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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 time series. Anchor table1 table1
Data Description | |
Dataset title | Global sea level change indicators from 1950 to 2050 derived from reanalysis and high resolution CMIP6 climate projections |
Data type | Reanalysis / Climate projections |
Topic category | Sea and coastal regions, Natural hazard |
Sector | Coastal flood risk, integrated coastal zone management, harbor and port |
Keyword | Extreme sea level, CMIP6, time series |
Domain | Global |
Horizontal resolution | Coastal grid points: 0.1° |
Temporal coverage | ERA5 reanalysis: from 1979 to 2018 |
Temporal resolution | 10min (all), hourly and daily maximum (reanalysis only) |
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 listed in the time series datasets (Table 2-2).
Table 2-2. Overview and description of variables for water level change time series. Anchor table3 table3
Variables | |||
Long Name | Short Name | Unit | Description |
Mean sea level | msl | m | Mean sea level height relative to the reference period (1986-2005). |
Storm surge residual | Surge | m | The storm surge residual is calculated as the difference between the total water level and the tide-only water level simulation. The effect of changes in annual mean sea level is included in both simulations and for both the historical and future period. |
Tidal elevation | Tide | m | The tidal elevation is derived from the tide-only simulation. The tide-only simulation is a hydrodynamic simulation without meteorological forcing (i.e. wind and pressure at mean sea level), the outputted water level includes only the pure tide without the storm surge residual. Sea level rise is included in both the historical and future period. |
Total water level | Water level | m | Total water level is derived from simulation including tidal and atmospheric forcing. Sea level rise is included for both the historical and future period. |
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 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 indicator 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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Table 2-3: Overview scenarios and epochs in the water level change time series simulation Anchor 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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Table 2-4: Key references of GTSM validation Anchor 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.
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Table 2-5: 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 | ||
R |
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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