The advent of expendable wave buoys has greatly expanded the data available for evaluating and calibrating wave models. Ideally, the newer buoys now drifting around the world's oceans would be merged with conventional time series measurements from moored buoys to form a consistent dataset of in situ observations. However, a comparison across several buoy types (moored Datawell, moored NDBC, and two types of drifting buoys) suggests large differences in the high frequency portion of the observed wave energy spectra (0.2 to 0.6 Hz). When binned by wind speed, the moored Datawell buoys have higher energy in the high frequency tail vs. drifting buoys, by factor 1.2 to 1.6. The moored Datawell buoys also have far better agreement with high-frequency energy levels predicted by a numerical wave model. The key to the difference appears to be the reference frame of the observations. To test this hypothesis, the spectra are adjusted from the drifting reference frame to the fixed reference frame. The adjustment is a two-step process, in which the buoy-observed frequencies are first shifted to an intrinsic reference frame, providing wavenumber at each frequency, and then the drifter-observed spectrum is Doppler shifted to the fixed reference frame. Two methods for estimation of buoy drift are tested; one is based on wind speed, and one is based on buoy positions. With this adjustment, the observations from the drifting buoys become more consistent with the moored Datawell buoys, though discrepancies still exist with the moored NDBC buoys.

This study discusses recent advances in modeling waves in sea ice in the U.S. Navy’s regional modeling system. It is applied in the marginal seas of the eastern Arctic Ocean, including the Barents Sea, Kara Sea, parts of the Greenland Sea, Norwegian Sea, and waters north of Svalbard. The focus is to assess the skills of two formulations of wave attenuation by sea ice used operationally in WAVEWATCH III. Both are derived from large field datasets, one from the Arctic and the other from the Antarctic. The new model (IC4M9) describes wave attenuation depending on the ice thickness in association with the dependence on wave frequency, while the earlier default scheme (IC4M6) omits the dependence on ice thickness. The modeling results are evaluated against the satellite wave observations from SWIM/CFOSAT and the buoy measurements from the Svalbard Marginal Ice Zone 2024 Campaign (SvalMIZ-24). The comparisons with SWIM data validate the wave model skill in regions of open water or with light ice coverage. When evaluated against the SvalMIZ-24 data, the statistical performance of IC4M9 is substantially better than that of IC4M6, showing the influence of ice thickness on waves in the MIZ. Moreover, diagnosing systematic errors in the predictions by IC4M9, we find that the ice thickness field provided by the sea ice model CICE to the wave model is biased high in the MIZ, thus penalizing the performance of IC4M9 while not affecting the model IC4M6, which depends on frequency only.

Utility of the empirical orthogonal function (EOF) decomposition for the processing and prediction of ambient noise in the open ocean is assessed. Using ambient noise observations in the Southern Ocean, it is found that 89%, 96%, and 98% of the ambient noise spectrum can be explained by the first, two, and three EOFs, respectively. This provides motivation for using singular value decomposition for the analysis, and pre-processing of the ambient noise observations, which usually include outliers and gaps. It is also shown that EOF-based re-filling of observational gaps provides approximately 35% higher accuracy than the conventional linear interpolation. EOFs significantly decrease the number of the regression parameters required for the ambient noise prediction using wind speed or other predictors. This allows for the prediction of the continuous ambient noise spectrum in computationally efficient ways and avoids subdividing ambient noise spectra into a limited number of frequency bands. It is also found that the accuracy of the wind-based ambient noise prediction is essentially controlled by the prediction of the frequency-averaged ambient noise magnitude so that accurate prediction of the ambient noise magnitude would decrease the RMS of the ambient noise spectral prediction from 4 to 1.6 dB.

Observational data from buoys are of primary importance during the development, calibration, and evaluation of ocean wave models, and these data are also used to make real-time corrections to operational models via data assimilation. By association, systematic inaccuracies in any buoy data are equally important, and thus when two buoy types provide systematically inconsistent information, this is a concern for anyone using an ocean wave model. This report is concerned with the accuracy of the high frequency portion of the ocean wave spectrum commonly observable by buoys, roughly 0.2 to 0.6 Hz. We evaluate four buoy types (two moored, two drifting) using two quantitative measures. The first involves comparing each type with a co-located ocean wave model. The second method involves evaluation of high frequency energy level as a function of wind speed. Both evaluation methods suggest that the Datawell Waverider (DWR) buoys have a strong tendency to report higher energy levels than the other three buoy types. We evaluate high frequency energy level using three different metrics (mean square slope, energy in a band of high frequencies, and spectral density at a single, specific band, 0.4 Hz), and the conclusions are found to be insensitive to the parameter used.

Visible and microwave satellite measurements can provide the global whitecap fraction. The bubble clouds are three-dimensional structures, and a space-based lidar can provide complementary observations of the bubble depth. Here, we use lidar measurements of the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite to quantify global bubble depth from depolarization. The relationship between CALIPSO bubble depth and wind speed from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) and AMSR2 is similar to a recently derived relationship based on buoy measurements. The CALIPSO-based bubble depth data show global distributions and seasonal variations consistent with the high wind speed (> 7 m/s) but with some variance. We also found similarities between the CALIPSO bubble depth and the whitecap fraction from AMSR2 and WindSat. Our findings support using spaceborne lidar measurements to advance the understanding of the 3D bubble properties and ocean physics at high wind speeds.

This study is concerned with prediction of the “wind noise” component of ambient noise (AN) in the ocean. It builds on the seminal paper by [1], in which the authors quantified the correlation between AN and individual wind/wave parameters. Acoustic data are obtained from hydrophones deployed in the north and northeast Pacific Ocean, and wind/wave parameters are obtained from moored buoys and numerical models. We describe a procedure developed for this study which isolates the correlation of AN with wave parameters, independent of mutual correlation with wind speed (residual correlation). We then describe paired calibration/prediction experiments, whereby multiple wind/wave parameters are used simultaneously to estimate AN. We find that the improvement from inclusion of wave parameters is robust but modest. We interpret the latter outcome as suggesting that wave breaking responds to changes in local winds quickly, relative to, for example, total wave energy, which develops more slowly. This outcome is consistent with prior knowledge of the physics of wave breaking, e.g. [2]. We discuss this in context of the time/space response of various wave parameters to wind forcing.

We present a method for predicting wave dissipation by sea ice that is based on the dimensional analysis of data with a scaling defined by ice thickness. Applying the method to an extensive dataset from the measurements during the "Polynyas, Ice Production, and seasonal Evolution in the Ross Sea" (PIPERS) cruise in 2017, we derive a new model of wave dissipation which describes a nonlinear dependence on ice thickness, and reveals the interrelation between the dependences on ice thickness and on wave frequency. This nonlinear dependence on ice thickness can have important implications on predicting low-frequency waves. The root-mean-square error of the prediction is significantly reduced using the new model, compared with other existing parametric models that are also calibrated for the PIPERS dataset. The new model also explicitly describes a condition of similarity between large- and small-scale observations, which is shown to exist when various laboratory datasets collapse onto the prediction. Thus, the new model improves the estimate of wave dissipation by ice across multiple scales.

Global wave hindcasts are developed using the third generation spectral wave model WAVEWATCH III with the observation-based source terms (ST6) and a hybrid rectilinear-curvilinear, irregular-regular-irregular grid system (approximately at 0.25 degrees x0.25 degrees). Three distinct global hindcasts are produced: (a) a long-term hindcast (1979-2019) forced by the ERA5 conventional winds U10 and (b) two short-term hindcasts (2011-2019) driven by the NCEP climate forecast system (CFS)v2 U10 and the ERA5 neutral winds U10,neu, respectively. The input field for ice is sourced from the Ocean and Sea Ice Satellite Application Facility (OSI SAF) sea-ice concentration climate data records. These wave simulations, together with the driving wind forcing, are validated against extensive in-situ observations and satellite altimeter records. The performance of the ST6 wave hindcasts shows promising results across multiple wave parameters, including the conventional wave characteristics (e.g., wave height Hs and wave period) and high-order spectral moments (e.g., the surface Stokes drift and mean square slope). The ERA5-based simulations generally present lower random errors, but the CFS-based run represents extreme sea states (e.g., Hs>10 m) considerably better. Novel wave parameters available in our hindcasts, namely the dominant wave breaking probability, wave-induced mixed layer depth, freak wave indexes and wave-spreading factor, are further described and briefly discussed. Inter-comparisons of Hs from the long-term (41 years) wave hindcast, buoy measurements and two different calibrated altimeter data sets highlight the inconsistency in these altimeter records arising from different calibration methodology. Significant errors in the low-frequency bins (period T>15 s) for both wave energy and directionality call for further model development.

This study is part of an effort to improve the Navy's ability to forecast wind-generated ocean waves in ice-infested regions, and here we are attempting to further this goal by improving prediction of dissipation of wave energy by sea ice. Rogers et al. (2021) presented new estimates of frequency-dependent dissipation of wave energy by sea ice, based on model-data inversion, and studied the correlation with various other parameters, such as ice thickness and sea state variables. Here, we use that dataset to propose a new dissipation parameterization which explicitly incorporates the dependence on the ice thickness, in addition to the wave frequency. The goal is to determine whether a parameterization dependent on wave frequency and ice thickness can be more accurate than one dependent only on wave frequency. Due to the dominant impact of frequency and confounding difficulties of field measurements, this is not a foregone conclusion. A parameterization is developed using the non-dimensionalization approach proposed by Yu et al. (2019). We find that the non-dimensionalization does result in significant scale collapse of the data, and inclusion of ice thickness does improve accuracy, most evidenced by reduced scatter when applied to the same dataset. However, evaluations against independent datasets are mixed. Possible reasons for this are discussed.

Despite the importance of diffraction of irregular ocean waves, there is a lack of reported measurements over scales larger than a few tens of wavelengths. In the coastal zone, Satellite Altimetry is hindered because the backscatter characteristics of waveforms deviate from Brown's theoretical model, specially up to 20 km from the coastline. Here, we combine a novel set of retracked - reprocessed - altimeter data with directional buoy spectra and simulations with a spectral numerical model where diffraction is computed with an approximation based on the Mild Slope Equation. The Channel Islands, off the coast of California, is an ideal spot for the analysis because of the sharp variations in bathymetry in the vicinity of the archipelago, increasing the relative importance of diffraction over refraction. Spatial variations of wave energy in the lee of the islands are investigated along and across the wave propagation direction. For the first time the lateral rate of energy spreading - across the wave propagation direction - was computed in oceanic conditions and are of the same magnitude as those found in previously published experiments in a wave tank. The importance of diffraction as a non-dissipative process during wave propagation in situations normally encountered in the ocean is also discussed.

Observations of ocean surface waves at three sites along the northern coast of Alaska show a strong correlation with seasonal sea ice patterns. In the winter, ice cover is complete, and waves are absent. In the spring and early summer, sea ice retreats regionally, but landfast ice persists near the coast. The landfast ice completely attenuates waves formed farther offshore in the open water, causing up to a two‐month delay in the onset of waves near shore. In autumn, landfast ice begins to reform, though the wave attenuation is only partial due to lower ice thickness compared to spring. The annual cycle in the observations is reproduced by the ERA5 reanalysis product, but the product does not resolve landfast ice. The resulting ERA5 bias in coastal wave exposure can be corrected by applying a higher‐resolution ice mask, and this has a significant effect on the long‐term trends inferred from ERA5.

A model-data inversion is applied to an extensive observational dataset collected in the Southern Ocean north of the Ross Sea during late autumn to early winter, producing estimates of the frequency-dependent rate of dissipation by sea ice. The modeling platform is WAVEWATCH III® which accounts for non-stationarity, advection, wave generation, and other relevant processes. The resulting 9477 dissipation profiles are co-located with other variables such as ice thickness to quantify correlations which might be exploited in later studies to improve predictions. An average of dissipation profiles from cases of thinner ice near the ice edge is fitted to a simple binomial. The binomial shows remarkable qualitative similarity to prior observation-based estimates of dissipation, and the power dependence is consistent with at least three theoretical models, one of which assumes that dissipation is dominated by turbulence generated by shear at the ice-water interface. Estimated dissipation is lower closer to the ice edge, where ice is thinner, and waveheight is larger. The quantified correlation with ice thickness may be exploited to develop new parametric predictions of dissipation.

Interaction between surface gravity waves and sea-ice in the marginal ice zone is complex, and most of the prior research focus has been in deeper oceans. Here, the regional wave model Simulating WAves Nearshore (SWAN) is configured to simulate reduced wind-generation and wave dissipation in the presence of sea-ice. The wind-generation process is modified by scaling the generation terms with the open-water fraction, while wave dissipation in the presence of sea-ice is simulated as an exponential energy decay as function of ice concentration, wave frequency and empirical coefficients determined from prior experiments. Modified SWAN is used to simulate interaction between regional sea-ice and a swell event in the Barents Sea. The simulation accounting for wave-ice interaction reasonably agrees with field measured significant wave height and the energy spectral density. Additional simulations are conducted for the shallow seas of Gulf of Bothnia, located in the northernmost reach of the Baltic sea. Modeled wave dynamics in this region agrees well with satellite altimetry based measurements. This model setup is further investigated to understand fetch scaling in the marginal ice zone, and non-dimensional energy scales well with a non-dimensional fetch determined from a cumulative fetch dependent on ice concentration. Additional implications for Stokes drift and Stokes drift shear are also discussed for the Bothnian bay. Finally recommendations for including dissipation due to ice thickness, and plans for future model coupling are considered.

Three dissipative (two viscoelastic and one viscous) ice models are implemented in the spectral wave model WAVEWATCH III to estimate the ice-induced wave attenuation rate. These models are then explored and intercompared through hindcasts of two field cases: one in the autumn Beaufort Sea in 2015 and the other in the Antarctic marginal ice zone (MIZ) in 2012. The capability of these dissipative models, along with their limitations and applicability to operational forecasts, are analyzed and discussed. The sensitivity of the simulated wave height to different source terms—the ice-induced wave decay S ice and other physical processes S other (e.g., wind input, nonlinear four-wave interactions)—is also investigated. For the Antarctic MIZ experiment, S other is found to be remarkably less than S ice and thus contributes little to the simulated significant wave height H s . The saturation of dH s / dx at large wave heights in this case, as reported by a previous study, is well reproduced by the three dissipative ice models with or without the utilization of S other in the ice-infested seas. A clear downward trend in the peak frequency f p is found as H s increases. As f p decreases, the dominant wave components of a wave spectrum will experience reduced damping by sea ice, and finally result in the flattening of dH s / dx for H s > 3 m in this specific case. Nonetheless, S other should not be disregarded within a more general modeling perspective, as our simulations suggest S other could be comparable to S ice in the Beaufort Sea case where wave and ice conditions are remarkably different.

Rapid decline in seasonal sea ice has been linked to increased surface wave activity and shoreline erosion in the coastal Arctic. This trend poses a risk to communities vulnerable to flooding and storm surges. Here we focus on quantifying the relationship between coastal erosion, increasing wave activity and the role of sea ice in protecting the coast. In November 2019, we observed a three day wave event in the Chukchi Sea along the coastal barrier system near Icy Cape, Alaska. The wave event was sampled using multiple drifting SWIFT (Surface Wave Instrument Float with Tracking) buoys, a cross-shore mooring array, and ship-based CTD casts. This provided datasets for different ice types in both Eulerian and Lagrangian reference frames. Pancake and frazil sea ice near the coast attenuated the incident wave field, such that the significant wave height reduced from 3 to 1.5 m over less than 5 kilometers. The wave data combined with in-situ ice observations and satellite imagery are used to calculate spectral attenuation of wave energy segregated by ice type. Furthermore, observed temperature, mean circulation and surface heat fluxes are used to address the evolution of sea ice throughout the event. Supported by the National Science Foundation and the Office of Naval Research.

A laboratory experimental study conducted in a freshwater wave flume installed in a refrigerated room characterized the modifications of wave propagation along on-site manufactured ice covers. Monochromatic surface waves of various amplitudes and frequencies were generated and propagated through three types of ice covers: sheet ice, broken ice floes, and grease ice. This study characterized the spatial evolutions of wave height attenuation and phase speed changes. Wave phase speed increased significantly in sheet ice relative to open water values while no significant changes in phase speed were present with other ice covers. The modifications by sheet ice are consistent with thin plate model predictions based on the elasticity of ice, using measured mechanical properties of sheet ice. Attenuation was strongest for shorter waves in sheet ice followed by grease ice and ice floes. Attenuation under grease ice is shown to be related to the surface wave orbital velocity, similar to dissipation by bottom friction and swell dissipation. The attenuation coefficient of grease ice normalized by wavenumber is proportional to wave steepness with a coefficient varying with ice properties. This laboratory experimental study examined and characterized wave dispersion and attenuation under these different ice properties and wave characteristics, which could be useful in understanding and modeling wave-ice interaction processes in open waters.

The existence and evolution of bedforms on the seafloor have significant effects in the areas of oceanography, marine geophysics, and underwater acoustics including the transport of sediment, wave energy attenuation, and seabed sonar scattering and penetration. Here, we present a wave-seafloor modeling system that couples a spectral seafloor boundary layer model (NSEA) with an operational wave model (SWAN) that includes the dynamic feedback between the predicted wave spectra and the wave generated bedforms on the seafloor through a bottom roughness parameter. NSEA is a seafloor spectral model that uses hydrodynamic input forcing forecasted by the wave model SWAN to predict the evolving seafloor spectra given a sediment grain diameter and an estimation of the biologic activity. The system can be used to determine the spatially and temporally varying bottom roughness under given wave forcing important for coastal morphology and acoustic applications.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/u66k6lZbEbw

Forty years (1979-2019) of global wave hindcasts are developed with the third generation spectral wave model WAVEWATCH III® using the state-of-the-art observation-based source term parameterizations (i.e., ST6) and the advanced irregular-regular-irregular (IRI) 1/4 grid system. The wave model has been forced with two distinct wind databases sourced from the latest NCEP Climate Forecast System (CFS) and the fifth generation of the ECMWF climate reanalyses (ERA5), together with the ice concentration available from the EUMETSAT OSI SAF (version 2). The hindcasts not only include traditional integral wave parameters (e.g., wave height, period) but also provide various novel parameters such as the dominant wave breaking probability, wave-induced mixed layer depth and whitecap coverage that are derived from wave spectrum based on previous theoretical and empirical studies. Wave parameters are extensively validated against observations from in-situ buoys and satellite altimeters on a global scale. Possible applications of these hindcasts in the fields of freak waves, sea spray and air-sea gas transfer will also be discussed.

We present here an empirical method aimed at decreasing the error in the significant wave height calculated through the Wave Watch model. The errors are calculated as the difference between the modeled and the locally observed measurement. We hypothesize that this error would be reduced if the model used a well calibrated method to account for thermal variations within the surface boundary layer. We compared then this error for 2015 to the air sea temperature difference in order to find a potential relationship among them. The statistical analysis performed show a clear correlation between these variables, hinting for the need of introducing a correction for stability based on a linear relationship between the error and the air ocean temperature difference.