Journal of Marine Science and Engineering

Article Numerical Analysis of Storm Surges on Canada’s Western Arctic Coastline

Joseph Kim 1,2,* , Enda Murphy 1,2 , Ioan Nistor 1, Sean Ferguson 2 and Mitchel Provan 2

1 Department of Civil Engineering, University of Ottawa, Ottawa, ON K1N 6N5, Canada; [email protected] (E.M.); [email protected] (I.N.) 2 Ocean, Coastal and River Engineering Research Centre, National Research Council Canada, Ottawa, ON K1A 0R6, Canada; [email protected] (S.F.); [email protected] (M.P.) * Correspondence: [email protected]

Abstract: A numerical study was conducted to characterize the probability and intensity of storm surge hazards in Canada’s western Arctic. The utility of the European Centre for Medium-Range Weather Forecasts Reanalysis 5th Generation (ERA5) dataset to force numerical simulations of storm surges was explored. Fifty historical storm surge events that were captured on a tide gauge near Tuktoyaktuk, Northwest Territories, were simulated using a two-dimensional (depth-averaged) hydrodynamic model accounting for the influence of sea ice on air-sea momentum transfer. The extent of sea ice and the duration of the ice season has been reducing in the Arctic region, which may contribute to increasing risk from storm surge-driven hazards. Comparisons between winter storm events under present-day ice concentrations and future open-water scenarios revealed that the decline in ice cover has potential to result in storm surges that are up to three times higher. The numerical model was also used to hindcast a significant surge event that was not recorded by



the tide gauge, but for which driftwood lines along the coast provided insights to the high-water
 marks. Compared to measurements at proximate meteorological stations, the ERA5 reanalysis

Citation: Kim, J.; Murphy, E.; Nistor, dataset provided reasonable estimates of atmospheric pressure but did not accurately capture peak I.; Ferguson, S.; Provan, M. Numerical wind speeds during storm surge events. By adjusting the wind drag coefficients to compensate, Analysis of Storm Surges on Canada’s reasonably accurate predictions of storm surges were attained for most of the simulated events. The Western Arctic Coastline. J. Mar. Sci. extreme value probability distributions (i.e., return periods and values) of the storm surges were Eng. 2021, 9, 326. https://doi.org/ significantly altered when events absent from the tide gauge record were included in the frequency 10.3390/jmse9030326 analysis, demonstrating the value of non-conventional data sources, such as driftwood line surveys, in supporting coastal hazard assessments in remote regions. Academic Editor: David Didier Keywords: storm surge; Arctic; flood; sea ice; coastal hazards; climate change; driftwood; reanaly- Received: 9 February 2021 sis; hydrodynamics Accepted: 14 March 2021 Published: 16 March 2021

Publisher’s Note: MDPI stays neutral 1. Introduction with regard to jurisdictional claims in published maps and institutional affil- Extreme weather events along Canada’s Arctic seaboard can generate storm surges, iations. defined as changes in water levels associated with atmospheric pressure and wind effects, which have led to flooding of coastal communities. Numerical modelling of storm surges in the Arctic Ocean has generally been scarce due to the lack of measurement data and the unique challenges that arise when modelling wave–ice interactions [1]. Additionally, the Arctic is greatly affected by climate change impacts [2]. The typical open-water season Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. of June to October along the north coast of Canada is predicted to lengthen to a season This article is an open access article spanning April to December by 2100 [3]. Ignoring potential climate change influences distributed under the terms and on storminess, which are highly uncertain [1], winter storm surges that would in the conditions of the Creative Commons past have been attenuated by sea ice cover have the potential to become more severe in Attribution (CC BY) license (https:// the future. Other projected climate change effects in the Beaufort Sea include relative creativecommons.org/licenses/by/ sea-level rise, thawing permafrost, increasingly extreme wave climate, and accelerating 4.0/). coastal erosion [4], which will compound declining sea ice cover and hence exacerbate the

J. Mar. Sci. Eng. 2021, 9, 326. https://doi.org/10.3390/jmse9030326 https://www.mdpi.com/journal/jmse J. Mar. Sci. Eng. 2021, 9, 326 2 of 20

existing hazards and vulnerabilities associated with extreme storm surges. In addition to the changes in ice conditions, projections indicate that relative sea level may rise by around 1 m by 2100 (95% confidence limit relative to 1986–2005) under the RCP8.5 scenario for the Tuktoyaktuk region [5]. The effects of relative sea-level rise on coastal hazards are intensified by the increasing permafrost temperatures [6], which can contribute to erosion and coastline retreat [7]. These climate change impacts are expected to increase the exposure of coastal communities and maritime infrastructure to storm surge hazards, exacerbating existing vulnerabilities, and causing a pressing need to study and understand the development of storm surges in the Arctic [6]. During the 1970s and 1980s, a series of studies were initiated by the Beaufort Sea Project focused on assessing storm surge hazards in the Canadian Arctic. These early studies characterized the weather patterns in the Beaufort Sea and the development of nu- merical storm surge models forced by surface winds calculated from estimated geostrophic winds [8–11], with spatial resolutions of roughly 20 km. During that time period, validation of storm surge models was not possible due to the lack of continuous, multi-year tide gauge records. Kowalik [12] simulated three storm surge events in the Beaufort and Chukchi Seas that occurred during ice-free periods using interpolated surface winds from 6-hourly calculations of geostrophic winds. High-resolution climatic forcing data have since become available in the form of global reanalysis datasets, which combine models with observations. Of particular interest is the ERA5 reanalysis [13], recently released by the European Centre for Medium-Range Weather Forecasts (ECMWF), and superseding ERA-Interim [14]. ERA5 promises a higher spatial and temporal resolution and improved storm physics compared to ERA-Interim and other global reanalyses, with direct implications for application to storm surge modelling. While there have been some studies that assess the performance of ERA5 in approximating surface winds and pressures [15–17], these studies did not investigate, in detail, ERA5’s performance in the Arctic region. The spatial coverage of weather station observations to support the development of the reanalysis is limited in the Beaufort Sea region. Thus, the quality of ERA5 as the forcing input for Arctic storm surge models is still unknown. Field measurements or observations of storm surges are needed to enable develop- ment, calibration, and validation of predictive numerical models. For the Arctic region, there are few reliable, long-term, continuous tide gauge records, reflecting the sparsity of human settlement and the challenges for deployment, maintenance, and survival of instrumentation in this harsh environment. In the absence of conventional water-level records, evidence inferred from seasonal field survey campaigns provides an alternative means to gain insight into historical storm surges. In 1977, Reimnitz and Maurer [18] con- ducted field investigations along the coast of northern Alaska, USA, and their observations provided insight into a significant storm surge event that occurred on 13 September 1970. Reimnitz and Maurer [18] deduced sea-level elevations associated with the 1970 event from a field survey of driftwood debris lines deposited by the storm. Similarly, Harper et al. [19] surveyed driftwood debris lines at Tuktoyaktuk, on Canada’s Beaufort Sea coast, and inferred estimates of the maximum storm surge elevations for the area. In addition, the survey produced detailed storm surge elevation estimates for an event that occurred on 23 August 1986. Records from a tide gauge installed at Tuktoyaktuk in 1961, though affected by several gaps and periods when the gauge was not in operation, suggest that storm surge elevations have not since exceeded the values inferred by Harper et al. [19]. Accurate simulation of storm surges in Arctic regions can be challenging owing to the presence and dynamics of sea ice, which can amplify or attenuate storm surges [20]. Kowalik’s [12] model incorporated coupled interactions between ice and water. However, the model’s performance in capturing the influence of ice on storm surges was not tested, as only events coinciding with ice-free conditions were simulated. Danard et al. [21] simulated eight storm surge events in the Beaufort Sea region that occurred during the months of August, September, and October in the 1970s and 1980s. These events coincided with low ice concentrations and an absence of fast ice. The study showed some agreement between J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 3 of 21

simulated eight storm surge events in the Beaufort Sea region that occurred during the months of August, September, and October in the 1970s and 1980s. These events coincided with low ice concentrations and an absence of fast ice. The study showed some agreement between the measured and modelled storm surges, but the timing of the modelled peak J. Mar. Sci. Eng. 2021, 9, 326 storm surge water level was delayed by up to 14 h. The authors noted that the3 of 20large grid size (18 km) and a lack of sufficient spin-up time were limitations to their study. Additionally, the work by Danard et al. [21] was not verified for winter events with high theice measuredconcentrations. and modelled Joyce et storm al. [22] surges, demonstrated but the timing the of performance the modelled peakof a new storm set surge of formulas water level was delayed by up to 14 h. The authors noted that the large grid size (18 km) that improve on the work of Chapman et al. [23] to account for the influence of ice cover and a lack of sufficient spin-up time were limitations to their study. Additionally, the on storm surges on the western Alaskan coast. Four simulated storm surge events yielded work by Danard et al. [21] was not verified for winter events with high ice concentrations. Joyceroot-mean et al. [-22square] demonstrated errors in thetotal performance water levels of of a new less setthan of formulas20 cm. that improve on the workIn this of Chapman study, the et al.utility [23] to of account the ERA5 for the reanalysis influence dataset of ice cover as atmospheric on storm surges forcing on input thefor western storm surgeAlaskan modelling coast. Four in simulated Canada’s storm western surge events Arctic yielded region root-mean-square was examined. A two- errorsdimensional in total water(depth levels-averaged) of less than hydrodynamic 20 cm. model of the region was developed using the In TELEMAC this study,- the2D utility (v7p3r1) of the ERA5 shallow reanalysis water dataset (Saint as-Venant) atmospheric equations forcing input solver [24]. forCustomized storm surge subroutines modelling inwere Canada’s employed western to incorporate Arctic region the was impacts examined. of sea A ice two- on storm dimensionalsurges, using (depth-averaged) the formulation hydrodynamic of Joyce et model al. [22]. of the Model region performance was developed was using evaluated the by TELEMAC-2D (v7p3r1) shallow water (Saint-Venant) equations solver [24]. Customized simulating fifty historical storm surge events that spanned a range of sea ice conditions, subroutines were employed to incorporate the impacts of sea ice on storm surges, using theranging formulation from full of Joyce ice cover et al. [ to22 ]. open Model-water performance conditions. was Subsequently, evaluated by simulating a hindcast of the fiftysignificant historical storm storm surge surge event events that that occurred spanned on a range 23 August of sea ice1986 conditions, was conducted, ranging and the fromresults full were ice cover compared to open-water to peak conditions. storm surges Subsequently, inferred from a hindcast driftwood of the significantlines surveyed by stormHarper surge et al.event [19] that more occurred than on 30 23 years August ago. 1986 The was comparison conducted, and provided the results insight were into the comparedveracity to and peak potential storm surges utility inferred of storm from driftwood surge records lines surveyed derived by fromHarper non et al.-conventional [19] moreobservation than 30 yearsmethods. ago. TheTo the comparison knowledge provided of the insight authors, into this the is veracity the first and attempt potential to conduct utilitya large of-scale storm numerical surge records investigation derived from of storm non-conventional surge hazards observation in the Beaufort methods. Sea To since the the knowledge of the authors, this is the first attempt to conduct a large-scale numerical work of Danard et al. [21]. Significant advances in atmospheric datasets and investigation of storm surge hazards in the Beaufort Sea since the work of Danard et al. [21]. Significantcomputational advances hydrodynamic in atmospheric models datasets have and occurred computational in the three hydrodynamic decades since, models which are haveleveraged occurred in in this the three study decades to provide since, which an improved are leveraged understanding in this study to of provide the probabilityan improveddistribution understanding of extreme storm of the probabilitysurges in the distribution Beaufort ofSea. extreme storm surges in the Beaufort Sea. 2. Methodology 2. Methodology An overview of the study methodology is shown in Figure 1. Each step is described An overview of the study methodology is shown in Figure1. Each step is described in further detail in the proceeding sections—the study area and computational domain is in further detail in the proceeding sections—the study area and computational domain isdefined defined in in Section Section 3; the the analysis analysis ofof tidetide gaugegauge records records is is presented presented in in Section Section4; the 4; the model modelsetup setup is described is described in in Section Section 5 5;; andand the the model model calibration, calibration, validation, validation and simulation, and simulation resultsresults are are shown shown in in Section Section6. 6.

FigureFigure 1. 1.Overview Overview of of the the study study methodology. methodology.

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 4 of 21

J. Mar. Sci.3. Eng. Study2021, 9 ,Area 326 4 of 20 The hydrodynamic model’s computational domain (Figure 2) was selected to encompass the approximate3. Study Area historical maximum extent of open water (i.e., coinciding with the minimum sea Theice hydrodynamiccover). As the model’s storm computational surge simulations domain (Figureperformed2) was in selected this study to encom- span a range of historicalpass the approximateice cover extents, historical this maximum methodology extent of openensures water sufficient (i.e., coinciding fetch with to the capture the developmentminimum of sea storm ice cover). surges As the due storm to surge wind simulations shear stresses performed and in barometricthis study span a range of historical ice cover extents, this methodology ensures sufficient fetch to capture pressure fluctuationsthe developmentassociated with of storm the surges passage due to of wind storm shear systems stresses andthrough barometric the pressureBeaufort fluctu- Sea. Monthly ice recordsations associated for the Beaufort with the passage Sea during of storm the systems period through of 1979 the Beaufort to 2019 Sea. from Monthly the ice U.S. National Snowrecords and for Ice the DataBeaufort Centre Sea during (NSIDC) the period [25] of 1979 were to 2019 used from to the determine U.S. National the Snow minimum ice extentand that Ice Dataoccurred Centre during (NSIDC) that [25] weretime usedperiod. to determine The computational the minimum icedomain extent that spans roughly 1000occurred km in the during east that–west time direction period. The and computational 800 km in domain the north spans–south roughly direction, 1000 km in the east–west direction and 800 km in the north–south direction, and touches multiple coastal and touches multiplecommunities coastal communities adjacent to the Beaufortadjacent Sea to and the Amundsen Beaufort Gulf. Sea Theand domain Amundsen extends to Gulf. The domain extendsoffshore water to offshore depths of wa moreter thandepths 3700 of m. more than 3700 m.

FigureFigure 2. Hydrodynamic 2. Hydrodynami modelc computationalmodel computational domain overlaid domain over overlaid the minimum over seathe ice minimum extent during sea theice periodextent of 1979–2019.during Mapthe period data from of Google 1979– [201926]. . Map data from Google [26].

4. Tide Gauge Analysis 4. Tide Gauge Analysis Water-level measurements were available for several tide gauges in the region oper- Water-level measurementsated by the Canadian were Hydrographic available Service for several (CHS) of tide Fisheries gauges and Oceans in the Canada, region with operated by the Canadiansome records Hydrographic starting in the 1960s.Service Many (CHS) of the of tide Fisheries gauge records and Oceans are associated Canada, with tem- with some recordsporary starting monitoring in the stations,1960s. Many and there of arethe few tide continuous, gauge records long-term are (i.e., associated multi-decadal) water-level records at locations in the Beaufort Sea. Of the 42 tide gauges in the numerical with temporary monitoringmodel domain, stations, the station and atthere Tuktoyaktuk are few (CHScontinuous, gauge 6485) long is- theterm only (i.e. permanent, multi- tide decadal) water-levelgauge records and provides at locations the longest-standing in the Beaufort and Sea. most Of complete the 42 record tide gauges of water in levels. the The numerical model Tuktoyaktuk domain, the gauge station was therefore at Tuktoyaktuk used as the primary (CHS source gauge of water-level6485) is the data onlyfor model permanent tide gaugecalibration and andprovides validation. the Thelongest water-level-standing records and are most referenced complete vertically record to local of chart datum (CD). Based on the tide gauge records between August 2008 and February 2018, CD water levels. The Tuktoyaktuk gauge was therefore used as the primary source of water- is approximately 0.454 m below the mean sea level (MSL) at the Tuktoyaktuk gauge for the level data for modelmean calibration epoch of December and validation. 2010. All total The water water levels-level and elevationsrecords are reported referenced in this paper vertically to local chartare referenced datum (CD). vertically Based to local on the CD attide Tuktoyaktuk. gauge records between August 2008 and February 2018, CD is approximately 0.454 m below the mean sea level (MSL) at the Tuktoyaktuk gauge for the mean epoch of December 2010. All total water levels and elevations reported in this paper are referenced vertically to local CD at Tuktoyaktuk. Following the procedures outlined in Foreman et al. [27], astronomical tidal constituents for the Tuktoyaktuk station provided by CHS were used to develop a continuous time series of tide predictions for the period 1961–2019. The predicted tidal elevations were subtracted from the total water-level measurements for the Tuktoyaktuk

J. Mar. Sci. Eng. 2021, 9, 326 5 of 20

Following the procedures outlined in Foreman et al. [27], astronomical tidal con- stituents for the Tuktoyaktuk station provided by CHS were used to develop a continuous J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 5 of 21 time series of tide predictions for the period 1961–2019. The predicted tidal elevations were subtracted from the total water-level measurements for the Tuktoyaktuk gauge, to obtain a time series of water-level residuals. The residuals were assumed to be representative gauge,of storm to obtain surges a arisingtime series from of water wind-level and residuals. atmospheric The residuals pressure were set-up assumed or set-down. to be This representativeassumption may of storm be a sourcesurges arising of uncertainty, from wind in and case atmospheric the water level pressure records set-up are or contaminated set- down.by wave This effects, assumption which may are be projected a source of to uncertainty, become more in case frequent the water and level intense records in are the Arctic contaminated by wave effects, which are projected to become more frequent and intense region [28,29]. The water-level residual record was linearly de-trended to remove the in the Arctic region [28,29]. The water-level residual record was linearly de-trended to removelong-term the trendlong-term associated trend associated with relative with relative sea-level sea rise.-level rise. FigureFigure 33a,ba,b show show the the measured measured water water levels levels and the and predicted the predicted tidal elevations, tidal elevations, while while Figure 3c shows shows the the residuals residuals calculated calculated from from the de the-trended de-trended sea-level sea-level elevations. elevations.

Figure 3. Station 6485, Tuktoyaktuk: (a) the measured water levels; (b) the predicted tides; (c) the Figure 3. Station 6485, Tuktoyaktuk: (a) the measured water levels; (b) the predicted tides; (c) the water-level residuals. water-level residuals. Identification of Storm Surge Events and Extreme Value Analysis Identification of Storm Surge Events and Extreme Value Analysis A peaks-over threshold (POT) approach [30] was applied to identify extreme storm A peaks-over threshold (POT) approach [30] was applied to identify extreme storm surge events. events. The The analysis analysis was was conducted conducted using using the Wave the Wave Analysis Analysis and Fatigue and Fatigueand and Oceanography (WAFO) (WAFO) toolbox toolbox [31]. [31 ]. Extreme Extreme values values occurring occurring within within 2 days 2 days of each of each other wereother countedwere counted as a singleas a single event event to ensure to ensure event event independence, independence, a a prerequisite prerequisite for the extreme valueextreme analysis. value analysis. An initial An initial threshold threshold based based on the on the average average residual residual plus plus two two standard standarddeviations deviations was used was for used preliminary for preliminary screening screening and identification and identification of extreme of extreme events. A final eventsthreshold. A final value threshold of 0.96 m value was of selected 0.96 m using was selected the ‘mean using residual the ‘mean life’ plotting residual methodlife’ [32]. Aplot Generalizedting method Pareto [32]. A Distribution Generalized (GPD)Pareto Distribution was fit to the (GPD) identified was fit peak to the water identified level residuals to estimate return periods associated with storm surge events. Figure4 shows the GPD fit

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 6 of 21

J. Mar. Sci. Eng. 2021, 9, 326 6 of 20 peak water level residuals to estimate return periods associated with storm surge events. Figure 4 shows the GPD fit to the data with 95% confidence intervals. to the data with 95% confidence intervals.

FigureFigure 4. 4.Generalized Generalized Pareto Pareto Distribution Distribution (GPD) fit(GPD to the) fit positive to the water-level positive residualwater-level peaks residual peaks identifiedidentified within within the Tuktoyaktuk the Tuktoyaktuk tide gauge tide record gauge (Station record 6485). (Station 6485). Although the Tuktoyaktuk gauge is the most complete tide gauge in the studied area, there areAlthough long periods the where Tuktoyaktuk the gauge wasgauge not operationalis the most and complete was not abletide to gauge capture in the studied area, largethere surge are eventslong periods (Figure3). where In particular, the gauge based onwas interviews not operational with the local and residents was not able to capture of Tuktoyaktuk, two separate storm surge events on 1 September 1944 and 14 September large surge events (Figure 3). In particular, based on interviews with the local residents of 1970 were not captured. These events reportedly led to water levels of approximately 3 m (MSL)Tuktoyaktuk, [33]. These two estimates separate are likely storm subject surge to some events uncertainty, on 1 September as they were 1944 based and 14 September solely1970 onwere the witnessnot captured. reports and These recollections events of reportedly residents. Harper led etto al.water [19] inferred levels peakof approximately 3 m water(MSL) levels [33]. closer These to 2.5 estimates m MSL (2.95 are m likely CD) for subject both events to basedsome onuncertainty, field surveys ofas they were based driftwood deposits. If these estimates are accurate, they exceed all high water-level events eversolely captured on the in witness the Tuktoyaktuk reports tide and gauge recollections record (Table 1of). Harperresidents. et al. Harper [ 19] noted et that al. [19] inferred peak theirwater driftwood levels linecloser elevation to 2. observations5 m MSL (2.9 are bounded5 m CD) by afor±0.3 both m measurement events based error. on field surveys of driftwood deposits. If these estimates are accurate, they exceed all high water-level events Tableever 1. capturedThe ten highest in the water Tuktoyaktuk levels recorded attide the gauge Tuktoyaktuk record tide gauge(Table station 1). Harper and the two et al. [19] noted that highest water-level events inferred by Harper et al. [19] from the driftwood line field surveys (shown intheir bold text).driftwood line elevation observations are bounded by a ±0.3 m measurement error.

Table 1. TheRank ten highest water levels Daterecorded at the Tuktoyaktuk Water Level tide (m CD) gauge station and the two highest water1-level events inferred 1 September by Harper 1944 et al. [19] from the 2.95 driftwood line field surveys 1 14 September 1970 2.95 (shown in bold3 text). 4 October 1963 2.23 4 4 September 1962 2.15 5Rank 4 November 2017Date 2.02 Water Level (m CD) 6 1 September 2013 1.96 71 17 August01 2018September 1944 1.95 2.95 81 21 July14 2019 September 1970 1.94 2.95 9 1 September 2018 1.89 103 30 July 196304 October 1963 1.85 2.23 114 27 August04 2015September 1962 1.82 2.15 12 1 September 1962 1.78 5 04 November 2017 2.02 6 01 September 2013 1.96 7 17 August 2018 1.95 8 21 July 2019 1.94 9 01 September 2018 1.89 10 30 July 1963 1.85 11 27 August 2015 1.82 12 01 September 1962 1.78

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 7 of 21

J. Mar. Sci. Eng. 2021, 9, 326 7 of 20 To examine the effects of the 1944 and 1970 event omissions from the tide gauge record on the estimated storm surge probability distributions, the extreme value analysis was Torepeated examine with the effects the two of the events 1944 and added 1970 eventto the omissions record, frombased the on tide the gauge peak record water levels oninferred the estimated by Harper storm et al. surge [19]. probability As Harper distributions, et al.’s [19] study the extreme inferred value peak analysis water was levels from repeatedthe maximum with the elevation two events of added driftwood to the record, deposits, based the on the time peak and water astronomical levels inferred tidal stage by Harper et al. [19]. As Harper et al.’s [19] study inferred peak water levels from the associated with the peak water-level events in 1944 and 1970 could not be determined maximum elevation of driftwood deposits, the time and astronomical tidal stage associated withfromthe the peak surveys. water-level The uncertainty events in 1944 surrounding and 1970 couldthe tide not level be determined during the from 1944 the and 1970 surveys.peak water The-level uncertainty events surrounding compounds the the tide measurement level during the error 1944 in and estimating 1970 peak the water- storm surge levelcontribution events compounds to the total the measurement water level. error Separate in estimating extreme the value storm surge analyses contribution were therefore toconducted the total waterfor different level. Separate possible extreme storm valuesurge analyses magnitudes were thereforeassociated conducted with the for 1944 and different1970 events, possible taking storm surge into magnitudes account measurement associated with the error 1944 and and 1970 the events, range taking of predicted intoastronomical account measurement tide stages on error each and date. the range Nine of scenarios predicted were astronomical tested to tide examine stages onthe impact eachof including date. Nine the scenarios two peak were water tested levels to examinefrom driftwood the impact lines of includingin the extreme the two value peak analysis. water levels from driftwood lines in the extreme value analysis. The scenarios consisted The scenarios consisted of combinations of the survey error (i.e., +0.3 m, 0 m, and −0.3 m) of combinations of the survey error (i.e., +0.3 m, 0 m, and −0.3 m) and the predicted tide leveland the for thepredicted day (i.e., tide minimum, level for average, the day and (i.e. maximum, minimum, tide level)average, for theand two maximum events. For tide level) example,for the two the events. maximum For possible example, storm the surge maximum for the 1944 possible and 1970 storm event surge would for correspond the 1944 and 1970 toevent a +0.3 would m measurement correspond error to a and+0.3 them measurement minimum predicted error tideand levelthe minimum on that day. predicted The tide upperlevel on and that lower day. bounds The upper of the GPD and fitlower to storm bounds surge of events, the GPD after fit considering to storm allsurge possible events, after combinationsconsidering alland possible estimates combinations of the 1944 and and 1970 estimates events, are of shown the 1944 compared and 1970 to the events fit , are relyingshown onlycompared on tide to gauge the fit records relying in only Figure on 5tide. The gauge results records show in that Figure the inclusion 5. The results of show thesethat thetwo eventsinclusion in the of analysis these two significantly events alters in the the analysis inferred significantlyprobability distribution alters the of inferred extreme storm surges at Tuktoyaktuk. probability distribution of extreme storm surges at Tuktoyaktuk.

Figure 5. 5.GPD GPD fit fit to to the the peak peak water-level water-level residuals residuals with with and and without without (baseline) (baseline) different different possible possible stormstorm surge surge magnitudes magnitudes associated associate withd with the 1944 the and1944 1970 and events, 1970 events, inferred inferred from Harper from et Harper al.’s [19 ]et al.’s driftwood[19] driftwood surveys. surveys. The baseline (best fit) return value of the storm surge associated with the 1 in 100 year returnThe period baseline was 2.10 (best m, fit) accounting return value only forof thethe surgestorm events surge captured associated by thewith gauge the 1 in 100 (dottedyear return line in period Figure 5was). The 2.1 inferred0 m, accounting estimates of only the peakfor the storm surge surges events associated captured with by the the gauge 1944(dotted and line 1970 in storm Figure surge 5). events The inferred increased estimates the 1 in 100-year of the return peak valuestorm to surges between associated 2.51 m with (lowerthe 1944 bound) and 1970 and 3.50 storm m (upper surge bound).events increased The range the reflects 1 in uncertainty100-year return associated value with to between the2.51 measurement m (lower bound) errors and and 3.5 the0 timingm (upper of the bound). peak storm The range surge relativereflects touncertainty astronomical associated tides.with Thethe resultsmeasurement of this analysis errors demonstrate and the timing the potential of the significance peak storm of considering surge relative to non-conventional observation methods (such as driftwood surveys) to support estimates astronomical tides. The results of this analysis demonstrate the potential significance of considering non-conventional observation methods (such as driftwood surveys) to support estimates of the probability distribution of extreme storm surges in remote

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 8 of 21 J. Mar. Sci. Eng. 2021, 9, 326 8 of 20

ofregions the probability with limited distribution continuous, of extreme long-term storm water surges-level in records, remote with regions implications with limited for continuous,flood hazard long-term and risk assessment, water-level and records, coastal with engineering implications design. for flood hazard and risk assessment, and coastal engineering design. 5. Model Setup 5. Model Setup A hydrodynamic model developed in TELEMAC-2D [24] was used to simulate storm surgesA hydrodynamic in the Beaufort model Sea. TELEMAC developed- in2D TELEMAC-2D solves the two [24-dimensional] was used to depth simulate averaged storm surgesNavier– inStokes the Beaufort equations Sea. (Saint TELEMAC-2D-Venant shallow solves water the two-dimensionalequations). Bathymetry depth data averaged were Navier–Stokes equations (Saint-Venant shallow water equations). Bathymetry data were obtained from the General Bathymetric Chart of the Oceans (GEBCO) [34], which is obtained from the General Bathymetric Chart of the Oceans (GEBCO) [34], which is derived derived from multiple data sources, interpolated to a grid with a resolution of from multiple data sources, interpolated to a grid with a resolution of approximately approximately 500 m. The 2019 version of the GEBCO gridded dataset was linearly 500 m. The 2019 version of the GEBCO gridded dataset was linearly interpolated to the interpolated to the unstructured computational mesh using Blue Kenue [35] and is shown unstructured computational mesh using Blue Kenue [35] and is shown in Figure6. in Figure 6.

Figure 6. Numerical hydrodynamic model computational mesh and bathymetry. Figure 6. Numerical hydrodynamic model computational mesh and bathymetry.

GEBCOGEBCO strivesstrives toto deliverdeliver aa comprehensivecomprehensive bathymetry bathymetry tiedtied toto thethe meanmean seasea levellevel (MSL)(MSL) datum;datum; however,however, itit isis anan amalgamationamalgamation ofof multiplemultiple bathymetrybathymetry datasets,datasets, somesome ofof whichwhich maymay notnot bebe tiedtied toto MSLMSL [[36]36] andand thisthis isis notednoted asas aa potentialpotential sourcesource ofof error.error. TheThe shorelineshoreline ofof the the model model was was based based on on digital digital shorelines shorelines obtained obtained from from Open Open Street Street Maps Maps [37]. Closed[37]. Closed boundaries boundaries were used were for used the entire for the domain entiresince domain the effect since of the artificial effect boundaries of artificial wasboundaries minimized was by minimized using a large by domain using a and large by domain having the and open-water by having boundary the open- beingwater locatedboundary in deepbeing water.located The in deep model water. was calibratedThe model by was adjusting calibrated the by wind adjusting drag coefficient the wind anddrag comparing coefficient theand simulated comparing storm the simulated surges (during storm open-water/ice-free surges (during open periods)-water/ice to-free the water-levelperiods) to residualsthe water derived-level residuals from the Tuktoyaktukderived from tide the gauge Tuktoyaktuk during storm tide gauge surge events. during Thestorm model surge was events. forced The by model spatially was and forced temporally by spatially varying and wind temporally and atmospheric varying wind pressure and fields.atmospheric Other modelpressure parameter fields. Other selections model included parameter a constant selections bed friction included (n = a 0.025 constant s/m 1/3bed), thefriction k-Epsilon (n = 0.025 turbulence s/m1/3), model,the k-Epsilon and inclusion turbulence of Coriolismodel, and force inclusion effects. Eachof Coriolis event force had aeffects simulated. Each durationevent had of a 6simulated days, including duration 3 daysof 6 days prior, including to the peak 3 days storm prior surge to tothe allow peak sufficientstorm surge time to forallow the sufficient model to spintime upfor fromthe model a zero-elevation to spin up initialfrom a water-level zero-elevation condition. initial water-level condition. 5.1. Reanalysis Dataset 5.1. ReanalysisThe ERA5 Dataset reanalysis dataset [13] was downloaded from the Copernicus Climate DataThe Store, ERA5 implemented reanalysis bydataset the European [13] was downloaded Centre for Medium-Range from the Copernicus Weather Climate Forecasts Data ◦ (ECMWF).Store, implemented Downloaded by data the includedEuropean hourly Centre wind for and Medium surface-Range pressure Weather fields on Forecasts a 0.25 (roughly(ECMWF). 30 Downloaded km) grid encompassing data included the hourly study wind area forand the surface period pressure of January fields 1979 on a to 0.25 the° present(roughly day. 30 k Asm) the grid total encompassing duration of thethe timestudy period area for spanned the period by the of reanalysis January 1979 dataset to the is shorterpresent thanday. theAs the tide total gauge duration record of at the Tuktoyaktuk, time period only spanned surge by events the reanalysis that occurred dataset after is 1979shorter (i.e., than within the thetide ERA5 gauge data record time at span) Tuktoyaktuk, were simulated only surge using events the numerical that occurred model, after to enable1979 (i.e. direct, within comparisons the ERA5 betweendata time the span) modelled were simulated storm surges using and the water numerical level residualsmodel, to derivedenable direct from thecomparisons tide gauge between measurements. the modelled For context, storm thesurges 50th and largest water storm level simulated residuals in this study is equivalent to the 89th largest storm captured on the gauge.

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 9 of 21

J. Mar. Sci. Eng. 2021, 9, 326 9 of 20 derived from the tide gauge measurements. For context, the 50th largest storm simulated in this study is equivalent to the 89th largest storm captured on the gauge. TimeTime seriesseries ofof surfacesurface windwind andand pressurepressure fromfrom the the ERA5 ERA5 dataset, dataset, extractedextracted atat gridgrid pointspoints approximately approximately co-located co-located to to weather weather stations stations nearnear Tuktoyaktuk,Tuktoyaktuk, were were compared compared to to thethe weatherweather stationstation records,records, toto assessassess thethe qualityquality ofof the the reanalysis reanalysis in in the the study study area. area. Five Five weatherweather stations stations (i.e., (i.e. Stations, Stations 1699, 1699, 1700, 1700, 26987, 26987, 53581, 53581, and and 53582), 53582), all all in closein close proximity proximity at Tuktoyaktuk,at Tuktoyaktuk, have have been beenoperated operated by Environment by Environment and Climate and Climate Change CanadaChange (ECCC) Canada since(ECCC) 1958. since The 1958. available The recordsavailable at theserecords stations at these have stations varying have sampling varying frequencies: sampling recordsfrequencies: were records available were for available Station 1699 for Station at six-hourly 1699 at intevals six-hourly from intevals 1958 to from 1993 1958 at an to elevation1993 at an of elevation 18.3 m (MSL), of 18.3 and m (MSL), for Station and for 26987 Station at hourly 26987 intervals at hourly from intervals 1994 fro onwardsm 1994 atonwards an elevation at an ofelevation 4.6 m (MSL). of 4.6 Stationm (MSL). 1700 Station was operational1700 was operational between 1970 between and2015 1970 and and the2015 archives and the forarchives this station for this contain station hourlycontain measurementshourly measurements between between 6 a.m. 6 and a.m 10. and p.m. 10 (Mountainp.m. (Mountain Standard Standard Time) Time) at an elevation at an elevation of 4.3 mof (MSL).4.3 m (MSL). The archives The archives for stations for stations 53581 and53581 53582 and contain 53582 hourly contain wind hourly speeds wind and speeds surface and pressures surface measured pressures at measured an elevation at of an 4.3elevation m (MSL), of 4. starting3 m (MSL) from, starting 2015. Duefrom to 2015. the overlapsDue to the in overlaps measurement in measurement periods between periods thebetween weather the stations, weather the stations, comparisons the comparisons between the between ERA5 reanalysis the ERA5 and reanalysis the measured and the weathermeasured data weather were performeddata were performed using data using from data the weatherfrom the station weather that station had thethat highest had the measurementhighest measurement frequency freq duringuency the during selected the stormselected surge storm event. surge event. AA comparison comparison between between the the atmospheric atmospheric pressure pressure recorded recorded at the at weatherthe weather stations stations and theand 10 the m surface10 m surface pressure pressure time series time at series the nearest at the ERA5 nearest reanalysis ERA5 reanalysis grid point grid is shown point in is Figureshown7 (ain timeFigure series 7 (a plottime for series a 7-day plot period for a 7 in-day 2019 period coinciding in 2019 with coinciding a storm surgewith a event, storm andsurge a correlation event, and plot a correlation showing measured plot showing and reanalysismeasured pressureand reanalysis minima pressure associated minima with 50associated historical with storm 50 surgehistorical events). storm surge events).

FigureFigure 7. 7.ERA5 ERA5 surface surface pressure: pressure: ( a(a)) a a direct direct comparison comparison of of the the surface surface pressure pressure field field for for the the largest largest simulated simulated surge surge event event (July(July 2019); 2019); ( b(b)) measured measured vs. vs. modelledmodelled minimumminimum surfacesurface pressurespressures forfor allall 5050 surgesurge events;events; (c(c)) the the mean mean and and standard standard deviationdeviation forfor the correlation, root root-mean-squared-mean-squared erro errorr (RMSE), (RMSE), and and % %difference difference between between the theminimums minimums for all for fifty all surge fifty events. surge events.

ERA5ERA5 surface surface pressure pressure minima minima showed showed good agreement good agreement with the with atmospheric the atmospheric pressure minimapressure recorded minima atrecorded the weather at the station weather during station the fifty during identified the fifty historical identified storm historical surge events,storm surgewith a events, correlation with coefficient a correlation of 0.996, coefficient a root-mean-squared of 0.996, a root error-mean (RMSE)-squared of 77 error Pa, and(RMSE) less than of 77 a 1%Pa, meanand less difference than a in1% pressure mean difference minima across in pressure all fifty minima events. Theacross coefficient all fifty ofevents. variation The (defined coefficient as theof variation ratio of the (defined standard as deviationthe ratio of and the the standard mean) was deviation less than and 1 forthe themean) aforementioned was less than values. 1 for These the comparison aforementioned metrics values. demonstrate These that comparison the ERA5 surface metrics pressure data exhibit reasonable accuracy for application to storm surge modelling in the

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 10 of 21

J. Mar. Sci. Eng. 2021, 9, 326 10 of 20 demonstrate that the ERA5 surface pressure data exhibit reasonable accuracy for application to storm surge modelling in the region, considering that a 100 Pa difference in region, considering that a 100 Pa difference in atmospheric pressure roughly corresponds atmospheric pressure roughly corresponds to a change in sea level of 1 cm [38]. to a change in sea level of 1 cm [38]. Figure 8 shows the comparison between the wind speed recorded at the weather Figure8 shows the comparison between the wind speed recorded at the weather station and the surface wind speed estimates provided in ERA5. station and the surface wind speed estimates provided in ERA5.

FigureFigure 8. 8.The The performance performance of of ERA5’s ERA5’s wind wind speed speed reanalysis: reanalysis (: a()a) direct direct comparison comparison of of the the surface surface wind wind field field for for the the largest largest simulatedsimulated surge surge event event (July (July 2019); 2019) (;b ()b measured) measured vs. vs. modelled modelled maximum maximum wind wind speeds speeds for for all all 50 50 surge surge events; events (;c ()c the) the mean mean andand standard standard deviation deviation for the for correlation, the correlation, root-mean-squared root-mean-squared error (RMSE), error (RMSE),and percent and difference percent between difference the between maximums the maximums for all fifty surge events. for all fifty surge events.

AlthoughAlthough thethe windwind speedspeed datadata fromfrom the the ERA5 ERA5 reanalysis reanalysis grid grid point point closest closest to to the the TuktoyaktukTuktoyaktuk meteorological meteorological stations stations generally generally capture capture the the magnitude, magnitude, timing, timing, trend, trend and, and variabilityvariability of of measured measured wind wind speeds, speeds, the the storm storm peak peak wind wind speeds speeds are are consistently consistently under- under- estimatedestimated by by ERA5. ERA5. For For the the 50 50 storm storm events events investigated, investigated, ERA5 ERA5 underpredicted underpredicted the the peak peak windwind speeds speeds by by an an average average of 27%of 27% (Figure (Figure8b). This8b). underThis under prediction prediction of peak of values peak byvalues ERA5 by heldERA5 true held for comparisons true for comparisons to both the hourly to both and the six-hourly hourly wind and speed six-hourly measurements. wind speed measurements. 5.2. Effect of Ice Presence on Storm Surges 5.2. AEffect unique of Ice aspect Presence of modellingon Storm Surges storm surges in the Arctic is the need to consider the presenceA unique of sea ice,aspect which of modelling is observed storm year-round surges in under the Arctic varying is the levels need of concentration.to consider the Seapresence ice has of been sea knownice, which to dampenis observed the year magnitude-round ofunder storm varying surges levels by acting of concentration. as a barrier betweenSea ice has the been water known surface to anddampen winds the [ 20magnitude,39]. Conversely, of storm marginal surges by ice acting concentrations as a barrier (definedbetween as the the water area fraction surface of and which winds is covered [20,39]. with Conversely, sea ice) can marginal amplify ice the concentr wind-drivenations components(defined as the of area storm fraction surges of by which increasing is covered the with form sea drag, ice) seacan surfaceamplify roughnessthe wind-driven and associatedcomponents shear of stresses storm surges [40]. Joyce by increasinget al. [22] performed the form a drag, numerical sea surface investigation roughness of storm and surgesassociated in western shear Alaskastresses and [40]. proposed Joyce et the al. following[22] performed modified a numerical formulation investigation of the drag of coefficientstorm surges that in characterizes western Alaska the transfer and proposed of momentum the following from air modified and ice toformulation the sea surface: of the drag coefficient that characterizes the transfer of momentum from air and ice to the sea C = (1 − AF)C + (AF)C + C (1) surface: d_mod d d−is d−i f

where AF is the area fraction퐶 of푑_푚표푑 ice coverage,= (1 − 퐴퐹 which)퐶푑 + can(퐴퐹 range)퐶푑−푖푠 from+ 퐶푑 0−푖푓 (open water conditions)(1) to 1 (complete ice cover); Cd_mod is the air–sea drag coefficient after the modification for the where AF is the area fraction of ice coverage, which can range from 0 (open water presence of sea ice; Cd is the air–sea drag coefficient in open water; Cd-is is the contribution conditions) to 1 (complete ice cover); Cd_mod is the air–sea drag coefficient after the of ice skin drag, set to a constant value of 0.0015 based on Lüpkes et al. [41]; and Cd-if is the formmodification drag contribution for the presence from the of ice,sea whichice; Cd dependsis the air– onseaAF drag[41 coefficient]: in open water; Cd- is is the contribution of ice skin drag, set to a constant value of 0.0015 based on Lüpkes et

al. [41]; and Cd-if is the form Cdragd−i f contribution= 4Cd−i f ,max from(AF)( the1 − ice,AF which) depends on AF [41]:(2)

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 11 of 21

퐶푑−푖푓 = 4퐶푑−푖푓,푚푎푥(퐴퐹)(1 − 퐴퐹) (2)

where Cd-if,max is the maximum value of Cd-if that occurs at 50% ice coverage. Cd-if,max was set to 0.0025 [22]. This modified drag formulation was added to TELEMAC-2D by the authors by modifying the model source code. AF was determined for each of the 50 events by J. Mar. Sci. Eng. 2021, 9, 326 importing weekly ice charts issued by CIS. 11 of 20

5.3. Sensitivity Study where Cd-if,max is the maximum value of Cd-if that occurs at 50% ice coverage. Cd-if,max was set toMesh 0.0025 sensitivity [22]. This tests modified were drag conducted formulation to assess was added the spatial to TELEMAC-2D resolution by required the to achieveauthors a by reasonable modifying the balance model sourcebetween code. computationAF was determined times forand each model of the accuracy. 50 events A 1.4 millionby importing element weekly mesh ice chartswith charaissuedcteristic by CIS. element edge lengths in the range 500 m (nearshore) to 50 km (offshore) was finally selected. A variable time step within the range 0–5.3.30 s Sensitivity was applied Study to satisfy a Courant number less than 0.9 for numerical stability during the simulation.Mesh sensitivity Computation tests wereal time conducted was roughly to assess 1 theh per spatial event resolution (6 day simulation required to period) usingachieve six a reasonable cores of balance an Intel between Core computation i7-8850H times processor. and model Given accuracy. these A 1.4relatively million short element mesh with characteristic element edge lengths in the range 500 m (nearshore) computational times, the numerical model has potential for future use as an operational to 50 km (offshore) was finally selected. A variable time step within the range 0–30 s stormwas applied surge forecasting to satisfy a Courantmodel. number less than 0.9 for numerical stability during the simulation. Computational time was roughly 1 h per event (6 day simulation period) using 6.six Results coresof an Intel Core i7-8850H processor. Given these relatively short computational 6.1.times, Calibration the numerical and Validation model has potential for future use as an operational storm surge forecasting model. The storm surge model was calibrated by adjusting the wind drag coefficient (Cd). Three6. Results large storm surge events that occurred during the summer months (i.e., July 2019, August6.1. Calibration 2018, and and September Validation 2013) were investigated as part of the calibration exercise.

The selectedThe storm events surge corresponded model was calibrated to ice-free by adjusting conditions the in wind the drag vicinity coefficient of Tuktoyaktuk, (Cd). basedThree on large weekly storm ice surge charts events from that the occurred Canadian during Ice Service the summer (CIS) months[42]. This (i.e., was July an 2019, important considerationAugust 2018, andto ensure September confidence 2013) were in the investigated model’s ability as part to of simulate the calibration storm exercise. surges under iceThe-free selected conditions, events prior corresponded to modifying to ice-free the source conditions code in to the include vicinity the of Tuktoyaktuk,effects of ice cover based on weekly ice charts from the Canadian Ice Service (CIS) [42]. This was an important on air-sea momentum transfer. The TELEMAC-2D default relationship between Cd and consideration to ensure confidence in the model’s ability to simulate storm surges under wind speed, based on Flather [43], was adjusted through trial and error until modelled ice-free conditions, prior to modifying the source code to include the effects of ice cover peak storm surges were within 5% of the measured values for the three events. As ERA5 on air-sea momentum transfer. The TELEMAC-2D default relationship between Cd and systematicallywind speed, based underpredicts on Flather [ 43the], was peak adjusted wind through speeds, trial the and threshold error until wind modelled speed for transitionpeak storm to surges the maximum were within drag 5% of coefficientthe measured was values reduced. for the three The events. final drag As ERA5 coefficient dependencesystematically on underpredicts wind speed, the as peak applied wind in speeds, the calibrated the threshold storm wind surge speed model for transition, is shown in Figureto the maximum 9 compared drag tocoefficient the default was reduced. Flather The [43] final formulation, drag coefficient and dependence other popular parametrizationson wind speed, asfrom applied Sheppard in the calibrated[44], Wu [45], storm Large surge and model, Pond is shown[46], and in Figure Yelland9 et al. [47].compared to the default Flather [43] formulation, and other popular parametrizations from Sheppard [44], Wu [45], Large and Pond [46], and Yelland et al. [47].

Figure 9. Final Cd values used for the storm surge model compared with the other common wind drag parametrizations.

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 12 of 21

Figure 9. Final Cd values used for the storm surge model compared with the other common wind J. Mar. Sci. Eng. 2021, 9, 326 drag parametrizations. 12 of 20

The time series of the measured water-level residuals and simulated (modelled) stormThe surges time seriesusing the of thecalibrated measured wind water-level drag coefficient residuals formula and is simulated shown in Fig (modelled)ure 10, for stormthree surges events. using the calibrated wind drag coefficient formula is shown in Figure 10, for three events.

Figure 10. Results of the storm surge model calibration exercise for the storm surge events that occurred on (a) Event 1—July Figure 10. Results of the storm surge model calibration exercise for the storm surge events that occurred on (a) Event 1— 2019;July ( b2019) Event; (b) Event 2—September 2—September 2013; 2013 (c) Event; (c) Event 4—August 4—August 2019. 2019. Results Results are are compared compared to theto the tide tide gauge gauge at at Tuktoyaktuk Tuktoyaktuk (Station(Station 6485). 6485). Refer Refer to to the the Appendix AppendixA—Table A—Table A1 A1for for event event numbering numbering reference. reference.

ForFor all all three three events, events, the the simulated simulated peak peak storm storm surge surge was was within within 3% 3% of of the the measured measured water-levelwater-level residual residual and and the timing the timing and duration and duration of the flooding of the flooding was well was captured. well captured. Follow- ingFollowing model calibration, model calibration, the remaining the remaining 47 historical 47 stormhistorical surge storm events surge were events then simulated were then (with calibrated model input parameters applied) under open-water conditions to validate simulated (with calibrated model input parameters applied) under open-water conditions the performance of the model. Figure 11a shows the performance of all fifty storm surge to validate the performance of the model. Figure 11a shows the performance of all fifty events that were simulated with the calibrated model. storm surge events that were simulated with the calibrated model. While the majority of the fifty events were adequately simulated, events occurring during the months of December and January, when fast ice is observed on the shorelines along the north coast of Canada, showed modelled surges much greater than the measured surge when assuming open-water conditions. Figure 12 shows a comparison between measured water-level residuals and modelled storm surges during a January 2005 winter event where high ice concentrations and fast ice were found near Tuktoyaktuk, for simulated scenarios with and without the effects of ice on air-sea momentum transfer incorporated. The latter scenario represents the model-predicted storm surge for this event under a hypothetical, ice-free condition.

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 13 of 21

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 13 of 21 J. Mar. Sci. Eng. 2021, 9, 326 13 of 20

Figure 11. (a) Comparison of measured and modelled storm surge values without any corrections for ice. (b) Comparison of measured vs modelled storm surge values after correcting for the effects of ice as per Joyce et al. [22]. (c) Correlation, RMSE, and percent difference between the maximum values for each model.

While the majority of the fifty events were adequately simulated, events occurring during the months of December and January, when fast ice is observed on the shorelines along the north coast of Canada, showed modelled surges much greater than the measured surge when assuming open-water conditions. Figure 12 shows a comparison between measured water-level residuals and modelled storm surges during a January 2005 winter event where high ice concentrations FigureFigure 11. 11. ((aa)) Comparison Comparison of ofand measured measured fast ice and and were modelled modelled found storm stormnear surgeTuktoyaktuk, surge values values without without for simulated any any corrections corrections scenarios for for ice. ice. with ( (bb)) Comparison Comparisonand without the of measured vs modelled storm surge values after correcting for the effects of ice as per Joyce et al. [22]. (c) Correlation, of measured vs modelled stormeffects surge of ice values on air after-sea correcting momentum for the transfer effects ofincorporated ice as per Joyce. The et al.latter [22]. scenario (c) Correlation, represents RMSE, and percent difference between the maximum values for each model. RMSE, and percent differencethe between model- thepredicted maximum storm values surge for eachfor this model. event under a hypothetical, ice-free condition. While the majority of the fifty events were adequately simulated, events occurring during the months of December and January, when fast ice is observed on the shorelines along the north coast of Canada, showed modelled surges much greater than the measured surge when assuming open-water conditions. Figure 12 shows a comparison between measured water-level residuals and modelled storm surges during a January 2005 winter event where high ice concentrations and fast ice were found near Tuktoyaktuk, for simulated scenarios with and without the effects of ice on air-sea momentum transfer incorporated. The latter scenario represents the model-predicted storm surge for this event under a hypothetical, ice-free condition.

FigureFigure 12.12. ComparisonComparison of thethe modelledmodelled andand measuredmeasured stormstorm surgesurge elevationselevations at StationStation 64856485 forfor thethe JanuaryJanuary 20052005 stormstorm surgesurge eventevent underunder ((aa)) actualactual iceice conditionsconditions andand ((bb)) aa futurefuture open-wateropen-water scenario.scenario.

With the the standard standard wind–sea wind–sea drag drag formulation, formulation, the hydrodynamic the hydrodynamic model over-predicted model over- thepredicted peak storm the peak surge storm by 171%. surge Following by 171%. modificationFollowing modification of the drag coefficientof the drag to coefficient incorporate to seaincorporate ice effects, sea the ice peak effects, predicted the storm peak surge predicted was within storm 12% surge of the was measured within 12% peak of water the level residual. These findings suggest that, under certain conditions, sea ice plays a significant role in reducing peak storm surges in the Beaufort Sea during winter months. If, as climate model projections suggest, sea ice extent and ice season duration continue to decline in the Arctic [4,48] throughout the 21st century, future winter storms could generate Figure 12. Comparison of themore modelled severe and storm measured surges storm than surge comparable elevations storms at Station today. 6485 Figure for the 11 Januaryb shows 2005 a comparison storm surge event under (a) actual ofice the conditions measured and and(b) a modelled future open storm-water surges scenario. after accounting for the presence of ice—the extreme outliers present in the correlation plot for simulations without consideration for ice areWith eliminated. the standard In addition, wind–sea Figure drag 11 formulation,c shows that the the hydrodynamic adjustment of modelthe model over to- predicted the peak storm surge by 171%. Following modification of the drag coefficient to incorporate sea ice effects, the peak predicted storm surge was within 12% of the

J. Mar. Sci. Eng. 2021, 9, x FOR PEER REVIEW 14 of 21

measured peak water level residual. These findings suggest that, under certain conditions, sea ice plays a significant role in reducing peak storm surges in the Beaufort Sea during winter months. If, as climate model projections suggest, sea ice extent and ice season duration continue to decline in the Arctic [4,48] throughout the 21st century, future winter storms could generate more severe storm surges than comparable storms today. Figure 11b shows a comparison of the measured and modelled storm surges after accounting for J. Mar. Sci. Eng. 2021, 9, 326 the presence of ice—the extreme outliers present in the correlation plot for simulations14 of 20 without consideration for ice are eliminated. In addition, Figure 11c shows that the adjustment of the model to incorporate ice effects yielded higher correlation coefficients, incorporatelower RMSE ice, and effects smaller yielded absolute higher differences correlation for peak coefficients, storm surge lower magnitudes. RMSE, and smaller absolute differences for peak storm surge magnitudes. 6.2. Hindcast of the 23 August 1986 Storm Surge Event 6.2. Hindcast of the 23 August 1986 Storm Surge Event Harper et al. [19] estimated the maximum water levels based on field observations of Harper et al. [19] estimated the maximum water levels based on field observations the driftwood lines for a significant high water-level event that occurred on 23 August of the driftwood lines for a significant high water-level event that occurred on 23 August 1986. This event was not captured on the Tuktoyaktuk (6485) tide gauge and was 1986. This event was not captured on the Tuktoyaktuk (6485) tide gauge and was simulated simulated to numerically validate the estimated water levels from driftwood line surveys. to numerically validate the estimated water levels from driftwood line surveys. The The simulation included the influence of sea ice as described in Section 5.2. Near simulation included the influence of sea ice as described in Section 5.2. Near Tuktoyaktuk, Tuktoyaktuk, Harper et al. [19] estimated a peak local water level of approximately 2.1 m Harper et al. [19] estimated a peak local water level of approximately 2.1 m CD (1.6 m MSL). CD (1.6 m MSL). This provides an estimated storm surge level ranging from 1.53 m to 1.90 This provides an estimated storm surge level ranging from 1.53 m to 1.90 m, depending on m, depending on the tide level during the storm, and considering the ±0.3 m measurement the tide level during the storm, and considering the ±0.3 m measurement error noted by error noted by Harper et al. [19]. The 23 August event was hindcast in the numerical model Harper et al. [19]. The 23 August event was hindcast in the numerical model and Figure 13 and Figure 13 shows the simulated storm surge elevations for areas covered by Harper et shows the simulated storm surge elevations for areas covered by Harper et al.’s [19] survey. al.’s [19] survey.

Figure 13. Hindcast of the 23 August 1986 storm surge event. Figure 13. Hindcast of the 23 August 1986 storm surge event.

TheThe simulated local maximum surge surge near near Tuktoyaktuk Tuktoyaktuk was was approximately approximately 1.4 1.400 m m,, whichwhich fallsfalls within the bounds of of the estimates by by Harper Harper et et al. al. [19] [19],, with with allowances allowances for for measurementmeasurement error (i.e.,(i.e., 1.531.53 m to 1.901.90 m ±0.0.33 m m).). However, However, the the model model does does not not simulate simulate overlandoverland flooding flooding oror wave wave effects, effects, which which would would likely likely have have influenced influenced driftwood driftwood eleva- tions,elevations and, thus and the thus high the water-level high water estimates-level inferred estimates from inferred driftwood from line driftwood measurements line cannotmeasurements be precisely cannot verified. be precisely However, verified the results. However, confirm the that results storm confirm surges of that magnitude storm similarsurges of to magnitude those inferred similar from to th theose survey inferred measurements from the survey are measurements possible, and couldare possible reason-, ablyand could have occurredreasonably during have theoccurred 23 August during 1986 the storm. 23 August This 1986 numerical storm. exercise This numerical suggests thatexercise driftwood suggests lines that can driftwood be used to lines provide can bereasonable used to estimates provide reasonable of elevated estimate water levelss of dueelevated to coastal water storm levels eventsdue to incoastal places storm where events tide gaugein places records where are tide short, gauge intermittent, records are or non-existent.short, intermittent, Driftwood or non lines-existent. may also Driftwood provide lines useful may information also provide on useful high water-levels information in areason high where water gauges-levels are in operational, areas where as gauges they can are malfunction operational during, as they extreme can malfunction events. The numericalduring extreme analysis events. lends The confidence numerical to Harperanalysiset lends al.’s [confidence19] estimates to ofHarper peak et water al.’s levels[19] reachingestimates upof peak to 2.95 water m, whichlevels reaching are thought up to to 2.9 exceed5 m, which all storm are thought surge events to exceed in theall storm region within the past 100 years. Thus, driftwood lines may provide additional information when calculating the probability distribution of the return periods and magnitudes of storm surge events, with Figure5 demonstrating the potentially significant implications if large events are omitted. J. Mar. Sci. Eng. 2021, 9, 326 15 of 20

7. Discussion 7.1. Quality of the ERA5 Dataset The advancement of atmospheric modelling has led to the development of high- resolution reanalysis datasets such as ERA5. Among the many atmospheric variables that these datasets offer, surface pressure and wind fields are of direct relevance for driving numerical storm surge models. Previous studies have demonstrated the improvements offered by ERA5 compared to predecessor datasets, when used to force storm surge models focused on regions in low latitudes [15,49]. This study compared surface pressure and wind data from ERA5 to measurements at stations in the Beaufort Sea region, to assess its utility for driving numerical simulations of storm surge events. Compared to local measurements, the ERA5 dataset demonstrated a high level of skill in reproducing observed surface pressures, with mean errors within 1% of the pressure minima during fifty historical storm surge events. The ERA5 reanalysis generally agreed with the timing, variability, and magnitude of mean wind speeds at weather stations but underestimated peak storm wind speeds by 27%, on average. The wind drag coefficient resulting from the calibration process described resulted in drag coefficient values exceeding those typically found in the literature [50]. Although some variability in drag coefficients may be expected due to a variety of factors, including fetch, sea surface roughness, and the methodology employed to measure wind drag coefficients, the majority of the discrepancies in the calibrated drag coefficients from typical values in the literature can be ascribed to ERA5’s underestimation of the peak wind speeds. Since Cd is proportional to the square of wind speed, the 27% underestimation of peak wind speeds by ERA5 (on average for the 50 events investigated) would require almost a doubling of the drag coefficient (or 88% increase) to compensate, which is consistent with values shown in Figure9. This was not a significant limitation for numerical simulation of storm surges, as scaling the wind speeds (e.g., by adjusting drag coefficients) gave accurate results when the model was calibrated to observations. Simulated peak storm surges associated with fifty-one historical surge events showed good agreement with the water-level residuals derived from the Tuktoyaktuk tide gauge record and inferred from driftwood surveys.

7.2. Quality of Historic Tide Gauge Records and Impacts on Uncertainty Extreme value statistics are often employed to describe the annual exceedance proba- bility or recurrence interval of a natural phenomenon for risk assessment or engineering design applications. The quality of the extreme value analyses is intrinsically linked to the length and quality of the data records on which they rely on. In this study, return period estimates for peak storm surge magnitude were determined based on water level records available from the Tuktoyaktuk tide gauge. As discussed in Section4, the tide gauge at Tuktoyaktuk was operational starting from 1961. However, the gauge record is interrupted by periods of inactivity where the gauge was not opera- tional. Most notably, the tide gauge record does not include two high-water-level events that occurred in 1944 and 1970, which Harper et al. [19] estimated, based on driftwood line surveys, to exceed any that were captured in the tide gauge record. It is uncertain whether these high-water events coincided with a low, mid, or high astronomical tidal stage. Therefore, it is not possible to elucidate a single storm surge magnitude for the two events and rather, a range of possibilities exist. However, even low-end estimates of storm surge magnitudes associated with the 1944 and 1970 events (approximately 2.10 m), inferred from the driftwood surveys, exceed any other water level residual events in the tide gauge record (up to 1.90 m). This suggests that the 1944 and 1970 events are the highest known storm surge events to have occurred in Canada’s western Arctic region in recent history. As such, the inclusion, or omission, of these events in extreme value analyses was demonstrated to have a significant impact on the GPD fit. J. Mar. Sci. Eng. 2021, 9, 326 16 of 20

7.3. Effects of Sea Ice Conditions on Storm Surges The use of the formulas presented by Joyce et al. [22] to account for the effect of ice on storm surges in western Alaska significantly improved the accuracy of the simulated peak storm surges. The modified drag formulation correctly accounted for the damping effects of sea ice on storm surges for winter events where fast ice was present in the vicinity of Tuktoyaktuk (Figure 11). These results demonstrate the role played by sea ice in attenuating winter storm surges in the Beaufort Sea (Figure 12). These findings are significant in the context of the lengthening open-water season durations in the Arctic, decreasing ice concentrations and declining ice-cover extents [48]. These changing ice conditions have potential to result in higher storm surges and associated impacts, and future studies that forecast storm surges under various climate change scenarios and ice conditions may be useful for community planners along Arctic coasts.

7.4. Limitations and Research Needs The formulas presented by Joyce et al. [22] consider only the effect of the presence of ice on storm surges. However, the type of ice can be a significant factor controlling its effect on the propagation of long waves. Land-fast ice attenuates waves more than floating ice, and the thickness of ice can also influence the degree of damping [38,51]. Ice floes can scatter wave energy, with the magnitude of scatter a function of wave frequency, ice floe distribution, and the ratio of wave length to ice floe length [39]. Differentiating the effects of fast ice and floating ice on the wind drag coefficient in the formulas by Joyce et al. [22] may improve the performance of the numerical model. Available ice charts from CIS would support advanced formulations as it already classifies the types of ice, thickness, and stage of development, in addition to ice concentrations. However, ice data at more frequent intervals (e.g., daily or sub-daily) would enable the influence of shifting ice conditions to be more accurately captured in storm surge models. While this study relied on the recently released, high-resolution ERA5 global reanalysis as the source of atmospheric forcing data, there are potential alternatives. The quality of other reanalysis datasets, such as the Japanese 55-year reanalysis [52] and the MSC Beaufort Wind and Wave Reanalysis [53], were not investigated. The latter has a higher spatial resolution than ERA5 (2 km grid resolution compared to 30 km). However, the spatial coverage of the MSC data is limited compared to the ERA5 reanalysis, and surface pressure data is not available. A comparative analysis of surface wind fields from various reanalysis datasets and weather station records, would help to identify the strengths and limitations of atmospheric forcing data for storm surge modelling in the Beaufort Sea region. The numerical storm surge model was calibrated using data from a single tide gauge at Tuktoyaktuk, which has the longest standing, and most complete records in the Beaufort Sea region. Future studies should examine other tide gauges in the region and simulate events where storm surges were captured on multiple gauges. This would lend insight to model bias outside of the Tuktoyaktuk region. Additionally, during the study, ERA5’s reanalysis period was from 1979 to present but, just recently, it was extended from 1950 to the present [54]. This provides the opportunity to simulate the September 1970 storm surge event, believed to be one of the highest storm surges that has occurred in the Beaufort Sea [19]. This exercise could further validate the use of driftwood lines as a non- conventional source for high water-level marks and provide information on the potential magnitude of extreme storm surges in the Beaufort Sea. Furthermore, a full, long-term water level or storm surge hindcast for the entire ERA5 duration could help to fill gaps in tide gauge records and identify historical storm surges that were not recorded. The regional model could then be used to feed higher-resolution inundation models for individual communities to enable risk assessment. The model also offers potential for application to investigating climate change impacts on storm surge frequency and severity. Forcing the storm surge model with Regional Climate Models (RCM) and/or simulating storm surge events under future hypothetical sea ice conditions [55] could help to quantify changes in storm surge risk, and identify implications to guide planning for coastal resiliency. J. Mar. Sci. Eng. 2021, 9, 326 17 of 20

8. Conclusions A numerical investigation of storm surges in the Beaufort Sea was conducted. A two- dimensional hydrodynamic model was developed and forced by surface pressure and wind fields to simulate storm surges in Canada’s western Arctic region. The model was calibrated and validated using tide gauge records and high water-level marks inferred from driftwood field surveys. The surface drag formulation was modified to incorporate the effects of sea ice on air–sea momentum transfer, significantly improving the accuracy of simulated peak storm surges during periods of ice cover. Surface pressure and wind data from the recently released ERA5 global reanalysis was compared to weather station records to assess its utility for driving numerical models of storm surges in the Beaufort Sea region. The following conclusions were drawn: • Inferences based on driftwood surveys and astronomical tide predictions identified two historical storm surge events that exceeded all events captured by the regional tide gauges. One of the events was successfully verified by a hindcast using the numerical model. • Inclusion of the two historical events in an extreme value analysis significantly altered the best fit probability distribution of storm surges in the region, with implications for risk assessment and coastal engineering design. • The ERA5 reanalysis was shown to be an adequate source of surface pressures and wind speeds, except for a systematic underprediction of peak wind speeds at the Tuktoyaktuk weather station. Acceptable numerical modelling results were achieved through calibration of the wind drag coefficient; large wind drag coefficients were prescribed to compensate for the generally low peak wind speeds reported in ERA5. • Sea ice has a significant damping impact on storm surges along the Beaufort Sea coast and should be considered in storm surge analyses for the region. Under open-water scenarios representative of future Arctic conditions, storm surges were found to be as much as three times higher than under present-day ice conditions.

Author Contributions: Conceptualization, J.K., E.M., I.N. and S.F.; formal analysis, J.K.; funding acquisition, E.M. and I.N.; investigation, J.K. and E.M.; methodology, J.K., E.M. and M.P.; project administration, E.M., S.F. and I.N.; resources, E.M., S.F. and M.P.; software, J.K., S.F. and M.P.; supervision, E.M. and I.N.; validation, J.K.; visualization, J.K.; writing—original draft, J.K.; writing— review and editing, J.K., E.M., I.N., S.F. and M.P. All authors have read and agreed to the published version of the manuscript. Funding: This work was funded as part of the Coastal Flood Mitigation Canada project by Defence Research and Development Canada through the Canadian Safety and Security Program, with support from National Research Council Canada’s Marine Infrastructure, Energy and Water Resources Program, Oceans Program, and Ioan Nistor’s NSERC Discovery Grant. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Acknowledgments: The authors would like to thank Nicky Hastings and Jackie Yip (Natural Re- sources Canada) and all project partners for helpful insights and co-ordination with other aspects of the project. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. J. Mar. Sci. Eng. 2021, 9, 326 18 of 20

Appendix A

Table A1. The fifty highest positive water-level residual events captured on Tuktoyaktuk Gauge 6485 that fall within the ERA5 reanalysis time period (1979–present).

Rank Datetime Measured Residual (m) 1 21 July 2019 1.49 2 1 September 2013 1.44 3 4 November 2017 1.42 4 5 August 2019 1.40 5 17 August 2018 1.34 6 12 September 2003 1.27 7 1 September 2018 1.25 8 3 September 2018 1.25 9 30 October 2013 1.23 10 2 August 2004 1.17 11 12 November 2013 1.17 12 15 November 2013 1.17 13 27 August 2015 1.12 14 3 September 2016 1.12 15 24 August 1991 1.11 16 30 July 2008 1.11 17 14 August 2004 1.08 18 10 January 2005 1.08 19 12 August 2019 1.07 20 15 August 2019 1.07 21 19 November 2010 1.05 22 8 September 2016 1.04 23 21 August 1982 1.03 24 16 September 2003 1.02 25 8 October 2008 1.00 26 30 August 1980 1.00 27 25 July 2017 0.99 28 8 October 2019 0.99 29 5 July 2018 0.99 30 29 October 2003 0.98 31 24 August 2019 0.97 32 17 August 2019 0.96 33 16 September 1980 0.96 34 5 October 2019 0.95 35 5 October 2003 0.94 36 13 October 2019 0.90 37 9 August 1991 0.90 38 6 August 1991 0.90 39 2 November 2019 0.90 40 1 October 2015 0.90 41 31 October 2019 0.88 42 4 September 2009 0.88 43 7 September 2009 0.88 44 19 September 2016 0.87 45 24 September 2016 0.86 46 2 January 2010 0.83 47 31 July 2018 0.82 48 3 December 2003 0.82 49 3 July 2006 0.82 50 27 September 2003 0.81 J. Mar. Sci. Eng. 2021, 9, 326 19 of 20

References 1. Casas-Prat, M.; Wang, X.; Swart, N. CMIP5-based global wave climate projections including the entire Arctic Ocean. Ocean Model. 2018, 123, 66–85. [CrossRef] 2. Lemmen, D.S.; Warren, F.J.; James, T.S.; Clarke, C.S.L.M. Canada’s Marine Coasts in a Changing Climate; Natural Resources Canada: Ottawa, ON, Canada, 2016. 3. Manson, G.K.; Solomon, S.M. Past and future forcing of Beaufort Sea coastal change. Atmosphere-Ocean 2007, 45, 107–122. [CrossRef] 4. Bush, E.; Lemmen, D.S. (Eds.) Canada’s Changing Climate Report; Government of Canada: Ottawa, ON, Canada, 2019. 5. James, T.S.; A Henton, J.; Leonard, L.J.; Darlington, A.; Forbes, D.L.; Craymer, M. Tabulated values of relative sea-level projections in Canada and the adjacent mainland United States. Geol. Surv. Can. 2015, 7942.[CrossRef] 6. Ford, J.D.; Couture, N.; Bell, T.; Clark, D.G. Climate change and Canada’s north coast: Research trends, progress, and future directions. Environ. Rev. 2018, 26, 82–92. [CrossRef] 7. Hoque, A.; Perrie, W.; Solomon, S.M. Evaluation of two spectral wave models for wave hindcasting in the Mackenzie Delta. Appl. Ocean Res. 2017, 62, 169–180. [CrossRef] 8. Burns, B.M. The Climate of the Mackenzie Valley—Beaufort Sea; Environment Canada, Atmospheric Environment: Toronto, ON, Canada, 1973. 9. Henry, R.F. Storm Surges in the Southern Beaufort Sea; Beaufort Sea Project, Department of the Environment: Victoria, BC, Canada, 1974. 10. Henry, R.F. Storm Surges; Beaufort Sea Project, Department of the Environment: Victoria, BC, Canada, 1975. 11. Henry, R.F.; Heaps, N.S. Storm Surges in the Southern Beaufort Sea. J. Fish. Res. Board Can. 1976, 33, 2362–2376. [CrossRef] 12. Kowalik, Z. Storm surges in the Beaufort and Chukchi Seas. J. Geophys. Res. Space Phys. 1984, 89, 10570–10578. [CrossRef] 13. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [CrossRef] 14. Berrisford, P.; Kållberg, P.; Kobayashi, S.; Dee, D.; Uppala, S.; Simmons, A.J.; Poli, P.; Sato, H. Atmospheric conservation properties in ERA-Interim. Q. J. R. Meteorol. Soc. 2011, 137, 1381–1399. [CrossRef] 15. Dullaart, J.C.M.; Muis, S.; Bloemendaal, N.; Aerts, J.C.J.H. Advancing global storm surge modelling using the new ERA5 climate reanalysis. Clim. Dyn. 2020, 54, 1007–1021. [CrossRef] 16. Graham, R.M.; Hudson, S.R.; Maturilli, M. Improved Performance of ERA5 in Arctic Gateway Relative to Four Global Atmospheric Reanalyses. Geophys. Res. Lett. 2019, 46, 6138–6147. [CrossRef] 17. Ramon, J.; Lledó, L.; Torralba, V.; Soret, A.; Doblas-Reyes, F.J. What global reanalysis best represents near-surface winds? Q. J. R. Meteorol. Soc. 2019, 145, 3236–3251. [CrossRef] 18. Reimnitz, E.; Maurer, D.K. Effects of Storm Surges on the Beaufort Sea Coast, Northern Alaska. Arctic 1979, 32, 329–344. [CrossRef] 19. Harper, J.R.; Henry, R.F.; Stewart, G.G. Maximum Storm Surge Elevations in the Tuktoyaktuk Region of the Canadian Beaufort Sea. Arctic 1988, 41, 48–52. [CrossRef] 20. Murty, T.S.; Polavarapu, R.J. Influence of an ice layer on the propagation of long waves. Mar. Geodesy 1979, 2, 99–125. [CrossRef] 21. Danard, M.B.; Rasmussen, M.C.; Murty, T.S.; Henry, R.F.; Kowalik, Z.; Venkatesh, S. Inclusion of ice cover in a storm surge model for the Beaufort Sea. Nat. Hazards 1989, 2, 153–171. [CrossRef] 22. Joyce, B.R.; Pringle, W.J.; Wirasaet, D.; Westerink, J.J.; Van Der Westhuysen, A.J.; Grumbine, R.; Feyen, J. High resolution modeling of western Alaskan tides and storm surge under varying sea ice conditions. Ocean Model. 2019, 141, 101421. [CrossRef] 23. Chapman, R.; Kim, S.-C.; Mark, D. Storm-Induced Water Level Prediction Study for the Western Coast of Alaska. US Army Corps of Engineers. October 2009. Available online: http://www.ariesnonprofit.com/SmithCorpsAKstormSurgeReport.pdf (accessed on 19 August 2020). 24. Hervouet, J. Hydrodynamics of Free Surface Flows, 1st ed.; John Wiley & Sons, Ltd.: Hoboken, NJ, USA, 2007. 25. Fetterer, F.; Knowles, K.; Savoie, M.; Windnagel, A.K. Sea Ice Index; National Snow and Ice Data Center: Boulder, CO, USA, 2017. [CrossRef] 26. Google. Canada. Available online: https://www.google.com/maps/@71.0146308,-130.9053989,5.83z (accessed on 7 March 2021). 27. Foreman, M.G.G.; Cherniawsky, J.Y.; Ballantyne, V.A. Versatile harmonic tidal analysis: Improvements and applications. J. Atmos. Ocean. Technol. 2009, 26, 806–817. [CrossRef] 28. Serafin, K.A.; Ruggiero, P.; Stockdon, H.F. The relative contribution of waves, tides, and non-tidal residuals to extreme total water levels on US West Coast sandy beaches. Geophys. Res. Lett. 2017, 44, 1839–1847. [CrossRef] 29. Casas-Prat, M.; Wang, X.L. Projections of Extreme Ocean Waves in the Arctic and Potential Implications for Coastal Inundation and Erosion. J. Geophys. Res. Oceans 2020, 125, 015745. [CrossRef] 30. Ferreira, J.A.; Soares, C.G. An Application of the Peaks Over Threshold Method to Predict Extremes of Significant Wave Height. J. Offshore Mech. Arct. Eng. 1998, 120, 165–176. [CrossRef] 31. Brodtkorb, P.A.; Johannesson, P.; Lindgren, G.; Rychlik, I.; Ryden, J.; Sjo, E. WAFO—A Matlab Toolbox for Analysis of Random Waves and Loads. In Proceedings of the International Offshore and Polar Engineering Conference, Seattle, WA, USA, 27 May–2 June 2000; International Society of Offshore and Polar Engineers: Seattle, WA, USA, 2000; p. 8. 32. Davison, A.C.; Smith, R.L. Models for Exceedances over High Thresholds. J. R. Stat. Soc. Ser. B Stat. Methodol. 1990, 52, 393–425. [CrossRef] J. Mar. Sci. Eng. 2021, 9, 326 20 of 20

33. Department of Public Works. Investigation of Storm, September 13–16, 1970—Mackenzie Delta Region, Beaufort Sea; Unpublished Technical Report by Public Works of Canada; Department of Public Works: Los Angeles, CA, USA, 1971. 34. Weatherall, P.; Marks, K.M.; Jakobsson, M.; Schmitt, T.; Tani, S.; Arndt, J.E.; Rovere, M.; Chayes, D.N.; Ferrini, V.L.; Wigley, R. A new digital bathymetric model of the world’s oceans. Earth Space Sci. 2015, 2, 331–345. [CrossRef] 35. Barton, A.J. Blue Kenue Enhancements from 2014 to 2019. In Proceedings of the XXVIth Telemac & Mascaret User Conference, Toulouse, France, 15–17 October 2019. 36. International Hydrographic Organization. Gridded Bathymetry Data (General Bathymetric Chart of the Oceans), GEBCO. 2019. Available online: https://www.gebco.net/data_and_products/gridded_bathymetry_data/ (accessed on 24 August 2020). 37. Topf, J. Osmcode/Osmcoastline. GitHub. 2019. Available online: https://github.com/osmcode/osmcoastline (accessed on 20 December 2020). 38. World Meteorological Organization. Guide to Storm Surge Forecasting; World Meteorological Organization: Geneva, Switzer- land, 2011. 39. Tolman, H.L. Treatment of unresolved islands and ice in wind wave models q. Ocean Model. 2003, 5, 13. [CrossRef] 40. Birnbaum, G.; Lüpkes, C. A new parameterization of surface drag in the marginal sea ice zone. Tellus A Dyn. Meteorol. Oceanogr. 2002, 54, 107–123. [CrossRef] 41. Lüpkes, C.; Gryanik, V.M.; Hartmann, J.; Andreas, E.L. A parametrization, based on sea ice morphology, of the neutral atmospheric drag coefficients for weather prediction and climate models. J. Geophys. Res. Space Phys. 2012, 117.[CrossRef] 42. CIS. Canadian Ice Service Digital Archive—Regional Chart: History, Accuracy, and Cavetas. CISDA. September 2006. Available online: https://ice-glaces.ec.gc.ca/IA_DOC/cisads_no_001_e.pdf (accessed on 7 March 2021). 43. Flather, R.A. Results from a Storm Surge Prediction Model of the North-West European Continental Shelf for April, November and December 1973; Institute of Oceanographic Sciences: Wormley, UK, 1976. 44. Sheppard, P.A. Transfer across the earth’s surface and through the air above. Q. J. R. Meteorol. Soc. 1958, 84, 205–224. [CrossRef] 45. Wu, J. Wind-Stress coefficients over Sea surface near Neutral Conditions—A Revisit. J. Phys. Oceanogr. 1980, 10, 727–740. [CrossRef] 46. Large, W.G.; Pond, S. Open Ocean Momentum Flux Measurements in Moderate to Strong Winds. J. Phys. Oceanogr. 1981, 11, 324–336. [CrossRef] 47. Yelland, M.J.; Moat, B.I.; Taylor, P.K.; Pascal, R.W.; Hutchings, J.; Cornell, V.C. Wind Stress Measurements from the Open Ocean Corrected for Airflow Distortion by the Ship. J. Phys. Oceanogr. 1998, 28, 1511–1526. [CrossRef] 48. Barnhart, K.R.; Miller, C.R.; Overeem, I.; Kay, J.E. Mapping the future expansion of Arctic open water. Nat. Clim. Chang. 2016, 6, 280–285. [CrossRef] 49. Muis, S.; Apecechea, M.I.; Dullaart, J.; Rego, J.D.L.; Madsen, K.S.; Su, J.; Yan, K.; Verlaan, M. A High-Resolution Global Dataset of Extreme Sea Levels, Tides, and Storm Surges, Including Future Projections. Front. Mar. Sci. 2020, 7, 263. [CrossRef] 50. Bryant, K.M.; Akbar, M. An Exploration of Wind Stress Calculation Techniques in Hurricane Storm Surge Modeling. J. Mar. Sci. Eng. 2016, 4, 58. [CrossRef] 51. Forbes, D.L.; Parkes, G.S.; Manson, G.K.; Ketch, L.A. Storms and shoreline retreat in the southern Gulf of St. Lawrence. Mar. Geol. 2004, 210, 169–204. [CrossRef] 52. Japan Meteorological Agency. JRA-55: Japanese 55-Year Reanalysis, Daily 3-Hourly and 6-Hourly Data; Research Data Archive at the National Center for Atmospheric Research; Computational and Information Systems Laboratory: Boulder, CO, USA, 2013. 53. Swail, V.R.; Cardone, V.J.; Callahan, B.; Ferguson, M.; Gummer, D.J.; Cox, A.T. The MSC Beaufort Wind and Wave Reanalysis. In Proceedings of the 10th International Workshop on Wave Hindcasting and Forecasting and Coastal Hazard Symp, Oahu, HI, USA, 11–16 November 2007; U.S Army Engineer Research and Development Center: Oahu, HI, USA, 2007; p. 22. 54. Giusti, M. ERA5 Back Extension 1950–1978 (Preliminary Veresion): Tropical Cyclones Are Too Intense. ECMWF ERA5. Novem- ber 2020. Available online: https://confluence.ecmwf.int/display/CKB/ERA5+back+extension+1950-1978+%28Preliminary+ version%29%3A+tropical+cyclones+are+too+intense (accessed on 20 November 2020). 55. Guarino, M.-V.; Sime, L.C.; Schröeder, D.; Malmierca-Vallet, I.; Rosenblum, E.; Ringer, M.; Ridley, J.; Feltham, D.; Bitz, C.; Steig, E.J.; et al. Sea-ice-free Arctic during the Last Interglacial supports fast future loss. Nat. Clim. Chang. 2020, 10, 928–932. [CrossRef]