Many processes that affect ocean surface gravity waves in sea ice give rise to attenuation rates that vary with both wave frequency and amplitude. Here we particularly test the possible effects of basal friction, scattering by ice floes, and dissipation in the ice layer due to dislocations, and ice breakup by the waves. The possible influence of these processes is evaluated in the marginal ice zone of the Beaufort Sea, where extensive wave measurements were performed. The wave data includes in situ measurements and the first kilometer‐scale map of wave heights provided by Sentinel‐1 SAR imagery on 12 October 2015, up to 400 km into the ice. We find that viscous friction at the base of an ice layer gives a dissipation rate that may be too large near the ice edge, where ice is mostly in the form of pancakes. Further into the ice, where larger floes are present, basal friction is not sufficient to account for the observed attenuation. In both regions, the observed narrow directional wave spectra are consistent with a parameterization that gives a weak effect of wave scattering by ice floes. For this particular event, with a dominant wave period around 10 s, we propose that wave attenuation is caused by ice flexure combined with basal friction that is reduced when the ice layer is not continuous. This combination gives realistic wave heights, associated with a 100–200 km wide region over which the ice is broken by waves, as observed in SAR imagery.

Highlights

• An algorithm is proposed to obtain orbital velocity maps from SAR images over sea ice.
• The algorithm is validated in terms of ocean wave spectra using in situ measurements.
• Wave height retrieval works best in the absence of unresolved short waves.

This paper presents a wave-in-ice model calibration study. Data used were collected in the thin ice of the advancing autumn marginal ice zone of the western Arctic Ocean in 2015, where pancake ice was found to be prevalent. Multiple buoys were deployed in seven wave experiments; data from four of these experiments are used in the present study. Wave attenuation coefficients are calculated utilizing wave energy decay between two buoys measuring simultaneously within the ice covered region. Wavenumbers are measured in one of these experiments. Forcing parameters are obtained from simultaneous in-situ and remote sensing observations, as well as forecast/hindcast models. Cases from three wave experiments are used to calibrate a viscoelastic model for wave attenuation/dispersion in ice cover. The calibration is done by minimizing the difference between modeled and measured complex wavenumber, using a multi-objective genetic algorithm. The calibrated results are validated using two methods. One is to directly apply the calibrated viscoelastic parameters to one of the wave experiments not used in the calibration and then compare the attenuation from the model with measured data. The other is to use the calibrated viscoelastic model in WAVEWATCH III over the entire western Beaufort Sea and then compare the wave spectra at two remote sites not used in the calibration. Both validations show reasonable agreement between the model and the measured data. The completed viscoelastic model is believed to be applicable to the fall marginal ice zone dominated by pancake ice.

"Sea State and Boundary Layer Physics in the Emerging Arctic Ocean" is an ongoing Departmental Research Initiative sponsored by the Office of Naval Research. The field component took place in the fall of 2015 within the Beaufort and Chukchi Seas and involved the deployment of a number of wave instruments including a downward looking Riegl laser rangefinder mounted on the foremast of the R/V Sikuliaq. Although time series measurements on a stationary vessel are thought to be accurate, an underway vessel introduces a Doppler shift to the observed wave spectrum. This Doppler shift is a function of the wavenumber vector and the velocity vector of the vessel. Of all the possible relative angles between wave direction and vessel heading there are two main scenarios: I) vessel steaming into waves and II) vessel steaming with waves. Previous studies have considered only a subset of cases, and all were in scenario I. This was likely to avoid ambiguities, which arise when the vessel is steaming with waves. This study addresses the ambiguities and analyzes arbitrary cases. In addition, a practical method is provided which is useful in situations when the vessel is changing speed or heading. These methods improved the laser rangefinder estimates of spectral shapes and peak parameters when compared to nearby buoys and a spectral wave model.

The ability of viscous layer models to describe the attenuation of waves propagating in grease‐pancake ice covered ocean is investigated. In particular, the Keller's model (Keller, 1998; https://doi.org/10.1029/97JC02966), the two‐layer viscous model (De Carolis & Desiderio, 2002; https://doi.org/10.1016/S0375-9601(02)01503-7) and the close‐packing model (De Santi & Olla, 2017; arXiv:1512.05631) are extensively validated by using wave attenuation data collected during two different field campaigns (Weddell Sea, Antarctica, April 2000; western Arctic Ocean, autumn 2015). We use these data to inspect the performance of the three models by minimizing the differences between the measured and model wave attenuation; the retrieved ice thickness is then compared with measured data. The three models allow to fit the observation data but with important differences in the three cases. The close‐packing model shows good agreement with the data for values of the ice viscosity comparable to those of grease ice in laboratory experiments. For thin ice, the Keller's model performance is similar to that of the close‐packing model, while for thick ice much larger values of the ice viscosity are required, which reflects the different ability of the two models to take into account the effect of pancakes. The improvement of performance over the Keller's model achieved by the two‐layer viscous model is minimal, which reflects the marginal role in the dynamics of a finite eddy viscosity in the ice‐free layer. A good ice thickness retrieval can be obtained by considering the ice layer as the only source in the wave dynamics, so that the wind input can be disregarded.

The reduction of the sea ice coverage increases the importance of wind waves in the Arctic. Updates to the standard spectral wave models in recent years have included many aspects of wave–ice interaction. Here we use high‐resolution wave parameters retrieved from TerraSAR‐X images and in situ SWIFT buoy observations to evaluate the performance of WAVEWATCH III® in ice‐free waters in the western Arctic over a 7‐week period including the fall freeze‐up. About two thirds of the analyzed data sets show a good agreement between observations and model results. In other cases, more accurate representation of the wind input fields and the ice coverage could improve the model predictions. Two data sets with larger discrepancy are discussed in more detail. In these two cases of low model skill, with off‐ice wind conditions, we show that the model wind‐sea growth is too weak to match TerraSAR‐X observations, and this situation is improved only slightly by including the effects of atmospheric stability using existing methods. Application of an effective fetch parameterization, which allows for reduced wave generation within the marginal ice zone prior to the open ocean, provides the best estimation of wave growth during off‐ice winds.

This study presents Arctic sea ice drift fields measured by shipboard marine X‐band radar (MR). The measurements are based on the maximum cross correlation between two sequential MR backscatter images separated ∼1 min in time, a method that is commonly used to estimate sea ice drift from satellite products. The advantage of MR is that images in close temporal proximity are readily available. A typical MR antenna rotation period is ∼1–2 s, whereas satellite revisit times can be on the order of days. The technique is applied to ∼4 weeks of measurements taken from R/V Sikuliaq in the Beaufort Sea in the fall of 2015. The resulting sea ice velocity fields have ∼500 m and up to ∼5 min resolution, covering a maximum range of ∼4 km. The MR velocity fields are validated using the GPS‐tracked motion of Surface Wave Instrument Float with Tracking (SWIFT) drifters, wave buoys, and R/V Sikuliaq during ice stations. The comparison between MR and reference sea ice drift measurements yields root‐mean‐square errors from 0.8–5.6 cm s^-1. The MR sea ice velocity fields near the ice edge reveal strong horizontal gradients and peak speeds > 1 m s^-1. The observed submesoscale sea ice drift processes include an eddy with ∼6 km diameter and vorticities < –2 (normalized by the Coriolis frequency) as well as converging and diverging flow with normalized divergences < –2 and > 1, respectively. The sea ice drift speed correlates only weakly with the wind speed (r²=0.34), which presents a challenge to conventional wisdom.

This paper investigates the attenuation and directional spreading of large amplitude waves traveling through pancake ice. Directional spectral density is analyzed from in situ wave buoy data collected during a 3‐day storm event in October 2015 in the Beaufort Sea. Two proxy metrics for wave amplitude obtained from energy density spectra, namely, spectral amplitude and significant wave height, are used to track the waves as they propagate along transects through the array of buoys in the predominantly pancake ice field. Two types of wave buoys are used in the analysis and compared, exhibiting significant differences in the wave energy density and directionality estimates. Although exponential decay is observed predominantly, one of the two buoy types indicates a potential positive correlation between wave energy density and the occurrence of linear wave decay, as opposed to exponential decay, in accord with recent observations in the Antarctic marginal ice zone. Factors affecting the validity of this observation are discussed. An empirical power law with exponent 2.2 is also found to hold between the exponential attenuation coefficient and wave frequency. The directional content of the wave spectrum appears to decrease consistently along the wave transects, confirming that wave energy is being dissipated by the pancake ice as opposed to being scattered by ice cakes.

The collection and processing of shipboard air, ice, and ocean measurements from the Sea State field campaign in the Beaufort/Chukchi Seas in autumn 2015 are described and the data used to characterize the near‐surface freezeup environment. The number of parameters measured or derived is large and the location and time of year are unique. Analysis was done of transits through the new, growing ice and of ice edge periods. Through differential surface energy fluxes, the presence of new, thin sea ice (<50 cm) produces lower tropospheric air temperatures in the ice interior that average ~4°C colder than those over open water near the ice edge, resulting in an ice edge baroclinic zone. This temperature difference doubles by late October and produces thermodynamic and dynamic feedbacks. These include off‐ice, cold‐air advection leading to enhanced surface heat loss averaging ~200 W/m² over the open water, formation of low‐level jets, suppression of the ice edge baroclinic zone, and enhanced ice drift. The interior ice growth rate is thermodynamically consistent with a surface heat loss of ~65 W/m² to the atmosphere and a heat flux of several tens of W/m² from the ocean below. Ice drift at times contributes to the southward advance of the autumn ice edge through off‐ice winds. The ocean thermohaline structure is highly variable and appears associated with bathymetric features, small‐scale upper‐ocean eddies, and the growing ice cover. Lower salinity under the ice interior compared to the nearby ice edge is an upper‐ocean impact of this thin ice cover.

New sea ice in the polar regions often begins as small pancake floes in autumn and winter that grow laterally and weld together into larger floes. However, conditions in polar oceans during freeze‐up are harsh, rendering in‐situ observations of small‐scale sea ice growth processes difficult and infrequent. Here, we apply image processing techniques to images obtained by drifting wave buoys (SWIFTs) deployed in the autumn Arctic Ocean to quantify these processes in situ for the first time. Small pancake ice floes were observed to form and grow gradually in freezing, low‐wave conditions. We find that pancake floe diameters are limited by the wave field, such that floe diameter is proportional to wavelength and amplitude over time. Floe welding correlates well with sea ice concentration, and the observations can be used to estimate a key model parameter for floe size evolution. There is some agreement between observed lateral growth rates and those predicted using a theoretical model based on heat flux balance, but the model lateral growth rates are too conservative in these conditions. These results will be used to inform description of lateral floe growth and floe welding in new models that evolve sea ice floe size distribution.

A model for wind-generated surface gravity waves, WAVEWATCH III®, is used to analyze and interpret buoy measurements of wave spectra. The model is applied to a hindcast of a wave event in sea ice in the western Arctic, October 11–14 2015, for which extensive buoy and ship-borne measurements were made during a research cruise. The model, which uses a viscoelastic parameterization to represent the impact of sea ice on the waves, is found to have good skill — after calibration of the effective viscosity — for prediction of total energy, but over-predicts dissipation of high frequency energy by the sea ice. This shortcoming motivates detailed analysis of the apparent dissipation rate. A new inversion method is applied to yield, for each buoy spectrum, the inferred dissipation rate as a function of wave frequency. For 102 of the measured wave spectra, visual observations of the sea ice were available from buoy-mounted cameras, and ice categories (primarily for varying forms of pancake and frazil ice) are assigned to each based on the photographs. When comparing the inversion-derived dissipation profiles against the independently derived ice categories, there is remarkable correspondence, with clear sorting of dissipation profiles into groups of similar ice type. These profiles are largely monotonic: they do not exhibit the "roll-over" that has been found at high frequencies in some previous observational studies. This article is protected by copyright. All rights reserved.

High‐resolution measurements of the air‐ice‐ocean system during an October 2015 event in the Beaufort Sea demonstrate how stored ocean heat can be released to temporarily reverse seasonal ice advance. Strong on‐ice winds over a vast fetch caused mixing and release of heat from the upper ocean. This heat was sufficient to melt large areas of thin, newly formed pancake ice; an average of 10 MJ/m² was lost from the upper ocean in the study area, resulting in ~3–5 cm pancake sea ice melt. Heat and salt budgets create a consistent picture of the evolving air‐ice‐ocean system during this event, in both a fixed and ice‐following (Lagrangian) reference frame. The heat lost from the upper ocean is large compared with prior observations of ocean heat flux under thick, multi‐year Arctic sea ice. In contrast to prior studies, where almost all heat lost goes into ice melt, a significant portion of the ocean heat released in this event goes directly to the atmosphere, while the remainder (~30–40%) goes into melting sea ice. The magnitude of ocean mixing during this event may have been enhanced by large surface waves, reaching nearly 5 m at the peak, which are becoming increasingly common in the autumn Arctic Ocean. The wave effects are explored by comparing the air‐ice‐ocean evolution observed at short and long fetches, and a common scaling for Langmuir turbulence. After the event, the ocean mixed layer was deeper and cooler, and autumn ice formation resumed.

Near‐surface turbulent kinetic energy dissipation rates are altered by the presence of sea ice in the marginal ice zone, with significant implications for exchanges at the air‐ice‐ocean interface. Observations spanning a range of conditions are used to parameterize dissipation rates in marginal ice zones with relatively thin, newly formed ice, and two regimes are identified. In both regimes, the turbulent dissipation rates are matched to the turbulent input rate, which is formulated as the surface stress acting on roughness elements moving at an effective transfer velocity. In marginal ice zones with waves, the short waves are the roughness elements, and the phase speed of these waves is the effective transfer velocity. The wave amplitudes are attenuated by the ice, and thus, the size of the roughness elements is reduced; this is parameterized as a reduction in the effective transfer velocity. When waves are sufficiently small, the ice floes are the roughness elements, and the relative velocity between the sea ice and the ocean is the effective transfer velocity. A scaling is introduced to determine the appropriate transfer velocity in a marginal ice zone based on wave height, ice thickness and concentration, and ice‐ocean shear. The results suggest that turbulence underneath new sea ice is mostly related to the wind forcing and that marginal ice zones generally have less turbulence than the open ocean under similar wind forcing.

In the marginal ice zone, surface waves drive motion of sea ice floes. The motion of floes relative to each other can cause periodic collisions, and drives the formation of pancake sea ice. Additionally, the motion of floes relative to the water results in turbulence generation at the interface between the ice and ocean below. These are important processes for the formation and growth of pancakes, and likely contribute to wave energy loss. Models and laboratory studies have been used to describe these motions, but there have been no in situ observations of relative ice velocities in a natural wave field. Here, we use shipboard stereo video to measure wave motion and relative motion of pancake floes simultaneously. The relative velocities of pancake floes are typically small compared to wave orbital motion (i.e. floes mostly follow the wave orbits). We find that relative velocities are well-captured by existing phase-resolved models, and are only somewhat over-estimated by using bulk wave parameters. Under the conditions observed, estimates of wave energy loss from ice–ocean turbulence are much larger than from pancake collisions. Increased relative pancake floe velocities in steeper wave fields may then result in more wave attenuation by increasing ice–ocean shear.

A storm with significant wave heights exceeding 4 m occurred in the Beaufort Sea on 11–13 October 2015. The waves and ice were captured on 12 October by the Synthetic Aperture Radar (SAR) on board Sentinel‐1A, with Interferometric Wide swath images covering 400 x 1,100 km at 10 m resolution. This data set allows the estimation of wave spectra across the marginal ice zone (MIZ) every 5 km, over 400 km of sea ice. Since ice attenuates waves with wavelengths shorter than 50 m in a few kilometers, the longer waves are clearly imaged by SAR in sea ice. Obtaining wave spectra from the image requires a careful estimation of the blurring effect produced by unresolved wavelengths in the azimuthal direction. Using in situ wave buoy measurements as reference, we establish that this azimuth cutoff can be estimated in mixed ocean‐ice conditions. Wave spectra could not be estimated where ice features such as leads contribute to a large fraction of the radar backscatter variance. The resulting wave height map exhibits a steep decay in the first 100 km of ice, with a transition into a weaker decay further away. This unique wave decay pattern transitions where large‐scale ice features such as leads become visible. As in situ ice information is limited, it is not known whether the decay is caused by a difference in ice properties or a wave dissipation mechanism. The implications of the observed wave patterns are discussed in the context of other observations.

The sea state of the Beaufort and Chukchi seas is controlled by the wind forcing and the amount of ice-free water available to generate surface waves. Clear trends in the annual duration of the open water season and in the extent of the seasonal sea ice minimum suggest that the sea state should be increasing, independent of changes in the wind forcing. Wave model hindcasts from four selected years spanning recent conditions are consistent with this expectation. In particular, larger waves are more common in years with less summer sea ice and/or a longer open water season, and peak wave periods are generally longer. The increase in wave energy may affect both the coastal zones and the remaining summer ice pack, as well as delay the autumn ice-edge advance. However, trends in the amount of wave energy impinging on the ice-edge are inconclusive, and the associated processes, especially in the autumn period of new ice formation, have yet to be well-described by in situ observations. There is an implicit trend and evidence for increasing wave energy along the coast of northern Alaska, and this coastal signal is corroborated by satellite altimeter estimates of wave energy.

Ocean surface waves propagating through sea ice are scattered and dissipated. The net attenuation occurs preferentially at the higher frequencies, and thus the spectral bandwidth of a given wave field is reduced, relative to open water. The reduction in bandwidth is associated with an increase in the groupiness of the wave field. Using SWIFT buoy data from the 2015 Arctic Sea State experiment, bandwidth is compared between pancake ice and open water conditions, and the linkage to group envelopes is explored. The enhancement of wave groups in ice is consistent with the simple linear mechanism of superposition of waves with narrowing spectral bandwidth. This is confirmed using synthetic data. Nonlinear mechanisms, which have been shown as significant in other ice types, are not found to be important in this data set.

The effects of instrument noise on estimating the spectral attenuation rates of ocean waves in sea ice are explored using synthetic observations in which the true attenuation rates are known explicitly. The spectral shape of the energy added by noise, relative to the spectral shape of the true wave energy, is the critical aspect of the investigation. A negative bias in attenuation that grows in frequency is found across a range of realistic parameters. This negative bias decreases the observed attenuation rates at high frequencies, such that it can explain the rollover effect commonly reported in field studies of wave attenuation in sea ice. The published results from five field experiments are evaluated in terms of the noise bias, and a spurious rollover (or flattening) of attenuation is found in all cases. Remarkably, the wave heights are unaffected by the noise bias, because the noise bias occurs at frequencies that contain only a small fraction of the total energy.

Ocean surface waves (sea and swell) are generated by winds blowing over a distance (fetch) for a duration of time. In the Arctic Ocean, fetch varies seasonally from essentially zero in winter to hundreds of kilometers in recent summers. Using in situ observations of waves in the central Beaufort Sea, combined with a numerical wave model and satellite sea ice observations, we show that wave energy scales with fetch throughout the seasonal ice cycle. Furthermore, we show that the increased open water of 2012 allowed waves to develop beyond pure wind seas and evolve into swells. The swells remain tied to the available fetch, however, because fetch is a proxy for the basin size in which the wave evolution occurs. Thus, both sea and swell depend on the open water fetch in the Arctic, because the swell is regionally driven. This suggests that further reductions in seasonal ice cover in the future will result in larger waves, which in turn provide a mechanism to break up sea ice and accelerate ice retreat.

Sea ice floe size distribution and lead properties in the Beaufort and Chukchi Seas are studied in the summer-fall transition 2014 to examine the impact on the sea ice cover from storms and surface waves. Floe size distributions are analyzed from MEDEA, Landsat8, and RADARSAT-2 imagery, with a resolution span of 1–100 m. Landsat8 imagery is also used to identify the orientation and spacing of leads. The study period centers around three large wave events during August–September 2014 identified by SWIFT buoys and WAVEWATCH III model data. The range of floe sizes from different resolutions provides the overall distribution across a wide range of ice properties and estimated thickness. All cumulative floe size distribution curves show a gradual bending toward shallower slopes for smaller floe sizes. The overall slopes in the cumulative floe size distribution curves from Landsat8 images are lower than, while those from RADARSAT-2 are similar to, previously reported results in the same region and seasonal period. The MEDEA floe size distributions appeared to be sensitive to the passage of storms. Lead orientations, regardless of length, correlate slightly better with the peak wave direction than with the mean wave direction. Their correlation with the geostrophic wind is stronger than with the surface wind. The spacing between shorter leads correlates well with the local incoming surface wavelengths, obtained from the model peak wave frequency. The information derived shows promise for a coordinated multisensor study of storm effects in the Arctic marginal ice zone.