshore wave characteristics and the offshore wind. These scenarios combine past real (flood and no flood) events in the 1900-2021 time span with extreme statistics based events, and some complementary fictive events. The post-processed outputs (called Y) includes, for each scenario, the maximal flooded area (m²) and the maximal water depth (m) in each of the 64 618 inland model grid points.

The modelling chain that allowed building this dataset relies on the joint use of a spectral wave model (WW3) to propagate the waves to the coast, and a non-hydrostatic wave-flow model (SWASH) to simulate the nearshore hydrodynamics and the flooding. The spatial and temporal resolution of the SWASH configuration validated on the Gâvres site are respectively 3 m and more than 10Hz. All the results are obtained for a Digital Elevation Model corresponding to the 2018 configuration of the site.

Such type of dataset is of use for local knowledge, risk prevention, metamodel testing/training, and local coastal flood forecast.

Part of this dataset has already been used in (Idier et al., 2021; López-Lopera et al., 2021; Betancourt et al., 2022), to develop metamodels and set up a coastal flood forecast and early warning prototype.

We hope and expect that making this dataset accessible will trigger further developments/investigations for improving risk knowledge on the considered site as well as methodological developments on machine-learning/metamodel-based techniques to support flood forecast.

The table below summarizes the variables contained in the dataset, for each scenario.

Variable name

Description and unit

Comment

Scenario n°

Number of the scenario.

INPUTS (X)

NM

Relative mean sea level, referenced to the French vertical datum (m, IGN69)

Time series over 6h

T

Tidal water level (m), referenced to the relative mean sea level

Time series over 6h

S

Atmospheric storm surge (m)

Time series over 6h

Hs

Significant wave height (m)

Time series over 6h

Tp

Wave peak period (s)

Time series over 6h

Dp

Wave peak direction (° in nautical convention)

Time series over 6h

U

Wind speed (m/s)

Time series over 6h

DU

Wind direction (° in nautical convention)

Time series over 6h

t

Relative time centered on the high tide of each event (min)

Not Concerned

High Tide date

UTC date for scenarios corresponding to past real events

Not Concerned

OUTPUTS (Y)

Smax

Maximum flooded area during the event (m²)

Post-processed scalar output

Hmax

Maximum water depth reached during the event (m), provided for each inland location

Post-processed functional (map) output

longitude

Longitude (°, WGS84)

For each inland location point

latitude

Latitude (°, WGS84)

For each inland location point

XL93

Longitude (m, Lambert 93)

For each inland location point

YL93

Latitude (m, Lambert 93)

For each inland location point