PROJECT SUMMARYWAVE-ICE-OCEAN INTERACTIONS ALONG THE ARCTIC COAST

Overview

Arctic coastlines, and in particular the northern coast of Alaska, are eroding at rates of meters per year.Coastal flooding events are becoming more common, as reductions in seasonal sea ice create large fetchesfor autumn storms. The proposed work concerns the oceanographic factors associated with coastal erosionand flooding, which are distinct from the geologic controls. Key among these oceanographic factors is thepreviously demonstrated increasing trend in surface wave activity throughout the western Arctic.

Field observations will be collected and a coupled modeling system will be developed that together quan-tify the wave-ice-ocean interactions along the northern coast of Alaska. This new model will be applied,after calibration and validation with the field observations, to generate a 20-year hindcast. The hindcastwill be used to investigate the climate signals in Arctic wave-ice-ocean coupling. The results will deter-mine: 1) the significance of coastal protection via scattering and dissipation of waves by sea ice, 2) thethermodynamic and mechanical effects of increasing wave energy, and 3) the changes in coastal floodingand circulation associated with increasing wave momentum.

Intellectual Merit

The proposed work will connect a rapidly changing climatology and the physical processes that are funda-mental to Arctic coastal ocean. This research topic is intrinsically multidimensional, with natural variabilityon a wide range of spatial and temporal scales. In order to make progress in this area, a modeling sys-tem capable of accommodating such variability is required, along with validation through detailed fieldobservations. The work proposed here will synthesize recent progress in process-based modeling of coastaldynamics, especially in regards to the coupling of specific process models. The proposed work will fill a gapbetween the recent progress modeling wave-ice interactions in deep-water and existing programs studyingerosion at the shoreline. Understanding wave-ice-ocean interactions along the Arctic coasts is essential toimproving the skill of forecast and climate models to the region. These interactions continue to be a fo-cal point for basic research, because of the complexities and possible non-linearities associated with theseprocesses.

Broader Impacts

The proposed work will result in development of an open-source, process-based modeling system for Arcticcoastal regions, including model grids and test cases for the north Alaska coastal zone. This capabilitywill have impacts across basic research, public infrastructure planning, climate scenario assessment, andpolicy-making. The work will include outreach in the form of K-12 events and public seminars. Also, thiswork will involve the mentoring and training of a postdoctoral researcher. Finally, the proposed work willalso generate a comprehensive set of field data and model results that will be publicly available to otherresearchers working to understand wave-ice-ocean interactions and Arctic coastal change.

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PROJECT DESCRIPTION

WAVE-ICE-OCEAN INTERACTIONS ALONG THE ARCTIC COAST

MotivationThe Arctic coastal region is extensively used for subsistence, including: hunting, fishing, and gathering[Braund and Associates, 2010]. Commercial activities, such as oil drilling and cargo shipping, are alsoconcentrated along Arctic coasts. Along the northern coast of Alaska, areas such as the National PetroleumReserve-Alaska (NPRA) and the Teshekpuk Lake Special Area (TLSA) support local Inupiaq communitiesand provide undisturbed regions for diverse wildlife.

Figure 1: Utqiagvik storm surge andflooding in September 2017.

As the whole Arctic shifts into a modern epoch, with a moreseasonal ice cover and warmer temperatures, the Arctic coastal pro-cesses are shifting as well. Storm systems with strong wave eventsnow occur more often in the Arctic, with less ice to protect the coast[ACIA, 2004]. These storm events cause coastal flooding and ero-sion, with associated damage to infrastructure. For example, a re-cent storm at Utqiagvik in late September 2017 flooded town sites(Figure 1), damaged several miles of protective beach berm, inun-dated two lagoons with saltwater, and undercut a large section ofbluff, initiating its collapse.

ObjectivesThe overall goal of this proposal is to improve scientific understanding of wave-ice-ocean interactions alongthe Arctic coast, with particular attention to the oceanographic parameters that affect erosion. The proposedwork will directly observe offshore wave conditions and shoreward wave transformations in the presence ofa variety of ice conditions. Results will inform a model capable of resolving wave-ice interactions, coastalcirculation, and water temperatures under changing Arctic ice conditions. The specific objectives are to:

• Quantify the role of reduced sea ice in causing increased wave action along the Arctic coast.• Understand the wave-ice dissipation and scattering occurring in the seasonal ice zone along the coast.• Develop hindcast climatology and forecast capability for coastal wave conditions, circulation, and

water temperature.

Proposal RevisionsThis proposal is substantially revised from the original submission in October 2016. Reviewer commentsare addressed by:

• Focusing the research on the oceanographic processes relevant to erosion, rather than erosion itself.• Clarifying the modeling effort and the connection from waves to coastal erosion.• Distinguishing between the oceanographic and geologic factors causing erosion, as well as differences

between sandy barrier spits and permafrost bluffs.• Expanding the field campaign to more sites, with a broader range of coastal types and conditions.• Simplifying the equilibrium framework, with application to the hindcast climatology only.• Increasing PI time commitment to the project, to ensure adequate management.• Employing a postdoc, rather than a graduate student, to ensure rapid productivity.• Engaging the model development expertise of Erick Rogers at NRL, with support committed from the

Office of Naval Research (ONR) Code 322.• Explicitly stating the intent to use the NSF Arctic Data Center for data dissemination and archiving.

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BackgroundErosion along the Arctic Coast

Figure 2: Erosion rates, in m/yr, (top) and coastline types(bottom) along the North Alaskan coast. From Gibbs and Rich-mond [2015].

Arctic coasts are generally erosional, witha pan-Arctic shoreline position, S, retreatrate of ∂S/∂ t = 0.5 m/yr [Lantuit et al.,2012]. The northern coast of Alaska, whichborders the Chukchi and the Beaufort seas,has an even higher regional average rate of∂S/∂ t = 1.4 m/yr [Gibbs and Richmond,2015]. For just the Beaufort coast, the re-gional average ∂S/∂ t = 1.7 m/yr is high aswell. There is a growing consensus that theseregional rates are increasing [e.g., Jones et al.,2009]. Beyond these regional averages, thelocal rates are highly variable, and suggest arange of processes specific to the geology andoceanography of a given site.

Figure 2 shows the erosion rates andcoastline types along the North Alaskan coast.Two common types are exposed coast andbarrier systems. On exposed coasts, per-mafrost bluffs can have erosion rates as highas 14.4 m/yr [Jones et al., 2009] or 19 m/yr[Barnhart et al., 2014b]. In some extremecases, the rates can be 30 m/yr [Wobus et al., 2011]. Warmer sea-surface temperatures are an obvioussignal that can undercut these bluffs [Reimnitz et al., 1988], however mechanical forcing by waves andcurrents can also be important. Jones et al. [2009] suggest that increasing fetch distances and increasingwave generation are also likely drivers of accelerating coastal erosion. Studies specifically focused on fetch-dependence indicate a strong connection between erosion and wave forcing [e.g., Overeem et al., 2011],along with the role of relative water level due to wind and wave-driven processes [e.g., coastal setup at DrewPoint, Alaska, Barnhart et al., 2014a,b].

Arctic coastal erosion is a complex combination of geologic factors (e.g., shoreline type, beach slope,and grain size), oceanographic factors (e.g., water levels, wave forcing, water temperature, and sea-ice cov-erage), and atmospheric factors (e.g., air temperature and solar insolation) [Reimnitz et al., 1988, Overeemet al., 2011, Lantuit et al., 2012, Barnhart et al., 2014a,b, Gibbs and Richmond, 2015]. This proposal isfocused on the oceanographic factors, in particular: 1) wave conditions, 2) coastal circulation, and 3) wa-ter temperature. These oceanographic factors likely are more important to barrier systems, rather than thepermafrost bluffs and open coasts (which have been more extensively studied), but the cross-shore andalongshore variability of aforementioned drivers is relevant to either coastal type.

As shown in Figure 2, about half of the northern Alaskan coastline is classified as a barrier shoreline (in-cludes barrier islands, spits, and beaches) [Gibbs and Richmond, 2015]. Barrier systems are often comprisedof sand and coarser material, and are associated with wave-dominated morphological regimes. Generally,barrier shorelines are more dynamic, with erosion and accretion occurring depending on forcing conditions,rather than purely erosion. Furthermore, barrier shorelines with low ground ice content and high backshore

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elevation may erode slowly, but supply more organic carbon-rich sediment into the water column, thus mod-ifying the nearshore carbon budget. As the western Arctic becomes more seasonally ice free, the role ofbarrier islands and spits may become more important to the coastal morphology.

Prior coastal erosion studies along the Arctic coast [e.g., Jones et al., 2009, Lantuit et al., 2012, Gibbsand Richmond, 2015] have used shoreline imagery to provide a comprehensive estimate of shoreline erosionrates along the north Alaskan coastline (e.g., Figure 2). Although extensive, these studies do not directlyaccount for event-driven wave and storm surge variability along the coastline, which is possibly one ofthe largest factors affecting coastal erosion along Arctic coasts [Lantuit et al., 2012]. Localized studieson exposed coasts with permafrost bluffs [e.g., Drew Point, Alaska, Barnhart et al., 2014a,b] do reinforcethe importance of wave events and water level changes on controlling erosion. Nonetheless, these studiesdo not quantify alongshore linkages in the shoreline, which are a common consequence of coastal circula-tion patterns. Previous studies have also not considered realistic wave-ice interactions [Squire et al., 2009]or wave breaking dissipation as a heating source to nearshore waters [e.g., Sinnett and Feddersen, 2014].Instead, most prior studies describe the shoreline retreat rates as a purely one-dimensional (cross-shore) phe-nomenon. A process-based representation is needed to accurately quantify wave forcing, interactions withsea ice, ice-bound sediments, and nearshore circulation patterns. The numerical model development pro-posed herein will provide a comprehensive framework for such understanding and practical implementation,but it must first be validated using field observations and case studies. The proposed development builds onthe substantial recent effort to understand and model wave-ice interactions in the deep Arctic [e.g., Rogersand Orzech, 2013, Rogers et al., 2016, Thomson et al., 2017] and apply these methods near the coast.

Reductions in Arctic Sea Ice

Figure 3: Average shift, in days per year,of autumn ice timing. From Thomsonet al. [2016a].

There have been significant reductions in the seasonal ice coverof the Western Arctic in recent years [e.g., Jeffries et al., 2013].One direct outcome of these reductions is an increase in the extentand duration of open water in the summer months [e.g., Barnhartet al., 2014a, Thomson et al., 2016b]. Figure 3 shows the trend inthe lengthening of the open water season, in days per year, overthe whole Arctic. In the Beaufort and Chukchi seas, the averageshift of one or two days later per year means that the ice advanceis now a full month later than it was a few decades ago. These icetrends are directly related to trends in the surface waves. The tem-poral signal is essential in linking the wave climate to coastal pro-cesses, because these are largely event-driven. An extra month ofopen water increases the likelihood that a storm will coincide witha large available fetch and send waves shoreward [e.g., Thomsonand Rogers, 2014].

Changing Wave ClimateDeep-water observations and modeling studies reveal a clear trend of increasing surface wave activity in theBeaufort and Chukchi seas [Thomson et al., 2016a, Wang et al., 2016], and this provides both motivationand context for understanding how wave-ice coupled processes drive coastal processes. The increasing wavetrend is directly related to the expanding fetch distances available for surface wave development [Thomsonand Rogers, 2014, Smith and Thomson, 2016]. The wave height increase occurs not only due to fartherretreat of seasonal ice during summers, but also because ice growth and advancement is slower in the autumn(Figure 3).

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The main result from Thomson et al. [2016a] is shown in Fig. 4, which uses the parameters of Weibulldistributions to describe the deep-water wave statistics over the Beaufort and Chukchi seas. To understandhow these trends from the larger domain might affect the coast, Thomson et al. [2016a] calculated the waveenergy flux incident to the coast,

F = E~cg · n, (1)

where E is wave energy, cg is wave group velocity, and n is the local unit vector normal to the coast. Theenergy is related to the wave height H by E = rgH2/16, and the group velocity is related to wave periodT by cg = gT/4p , and thus the flux calculation incorporates the increasing trends in both wave parameters(i.e., Fig. 4a and b). There is a clear trend in the shoreward flux, confirming that not only are waves increasingout in the open ocean, but specifically that more wave energy flux impinges on the coasts.

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Hs [

m]

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Tp [

s]

1990 1995 2000 2005 2010 20150

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U1

0 [

m/s

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Figure 4: Trends in the Weibull fit parameters forsignificant wave height, peak period, and wind speed.Diamonds are the scale parameter and the verticalbounded lines are the 95% confidence intervals of theshape parameter divided by a factor of ten (for visualsimplicity). The black dashed lines are the estimatedtrend lines of the scale parameter. From Thomsonet al. [2016a].

Of course, the presence of any sea ice near thecoast will dissipate and scatter the incoming waves[Squire et al., 1995, Squire, 2007], such that the coastsare partially protected. The process is likely to bedifferent in the spring, when shorefast ice is break-ing out and melting into brash, compared with the au-tumn, when new ice is forming as grease and pancakeice [Rogers et al., 2016]. Thus, modeling of Arcticcoastal processes must include wave-ice interactionsat a level of sophistication to distinguish between icetypes and ice properties.

Recent Progress in Arctic Wave Modeling

Multiple theoretical models represent wave-ice inter-action for different ice types [e.g., Squire et al., 1995,Squire, 2007]. For sparsely spread ice floes, wave-ice interaction is modeled through exponential wave-attenuation determined as a function of water depth,floe diameter and thickness [Wadhams et al., 1988,Kohout and Meylan, 2008]. For compact and poten-

tially colliding ice floes, mathematical models treat ice floes as a single viscous fluid layer with specificrheology [Weber, 1987, Keller, 1998]. Such models require eddy viscosity and density and are applied tostudy grease ice [Newyear and Martin, 1999]. Complex two-layer viscoelastic theoretical models provide aunified rheology for ice layer treated as a viscous fluid layer by considering the elasticity quantified throughthe shear modulus [Wang and Shen, 2010]. Other theories also exist for highly compact ice (e.g., shorefastice) which attribute wave dissipation due to turbulence in the boundary layer beneath the ice layer [e.g., Liuand Mollo-Christensen, 1988, Liu et al., 1991, 1994]. Note that these theories do not allow for simultaneousevolution of sea ice due to forcing from wave-attenuation or evolving hydro- and thermodynamics.

Application of wave-ice interaction theories for operational wave modeling is challenging, as some ofthe assumptions in theoretical model derivations are not always true in reality. Also, some of the inputvariables required might not be readily available, or might be of poor fidelity (e.g., ice type). Nonetheless,substantial progress has been made in the adaptation of the operational wave model WAVEWATCH3 [WW3,Tolman and The WAVEWATCH III R� Development Group, 2014] to include wave-ice interactions [Rogersand Orzech, 2013, Rogers et al., 2016]. The progress in WW3 is intended for deep water applicationsand large domains. Figure 5 shows an example of this progress and comparison to an in situ observation,

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including the various ‘IC’ routines that have been implemented (and are described later in the proposal).Significant work remains to model the coupled wave-ice-ocean processes occurring near the coast, which isbeyond the scope of WW3. For example, surface wave energy dissipation by ice is a momentum source thatdrives both ocean currents and ice drift [Steele et al., 1989b, Tang and Fissel, 1991, Perrie and Hu, 1997].In addition, wave-driven ocean circulation changes the ocean stress on the ice, and further modifies ice drift.To correctly estimate these complex interactions, a coupled framework of surface waves, ocean circulation,and sea ice is required, which will help better understand the oceanographic conditions along the Arcticcoast.Recent Progress in Arctic Coastal Modeling

Figure 5: Results from various WW3ice algorithms (colors) in a hindcast ofa moored Acoustic Wave and Current(AWAC) observation of significant wavesheights for Autumn of 2015 (black dots).From Collins and Rogers [2017].

One-dimensional wave modeling based on empirical formula-tions for fetch and wind speed have been recently applied tostudy wave and wind-induced setup, and coastal dynamics ad-jacent to permafrost bluffs in the Arctic [e.g., Overeem et al.,2011, Barnhart et al., 2014a,b]. In these studies, modeled waveheight and water level variability agree well with observations.Such empirical models however do not account for processes likewave refraction, wave-driven circulation, wave-ice dissipation,and wave-ice scattering. DELFT3D with a hydrodynamic mod-ule and the SWAN (Simulating Waves Accurately Nearshore)model has been applied to study wave dynamics east of Macken-zie Bay and validated against measurements from early 1980s and2006 [Lintern et al., 2013]. At present, SWAN does not includewave-ice interactions.

Fundamentally, there is a need for high-resolution realisticwave-ice-circulation modeling along the Arctic coasts. To date, the substantial recent advances in modelingwave-ice interactions in the deep basins have not been ported to high-resolution coastal models. In addition,nearshore wave and circulation models neither include basic wave propagation dynamics, nor accuratelysimulate wave-ice interaction in coastal waters. Development of the calibrated model will simultaneouslyaddress several basic research questions (see below) and provide a state-of-the-art tool for the community.

HypothesesThe overall hypothesis is that coastal processes in the Arctic are changing in response to increased waveenergy. Historically, Arctic coastlines have been protected against wave action by perennial ice cover overthe deep Arctic ocean, which severely limited the fetch for wave generation, and by the presence of shorefastice, which dissipated and scattered what little wave energy was incident to the coast. Now, with a moreseasonal ice cover, the fetch is increasing and shorefast ice is absent several months of each year. Specificprocesses to be addressed include wave-ice interactions and wave-driven circulations, as well as the recentindication that wave breaking dissipation can be a significant source of heating to nearshore waters [Sinnettand Feddersen, 2014]. Specifically, the proposed work will test:

H1. Wave-ice interactions: The extent and type of remaining seasonal coastal sea ice significantly im-pacts coastal wave energy via wave-ice scattering and dissipation.

H2. Wave energy effects: Increasing wave activity is accelerating erosion along Arctic coasts, via bothsupply/transport of heat and mechanical energy.

H3. Wave momentum effects: Increasing wave activity is exacerbating coastal flooding events, via wave-driven setup, and enhancing coastal circulation, via alongshore forcing.

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Science PlanAddressing these hypotheses requires accurate estimates of wave energy, wave energy fluxes, wave direc-tions, sea surface temperatures, ice coverage, ice thickness and coastal currents. The proposed approach usesa combination of climatology, field measurements, and a numerical modeling system. Acoustic and highfrequency pressure measurements will quantify seasonal variability in coastal wave-dynamics and circula-tion at three sites along the Arctic coast. An existing open-source community model (COAWST, includingSWAN as the wave module) will be improved to account for interactions between the waves, the sea ice,and the ocean. This coupled model will be the first to offer these kind of detailed interactions for the Arcticenvironment, and it will be validated against the field measurements.

Case Study Sites

Figure 6: Subsistence hunting areas(shaded circles) and proposed field sites(numbered boxes), with a focus on bar-rier system coastal types. Modified fromKonar et al. [2017].

The field work and model development will focus on three repre-sentative sites, initially. Later, the validated model will be used toscale-up the results to the whole northern coast of Alaska, with agoal of mapping the variability in surface waves and coastal cir-culation. The representative sites are selected based on coastaltype and in deference to subsistence whaling activities. Figure6 shows the three proposed sites along the Alaska coast, alongwith regions of subsistence hunting. The sites are all classifiedas sandy barrier systems (see Figure 2), which are expected tohave the most sensitivity to wave forcing. Following the NSFCommunity and Environmental Compliance Standard OperatingProcedure (CECSOP) for Sikuliaq [Konar et al., 2017], the sitesare selected to avoid subsistence hunting regions.

Field Observations

PI Thomson will lead the field study. Thomson has over 100 days of at sea experience in the Beaufort andChukchi seas, including several days along the coast east of Prudhoe Bay in August 2014 during Office ofNaval Research Marginal Ice Zone program. The field observations will be designed around two researchcruises using the R/V Sikuliaq, in Autumn 2019 and Autumn 2020, with long-term mooring observationsbetween the cruises. The Sikuliaq cruises have been entered in the UNOLS system as Ship Time Request(STR 105964), and PI Thomson has already coordinated with Interim Marine Superintendent Doug Bairdabout the suitability of Sikuliaq for coastal and nearshore work. Captain Baird has confirmed that Sikuliaqcan operate in as shallow as 20 m depth, and the small workboat carried by Sikuliaq can operate in as shallowas 5 m depth. The observations are planned with these limitations in mind. Although the proposed workis motivated by coastal erosion and flooding, the observational focus is on the coastal waters just offshoreof these phenomena, nominally 5 to 30 m water depths, where wave-ice-ocean interactions determine theincident forcing to the coast.

The two cruises will focus on deployment and recovery, respectively, of mooring arrays at each of thethree field sites. Opportunistic sampling of wave events will occur during the cruises, and the STR includesscience days for such sampling. The mooring arrays and sampling are shown in Figure 7 and listed in Table1. The arrays each center around Nortek Acoustic Wave and Current (AWAC) meter in 25 m depth on aseafloor tripod. This depth will be sufficient to avoid ice keels in the winter, yet close enough to providehigh-fidelity incident wave conditions and current profiles every 30 minutes. An example of AWAC data hasalready been shown in Figure 5. The AWAC is a well-tested instrument, having now been used by Thomson

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Figure 7: Schematic of a mooring array and sampling. Each array includes pressure and temp loggers (greycylinders), temp loggers (red cylinders), an Acoustic Wave and Current sensor on a tripod (orange triangle),SWIFT buoys (yellow cylinders), a shipboard stereo system (green field of view), and a small UAS (purple fieldof view). The array is designed to measure incident wave energy and direction, as well as profiles of currents andwater temperature. An array will be deployed at each of the three field sites.

Table 1: Instrumentation for each field site (three total).

Instrumentation Duration PurposeTemp and pressure recorders 1 year (moored) Waves and water tempAWAC on tripod 1 year (moored) Waves and currentsSatellite images 1 year Ice conditions and shoreline positionSWIFT buoys days (opportunistic) Waves, winds, currents, dissipationShipboard stereo video days (opportunistic) Wave-ice interactionsShipboard met station days (opportunistic) Wind stress and heat fluxShipboard ADCP and CTD days (opportunistic) Currents and heat transportShipboard multibeam days (opportunistic) Bathymetry surveyUAS (drone) days (opportunistic) Ice coverage

and others in the Arctic since 2012 [e.g., Thomson and Rogers, 2014]. The three required AWACs, withtripods with acoustic recovery systems, are existing equipment available at APL-UW (i.e., they are notincluded in the budget).

The rest of each cross-shore array is comprised of 4 seafloor pressure and temperature loggers, each withadditional temperature loggers strung along the sub-surface moorings (Figure 7). The sub-surface design isintended to improve survivability in the presence of shorefast ice and ice keels traversing the area duringthe winter (still, some attrition of these inexpensive instruments is expected). The logger positions will bemarked using GPS, and each logger will also have acoustic tracking beacons. Contingency for recovery ofthe instruments will be SCUBA diving (Thomson and two other AAUS [American Academy of UnderwaterSciences] divers will be on each Sikuliaq cruise).

The mooring observations will quantify the amount of wave energy propagating through the coastal zoneover the seasonal cycle of shorefast ice formation (in autumn), ice drift (in winter), ice break out (in spring),and open water (in summer). Thermistors at each cross-shore mooring location will record the seasonalnearshore temperature evolution. The moorings may also capture specific wave events that constitute theextremes that are perhaps the dominant, albeit rare, forcing for coastal morphology. The single year ofobservations will provided a detailed view of the wave forcing and temperature evolution, and this year willbe placed in context using climatology [e.g., Thomson et al., 2016b].

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Figure 8: Pancake ice via stereo camera systemmounted on the Sikuliaq. From Smith and Thomson(in prep).

Given the autumn timing requested in the STR,it is expected that at least one large wave event willoccur during each cruise. During these events, theSikuliaq will deploy temporary arrays of SWIFTbuoys and conduct surveys with a ship-based stereovideo system, along with underway ADCP andCTD profiling. Figure 8 shows an example of thestereo data collected during pancake ice conditions.The image pairs are processed to render 4D mo-tion maps detailing interactions of ice floes and in-dividual waves. A small Unmanned Aerial Sys-tem (UAS,DJI Phantom 4 Pro) will be used to sup-plement the shipboard stereo and obtain broaderice coverage, albeit with only 2D mapping. TheSWIFT buoys measure waves, sea surface tempera-tures, wind forcing, current profiles, and near-surface turbulence [Thomson, 2012]. SWIFT wave measure-ments have been used to calibrate the recent advances for wave-ice modeling in deep water [Rogers et al.,2016]. SWIFT turbulence measurements will be related to the dissipation of wave energy by ice floes [Zip-pel and Thomson, 2016], and this will be an important validation measurement for the new modules of thenumerical model (see next section). The stereo system, SWIFT buoys, and drone are available from priorsupport (i.e., they are not included in the budget).

Figure 9: Example of RadarSat2 image fromthe National Ice Center (NIC) outside thesandy barrier spit/island system in the easternportion of Prudhoe Bay, AK. The area outsideof Maquire and Flaxman Islands is proposed asfield site 3, which is approximately 50 nm fromthe Cross Island Whaling Camp. The yellowline is the shoreline from Google Earth.

Planning and execution of the field program will bedone in accordance with the CECSOP [Konar et al.,2017], including coordination with local leaders and theAlaska Eskimo Whaling Commission. Thomson willattend the spring Alaska Waterways Safety Committeemeeting each year in March.

Satellite remote sensing

Satellite observations of the ice conditions will be es-sential to the interpretation of the hourly mooring data.These will be provided at an interval of a few days, usinga combination of: RadarSat2 images provided via spe-cial support request to the National Ice Center, Sentinel1images provided via European Space Agency portal, andLandSat8 images provided via the US Geological Surveyweb portal. Additional products are available via PolarView. The RadarSat2 and Sentinel1 products will be pre-ferred, as Synthetic Aperture Radar (SAR) images are notsensitive to clouds. Figure 9 is an example RadarSat2image that includes field site # 3, in which the coast issurrounded by a mixture of open water and brash ice inthe early summer of 2014.

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Model Development and Application

Co-PIs Kumar and Rogers will lead the modeling study. Kumar has extensive experience in developingpublic-domain ocean circulation and surface wave propagation models, and Rogers is leading the develop-ment of wave-ice interaction module in WW3 (Note: Rogers will be funded separately by ONR Code 322,Dr. Scott Harper.) The model framework development will involve (i) surface wave propagation modelmodification to allow for interaction with sea ice, and (ii) sea ice and ocean circulation model modificationto account for surface wave effects. These tasks will occur in conjunction with the field observations.

Model FrameworkThe Coupled Ocean Wave Atmosphere Sediment Transport modeling system [COAWST, Warner et al.,2010] will be used, which consists of the ocean circulation model Regional Ocean Modeling System (ROMS),wave propagation model Simulating Waves Nearshore (SWAN), Sea-Ice Model, and the Community Sed-iment Transport Modeling System. Model interaction is through the Model Coupling Toolkit (MCT).COAWST is validated for wave-current interaction in the surf zone, subtidal and internal tidal circula-tion [Kumar et al., 2011, 2012, 2015, 2016], and shoreline evolution over decadal time scales [Safak et al.,2016]. The ocean-circulation (ROMS) and the Sea-Ice model [Budgell, 2005] are used to study deep-waterto coastal circulation in the Bering sea, and validated against tidal circulation, temperature, salinity and icecover [e.g., Danielson et al., 2011, Hermann et al., 2013].

Model Development SummaryMultiple wave-ice interaction formulations will be incorporated into SWAN to accurately simulate wavedissipation and scattering due to ice. The inputs required for these interactions will be either user specified,or obtained through the Sea-Ice model. These wave-ice coupling methods will be validated against academictest cases [e.g., Rogers and Orzech, 2013] and field observations (see Table 3). Wave dissipation via ice willlead to additional stresses driving ocean circulation and ice drift, and act as source term for water temperatureevolution. The ocean-circulation modification will influence wave-current interaction, and also modify thestresses responsible for ice velocity. Finally, the modified ice drift will change the ice-ocean drag. Thistightly coupled framework will allow for offshore generated waves propagating towards the coast to interactwith ice floes in the coastal ocean and shorefast ice, and in process change the nearshore circulation andtemperature that cause erosion.

SWAN Development: Wave-Ice InteractionsSWAN is a spectral wave model [Booij et al., 1999, Ris et al., 1999] that simulates shoaling, wave refraction,energy input due to winds, energy dissipation through bottom friction, whitecapping, depth-limitation andmud layer, and nonlinear wave-wave interaction. Like SWAN, the operational wave model WW3 is governedby similar set of equations; however, WW3 uses an explicit time-stepping scheme and is more efficient atlarger scales (i.e., grid spacing > 2 km), while SWAN uses implicit time stepping and is better for regionaland coastal scales [Rogers et al., 2007].

WW3 has been updated for surface wave-ice interaction [Rogers and Orzech, 2013, Rogers et al., 2016],and provides multiple methods for sea-ice induced wave-dissipation [Rogers and Orzech, 2013]. Thesemethodologies include (i) exponential wave energy decay as a function of ice coverage ( IC1), with user-specified exponential decay; (ii) wave-dissipation due to turbulence at ice-water interface [IC2, Liu et al.,1991]; (iii) wave-dissipation through interaction with an ice continuum model which treats ice as viscoelasticlayer [IC3, Wang and Shen, 2010]; and (iv) exponential wave energy decay as a function of significant waveheight and wave frequency, as established from empirical fits to field measurements [ IC4, Collins andRogers, 2017]. The modified WW3 is validated against measurement of wave properties in the Beaufort and

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Chucki Seas [Thomson and Rogers, 2014, Thomson et al., 2016a, Rogers et al., 2016, Collins and Rogers,2017]. In addition to dissipation through interaction with ice, wave scattering occurs as well. Recent studieshave simulated wave scattering due to ice in the Arctic [e.g., Ardhuin et al., 2016], to be incorporated intothe WW3 code [Rogers et al., 2016].

To date, wave-ice interaction and associated dissipation is not incorporated into SWAN or other nearshorewave models. Previous efforts have involved: i) running SWAN in open water to provide incident waveconditions for use in simple, external calculations of wave transmission and losses, or ii) deactivating ice-covered grid points in SWAN by changing them to land points. In contrast, this work will focus on physicsbased wave-ice modeling. Accurate wave-ice interaction modeling in SWAN will allow coupling with theSea-Ice model and ROMS circulation model.

Table 2: IC4 variations for wave-ice interactions.

M1 Exponential fit to the field observationsM2 Provide coefficients of a 4 degree polynomial to rep-

resent wave attenuation as a function of frequencyM3 Represents wave attenuation as a quadratic equation

dependent on ice thickness and wave periodM4 M4 uses the same dataset as M2 to prescribe wave

attenuation as function of wave heightM5 Prescribe attenuation as step functions in frequency

space, optionally non-uniform in space and timeM6 Allows direct use of dissipation rates that have been

inverted from prior observational studies

Weighing the relative success of thefour approaches now used in WW3(i.e., IC1-IC4), dissipation of wave en-ergy by sea ice will be incorporated inSWAN only using IC1 and IC4 method-ologies. These methods provide an op-timal combination of simplicity, robust-ness, and flexibility. For IC1, a horizon-tally varying exponential decay constantwill be a user-specified quantity. IC1is relatively simplistic, being uniform infrequency space. The IC4 methodologyoffers high flexibility: it is subdivided toprovide 6 different options representedby M# [Collins and Rogers, 2017]. Methods M1, M2, M3, M5, and M6 are a function of the wavefrequency, while M4 like IC1 is uniform in frequency space, but dependent on the wave height. Eachmethod provides a different empirical approach to wave attenuation (Table. 2). The M2 and M6 method-ologies do not directly use ice thickness from the sea-ice model; however, inputs to these methods can bedetermined in the coupling layer using ice thickness. SWAN will be coupled to the Sea-Ice model such thathorizontally varying ice-thickness estimated by the latter is provided to SWAN through MCT. Finally, wavescattering due to ice will also be incorporated in SWAN following recent advancements in WW3 [Ardhuinet al., 2016].

The applicability of both IC1 and IC4 algorithms in SWAN will be validated against academic test cases[Rogers and Orzech, 2013], observations from publicly available deep water measurements [Rogers et al.,2016, Thomson et al., 2016b], with further calibration and validation from measurements obtained as a partof this study (see Table. 3).

Sea-Ice Model Development: Interaction with Waves and Ocean CirculationThe Sea-Ice model within COAWST consists of the elastic-viscous-plastic (EVP) rheology [Hunke andDukowicz, 1997, Hunke, 2001] and one-layer ice and snow thermodynamics with a molecular sublayerunder the ice [Mellor and Kantha, 1989]. This model solves for ice velocity due to the Coriolis force,surface ocean tilt, air and water stress, and internal ice stresses. The surface wave stresses on the ice[e.g., Steele et al., 1989b, Perrie and Hu, 1997] are not yet included, and these will be implemented asa part of this work. The internal ice stresses are determined through the EVP rheology. Ice velocities controlice advection, thickness and concentration, snow thickness, internal ice temperature, and surface melt pond.

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Finally, the thermodynamic model estimates the ice growth and melt on surface, bottom and side of ice floes,as well as frazil ice formation [Mellor and Kantha, 1989, Steele et al., 1989a].

Interaction between ROMS and Sea-Ice model already exists and involves (i) providing flow velocityfrom ROMS to Sea-Ice model which are used to estimate water stresses required to solve for the ice velocity;and (ii) surface stress at the ice-water interface from the Sea-Ice model to ROMS. With interaction of wavesand ice, it is expected that the ice velocity will change, thus modifying the stress at the ice-water interface.ROMS Development: Interaction with Waves and Sea-Ice ModelWithin the COAWST framework, ROMS is already coupled to both SWAN [Kumar et al., 2012, 2015] andthe Sea-Ice [Budgell, 2005] models. Here, the additional interaction between SWAN and Sea-Ice modelwill change the the existing coupling to ocean circulation model. In particular, dissipation of surface wavesthrough interaction with ice will induce additional stresses leading to changes in oceanic flows. Here, thiseffect will be added into ROMS as an additional term in the Reynolds Averaged Navier Stokes Equation[e.g., Kumar et al., 2012]. The modification to ROMS flow velocities has implications for the Sea-Icemodel as well. In particular, the ROMS flow velocities determine the water stresses acting on ice, and thusmodify the ice velocity. Further, surface wave dissipation will be added as a source term to temperatureevolution, in orded to account for wave breaking induced heating [Sinnett and Feddersen, 2014].

Analysis and SynthesisThe field observations and model development described above will be brought together to evaluate thethree hypotheses (H1, H2, H3). Each hypothesis can be independently tested using the field observationsover the range of observed conditions. This limited experimental space will be expanded using the newSWAN/COAWST model to run a 20 year hindcast for the entire northern coast of Alaska. The modelhindcast will use (i) pre-existing WW3 model results as the boundary condition providing incident surfacewave conditions from deep water, as conventionally done in many other coastal modeling efforts [Thomsonet al., 2016a]; (ii) initial and boundary conditions for circulation pattern, temperature, salinity and sea-surface elevation from public domain Pan-Arctic regional ocean model hindcast [up to 500 m resolution,Hedstrom et al., 2016]; (iii) combination of publicly available Nimbus 7SMMR/SSM/Land DMSP SSMIPassive Microwave Sea Ice concentration (25 ⇥ 25 km), Interactive Multisensor Snow and Ice MappingSystem (4 ⇥ 4 km), and MODIS imagery (250 ⇥ 250 m); (iv) North American Regional Analysis wind andatmospheric fluxes; and (v) bathymetry product developed from combination of GEBCO, NOAA CoastalRelief Model, and historic and recently archived USGS bathymetric products. The following analysis willuse both the new field observations, and the full coastal hindcast. For each, the key parameter is the temporaland spatial distribution of the wave energy flux incident to the coast (Eq. 1).

The modeled 20 year hindcast will provide an extensive database to evaluate trends in (a) wave energyflux normal to the shoreline (F , Eq. 1); (b) dominant frequency ( f ) and direction (q ) of wave propagation(indicating shift in offshore wave generation location); and (c) gradient of radiation stress (d(F sinq/c)/dx,responsible for wave set up and alongshore currents. Furthermore, along coast variability of these waverelated quantities will identify locations which have experienced extensive change in local wave activitywith modified ice coverage and temperature variability in the Arctic.

The timeline for the proposed work is presented in Table 3. The plan is to conduct the field worksimultaneously with the model development work, such that the validation data are ready when the modeldevelopment is complete.

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Wave-ice interactions (Assessing H1)

The field measurements will provide detailed observation of cross-shore wave dissipation and scattering,which will determine the role of near coast sea ice in transforming the waves on seasonal and shorter timescales. It is expected that the observed wave transformation will be sensitive to the particular ice coverand ice type (as observed by satellite imagery and using the visual ASPeCT protocols during the Sikuliaqcruises).

The rich dataset will calibrate the COAWST model setup with wave-ice interaction to accurately repre-sent wave action cross-shore variability. Figure 10 shows an example of wave attenuation for various IC4routines as a function of distance propagated into an ice-covered region. The calibration effort will deter-mine which ‘IC’ is most accurate for SWAN applied to the Arctic coast. The calibrated model will be usedto conduct a 20 year model hindcast, in order to test the extent to which sea ice protects Arctic coasts.

Figure 10: Significant wave height as a function of distanceinto the ice. Top plot y-axis is linear scale, bottom plot y-axis is on a log scale. Collins and Rogers [2017].

The extent to which sea ice cover and typecontrols the amount of wave energy reaching theshoreline will be evaluated by identifying thekey processes which control wave propagationinto the coastal regions. This task will distin-guish between wave scattering and wave dissipa-tion mechanisms (i.e., conservative versus non-conservative wave-ice interactions). In particu-lar, the new data set will capture both the com-plete wave evolution from offshore to the shore-line, as well as the turbulent dissipation resultingfrom wave-ice interaction and wave-breaking.The local dissipation rates e will be measured us-ing the SWIFTs during the cruise sampling. The regional dissipation rates e will be measured from themoorings by calculating the gradient of shoreward wave energy flux over the wave layer zw,

e =1zw

∂F∂x

. (2)

Wave scattering will be measured via changes in the directional distribution of waves propagating throughthe domain (as estimated from the AWACs and SWIFTs). The dataset will also provide insights into localwave generation by coastal winds, as well as interaction between surface waves and nearshore circulationpattern.

Wave energy effects (Assessing H2)The extent to which increasing wave energy forces erosion of barrier systems will be evaluated from both amechanical and thermal standpoint using the equilibrium shoreline concept and estimates of nearshore heat-ing from wave dissipation in the surfzone, respectively. The equilibrium framework is a highly simplifiedapproach intended to capture broad patterns of cross-shore shoreline changes in response to wave forcing.Wave-dominated sandy shorelines in lower-latitudes are often described using the coastal equilibrium con-cept, wherein the shoreline position S changes at a rate [e.g., Yates et al., 2009]

∂S/∂ t ⇡ E1/2DE, (3)

where DE = Eeq �E is the difference between a given wave energy E and the climatological equilibriumwave energy Eeq of that shoreline. Here, changes in wave energy E from the new coastal 20-year hindcast

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will be tested against existing published values of shoreline retreat ∂S/∂ t. The upper panel of Figure 11shows a preliminary test of this framework, using the existing basin-wide WW3 hindcast for wave energyE. The signal is broadly consistent with observed erosion rates of a few m/yr, despite the poor quality of theexisting hindcast E values for coastal applications.

Figure 11: Preliminary rates of shoreline ero-sion from equilibrium wave exceedance (top)and nearshore heating from wave dissipation(bottom).

One previously ignored mechanism for Arctic coastalerosion is heating due to wave dissipation. Shoreward surfacewave energy flux is dissipated in the surfzone through turbu-lence and eventually heat [Sinnett and Feddersen, 2014]. Thisheat flux into the wave-driven layer very near the shoreline is

DH ⇡Z

edz = ∂Ecg/∂x. (4)

The existing basin-wide WW3 hindcast of wave energy fluxEcg allows for estimation of heating from breaking wavesnear shore (Figure 11, bottom). There is an increasing trend,with enough additional energy to adiabatically heat the wave-driven layer roughly 0.02�C hr�1. During storms, the wave-heating contribution can be much larger. The temperature ofthe wave-driven near-surface layer equilibrates with the sur-rounding nearshore water and sediment. Increased heating

through this process (due to increased wave activity) is an additional factor which may accelerate erosion ofice-bound sediments in Arctic coastal systems. Nearshore wave and temperature observations, along withmodeled DH will inform the (as yet unknown) the importance of this mechanism to coastal erosion.

Wave momentum effects (Assessing H3)Gradients in wave momentum flux, or radiation stress, drive changes in cross-shore water levels (i.e., coastalflooding from wave set-up) and alongshore currents. Wave setup can be evaluated as a slope in the still waterlevel, h ,

dhdx

=� 1rgh

ddx

(F/c) , (5)

where r is density, g is gravity, and h is water depth. With modeled or observed estimates of F/c atthe coast, this formulation has an analytic solution for the profile of wave setup that will be evaluated fortrends and events. Similarly, alongshore gradients, d(F sinq/c)/dx, can drive circulations. The COAWSTcirculation module will provide a detailed picture of the role sea-ice and wave dissipation play in changingthe nearshore circulation pattern. These alongshore circulation assessments will compliment and extend thesimplistic cross-shore analysis using the equilibrium wave energy formulation.

Broader Impacts (of this Proposal)This work will directly improve the physical understanding of wave-ice-ocean systems and the impactchanging climatology has on Arctic coastal waters. The enhancements to an existing coupled modelingsystem (the open-source COAWST system) will be readily available to the community, for both researchand operational usage. One clear application will be in prescribing forcing parameters for past and futurecoastal erosion in the Arctic.

Additional broader impacts will include outreach and education. The proposed team will participatein the Polar Science Weekend at the Pacific Science Center in Seattle. This annual outreach event attracts

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Table 3: Proposed timeline and tasks.

Timeline Field ModelAutumn 2018 Planning Add IC1 method to SWANWinter 2019 Field prep and logistics Validate IC1 with existing data (deep water)Spring 2019 Meet with Waterways Committee Add IC4 to SWANSummer 2019 Field prep and logistics Add IC4 to SWANAutumn 2019 Sikuliaq cruise # 1 (mooring deploy) Validate IC4 with existing data (deep water)Winter 2020 Process cruise data Couple SWAN to ROMS and Sea-Ice modelsSpring 2020 Meet with Waterways Committee Validate coupled setup with cruise #1 dataSummer 2020 Process satellite data Develop 20-year hindcastAutumn 2020 Sikuliaq cruise # 2 (mooring recover) Evaluate wave-ice interactions (H1)Winter 2021 Evaluate wave energy effects (H2)Spring 2021 Evaluate wave momentum effects (H2)Summer 2021 Journal articles with results, including future scenariosProject Submit all field data and 20-yr model hindcast to NSF Arctic Data Centercompletion Add ice routines in official SWAN/COAWST release

over 10,000 visitors, who engage with researchers via display booths and demonstrations. PI Thomsonhas participated in this events for several years and will develop a new interactive exhibit related to thisproject. This exhibit also will be used at another annual outreach event: the University of Washington“Discovery Days”, which brings over 5,000 elementary and secondary school children to campus to learnabout ongoing research in science and engineering. Further outreach will be conducted in the numerouspublic seminars that PI Thomson and co-PI Kumar regularly conduct, such as annual town hall meetings inBarrow (AK) and the Sea Grant “Strait Science” series in Nome (AK). Another broader impact will be thetraining of postdoctoral researcher, who will participate in all aspects of the project (i.e., field work, modeldevelopment, climatology/data analysis).

Results From Prior NSF SupportThomson has had three prior NSF awards as lead PI and several other prior NSF awards as a co-PI. Themost relevant award for Thomson is OCE-1332719: “Phase-resolved wave breaking dissipation” ($708,615,October 2013 to September 2018). This project concerns the breaking ocean surface waves in deep water.A field experiment was conducted at Station P in the North Pacific during the winter of 2015. The metadataand data collected under this award are available from the data tab at www.apl.uw.edu/swift, and the results(to date) under this award have been disseminated in the following four papers:

• Schwendeman, M. and J. Thomson, Evidence of Stokes’ Limiting Steepness in Breaking Waves froma Ship-Based Stereo Video System, J. Phys. Oceanog., 47 (2017).

• Thomson, J., M. Schwendeman, S. Zippel, S. Moghimi, J. Gemmrich, E. Rogers, Turbulence in theocean surface layer, J. Phys. Oceanog., 46 (2016).

• Schwendeman, M. and J. Thomson, Observations of whitecap coverage and the relation to wind stress,wave slope, and turbulent dissipation, J. Geophys. Res., 120 (2015).

• Schwendeman, M. and J. Thomson, A Horizon-Tracking Method for Shipboard Video Stabilizationand Rectification, J. Atmos. & Ocean. Tech., 32, (2015).

Addition products from this prior support include two publicly available software libraries:• Stabilization of shipboard images using horizon tracking:

http://www.mathworks.com/matlabcentral/fileexchange/47606-horizon-stabilization

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• Stereo image processing for wave surface reconstruction:https://github.com/mikeschwendy/ShipBasedStereoVideo

Many of the wave measurement techniques developed under prior support are relevant to the proposed work.The stereo methods and SWIFT buoys, in particular, will aid in the proposed research.

Kumar has one prior NSF awards as lead PI, OCE-173546:“Transient Rip Current Dynamics: LaboratoryMeasurements and Modeling of Surfzone Vorticity” ($716,960, September 2017 to August 2020). Data col-lection for this award will occur in autumn 2018. Primary modeling and data comparison will be presentedin the upcoming ocean sciences meeting.

• Baker, Moulton, Elgar, Raubenheimer, and Kumar. “Rip-current driven cross-shore exchange dynam-ics on a natural barred beach”, CD14B-0042, Ocean Sciences Meeting, 2018.

Intellectual Merit (from Prior Support)Thomson: The central merit of OCE-1332719 is the direct observation of individual breaking waves at theocean surface. These observations of individual waves have expanded understanding of the wave breakingprocess beyond the statistical/ensemble approaches that are commonly used in the field. First, the applica-tion of a new shipboard stereo wave system has shown that breaking waves have a rapid and highly localizedsteepening at the crest just before breaking (which has not be observable with buoys). Second, the appli-cation of new autonomous SWIFT platforms has shown that the turbulence generated by individual waveslasts at least a full wave period, such that strong turbulence is carried down from the crest level to the troughlevel.Kumar: OCE-173546 -The hypothesis that eddies in the surf zone behave as a forced two-dimensionalturbulence system has not been tested. Laboratory experiments with known wave forcing conditions providea controlled framework with which to investigate this process. The lab experiments will investigate if short-crested waves are an important source of vorticity at short spatial and temporal scales, do smaller eddiescoalesce to create bigger eddies, mechanisms for decay of surfzone eddies, hypothesized to be controlledby bottom friction, do eddies ejected outside of the surf zone onto the sloping shelf deviate from the two-dimensional turbulence assumption will be investigated.

Broader Impacts (from Prior Support)Thomson: The impacts of OCE-1332719 include parametrizations for wave breaking in models, software li-braries for general application in imaging the ocean surface, training of a graduate student (Michael Schwen-deman, PhD, August 2016), and outreach efforts. PI Thomson gave several public seminars under priorsupport, including at local elementary schools and the Pacific Science Center. PI Thomson also appearedtwice on the “The Bait” (part of the Deadliest Catch franchise) to discuss wave research with fishermen.

Another impact under prior support is the continuation of a long-term wave record at Ocean Station P.Thomson has maintained a Datawell Waverider mooring at the site since 2010, and it was serviced duringthe winter 2015 experiment. These continual wave measurements are valuable context for the recently addedOOI moorings (which have no surface measurements). The Waverider data goes directly to NDBC (station46246) and to CDIP (station 166) so that it is available to other researchers and to mariners in near-realtime.Kumar: OCE-173546 will provide significant advances toward a comprehensive understanding of surfzoneeddy generation, the dynamics of transient rip currents, and transient rip current driven cross-shelf exchange.The laboratory measurements and modeling frameworks will be available to the larger community. Theproposed work also will include collaboration with NOAA NWS and NOS scientists to develop predictorsof transient rip currents to improve hazard forecasts for a broad range of environments. A graduate studentwill be trained and will participate in all aspects of the project.

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Data Management Plan

The proposed research will generate oceanographic measurement data, specifically:

• Time series data of waves, currents, and temperature from several fixed locations.

• Drifter data of waves, currents, and temperature.

• Shipboard stereo wave data and bathymetry maps

• aerial (drone) images of sea ice

Observational Dataset: Data will be recorded locally in the field, then backed up to an offsite server.The data collection and meta-data will be documented in daily operations logs and cruise reports. Thedata will be archived using a local server at APL-UW with RAID (Redundant Array of Independent Disks)storage. Upon successful processing of the data, the data products will be publicly available for downloadvia a project page on the APL-UW website and post to the NSF Arctic Data Center (which is the primarydata and software repository for NSF Arctic research). This is expected to take two years. There will be norestrictions on the use of these data.

Model Results: Model application will generate results for validation against field measurements, andapplication of the modeling framework for the Northern Alaskan coast. Model results will be stored in theLolo Archival Storage of University of Washington, along with backup RAID storage. Upon successfulcompletion of model development and verification with observations, these model results will be publiclyavailable for download through a File Transfer Protocol (FTP) and THREDDS data server linked to theproject page on the APL-UW website. Model output will also be archived at the NSF Arctic Data Center.There will be no restrictions on the use of these model results.

Data Format: Time series of waves, current, temperature, drifter data, shipboard stereo wave data, andbathymetry maps will be available both in MATLAB (.mat) and ASCII (.asc, .txt) format. Model resultswill be primarily stored in NETCDF (.nc) format. It is expected that total data size from observations willbe ⇡ 5 TB, and modeled products will be up to ⇡ 10 TB.

Source Code: An additional product will be the source code for wave-ice-ocean interactions in theSWAN, SEA-ICE and ROMS models, which are component of the COAWST modeling system. This codewill be included in the open-source repository maintained for COAWST. The methodology to conduct wave-ice-ocean interactions in COAWST will be added to the COAWST manual.

The final product of this research will be the presentation of results in journal articles and conferenceproceedings. Open-access journals with permanent archives will be used.

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