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In this section more details are given about the variables listed in the time series datasets (Table 2-32).

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Table 2-32. Overview and description of variables for water level change time series.

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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 2-73). The experimental design of HighResMIP is explained in more detail in Haarsma et al., (2018).

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Table 2-73: Overview scenarios and epochs in the water level change time series simulation

Model / Algorithm

Global Tide and Surge Modelling (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 2-8 4 provides a summary of the key references and description of the results.

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Table 2-84: Key references of GTSM validation

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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 2-95). In general, GTSM seems to overpredict tidal amplitudes (Table 2-95). 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.

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Table 2-95: 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.

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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., ading R

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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