Last modified on Oct 09, 2023 16:15

Contributors: Kun Yan (Deltares), Sanne Muis (Deltares), Robyn Gwee (Deltares), Jelmer Veenstra (Deltares), Job Dullaart (VU Amsterdam), Jeroen Aerts (VU Amsterdam), Trang Duong (IHE), Roshanka Ranasinghe (IHE)

Issued by: Kun Yan, Sanne Muis (Deltares)

Issued Date: 16/07/2021

Ref: 

Official refence number service contract: 2020/C3S_435_Lot8_Deltares

Acronyms

1. Introduction

1.1. Executive Summary

The Global Sea Level Change Indicators for 1950 to 2050 Derived from Reanalysis and High Resolution CMIP6 Climate Projections forms one of two catalogue entries consisting of tides, storm surges and sea level rise data and can be used to characterize water levels in present day and future conditions. The second catalogue entry provides the variables as time series at 10-minute temporal resolution for the climate projections and reanalysis, as well as hourly and daily maximums for just the reanalysis product. The two datasets may be used to evaluate sea level variability, coastal flooding, coastal erosion, and accessibility of ports in both present day conditions and to assess changes under climate change.

The indicators include: tidal indicators, extreme-value indicators (e.g. return periods including confidence bounds for total water levels and surge levels), probability indicators (e.g. percentile for total water levels and surge levels). The indicators are computed for three different 30-years periods corresponding to historical, present and future climate conditions (1951-1980, 1985-2014, and 2021-2050). For each of the indicators, the dataset also provides ensemble statistics computed across the 5 members of the HighResMIP ensemble. (e.g. median, mean, standard deviation, range). Absolute and relative changes for the future period (2015-2050) relative to the present-day (1985-2014) are provided to assess climate change impacts on water levels.

This dataset was produced by a Deltares led consortium including VU Amsterdam, IHE Delft on behalf of the Copernicus Climate Change Service.

1.2. Scope of Documentation

This document describes the global water level change indicator products ingested into the CDS as two catalogue entries. It provides an overview of metadata and variable description, as well as how the product is produced. This includes a description of the model, product validation, and indicator calculation.

Specifically, section 1 provides an executive summary of the product. Section 2.1 summarize the data gap and product added value. Section 2.2 provides product overview with metadata information and variable description. Section 2.3 describes input data, climate scenarios, modelling and validation. Section 3 concludes the user guidance and the product limitations.

1.3. Version History

This is the 1st version of global water level change time series and indicator based on the CIMP6 multi-model ensemble.

The production of this dataset adopted the similar modelling technique, compared to the pan-European water level time series and indicator. The model in the Pan-European dataset is forced by the EURO-CORDEX single climate model.

2. Product Description

2.1. Product Target Requirements

Extreme sea levels, consisting of tides, storm surges, and mean sea levels, can cause a range of coastal hazards. The world's coastal areas are increasingly at risk due to rising mean and extreme sea levels, which can lead to the permanent submergence of land; increased coastal flooding; enhanced coastal erosion; loss of coastal ecosystem; and salinization (Oppenheimer, et al., 2019). Global projections of extreme sea levels can be used to assess the impacts of these coastal hazards and provide information on the projected changes for the coming decades.

This User Guide provides a description of a new global dataset of extreme sea levels for a multi-model ensemble of high-resolution climate models. The dataset provides indicators that can be used to characterize water level in present-day conditions, but also assess changes under climate change. This dataset provides an update of a previous dataset which was developed in the contract C3S_422 Lot2. There are two main improvements compared to the previous dataset. First, the geospatial coverage is expanded from pan-European to global. Second, uncertainty from the climate forcing and the extreme value analysis has been considered. More details are provided in Section 2.3.

2.2. Product Overview

2.2.1. Data Description

In this section an overview of the dataset is provided (Table 2-1).

Table 2-1: Overview of key characteristics of the water level change indicator

2.2.2. Variable Description

In this section more details are given about the variables (Table 2-2).

Table 2-2: Overview and description of variables for water level change indicators: tidal indicators.

Table 2-3: Overview and description of variables for water level change indicators: extreme-value indicators and probability indicators including changes

Table 2-4: Overview and description of variables for water level change indicators: Ensemble statistics for extreme-value and probability indicators

2.3. Method

2.3.1. Background

Extreme sea levels, consisting of tides, storm surges, and mean sea levels, can cause a range of coastal hazards. The world's coastal areas are increasingly at risk due to due to rising mean and extreme sea levels, which can lead to the permanent submergence of land; increased coastal flooding; enhanced coastal erosion; loss of coastal ecosystem; and salinization (Oppenheimer, et al., 2019). Global projections of extreme sea levels can be used to assess the impacts of these coastal hazards and provide information on the projected changes for the coming decades. In a previous contract (C3S_422_Lot2), a pan-European dataset with consistent projections of mean sea level, tides, surges and wave condition has been developed (Muis et al., 2020). The time series and indicators are made available via the Climate Data Store (CDS), and have been used for coastal applications such as offshore wind maintenance, port operations and planning, and coastal flood risk assessment.

The dataset described in this User Guide (C3S_435 Lot8) implements several improvements compared to the previous dataset. First, the spatial coverage is expanded from pan-European to global. The global coverage is highly valuable for global studies of the climate change impact on extreme sea levels and coastal flooding, including the effect of climate mitigation and adaptation. Moreover, the global coverage is very beneficial for countries where no regional information is available. Second, uncertainty from the climate forcing and the extreme value analysis has been considered. We provide extreme sea level projections based on a multi-model ensemble of CMIP6 climate models opposed to the previous pan-European dataset which was based on a single CMIP5 climate model. Climate models have large uncertainties and the use of multi-models can be used to increase the confidence in the extreme sea level projections. Moreover, the CMIP6 models have a higher model resolution and improved physics, and as such they are expected to better capture storm surges compared to the previous generation of climate models. In addition, compared to the previous one, this dataset applies more advanced extreme values analysis (peaks-over-threshold instead of annual maxima), and provides an estimate of the uncertainty of the fitted distribution (low and high bound in addition to the best fit).

2.3.2. Dataset design

This User Guide describes a global dataset of (changes in) extreme sea level under future climate change based on the HighResMIP multi-model ensemble. The dataset is produced by simulations with the Global Tide and Surge Model version 3.0 (GTSMv3.0), a 2D hydrodynamic model with global coverage which incorporates tides, surges and mean sea-levels dynamically. The HighResMIP ensemble, part of the CMIP6 experiments, is used as atmospheric forcing (Haarsma et al., 2016). The HighResMIP ensemble consists of high-resolution climate models with resolutions of at least 50 km in the atmosphere and 0.25° in the ocean. The enhanced resolution of HighResMIP has added value for resolving climate extremes such as tropical cyclones (Roberts et al., 2020), which is important for the modelling of storm surges.

This new dataset is developed with the aim to assess how extreme sea levels change between 1950 to 2050 under influence of sea-level rise and climate change based on SSP5-8.5. Two main products are ingested in the CDS:

• Time series of total water levels, tides, storm surges and mean sea level of 1950-2050 for the HighResMIP ensemble, as well as for the ERA5 reanalysis;
• Indicators of total water level, tidal and storm surge statistics for historical, present and future time periods (1951-1980, 1986-2015, and 2021-2050), including changes and uncertainties;

2.3.3. Input Data

The global dataset of extreme sea levels described in this User Guide is based on the ERA5 climate reanalysis and the HighResMIP climate simulations. For the simulations of total water levels we force GTSM with wind and atmospheric pressure. More specially, the following variables are required: the mean sea level pressure (psl), zonal surface wind speed (uas), and meridional surface wind speed (vas). For each of these variables, we use a the highest temporal resolution available. For ERA5 this is hourly. For the HighResMIP ensemble, this is 3- or 6-hourly depending on the climate model.

ERA5 is the global climate reanalysis of the Copernicus Climate Change Service, which is available at the CDS (Hersbach et al., 2020). It is the successor of the ERA-Interim dataset and has a spatial resolution of 0.25° × 0.25° (∼31 km), and is available from 1950 to present. The ERA5 back extension to the period 1950-1978 has recently become available, and therefore, the simulations presented here cover the period 1979-2018. Note that it is reported that the 10m winds in ERA5can become unrealistically large in a particular location (see https://confluence.ecmwf.int/display/CKB/ERA5%3A+large+10m+winds). Also, it is reported that some of the ERA5 data on the CDS was corrupted, resulting in spurious values (see https://confluence.ecmwf.int/display/CKB/ERA5+CDS%3A+Data+corruption). These caveats were issued after the C3S-435 Lot8 simulations were completed, and may affect the products described in this User Guide.  Nevertheless, the GTSM results have been extensively validated as reported in Section 2.3.5 below and show no major issues of concern. The impact of unrealistic wind values and corrupted files would result in the output dataset with erroneous value and unavailable. For the extreme value statistical indicators, we have attempted to remove erroneous values, but for the time series this was not desirable. We do not expect the ERA5 caveats to have large-scale effects, but the unrealistic wind values can result in localised extreme water levels that are too high. When using the time series for a specific event in a local setting, we would recommend users to verify the results and validate.

HighResMIP is a set of model experiments carried out as part of the Climate Model Intercomparison Phase 6 (CMIP6). The experiments cover the period 1950-2050 for a set of climate models with a resolution higher than 50 km. The historical periods (1950-2014) is constrained by observations, while the future period (2015-2050) is based on a high-emission scenario (i.e. SSP5-8.5). In general, the difference between emission scenarios are rather small until the mid-century. The members that are included are largely based on the availability at the time the GTSM simulations were carried out. An ensemble of 5 climate models is used. This ensemble consist of a mix of are both coupled and atmosphere-only (i.e. SST-forced) simulations (Table 2-5). The experimental design of HighResMIP is explained in more detail in Haarsma et al., (2018).

Both the historical and future simulations with GTSM include sea-level rise. A spatially-varying SLR fields at a 1°x1° resolution is used as input and annually updated, using 1986-2005 as the reference period. Processes are computed and combined using the probabilistic model described in Le Bars (2018), here we use the median of these probabilistic SLR projections. The period 1950-2016 is informed by observations-based products; Antarctic and Greenland ice sheets (Mouginot et al., 2019; Rignot et al., 2019), the glaciers (Marzeion et al., 2015), thermal expansion between 0 and 2000 m depth (Levitus et al., 2012), and climate-driven water storage (Humphrey and Gudmundsson, 2019). The ice sheets are assumed to be in equilibrium before 1979 for Antarctica and 1972 for Greenland because no data are available before these dates. The period 2016-2050 is based on the Fifth Assessment Report (AR5) of the Intergovernmental Panel on Climate Change (IPCC) for the SSP5-8.5 scenario (Church et al., 2013). For the dynamics of the Antarctic contribution we use the re-evaluation presented in the Special Report on the Ocean and Cryosphere in a Changing Climate (SCROCC) by the IPCC (Oppenheimer et al., 2019). Additionally, glacial isostatic adjustment is taken from the ICE-6G model (Peltier et al., 2015) but do not take into account other changes in land elevation.

Table 2-5: Overview scenarios and epochs in the water level change time series simulation

2.3.4. Model / Algorithm

2.3.4.1. Global Tide and Surge Modelling (GTSM)

The Global Tide Surge Model (GTSM v3.0) is used as modelling tool to produce the C3S-435 dataset. The GTSMv3.0 is a depth-averaged hydrodynamic model with global coverage that dynamically simulates water levels resulting from tides and storm surges. GTSM has no open boundaries. Tides are modelled by forcing with the tide-generating forces using a set of 60 frequencies. Self-attraction and loading and dissipation of energy through generation of internal tides are considered (Irazoqui Apecechea et al., 2017). Storm surges are modelled by forcing with wind speed and surface pressure. Because tides and storm surge are modelled simultaneously, non-linear interaction effects are included.

GTSMv3.0 uses the unstructured Delft3D Flexible Mesh software (Kernkamp et al., 2011). The spatially-varying resolution leads to high accuracy at relatively low computational costs. It has an unprecedented high coastal resolution globally (2.5 km, 1.25km in Europe, Figure 2-1). The resolution decreases from the coast to the deep ocean to a maximum of 25km. Grid resolution is refined in areas in the deep ocean with steep topography areas to enable the dissipation of barotropic energy through generation of internal tides. See User Guide (Yan et al., 2019) for more detailed regarding GTSM.

Figure 2-1: GTSMv3.0 grid in SouthEast Asia (left) and Europe (right).

2.3.4.2. Water level change indicators

Various statistics are derived from the time series using the post-processing toolbox written in Python. We provide indicators for tidal levels, extreme values, frequency distribution for which we provide actual values, as well as changes from the historical to future period. In addition, we provide ensemble statistics derived from the indictors derived for the five climate models. In the section below we describe each indicator category in more detail.

2.3.4.3. Tidal indicators

Tide control many coastal processes. They are important for the occurrence of high waters, sediment transport and erosion, and tidal mixing and stratification. As such, they are important for the functioning of intertidal ecosystems, but also for society as, for example, high tides can contribute to coastal flooding, and low tides are important for marine navigation. Tides may change in response to sea-level rise, as this will increase the water depth and therefore the tidal propagation. This is most relevant in wide shallow areas, such as the North Sea, where changes are can have wide-ranging implications and are important to consider. See Haigh et al. (2019) for a comprehensive review.
The dataset contains the following tidal indictors based on tidal levels:

• Highest Astronomical Tide (HAT);
• Mean High High Water (MHHW);
• Mean Sea Level (MSL);
• Mean Low Low Water (MLLW);
• Lowest Astronomical Tide (LAT);
• Tidal range (TR).

We include tidal indicators for the three epochs (i.e. 1951-1980, 1985-2014 and 2021-2050) and for annual values (i.e. from 1950 to 2050). MHHW and MLLW are computed using a window of 25 hours, and then averaged over the time period considered. HAT and LAT are respectively the highest and lowest value over the time period considered. For the epoch indicators, the effect of sea-level rise is removed in order to purely characterize the tide and its changes. Thus, the changes between the historical and future time periods (1985-2014, and 2021-2050) for both surge levels and total water levels are assessed. The dataset includes changes between the historical and future epochs (1985-2014 to 2021-2050) calculated as the absolute change (in m) and relative change (in %). For the annual indicators, the effect of sea-level rise included. In addition to sea-level rise, the annual indicators will also reflect long-term tidal variations such as the 18.6-year nodal cycle. This is time-dependent and relevant for the evolution of tidal levels over time.

2.3.4.4. Extreme-value indicators including confidence bounds

Coastal flooding is driven by extreme sea levels composed of mean sea level, astronomical tide, and storm surges. Extreme water level values for different return periods can be used assess the frequency of an event and form the basis of flood-risk assessments. Return periods reflect the exceedance probability of a water levels, for example the water level corresponding to a return period of 100 year has a 1% change to be exceeded in a given year.
The dataset contains the following extreme-value indicators for the three epochs and climate forcing considered:

• Return periods for total water levels (1, 2, 5, 10, 25, 50 and 100 years);
• Return periods for surge levels (1, 2, 5, 10, 25, 50, and 100 years).

The return periods are computed by fitting a Generalized Pareto Distribution (GDP) to the independent peaks above the 99^th percentiles. A 3-day window is used to make sure the peaks are independent. The fitting of the GPD is sensitive to outliers and a few high data points can result in a large overestimation of the return levels, therefore, the shape parameter is set close to zero. In addition to the best fit, confidence bounds are computed using bootstrapping technique. This is done by resampling of peaks (n=599), from which we extract a low bound (5^th percentiles) and a high bound (95^th). By analyzing the difference in given return periods between epochs, one can assess how the extremes may changes under future climate change. The dataset includes changes between the historical and future epochs (1985-2014 to 2021-2050) calculated as the absolute change (in m) and relative change (in %).

Probability indicators
In addition to information on the return period of the extreme events, it is useful to know the frequency distribution. This provides information about the percentage of time a water level is likely to exceed a specific value, reflecting which water levels indicate below or above normal conditions. The dataset contains the following extreme-value indicators for each of the epochs and climate forcing considered:

• Return periods for total water levels (5^th, 10^th, 25^th, 50^th, 75^th, 90^th and 95th);
• Return periods for surge levels (5^th, 10^th, 25^th, 50^th, 75^th, 90^th and 95th).

In order to look at the frequency distribution, we use percentiles. The percentile indicates the value below which a given percentage of data falls. For example, the 75^th percentile indicates above normal conditions and is the value (of total water level or surge level) below which 75% of the data is found. By analyzing difference in a given percentile between epochs , one can assess how the probability distribution changes under future climate change. The dataset includes changes between the historical and future epochs (1985-2014 to 2021-2050) calculated as the absolute change (in m) and relative change (in %). In addition to the epoch probability indicators, we provide annual frequency distribution based on the ERA5 reanalysis (i.e. 1979 to 2018).

2.3.4.5. Ensemble statistics for extreme-value and probability indicators

The extreme-value and probability indicators are computed for each of the five climate models of the HighResMIP ensemble. In addition, the dataset provides absolute and relative changes for future epoch compared to the historical epoch. The actual values of the indicators, as well as the projected changes, have large uncertainties as a result of the large spatial bias of the individual climate models in combination with the relative short period that is used to characterize the water levels. It is therefore important to understand the uncertainties linked to climate model forcing in the extreme sea level calculation, which can be assessed by the ensemble statistics. The dataset contains the following ensemble statistics which are computed based on the five climate models:

• Mean of the actual values;
• Median of the actual values;
• Range of the actual values (difference between minimum and maximum value;
• Standard deviation of the actual values;
• No. of models that show an increase or decrease of the absolute/relative changes.

The ensemble statistics can be used to assess the uncertainty of both the actual values as well as the projected changes. For example, the median of the ensemble for the historical period will have a smaller bias than the individual climate models and is more likely to correspond to the actual conditions (i.e. in agreement with ERA5 indicators). For the projected changes, a large intermodel agreement in the sign op change (increase vs. decrease) may indicate a higher confidence compared the a large intermodel disagreement.

2.3.5. Validation

GTSM is the backbone of the global water level change time series and indicator products, and the validation of the model is thus key to the product validation. The GTSM model has been thoroughly calibrated and validated based on tide gauge observations, satellite products, and comparison with other hydrodynamic models. Table 2-6 provides a summary of the key references and description of the results. A summary is provided below.

Table 2-6: Key references of GTSM validation

The validation of total water levels and storm surges in Muis et. al. (2016; 2020) indicates a good performance for the modelling of extreme sea levels. In general, when comparing modelled and observed time series the root-mean-squared-errors are low (<10cm) and correlation coefficients are high (>0.7). Also return periods show a good performance with a mean bias of -10cm for a 10-year return period. The high accuracy of GTSM is attributed to the increased model resolution at the coast, which is where the highest storm surges are generated. Moreover, the good quality of the ERA5 climate reanalysis also contributes to the model performance. The model performance is generally lower in regions with little variability and storm surges dominantly induced by tropical cyclones. This is linked to the resolution of the meteorological forcing which is too low to fully resolve tropical cyclones. In topographically complex areas, such as estuaries and semi-enclosed bays, the model resolution of GTSM may be insufficient to accurately capture the storm surge.

The validation of tides is not published, and it is described in more detail here. We validated GTSM against observed tides from the University of Hawaii Sea Level Center (UHSLC) dataset, which contains 251 tide gauge stations. In addition, we validated GTSM against modelled tides derived with the FES2012 model, which is an assimilative global tide model. GTSM shows, in general, a good agreement with the observed and models tides (Table 2-7). In general, GTSM seems to overpredict tidal amplitudes (Table 2-7). Errors near the coast are larger than in the open ocean. However, with an average M2 vector difference error of 10.5cm at the coast, the model has a comparable accuracy to state-of-the-art assimilative global tide models. Moreover, when compared to non-assimilated global tide models, the GTSMv3.0 performs significantly better. For the semi-enclosed seas, a good agreement with observations proves more difficult. The semi-enclosed sea of the Baltic is sensitive to the narrow connection with the adjacent North Sea and the geometry of such connection, which seems to negatively affect the model performance. In some areas we cannot properly assess the model performance because of the lack of a full spatial coverage and long records of time-series.

Table 2-7: Model performance of GTSM against the UHSL dataset and the FES2012 model. The metrics used are standard deviation of errors (STDE), relative range, and correlation coefficient (R).

The model performance is also assessed in terms of energy budget. In general, the global and regional estimates of M2 energy dissipation through bottom friction and internal wave drag are in good agreement with satellite altimetry derived estimates by Egbert and Ray (2001). Sensitivity tests show that these energies are slightly sensitive to bottom friction coefficient changes within a range of typical values. The dissipation estimated seems quite sensitive to changes of similar order to the internal wave drag coefficient, showing a positive response in terms of STDE to increasing values of the parameter. However, it is concluded from these tests that spatially non-uniform calibration of both dissipation parameters is needed to optimize the model solution and the agreement with the observed regional dissipation estimates.

Figure 2-2: GTSM model validation against the UHSL dataset and the FES2012 model showing the standard deviation of and correlation coefficient (R).

In summary, the validation demonstrates a good model performance of GTSMv3.0 on representing total water-levels, tides and storm surges. The high accuracy is the result of the continuous model developments, which has been focused at improving the model physics, grid resolution, and input datasets. GTSM is capable of simulation of historical events as well as multi-decadal simulations for historical and future climate scenarios.

3. Concluding Remarks

Use of the dataset
This dataset presents sea level change indicators resulting from tides, surges and sea level rise computed for the whole globe. The dataset is based on the multi-model HighResMIP ensemble, one of the CMIP6 model experiments. The projections cover the period 1950-2050 and are based on SSP5-8.5. The dataset constitutes a major improvement compared to dataset provided in the C3S 422 Lot2 contract, which was limited to the pan-European domain and included a single CMIP5 model.

The water level change time series and indicator allow for evaluation of the impacts of climate change and sea level rise including an assessment of uncertainty. Different coastal sectors and industries, such as flood risk authorities, harbors and ports, coastal zone management institutes, can use the dataset for a first-cut assessment of changes in extreme sea level for the mid-century. In addition, the time series can be used for dynamic downscaling for regional studies of climate change impacts.

Limited applicability at the local level
The dataset is designed specifically for global to continental scale assessment of climate change impacts on coastal water levels, and users should take caution when applying the dataset to smaller scale studies. Although the coastal resolution of GTSM is high for a global model (e.g. 1.25km in Europe, 2.5km rest of the world), this resolution is rather coarse for areas with a complex bathymetry such a estuaries or semi-enclosed bays. Moreover, at the local scale it may be important to consider additional physical processes which are not simulated by GTSM. This includes, for example, the effect of waves during extremes and the seasonal variability in mean sea level driven by baroclinic currents. It is therefore recommended to use this dataset only in large-scale studies. For regional studies, we would recommend using the time series as boundary conditions for a local models, in which way, local data and knowledge can be included.

Uncertainties in projected changes
The dataset contains indicators that may be used to assess the climate change impact on extreme sea levels. The extreme-value indicators are prone to large uncertainties due to the limited length of the dataset in combination with the fitting of extreme value distribution using automatic procedures. To account for the uncertainty, we provide a low and high bound of the return periods in addition to the best fit. For extreme-value indicators, experienced user could consider to carry out their own extreme value analysis tailored to their specific need. Another source of uncertainty is the performance of the individual HighresMIP climate models. Climate models can have large spatial bias and may under- or overestimate the storm surges related statistics. In many places the projected changes in extreme are small compared to the uncertainties. We include ensemble statistics for the extreme-value and probability indicators to account for the uncertainties associated with climate model ensemble. The intermodel agreement may indicate how large the uncertainties are. Users could consider to carry out of more detailed evaluation of the spatial bias of the individual climate models for their specific region of interest, and subsequently, they could decide to give more weight to a good-performing model. In general, users of the dataset should take note of the uncertainties presented in this user guide.

4. Appendix I

4.1.1.1. FAQ

Q: What is the vertical datum/reference of the dataset? How is it derived?

A: The vertical reference level of the time series is the mean sea level (MSL) calculated over the 1986-2005 reference period as used by the Intergovernmental Panel on Climate Change in the Fifth Assessment Report (IPCC AR5). Note the reference mean sea level values may be different from the IPCC AR5 values due to minor differences in the calculation methods. To harmonize the definition of MSL with the vertical datum used for sea-level rise field, the mean sea-level pressure field (MSLP) over 1986–2005 is removed. The MSLP calculation is based on the ERA-Interim. MSL datum is applicable for all time series and indicators provided in this dataset and does not change over time (e.g. from historical to future period). However, do note that the time series for total water levels and tides also include sea-level rise field, while for the surge level the sea-level rise is removed.

Q: How is sea level rise considered in the model simulation?

A: Both historical and future period simulations include spatially-varying sea level rise (SLR) contributions. The SLR fields are computed using a probabilistic model (Le Bars, 2018) based on observations (1950-2015) and CMIP5 climate models according to RCP8.5 for 2016-2050 and hence is independent of the model selection in this catalouge entry. Included are changes in sea level from various processes including thermal expansion of the ocean, changes in ocean circulation, ice sheet contributions, and glacio-isostatic adjustment (but not subsidence or tectonics). The annual SLR fields are referenced to the mean level over the period 1986–2005, with a spatial resolution of 1° × 1° and interpolated to the model grid using nearest neighbor. The SLR field is used to initialize the GTSM model at annual timesteps.

5. References

Dullaart, J.C.M., Muis, S., Bloemendaal, N. & Aerts, J. C. H. (2020). Advancing global storm surge modelling using the new ERA5 climate reanalysis. Climate Dynamics 54, 1007–1021, doi:10.1007/s00382-019-05044-0

Egbert, G. D., and Ray, R. D. (2001). Estimates of M2 tidal energy dissipation from TOPEX/Poseidon altimeter data, J. Geophys. Res., 106(C10), 22475–22502, doi:10.1029/2000JC000699.

Irazoqui Apecechea, M., Verlaan, M., Zijl, F., Le Coz, C., & Kernkamp, H. (2017). Effects of self-attraction and loading at a regional scale: a test case for the Northwest European Shelf. Ocean Dynamics, 67(6), 729-749

Haarsma, R. J., Roberts, M. J., Vidale, P. L., Senior, C. A., Bellucci, A., Bao, Q., Chang, P., Corti, S., Fučkar, N. S., Guemas, V., von Hardenberg, J., Hazeleger, W., Kodama, C., Koenigk, T., Leung, L. R., Lu, J., Luo, J.-J., Mao, J., Mizielinski, M. S., Mizuta, R., Nobre, P., Satoh, M., Scoccimarro, E., Semmler, T., Small, J., and von Storch, J.-S.: High Resolution Model Intercomparison Project (HighResMIP v1.0) for CMIP6, Geosci. Model Dev., 9, 4185–4208, https://doi.org/10.5194/gmd-9-4185-2016, 2016.

Hersbach, H., Bell, B., et al., (2020) The ERA5 global reanalysis. Quarterly Journal of the Royan Meteorological Society https://doi.org/10.1002/qj.3803

Irazoqui Apecechea, M., Muis, S (2019) D422Lot2.DEL.2.5_GTSM_setup_validation. Project report C3S_422 Lot2 Deltares

Irazoqui Apecechea, M., Rego, J., Verlaan, M (2018) GTSM setup and validation. Project report C3S_422_Lot2_Deltares - European Services

Muis, S., Verlaan, M., Winsemius, H. C., Aerts, J. C. H., & Ward, P. J. (2016). A global reanalysis of storm surges and extreme sea levels. Nature Communications, 7(1), 1-12, doi:10.1038/ncomms11969

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