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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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Table 2-1: Overview of key characteristics of the water level change indicatorAnchor table2 table2
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° |
Temporal coverage | Statistics for historical: from 1951 to 1980 |
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).
Table 2-2: Overview and description of variables for water level change indicators: tidal indicators.Anchor table4 table4
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.[[ |
Table 2-3: Overview and description of variables for water level change indicators: extreme-value indicators and probability indicators including changesAnchor table5 table5
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 (5th percentile) and high bound (95th 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. |
Table 2-4: Overview and description of variables for water level change indicators: Ensemble statistics for extreme-value and probability indicatorsAnchor table6 table6
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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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).
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Table 2-5: Overview scenarios and epochs in the water level change time series simulationAnchor table7 table7
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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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.
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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 validationAnchor table8 table8
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.
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).Anchor table9 table9
Geographical Area | UHSCL tide gauge stations | FES2012 assimilative tide model | ||||||
No. of stations | STDE | Relative | R | No. of stations | STDE | Relative | 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.
Anchor figure2 figure2
UHSCL tide gauge stations | FES2012 assimilative tide model | |
STDE |
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R |
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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.
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.
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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.,
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
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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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