Gumbel parameters Global Tide and Surge Reanalysis (GTSR) dataset

GTSR (Global Tide and Surge Reanalysis) is the first global reanalysis of storm surges and extreme sea levels based on hydrodynamic modelling. GTSR covers the entire world's coastline based on the DIVA coastline segmentation. To estimate the probabilities of extreme sea levels, we apply extreme value statistics using the annual maxima method. For each output location, we extract the annual maximum for the calendar years 1979–2014 and fit a Gumbel distribution using the maximum-likelihood method. This files provides the location and scale parameters

Daily maxima of storm surge from the Global Tide and Surge Reanalysis (GTSR) dataset

GTSR (Global Tide and Surge Reanalysis) is the first global reanalysis of storm surges and extreme sea levels based on hydrodynamic modelling. GTSR covers the entire world's coastline and provides time-series of tides and storm surge from 1979-2014. Tides are simulated with FES2012, while storm surges are simulated by forcing the Global Tide and Surge Model (GTSM) with wind and pressure fields from ERA-Interim. In addition to timeseries, GTSR also provides estimates of extreme sea levels for various return period based on the Gumbel distributio

Daily maxima of total water levels from the Global Tide and Surge Reanalysis (GTSR) dataset

GTSR (Global Tide and Surge Reanalysis) is the first global reanalysis of storm surges and extreme sea levels based on hydrodynamic modelling. GTSR covers the entire world's coastline and provides time-series of tides and storm surge from 1979-2014. Tides are simulated with FES2012, while storm surges are simulated by forcing the Global Tide and Surge Model (GTSM) with wind and pressure fields from ERA-Interim. In addition to timeseries, GTSR also provides estimates of extreme sea levels for various return period based on the Gumbel distributio

Time-series of monthly mean steric sea levels

This dataset with monthly mean sea levels complements the Global Tide and Surge Reanalysis (GTSR) dataset. The GTSR time-series do not includes variations in mean sea level driven by changes in water mass and water density. We estimated the density effect by computing the monthly mean steric sea levels based on temperature and salinity data profiles. The algorithm is developed by Amiruddin et al., (2015; ). We use monthly means of global gridded temperature and salinity data from Ishii and Kimoto (2009). This data set ends in 2012, so we use the EN4.1.1 dataset from the UK MET Office for the period 2012-2014 (Good et al., 2013; Gouretski & Reseghetti, 2010)

STORM tropical cyclone wind speed return periods

Datasets containing tropical cyclone maximum wind speed (in m/s) return periods, generated using the STORM datasets (see https://www.nature.com/articles/s41597-020-0381-2). Return periods were empirically calculated using Weibull's plotting formula. The STORM_FIXED_RETURN_PERIOD dataset contains maximum wind speeds for a fixed set of return periods at 10 km resolution in every ocean basin. The STORM_FIXED_WIND_SPEED dataset contains return periods for a fixed set of maximum wind speeds at 10 km resolution in every ocean basin. The STORM_CITIES dataset contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from a selection of 18 coastal cities. The STORM_ISLANDS contains return periods at fixed wind speeds and wind speeds at fixed return periods (on two seperate sheets), occurring within 100 km from the capital city of an island. We included the Small Island Developing States and a set of other islands

Data underlying the paper: Spatiotemporal patterns of extreme sea levels along the western North-Atlantic coasts

ATSR (Atlantic Tide and Surge Reanalysis) provides time-series of tides and surges from 1988-2015 for the western North-Atlantic coastline. Simulations are based on the Global Tide and Surge Model version 2.0. Tropical cyclones are explicitly included based on the Extended Best Track dataset and parametric cyclone model (Holland, 1980)

STORM EC-Earth present climate synthetic tropical cyclone tracks

Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al, Generation of a Global Synthetic Tropical cyclone Hazard Dataset using STORM, in prep.). The dataset is generated using data the EC-Earth model and resembles present-climate conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones

STORM IBTrACS present climate synthetic tropical cyclone tracks

Datasets consisting of 10,000 years of synthetic tropical cyclone tracks, generated using the Synthetic Tropical cyclOne geneRation Model (STORM) algorithm (see Bloemendaal et al, Generation of a Global Synthetic Tropical cyclone Hazard Dataset using STORM, in review). The dataset is generated using historical data from IBTrACS and resembles present-climate conditions. The data can be used to calculate tropical cyclone risk in all (coastal) regions prone to tropical cyclones

COAST-RP: A global COastal dAtaset of Storm Tide Return Periods

Storm surges that occur along low-lying, densely populated coastlines can leave devastating societal, economical, and ecological impacts. To protect coastal communities from flooding, return periods of storm tides, defined as the combination of the surge and tide, must be accurately evaluated. Here we present storm tide return periods using a novel integration of two modelling techniques. For surges induced by extratropical cyclones, we use a 38-year time series based on the ERA5 climate reanalysis. For surges induced by tropical cyclones, we use synthetic tropical cyclones from the STORM dataset representing 10,000 years under current climate conditions. Tropical and extratropical cyclone surge levels are probabilistically combined with tidal levels, and return periods are computed empirically. The COAST-RP dataset contains storm tide levels representing the 1, 2, 5, 10, 25, 50, 100, 250, 500, and 1000-year return period.</p