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Acronyms

Acronym	Description
C3S	Copernicus Climate Change Service
CDR	Climate Data Record
CDS	Climate Data Store
CMS	Content Management System
EQC	Evaluation and Quality Control
RCP	Representative Concentration Pathway
RCM	Regional Climate Model
SIS	Sectoral Information System
GTSM	Global Tide and Surge Model
CMIP6	Coupled Model Intercomparison Project Phase 6
HighResMIP	High Resolution Model Intercomparison Project

Introduction

Executive Summary

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	table2
	table2

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

Data Description
Dataset title	Global sea level change indicators from 1950 to 2050 derived from high resolution CMIP6 climate projections
Data type	Indicators
Topic category	Sea and coastal regions, Natural hazard
Sector	Coastal flood risk, integrated coastal zone management, harbor and port
Keyword	Extreme sea level, CMIP6, indicator
Domain	Global
Horizontal resolution	Coastal grid points: 0.1° Ocean grid points: 0.25°, 0.5°, and 1° within 100 km, 500 km, and >500 km of the coastline, respectively
Temporal coverage	Statistics for historical: from 1951 to 1980 Statistics for present: from 1985-2014 Statistics for future (SSP5-8.5): from 2021 to 2050
Temporal resolution	No temporal resolution as the indicators are derived from the 10-min time series and represents statistics over the temporal coverage
Vertical coverage	Surface
Update frequency	No updates expected
Version	1.0
Model	Global Tide and Surge Model (GTSM) version 3.0
Provider	Deltares (Kun Yan)
Terms of Use	Copernicus Product License

Variable Description

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

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	table4

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

Variables
Long Name	Short Name	Unit	Description
Mean sea level	msl	m	The average water level of a 30-year tide-only simulation. This includes the interaction effects with tides and the sea level rise over the 30-year period simulated. Storm surge caused by atmospheric forcing is not taken into account. Please refer to Appendix I for details on the vertical datum.
Highest astronomical tide	HAT	m	Highest Astronomical Tide (HAT) is the elevation of the highest predicted astronomical tide expected to occur at a specific location over the datum (i.e. MSL). HAT is calculated as the maximum (minimum) over the 30-year simulation period. All tide variables are derived from a tide-only simulation with GTSM. Please refer to Appendix I for details on the vertical datum.
Lowest astronomical tide	LAT	m	Lowest Astronomical Tide (LAT) is the elevation of the lowest predicted astronomical tide expected to occur at a specific location over the datum (i.e. MSL). LAT is calculated as the minimum over the 30-year simulation period. All tide variables are derived from a tide-only simulation with GTSM. Please refer to Appendix I for details on the vertical datum.
Annual mean lowest low water	MLLW	m	Annual average of the lowest low tide of each tidal day (25-hour window) including sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Please refer to Appendix I for details on the vertical datum.
Annual mean highest high water	MHHW	m	Annual average of the highest high tide of each tidal day (25-hour window) including sea level rise. Storm surge caused by atmospheric forcing is not taken into account. Please refer to Appendix I for details on the vertical datum.
Tidal range	TR	m	Average tidal range observed over the 30-year period simulated. Please refer to Appendix I for details on the vertical datum.[[

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	table5

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

Total water level/surge level for different return periods with confidence intervals	Water level/surge level return periods	m	Total water level and surge level for the following return periods: 1, 2, 5, 10, 25, 50, 75 and 100 years. In addition to the best fit, a low bound (5^th percentile) and high bound (95^th percentile) are provided. Total water level and surge level simulations are forced by ERA5 reanalysis and the HighResMIP ensemble. Total water levels include (changes in) tidal levels, surge levels and interactions, but with sea level rise removed. Surge level are defined as the difference between the tide-only and the total water level simulations, and include (changes in) surge levels and interactions.
Total water level/Surge level for different percentiles	Water level/Surge level percentiles	m	Total water level and surge level for the following percentiles: 5th, 10th, 25th, 50th, 75th, 90th and 95th. Simulations are forced by EAR5 reanalysis and the HighResMIP ensemble. Total water levels includes (changes in) tidal levels, surge levels and sea level rise. Surge level are defined as the difference between the tide-only and the total water level simulations, and include (changes in) surge levels and interactions.
Absolute change of total Water level/surge level for different percentiles	Absolute change Water level/surge level percentiles	m	The absolute change of total water level/surge level for the following percentiles: 5th, 10th, 25th, 50th, 75th, 90th and 95th. The absolute change is computed for the HighResMIP ensemble for 2021-2050 and 1951-1980, using 1985-2014 as the reference period.
Relative change of total Water level/surge level for different climate model and different percentiles	Absolute change Water level/surge level percentiles	%	The relative change of total water level/surge level for the following percentiles: 5th, 10th, 25th, 50th, 75th, 90th and 95th. The relative change is computed for the HighResMIP ensemble for 2021-2050 and 1951-1980, using 1985-2014 as the reference period.

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

Total Water level/surge level ensemble standard deviation for different percentiles/return periods	Water level/surge level ensemble std percentiles/return periods	m	Standard deviation of total water level/surge level across the five members of the HighResMIP ensemble for the following percentiles: 5, 10, 25, 50, 75, 90, 95; and return periods: 1, 2, 5, 10, 50, 75 and 100 years. See the variable description to see how the percentiles and return periods are defined.
Total Water level/surge level ensemble range for different percentiles/return periods	Water level/surge level ensemble range percentiles	m	Range of the total water level/surge level (differences between maximum and minimum value) across the five members of the HighResMIP ensemble for the following percentiles: 5, 10, 25, 50, 75, 90, 95; and return periods: 1, 2, 5, 10, 50, 75 and 100 years. See the variable description to see how the percentiles and return periods are defined.
Total Water level/surge level ensemble median for different percentiles/return periods	Water level/surge level ensemble median percentiles	m	Median of total water level/surge level across the five members of the HighResMIP ensemble for the following percentiles: 5, 10, 25, 50, 75, 90, 95; and return periods: 1, 2, 5, 10, 50, 75 and 100 years. See the variable description to see how the percentiles and return periods are defined. .
Total Water level/surge level ensemble mean for different percentiles/return periods	Water level/surge level ensemble mean percentiles	m	Mean of total water level/surge level across the five members of the HighResMIP ensemble for the following percentiles: 5, 10, 25, 50, 75, 90, 95; and return periods: 1, 2, 5, 10, 50, 75 and 100 years. See the variable description to see how the percentiles and return periods are defined.
Number of models that a positive change in total water level/surge level for different percentiles/return periods	Water level/surge level ensemble positive count	-	The number of members (out of 5) of the HighResMIP ensemble that show an increase in total water level/surge for the following percentiles: 5, 10, 25, 50, 75, 90, 95; and return periods: 1, 2, 5, 10, 50, 75 and 100 years. See the variable description to see how the percentiles and return periods are defined.
Number of models that a negative change in total water level/surge level for percentiles/return periods	Water level/surge level ensemble negative count	-	The number of members (out of 5) of the HighResMIP ensemble that show a decrease in total water level/surge for the following percentiles: 5, 10, 25, 50, 75, 90, 95; and return periods: 1, 2, 5, 10, 50, 75 and 100 years. See the variable description to see how the percentiles and return periods are defined.

Method

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.

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	table7
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Table 2-5: Overview scenarios and epochs in the water level change time series simulation

Scenario	Type	Period	Meteorological forcing
ERA5 Reanalysis	Climate reanalysis	1979-2018	ERA5
Historical	Baseline climate scenario	1950-2014	HighResMIp ensemble, consisting of a mix of SST-forced (HadGEM3GC31-HM, and GFDL-CMC192) and coupled (EC-Earth3P-HR, CMCC-CM2-VHR4, and HadGEM3-GC31-HM) climate simulations
Future	Future climate scenario based on SSP5-8.5	2015-2050	HighResMIP ensemble, consisting of a mix of SST-forced (HadGEM3-GC31-HM and GFDL-CMC192) and coupled (ECEarth3P-HR, CMCC-CM2-VHR4, and HadGEM3-GC31-HM) climate simulations
Tide only	Tide-only simulation	1950-2050	N/A

Model / Algorithm

Global Tide and Surge Modelling (GTSM)

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	table8
	table8

Table 2-6: Key references of GTSM validation

Reference	Description
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	Validation of GTSM2.0 for the modelling of storm surges and estimation of return periods. Results show good agreement with observations. Storm surges, especially those induced by tropical cyclones, are slightly underestimated; this is mainly due to the resolution of the meteorological forcing.
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	Evaluation of the performance of GTSM3.0 for the global modelling of storm surges for historical extreme events, and the advances due to ERA5 climate reanalysis
Muis, S., Apecechea, M. I., Dullaart, J., de Lima Rego, J., Madsen, K. S., Su, J., Kun, Y. & Verlaan, M. (2020). A High-resolution global dataset of extreme sea levels, tides, and storm surges, including future projections. Frontiers in Marine Science, 7, 263, doi:10.3389/fmars.2020.00263	Validation of GTSM3.0 for application to climate change projections. Comparison against observations shows a good performance with observed sea levels demonstrated a good performance with the annual maxima having mean bias of -0.04 m.
Wang, X., Verlaan, M., Apecechea, M. I., & Lin, H. X. (2021). Computation‐Efficient Parameter Estimation for a High‐Resolution Global Tide and Surge Model. Journal of Geophysical Research: Oceans, 126(3), e2020JC016917.	Description of the calibration of the GTSM. Result show that the accuracy of the tidal representation can be improved significantly at affordable cost.
Irazoqui Apecechea, M., Rego, J., Verlaan, M (2018) GTSM setup and validation. Project report C3S_422_Lot2_Deltares - European Services	Description of the calibration of the GTSM.

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.

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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).

Geographical Area	UHSCL tide gauge stations				FES2012 assimilative tide model
	No. of stations	STDE	Relative range (%)	R	No. of stations	STDE	Relative range (%)	R
Antarctic	1	0.07	101	0.98	3	0.14	107	0.96
Arctic	3	0.12	115	0.94	40	0.05	125	0.85
South East Asia	27	0.28	113	0.90	0	-	-	-
Indian Ocean	39	0.20	114	0.94	72	0.07	112	0.98
North Atlantic	48	0.18	106	0.86	30	0.07	102	0.97
North Pacific	75	0.15	102	0.95	65	0.07	104	0.98
South Atlantic	13	0.16	114	0.94	43	0.05	111	0.99
South Pacific	45	0.14	109	0.93	94	0.07	111	0.97
Total	251	0.18	108	0.92	347	0.06	111	0.96

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.

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	UHSCL tide gauge stations	FES2012 assimilative tide model
STDE	Image Modified	Image Modified
R	Image Modified	Image Modified

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

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Yan, K., Minns, T., Irazoqui Apecechea, M., Muis, S., et al. (2019) C3S_D422Lot2.DEL.3.3_User_Guide. Project report C3S_422 Lot2 Deltares

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_{This document has been produced in the context of the Copernicus Climate Change Service (C3S).}

_{The activities leading to these results have been contracted by the European Centre for Medium-Range Weather Forecasts, operator of C3S on behalf of the European Union (Delegation agreement Agreement signed on 11/11/2014 and Contribution Agreement signed on 22/07/2021). All information in this document is provided "as is" and no guarantee or warranty is given that the information is fit for any particular purpose.}

_{The users thereof use the information at their sole risk and liability. For the avoidance of all doubt , the European Commission and the European Centre for Medium - Range Weather Forecasts have no liability in respect of this document, which is merely representing the author's view.}

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Introduction