...

Info
icon false title Table of Contents
Table of Contents maxLevel 3

Easy Heading Macro

Acronyms

Introduction

Executive Summary

...

Anchor
table1 table1

Table 2-1: Overview of key characteristics of the water level change time series.

Variable Description

In this section more details are given about the variables listed in the time series datasets (Table 2-2).

Anchor
table3 table3

Table 2-2. Overview and description of variables for water level change time series.

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.

...

Anchor
table7 table7

Table 2-3: Overview scenarios and epochs in the water level change time series simulation

Model / Algorithm

Global Tide and Surge Modelling (GTSM)

...

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 the model. GTSMv3.0 also has high temporal resolution producing output at 10-minute intervals. The 10-minutes time series are physically realistic since two types of forcing are used; that is, tidal and meteorological forcing. The tidal forcing is internally generated based on position of the earth, moon and sun. The meteorological forcing is available at hourly (or coarser) resolution, but is internally interpolated to the model timestep. Because tides vary at high-frequency and can non-linearly interact with storm surge (so the sum of the two is different from the individual components) we use an temporal resolution of 10 minutes. Especially for output stations with a wide and shallow continental shelf (such as the North Sea) lower temproal resolution, i.e. hourly resolution, can be too coarse and may miss the peak water levels.

...

Anchor
table8 table8

Table 2-4: 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.

...

Anchor
table9 table9

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).

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

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

...

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

Q: Is the model output at 10-minute temporal resolution physically realistic?

The 10-minutes time series are physically realistic because two types of forcing are used; tidal forcing and meteorological forcing. The tidal forcing is internally generated based on position of the earth, moon and sun. The meteorological forcing is available at hourly (or coarser) resolution and is internally interpolated to the model timestep. Since tides vary at high-frequency and produce non-linear interactions with storm surges (the sum of the two is different from the individual components), we use a temporal resolution of 10-minutes to capture the high frequency signal. This is especially relevant for stations on wide and shallow continental shelves, such as the North Sea, where an hourly resolution may be too coarse and potentially miss the peaks in water level. For further details on the model and validation, see Kernkamp et al. (2011) and Muis et al. (2016, 2022).

References

Dullaart, J.C.M., Muis, S., Bloemendaal, N. & Aerts, J. C. H. 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), 729-749/2000JC000699.

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 HardenbergStorch, J.-S., 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: 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., 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

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

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

Kernkamp, H. W. J., RegoVan Dam, JA., Verlaan, M (2018) GTSM setup and validation. Project report C3S_422_Lot2_Deltares - European ServicesStelling, G. S. & de Goede, E. D. (2011) Efficient scheme for the shallow water equations on unstructured grids with application to the Continental Shelf. Ocean Dyn. 61, 1175–1188.

Le Bars, D (2018) Uncertainty in sea level rise projections due to the dependence between contributors.Earth's Future, 6, 1275– 1291. https://doi.org/10.1029/2018EF000849

...

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

Info
icon false
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.

Related articles

Content by Label
showLabels false max 5 spaces CKB showSpace false sort modified reverse true type page cql label in ("sis","water_indicators") and type = "page" and space = "CKB" labels era-interim