A global wave parameter database for geophysical applications. Part 2: Model validation with improved source term parameterization

Abstract

a b s t r a c tA multi-scale global hindcast of ocean waves is presented that covers the years 1994-2012, based on recently published parameterizations for wind sea and swell dissipation [, Aouf, L., Collard, F., 2010. Semi-empirical dissipation source functions for wind-wave models: Part I. Definition, calibration and validation. J. Phys. Oceanogr. 40 (9), 1917Oceanogr. 40 (9), -1941. Results from this hindcast include traditional wave parameters, like the significant wave height and mean periods, and we particularly consider the accuracy of the results for phenomenal sea states, with significant heights above 14 m. Using unbiased winds, there is no evidence of a bias in wave heights even for this very high range. Various spectral moments were also validated, including the surface Stokes drift and mean square slopes that are relevant for wave-current interactions modelling and remote sensing, and also spectra of seismic noise sources. The estimation of these parameters is made more accurate by the new wave growth and dissipation parameterizations. Associated air-sea fluxes of momentum and energy are significantly different from what is obtained with the WAM-Cycle 4 parameterization, with a roughness that is practically a function of wind speed only. That particular output of the model does not appear very realistic and will require future adjustments of the generation and dissipation parameterizations.

Tide-surge interaction in the English Channel

“…The last input is the wave data, used to compute the Charnock coefficient in every cell and for every time step, and then the drag coefficient C D . These data derive from modelling work carried out with the WW3 model (Rascle and Ardhuin, 2012). Such approach yields drag coefficients C D of about 0.0025 to 0.0035 for stormy events.…”

Periodicity and patterns of ocean wind and wave climate

“…showed ERA-I is more consistent through time, while CFSR better resolves the upper percentiles and variability in comparison to buoy and altimetry measurements. Reanalysis wind and wave data sets have advantage over in situ and remotely sensed observations because they resolve multiscale processes by their high spatial and temporal resolution [Semedo et al, 2011;Rascle and Ardhuin, 2013]. This allows more intricate studies of the climate impacts from extratropical cyclones to nearshore processes [e.g., Wang et al, 2006;Bromirski et al, 2013].…”

Atmospheric storm surge modeling methodology along the French (Atlantic and English Channel) coast

“…An important difference, though, is that the parameterization for the wave breaking and generation terms are different from those of ECMWF (Bidlot 2012), in particular because Ardhuin et al (2010) include a reduction of the wavesupported stress in the high frequencies to represent a sheltering of short waves by longer waves. Although this parameterization leads to better short wave properties, it removes most of the wave-induced variability in the Charnock coefficient (Rascle and Ardhuin 2013). We thus expect that our results are much less sensitive to the wave age than using other parameterizations (e.g., Mastenbroek et al 1993).…”

“…(3). In the present study, α c is constant or variable (α cvar ) and, in this case, issued from the IOWAGA modeling system (Rascle and Ardhuin 2013) which is based on the wave model WAVEWATCH III® (Tolman 2008;Ardhuin et al 2010;Tolman et al 2013). Within the wave model, α c comes from Eq.…”