Diminishing sea ice is impacting the wave field across the Arctic
region. Recent observation- and model-based studies highlight the
spatiotemporal influence of sea ice on offshore wave climatologies, but
effects within the nearshore region are still poorly described. This study
characterizes the wave climate in the central Beaufort Sea coast from 1979
to 2019 by utilizing a wave hindcast model that uses ERA5 winds, waves, and
ice concentrations as input. The spectral wave model SWAN (Simulating Waves Nearshore) is calibrated and
validated based on more than 10 000 in situ time point measurements collected over
a 13-year time period across the region, with friction variations and
empirical coefficients for newly implemented empirical ice formulations for
the open-water and shoulder seasons. Model results and trends are analyzed
over the 41-year time period using the non-parametric Mann–Kendall test,
including an estimate of Sen's slope. The model results show that the
reduction in sea ice concentration correlates strongly with increases in
average and extreme wave conditions. In particular, the open-water season
extended by ∼96 d over the 41-year time period
(∼2.4 d yr-1), resulting in a 5-fold increase in the
yearly cumulative wave power. Moreover, the open-water season extends later
into the year, resulting in relatively more open-water conditions during
fall storms with high wind speeds. The later freeze-up results in an
increase in the annual offshore median wave heights of 1 % yr-1 and an
increase in the average number of rough wave days (defined as days when
maximum wave heights exceed 2.5 m) from 1.5 in 1979 to 13.1 d in 2019.
Trends in the nearshore areas deviate from the patterns offshore. Model
results indicate a saturation limit for high wave heights in the shallow
areas of Foggy Island Bay. Similar patterns are found for yearly cumulative
wave power.
Introduction
Receding Arctic Ocean ice coverage is increasing commercial opportunities
such as the shipping of goods and oil and gas interests along the shores of
Alaska's northern coast (O'Rourke et al., 2020; Perrie et al., 2013; Aksenov et al., 2017).
However, rising air and ocean temperatures are changing the climate regime
(Navarro et al., 2016; Overland, 2016) and may pose new challenges
to commercial activities in the region. Additional oceanographic data will
improve the understanding of how future changes will affect wave climatology
and its impact on existing and planned infrastructure. Coastal Arctic
activities and marine infrastructure will be susceptible to disruption,
decay, and catastrophic failure if wind-wave energy increases, if swell
waves emerge along the otherwise fetch-limited Alaska Arctic coast, and if
storm surge levels increase (Erikson et al., 2015; Pisaric et al., 2011;
Thomson et al., 2016; Thomson and Rogers, 2014).
Recent interest and advancements in satellite technology, processing
techniques, and modeling have resulted in several new studies that highlight
and illuminate the effects of increasing median and extreme wave conditions
across the Arctic Ocean (Casas-Prat et al., 2018; Casas-Prat and Wang, 2020;
Liu et al., 2016; Stopa et al., 2016; Francis et al., 2011). Few studies,
however, can resolve changes within the nearshore region, here defined as
the portion of the shelf between the coast and the ∼20 m isobath.
Nearshore wave climate is a function of all factors that generate and
dissipate wave energy (e.g., winds, coastal orientation, continental shelf
size, and slope); however, in the Arctic, sea ice plays an additional
crucial role in the development and mitigation of wave energy within the
coastal margins. Seasonal sea ice forms in early to late fall, with ice
first forming in the protected bays and shallows, eventually merges
with basin-wide multi-year and accumulated pack ice and eliminates any
surface wave action at the coast, and subsequently breaks up sometime in
late spring or early summer. During the transitionary “shoulder seasons”
when landfast ice breaks up or forms, wave growth and energy transfer are
mitigated by reduced wind–sea surface drag and dissipation by the presence
of ice, further complicating the accurate depiction of nearshore wave
conditions. Landfast ice is sea ice that is attached to the coastlines or
shallow sea floor on the continental shelves and therefore does not drift
with currents and wind (Mahoney et al., 2014).
Since the satellite era, it has become increasingly clear that freeze-up and
thaw occur later and earlier, respectively, resulting in extended periods
over which wave generation can occur (Frey et al., 2015; Thomson and Rogers,
2014; Wang and Overland, 2015). Additionally, minimum sea ice extents, which
typically occur in September, have been, since the year 2000, decreasing at a rate
of 3.4 % per decade across the Arctic basin, with the most expansive
changes in open-water area occurring across the Beaufort Sea and Chukchi Sea
coasts (Frey et al., 2015; Stopa et al., 2016; Stroeve and Notz, 2018). The
resulting increase in fetch, defined by the time-varying shape and size of
the ice pack, has resulted in the emergence of swell energy notwithstanding
any changes in wind magnitude, direction, and duration (Stopa et al., 2016;
Thomson et al., 2016). Previous works have shown increases in mean and
extreme wind speeds, as well as increasing frequency of occurrence of
extreme winds in October when landfast sea ice often begins to form.
However, due to limited observations, it remains unclear whether such
changes exist in overwater winds and if they are driving observed and
hindcasted increasing wind-wave energy and swell either offshore or
nearshore.
The objectives of this study are twofold: first, to compare trends in median
and extreme wave climatology within the nearshore region to those offshore and
second, to illuminate the underlying causes of noted changes. We investigate
changes in nearshore wave conditions along a stretch of the Alaska central
Beaufort Sea coast where there is renewed interest in nearshore oil
exploration and production. The proposed construction of an additional
artificial island in the Liberty Prospect area (near the existing Northstar
Island) and exploration-supporting infrastructure has raised concerns for
potential negative impacts on marine mammals, subsistence whaling, and
nearshore habitats, especially around the nearby Boulder Patch. The Boulder
Patch is an ecologically important area within Stefansson Sound believed to
support Beaufort Sea's richest and most diverse biological communities
(Dunton et al., 1982). A high-resolution SWAN (Simulating Waves Nearshore;
Booij et al., 1999) wave model, forced with winds from a state-of-the-art
global reanalysis, is calibrated and validated against in situ offshore and
nearshore wave measurements and used to compute a continuous 3-hourly
time series of wave conditions from 1979 through 2019. The model includes
newly implemented formulations (Rogers, 2019) to account for limited wave
growth and energy dissipation within the marginal ice zone (MIZ). The MIZ is
defined here where waves interact with the sea ice (e.g., Dumont et al.,
2011) and typically has an ice concentration of larger than 5 % sea ice
(e.g., Aksenov et al., 2017).
This paper begins with a description of the greater Stefansson Sound
region and field measurements obtained therein. The model setup,
calibration, and validation are then presented, followed by analyses of
changes in hindcasted winds and waves both within the nearshore region of
Stefansson Sound and offshore. Limitations and implications are then
discussed in the final two sections.
Site description
Foggy Island Bay (FIB) is relatively shallow with a mean water depth of
∼7 m and is sheltered by several offshore shoals and barrier
island complexes (Fig. 1). FIB is fronted by the
Beaufort Shelf that extends 60 to 120 km offshore with an average depth of
37 m. The slope of the shelf is mild, with bottom slopes typically being
∼10-3 inshore of the 10 m isobath (Curchitser et al.,
2017). Meteorological conditions along the Beaufort Sea coast are a major
controlling factor in determining the physical environment of the entire
region. Wind directions are largely bimodal, blowing from either east or
west, with prevailing winds from the east (Mahoney et al., 2019;
Fig. 2a). Both regional-scale atmospheric
circulation patterns and mesoscale coastal wind phenomena contribute to the
distinct wind patterns. Wave directions are similarly bimodal with a
predominant direction from the east (Erikson et al., 2020;
Fig. 2b).
The region experiences subfreezing temperatures for 9 months of the year
when air temperatures can reach to -45∘C (Overland, 2009) and
with strong winds can produce even colder wind chills. The mean annual
temperature is around -10∘C, but during the summer months, air
temperatures occasionally exceed +20∘C (Curchitser et al.,
2017). Air temperature largely controls the timing of sea ice formation and
breakup.
Sea ice initially forms in the shallows of FIB in late September and early
October, then slowly thickens and grows seaward until the Beaufort Shelf is
ice-covered by the second or third week of October
(Fig. 3a). In the fall, when the floating ice
sheet grows seaward, the ice gradually attaches to the near-freezing seabed,
gradually thickens to ∼1.7 to 2.2 m by mid-March, and then
remains constant through mid-June (Mahoney et al., 2014; Curchitser et al.,
2017).
Breakup of the nearshore landfast ice zone begins in late May or June, and
it typically disappears by mid-July (Fig. 3a). During
breakup, coastal rivers discharge warmer fresh sediment-laden water onto
the landfast ice, hastening its nearshore melting (Dmitrenko et al., 1999).
Through June, the offshore sea ice (once attached to land as landfast ice)
rapidly breaks up, often sped up by winds, freshening the surface waters
while dispersing large amounts of sediment and organic matter into the water
column (Mahoney et al., 2007). Typically, by July, FIB is ice-free, although
small floating ice can drift into the waters (Stroeve and Notz, 2018).
Wave conditions are strongly influenced by these seasonal variations in ice
concentration and wind speeds. During the frozen months from early to
mid-November through May, no wave action is observed
(Fig. 3c). However, once ice concentrations start
to decrease, waves begin to emerge in the region (e.g., Thomson et al.,
2016). Wave heights increase throughout the open-water season due to
increasingly higher wind speeds and larger fetch. The highest wave heights
are typically observed in late October when wind speeds are high and ice is
not yet present (e.g., Stopa et al., 2016).
Wind (left; a) and wave (right; b) climate at the single ERA5 output
point of 72∘ N, 147∘ W for the time period 1979–2019 (see Fig. 1 for the location). In panel (a) the black line depicts the overall
coastline orientation of 110∘ N, and in panel (b) it is the mean wave
direction of 75∘ N.
Ice concentrations (a), wind speed magnitude (b), and significant
wave height (c) at the single ERA5 output point of 72∘ N, 147∘ W (see Fig. 1 for the location) averaged daily over the
time period 1979–2019. Each subplot shows the mean, 5 %, 25 %, 75 %, and 95 %
exceedance probability. The interquartile range (IQ) is the area shaded green
between the 25th and 75th percentiles.
Materials and methodsData sourcesERA5
ERA5 (Hersbach et al., 2020) is a detailed reanalysis of the global
atmosphere, land surface, and ocean waves from 1950 onwards produced by the
European Centre for Medium-Range Weather Forecasts (ECMWF). This
meteorological dataset provides, among other variables, estimates of
atmospheric parameters such as air temperature, pressure, wind, ice
concentration, and information on waves over the global oceans. Atmospheric
data are available at a resolution of 0.25∘ (∼30 km), while
wave data can be retrieved at 0.5∘ resolution. The reanalysis combines
model data with observations from across the world into a globally complete
and consistent dataset using the laws of physics. ERA5 has been shown to
perform well in capturing observed weather and climate variability in Alaska
and the Arctic (Graham et al., 2019). It is however not able to resolve
landfast ice (Hošeková et al., 2021). In particular, the authors
show that the global reanalysis model has a good agreement with observed
offshore wave heights. However, the persistence of landfast ice in the late
spring/early summer is not well resolved, resulting in an overestimation of
the cumulative spring coastal wave exposure. In this paper, offshore
significant wave height (Hs), mean period (Tm), and mean direction
(Dm) are used to drive the SWAN model. Wind conditions (u10,
v10) and ice concentration (IC) from this reanalysis dataset are
additionally applied across all model domains.
Field measurements
Limited in situ observational wave data exist within Beaufort Sea and particularly
within FIB. As part of this study, existing wave observations from the 1980s
until 2013 were gathered by combing several existing databases. High-quality
observations collected by Shell Energy from 2007–2013 were selected to
calibrate and validate the model. Data prior to 2007 provided daily wave
height estimates measured with a “yardstick” and therefore deemed
insufficiently accurate for this study. With the exception of one
shallow-water (∼3 m) time series measurement in 1982 that was
located outside the high-resolution model domain (Gallaway and Britch, 1983), all
previously collected wave observations were in deep water (depth
>20 m). Therefore, additional measurements were collected
as part of the Bureau of Ocean Energy Management (BOEM) Central Beaufort
Sea Wave and Hydrodynamic Modeling Study. Sofar Spotter wave buoys
(Raghukumar et al., 2019) were deployed in shallow water for approximately 4 weeks each in the summer of 2019 and 2020. The buoys were set to broadcast
standard bulk wave parameters (Hs, Tm, Dm, etc.) every
hour via the Iridium satellite communication network. Three Spotter buoys
were used in this study, deployed in 2019 (one time) and 2020 (two times). Spotter
no. 0519 deployed in 2020 was dragged by ice and changed position and is
therefore included twice in summary Table 1.
Overview of wave observations used for calibration and validation
purposes in this paper. The name of each observation is a combination of the
calendar year of deployment and a letter. Longitude and latitude are
coordinates in degrees (WGS84). Depth is in meters relative to mean sea
level. The start and end dates (mm-dd) of deployment are indicated. The
comment provides more information including which measurement was used for
calibration and validation for sea ice coefficients and friction
coefficients and formulation.
The spectral wind-wave model SWAN is widely used to compute wave fields over
shelf seas, in coastal areas, and in shallow lakes. The accurate estimation
of wave field statistics by such models is essential to various applications
in these environments. SWAN computes the evolution of wave action density N=E/σ, where E is the wave variance density spectrum and σ
is the relative radian frequency, using the action balance equation.
SWAN supports several bottom friction formulations (BFFs) that can be found
in the literature. In this study, three formulations were tested: Hasselmann
et al. (1973; Joint North Sea Wave Project, JONSWAP), Collins (1972; called Collins-BFF), and Madsen
et al. (1988; called Madsen-BFF). Hasselmann et al. (1973) derived the
simplest expression for bottom dissipation in which friction is a constant.
From the results of the JONSWAP experiment, they found a value of 0.038 m2 s-3, which is also the default in SWAN. Madsen et al. (1988)
derived a bottom friction formulation based on the eddy-viscosity concept in
which the user specifies a bottom roughness length. The default bottom
roughness length used by SWAN is 0.05 m. Collins (1972) derived a formulation
for the bottom friction dissipation in which the turbulent bottom stress is
related to the external flow. The user-definable variable is the drag
coefficient which has a default value of 0.015 in SWAN.
Recently, Rogers (2019) implemented input–output for sea ice in SWAN, a
dissipation source term, and scaling of wind input source functions by sea
ice. This functionality is built on lessons learned during the
implementation of sea ice in WAVEWATCH III (Collins and Rogers, 2017). The
formulations use a simple empirical parametric model (polynomial function)
for dissipation by sea ice, following Meylan et al. (2014) and Collins and
Rogers (2017), which prescribe the dissipation rate as a function dependent
on the wave frequency. Thus, the temporal exponential decay rate of energy
can be written as
Dice=SiceE=-2cgki,
where Sice is the sea ice sink term and E is the wave energy spectrum.
Here, ki has units of 1 m-1 and is the linear exponential attenuation
rate of wave amplitude in space. Factor 2 provides a conversion from
amplitude to energy decay. The group velocity, cg, enables conversion
from spatial decay to temporal decay. Sice and E vary with frequency and
direction. In the implementation of Rogers (2019), ki varies with
frequency according to
kif=c0+c0f+c2f2+c3f3+c4f4+c5f5+c6f6,
with c0 to c6 being the user-defined empirical (calibration)
polynomial coefficients. Rogers (2019) only used c2 and c4 and
excluded the other coefficients (i.e., the remaining coefficients are zero).
Throughout this study, we follow previous work and only calibrate using
c2 and c4. The sea ice sink term is scaled with ice concentration.
Sea ice thickness is not explicitly part of the equation but implicitly
considered via the calibration coefficients.
Furthermore, the scaling of the wind input source functions allows the user
to control the scaling of wind input by open-water fraction with the
variable Ωiw (Rogers, 2019). The default value of Ωiw=0, used throughout this study, corresponds to the case where
wind input is scaled by the total fraction of open water. For example, when
25 % of a grid cell is covered with ice, only 75 % of the original input
source function of wind is applied in the simulation (1–0.25 = 0.75).
These formulations, also referred to as IC4M2, have been implemented in the
main sub-version of SWAN since version 41.31, which is the version used in
this study. Here, a three-level SWAN nested grid setup is used
(Fig. 4) with grid resolutions of 5000, 1000,
and 200 m for the overall, intermediate, and detail grids, respectively.
SWAN is run in third-generation mode and includes parameterizations for wind
input, quadruplet interactions, triads, and whitecapping. SWAN is run with
physics package ST6 (Rogers et al., 2012) that allows for a multiplier on
the drag coefficient. Here we base the drag coefficient multiplier on the
work of Le Roux (2009), which accounts for differences in air–water
temperatures. SWAN normally does not include this effect, but the Le Roux
formulation based on temperature difference is included here via the ST6
implementation. Based on the analytical wave height formulation of Le Roux,
variations to the wave height because of variations in the drag coefficient
multiplier are estimated to be between -10 % to +10 % (95 % confidence
interval, CI) or a drag coefficient multiplier of ±20 %. Wave
boundary conditions and meteorological forcing conditions are based on ERA5.
In particular, u10 and v10 were used to generate wind waves. IC was
used for the IC4M2 computation. Air temperature and sea temperature were used to estimate
the drag coefficient of Le Roux. Numerical frequency resolution ranges
lognormally from 0.03 Hz up to 2.5 Hz in 46 frequency bins (33.3–0.4 s); 5∘ bins are used to resolve wave direction.
Calibration was performed via the testing of several friction formulations
and coefficients. In particular, three bottom friction formulations
(JONSWAP, Collins-BFF, and Madsen-BFF) were tested for the three coefficients
each (i.e., 3×3 variations =9 times). Moreover, several empirical coefficients
of the newly implemented ice formulations by Rogers (2019) were tested
regarding the empirical (calibration) polynomial coefficients for
dissipation and Ωiw.
Methods
Wave conditions across Beaufort Sea, Beaufort sound, and FIB were
simulated with 3-hourly stationary SWAN simulations. First, the model
was run over time periods with available field measurements to perform
calibration and validation of the friction and empirical ice coefficients.
Observations collected in 2007–2013 (offshore) and 2019–2020 (nearshore)
were used to calibrate and validate the SWAN grid models (see next
sections). Offshore measurements collected between 2007 and 2013 during
partial ice cover were split into time periods for calibration and
validation of the sea ice implementation. All model domains were utilized
for the calibration and validation. In particular, 1439 time points during
the partial ice season were selected for calibration purposes
(∼20 % of the available timestamps with IC >5 %), and 11 430 time stamps in both the open-water and ice season were
used for validation. Spotter data collected from within the shallow region
of FIB during the 2019 open-water (i.e., ice-free) season were used to
calibrate the friction formulations and coefficients. In addition, 2020
nearshore Spotter data were used to validate the finest-resolution grid and
nearshore wave heights. Second, the calibrated SWAN model was used to
hindcast wave conditions from 1979 to 2019. Both the open-water (IC <5 %) and ice (IC >5 %) season were simulated. Years were
simulated individually, and once completed, they were combined into one
41-year time series per grid cell with a temporal resolution of 3 h.
Wave parameters
Throughout this paper, the following wave parameters were used. In
particular, the significant wave height (Hs; Eq. 3 in meters), mean wave
period (Tm or Tm0,1; Eq. 4 in seconds), steepness (s; Eq. 5;
dimensionless), mean wave direction (Dm; Kuik et al., 1988; in degrees
relative to north), and wave power (P; Eq. 6 in J m-1 s-1) were used. We
acknowledge that other wave period could be used that give more
weight to either lower frequencies (Tm-1,0) or higher frequencies
(Tm0,2). Sofar Spotter wave buoys directly reported Tm01, while
2007–2013 data from Shell were converted from peak wave period to Tm01
with a transformation constant of 1.2 (Goda, 2010):
3Hs=4m0,4Tm0,1=m0/m1,5s=H/L,6P=Ecg=116ρgHs2,
in which m0 is the zero moment of the spectrum and m1 is first moment
of the spectrum. L is wavelength; ρ is the density of water; and g is the
gravitational constant.
Skill scores
To assess model skill, several metrics were used. In particular, the model
bias, mean absolute error (MAE; Eq. 7), root mean square error (RMSE; Eq. 8),
and scatter index (SCI; Eq. 9) were computed. The latter gives a relative
measure of the RMSE compared to the observed variability.
7MAE=1N∑yi-xi,8RMSE=1N∑(yi-xi)2,9SCI=1N∑(yi-xi)21N∑yi2,
in which yi is the computed value, xiis the observed value, and N is
the total number of data points.
Trend analysis
Summary statistics of Hs, Tm, Dm, P, s, IC, and wind speed
(umag) were computed. The median, 90th percentile (or 10 %
exceedance probability), and maximum values for each variable were computed
for several daily, monthly, seasonal, and yearly periods. Additionally, the
annual count of rough wave days (τro), defined as the number of
days when Hs exceeds 2.5 m yr-1 (WCRP, 2020), were computed. Also,
the number of open (IC <5 % ice) and closed days (IC >85 %) were determined for the area of interest.
The non-parametric Mann–Kendall (MK; Mann, 1945; Kendall, 1975) test was then
applied to detect monotonic trends, and the magnitude of the trends was
calculated with Sen's slope (Sen, 1968). The MK test is a test to
statistically assess if there is a monotonic upward or downward trend of the
variable of interest over time. The MK test is non-parametric (distribution-free) and does not require that the residuals of the fitted regression line
be normally distributed. However, the standard p values derived from the MK
test assume that the observations are independent realizations. Following
the method used by Wang and Swail (2001), the effects of autocorrelations
are accounted for in assessing trends and their significance. A pre-whitened
time series (i.e., processed to make it behave statistically like white
noise) that possesses the same trend as the original signal is computed and
re-computed via an iterative approach to find the best fit line (Sen's
slope) and adjusted p value (Reguero, 2019).
Wave calibration
The wave calibration is divided into simulations for observation periods
during the open-water season and ice season. This division is made by
partitioning the observations based on the mean IC in the area of interest.
When the mean IC was higher than 5 %, it was deemed part of the ice
season. When the mean IC was smaller than 5 %, it was deemed part of the
open-water season. In particular, 2019 observations were used for open-water
season calibration, and ∼20 % of the available timestamps in
the data from 2007–2013 were used for the ice season calibration.
Open-water season
Observed and computed wave heights and periods for the 2019 measurement
period are shown in Fig. 5. Individual
combinations of bottom friction formulation and friction coefficient are
plotted with different colors. Observed wave heights and periods are plotted
as black dots. The figure shows strong sensitivity to different friction
options used for both the wave height and period. The range of coefficients
used for the Madsen et al. (1988) formulation (Madsen-BFF) resulted in too
much dissipation due to bottom friction and underestimated wave heights.
Whereas default SWAN values for Collins-BFF and JONSWAP (see Table 2)
performed well, the overall best fit, based on visual inspection of the
time series in Fig. 5 and residual plots (not shown) as well as quantitative
error statistics, was the formulation of Collins-BFF with a coefficient of
0.020 (RMSE = 0.126 m; bias = 0.005 m).
Wave height (a) and wave period (b) as observed (black
dots) and modeled (colored lines) with the detailed domain using various
friction formulations and coefficients for the observation period in 2019.
Measurements were obtained with a Sofar Spotter anchored at 70.32∘ N, 147.76∘ W in approximately 3 m water depth. Please note that the date format in this and following figures is month/day.
Skill scores for computed significant wave heights (Hs) using
various bottom friction formulations (BFFs) and coefficients. The Collins
bottom friction formulation (Collins-BFF; Collins, 1972), with a coefficient
of 0.020, was chosen for the remainder of this study (denoted in bold).
Friction coefficients with an asterisk (*) are SWAN “default” values. The JONSWAP
friction formulations are from Hasselmann et al. (1973), and the Madsen
friction formulations are from Madsen et al. (1988).
Observed and computed wave heights and periods for the 2007 measurement
campaign are shown in Fig. 6. All individual
combinations of empirical ice formulations are plotted with a different
color (see Table 3 for a description per combination). Observed wave heights
and periods are plotted as black dots. The results show a strong sensitivity
to these empirical coefficients. Moreover, the SWAN models miss certain
events that ERA5 can reproduce likely due to the assimilation of altimeter
measurements. For example, the event at the end of November 2007, when wave
heights around 5–6 m were observed, was captured in ERA5 but strongly
underestimated by SWAN. The observations also have gaps when no waves were
observed.
Table 3 summarizes model skills for wave height and period for 20 % of the
offshore data between 2007–2013 combined (1439 time points). Based on
these results, the lower IC4M2 coefficient is most appropriate. Values of
1.06×10-3 and 2.30×10-2, for c2 and c4, respectively, in the
equation, as Meylan et al. (2014) found for ice floes in the MIZ near
Antarctica, resulted in a strong negative bias (i.e., model underestimates;
too much dissipation). On the other hand, values of 2.84×10-4 and 1.53×10-2,
as found by Rogers (2019) for pancake and frazil ice, resulted in a better
agreement with observations.
Wave height (a) and wave period (b) as observed (black
dots) and modeled (colored lines) with the detailed intermediate domain
using various combinations of empirical ice formulations (Table 3) for the
observation period in 2007. Ice concentrations (IC; panel c) are high across
the domain. The different colors in panel (c) show the mean, 5 %, 25 %, 75 %, and
95 % exceedance probability. The interquartile range (IQ) is the area shaded green
between the 25th and 75th percentiles.
Significant wave height model skill for different combinations of
empirical ice coefficients describing dissipation and reduction in wave
growth; var07 (bold) is the chosen value for the remainder of this study.
All 1439 observations with at least a mean ice concentration of 5 % from
2007–2013 are considered.
Observed and computed wave heights and periods for the 2020 measurement
campaign are shown in Fig. 7. Similar to previous figures, model results
are plotted with colored lines, and observed data are black dots. The figure
shows that generally increasing model resolution improves reproductive
skill. In particular, the detail model domain has the lowest RMSE of 0.133
and 0.118 m for 2020A and 2020B+C, respectively. However, the overall
and intermediate model domains also have good model skill for the nearshore
Spotter observations. The detailed domain results in a ∼20 % reduction in RMSE but with an ∼80 % increase in
computation time. Model resolution cannot explain the mismatch for the time
periods of 27 July–1 August and ca. 6 August, where larger differences
between observations and measurements can be seen.
Significant wave height as observed (black dots) and modeled
(colored lines). Three model domains are presented: overall (red),
intermediate (blue), and detailed (green) domain. Upper panel (a) is 2020A
(Spotter no. 0518), and bottom panel (b) is 2020B and 2020C (Spotter
nos. 0519-1 and 0519-2). See Fig. 1 for the extent and locations of
these grids.
Large-scale validation
The calibration coefficients found in the previous section for the
open-water (i.e., friction formulation and coefficient; Collins-BFF of
0.020) and ice season (i.e., empirical coefficients for ice dissipation and
reduction on wave growth; var07) are validated for the remaining observation
time points not used within the calibration period. In particular, 11 430
time stamps (80 % of the offshore data between 2007–2013) in both the
open-water and ice season were used for large-scale validation. This
approach allows for independent validation of the model.
Figure 8 presents scatter density plots for the
modeled and observed significant wave height (Fig. 8a) and mean wave period (Fig. 8b) as modeled
with the intermediate grid. The model slightly overestimates both the wave
height (bias of 19 cm) and period (1.3 s). SCIs for wave heights and periods
are around 30 %. This is deemed acceptable to assess changes in the wave
climate.
Scatter density plots of the modeled and observed wave parameters
for >10000 timestamps for the combined dataset of observations
collected between 2007–2013 for the intermediate domain. (a) Significant wave
height and (b) mean wave period.
Changes in wave and meteorological climatologies
In this section, a 41-year hindcast of waves simulated with SWAN is
analyzed. First, changes in meteorological conditions, including changes in
ice concentration and the number of open-water days and historical winds,
are presented (Figs. 9 and 10). Second, changes in wave height, period,
wave power, and direction are visualized and quantified. Table 4 presents an
overview of the results per month, season, and year. Figure 11 presents an
overview of the main changes in climate for September, October, and November
(SON).
Changes in meteorological climateWind
Wind speeds and direction vary from month to month, with higher extremes
between about October and May. Figure 9 presents the number of days during
which the study area had a Beaufort scale value of <4 (gentle breeze), 4
(moderate breeze), 5 (fresh breeze), 6 (strong breeze), and >6
(gale force) based on the wind speed magnitude in ERA5. Although there is
year-to-year variability, visually, no trends emerge. Spatial variability
(not shown) reveals that median wind speeds are fairly constant along the
coast but decrease in the cross-shore direction from sea to land. In
contrast, annual extreme wind speeds are higher in the southeastern corner of
the domain with an annual wind speed close to 21 m s-1. The MK test of the
annual extreme winds reveals a statistically insignificant median trend of +0.01 m s-1 yr-1 (or less than +0.1 % yr-1).
The number of days with the Beaufort scale: <4 (gentle
breeze), 4 (moderate breeze), 5 (fresh breeze), 6 (strong breeze), and
>6 (gale force) as simulated by ERA5 for the area of interest.
Data are based on the average wind speed for the intermediate domain.
Sea ice
Ice concentration varies considerably from month to month. As shown in
Fig. 3a, the maximum duration of the open-water
season is from June–November with the lowest concentration around September–October. Figure 10 presents the number of days per year during which central Beaufort Sea
(see Fig. 1 for location) was fully closed (IC >85 %), open
(IC <5 %), or in an intermediate state. The trend lines reveal
a large decrease in the number of days the area of interest was covered with
ice and a similar increase in the number of days it was fully ice-free. For
example, in 1979, on average, the area was closed for ∼250 d and only fully open for a few weeks. In 2019, 41 years later, this has
changed to 195 and 110 d, respectively. This equates to an 8-fold
increase in the number of open-water days. This increase in open-water days
is driven both by earlier sea ice breakup and later freeze-up.
Moreover, the MK test reveals a statistically significant trend of
decreasing median IC of -1.3 % yr-1 and -1.7 % yr-1 for the summer (June,
July, and August; JJA) and fall (September, October, and November; SON),
respectively. Figure 11a presents the 41-year median IC for SON and the
trend of IC for SON (Fig. 11b) for the area of interest. Spatial
variability reveals median ice concentrations (IC50) to be the
lowest (close to zero) in the northwest of the area of interest and highest
in the southeast (around 25 %). A larger negative gradient occurs closer to
the shoreline (around the 10 m depth contour) and in the areas with
generally higher concentrations. IC50 trends have a statistically
significant trend across the area of interest. Table 4 shows similar
patterns as seen visually. Statistically significant decreasing trends in
ice concentration occur in the months of July–November, with October being the
month with the most significant negative trend.
The number of closed (IC >85 %), intermediate, and
open (IC <5 %) days based on the percentage ice cover as simulated
by ERA5 for the central Beaufort Sea. Trend lines for the number of closed days
(green) and open days (red) are presented as dashed lines. Data are based on the
single ERA5 output point of 72∘ N, 147∘ W for the time
period 1979–2019 (see Fig. 1 for the location).
Changes in wave climateWave heights
Wave heights vary widely from month to month because of the seasonality of IC. As shown in Fig. 3, waves occur mostly
from late May to November, depending on the ice concentration.
Figure 12a presents the daily median wave height
(Hs50) for 41 years based on simulated conditions averaged across FIB.
In general, no waves are present during the months of December to June, when
ice concentration is near 100 %. Strong year-to-year variations are
evident, but visually it is clear that wave heights have increased
substantially in the last 40 years. In 1979, Hs50 values higher than 0.5 m were present only from ca. August–October. In 2019, this period extended
to ca. July–November. This pattern correlates strongly with changes in IC in
the area (correlation coefficient r of -0.70 for daily Hs50 and
IC50).
These visually observed trends are quantified by the MK test of the 10 %
exceedance wave height (Hs90). Spatial variability in Hs90 and
trends for the fall season (SON) are presented in Fig. 11c and d. Wave heights
are higher in the northwest (Fig. 11c). This is a similar pattern as seen
in IC shown in Fig. 11a. Moreover, a clear trend of increasing
Hs90 can be seen in Fig. 11d. There is hardly any alongshore
variability in the increasing trend. This might be because of the somewhat
coarse ERA5 wave and wind resolutions. However, there is a cross-shore
variability with larger increases in Hs90 offshore than closer to the
shoreline. The increase in Hs90 is estimated to be around +2.0±0.3 % yr-1
(or 3.92±1.06 cm yr-1)
Throughout this paper, median trend values are
reported, including 1 standard deviation, depicted with ±.
. Table 4 presents the median in Sen's trend
values for the Hs90 for all months, seasons, and annually. These larger
(Hs90) waves mainly occur in September and October, with a median value
of around 2.0 and 2.4 m, respectively, for September and October based on the 41-year-long hindcast. For October, Hs90 is increasing by 6.5±1.7 cm yr-1 (or +2.7±0.7 % yr-1). This pattern correlates with
the largest decrease in IC. In particular, a negative correlation of
0.87±0.02 is found for the entire dataset for monthly Hs90
and IC50.
Similar trends are found for the more extreme wave conditions. In
particular, the annual maximum wave height (Hs,max) and the number of
rough days (τro) were computed from the 41-year dataset.
Figure 13a presents the Hs,max across FIB as a
function of time. The spatial median annual Hs,max is depicted as black
dots, and the spatial variability in the annual Hs,max is depicted as
uncertainty bars. Strong year-to-year variability is visible; however, a
statistically significant increasing trend of around 4.1 cm yr-1 (or
+1.1 % yr-1) was found, resulting in an increase in a
spatially median Hs,max from 2.90 m in 1979 to 4.62 m in 2019.
Similar to the Hs90 during SON (Fig. 11c), annual Hs,max values show
cross-shore variations but little to no alongshore variation. Closer to
the shoreline, processes such as whitecapping and breaking dissipate wave
energy occur. This depth-induced saturation aligns very closely with the 10 m
depth contour. Within the shallow FIB, wave height tends to have a maximum
of ∼1.9 m, implying a depth-induced saturation that
corresponds to a height–water depth ratio (γ) of 0.4 (depth is
∼5 m). The rather low γ value is typical of
field studies (Raubenheimer et al., 1996) and smaller than γ of
∼0.6–0.78 found for more simplified cases. Raubenheimer et
al. (1996) report γ as low as ∼0.3 for field studies
and attribute such low values to bottom friction and whitecapping by strong
winds through wave energy dissipation near the shore.
Moreover, the largest wave events seem to be happening later and later in
the calendar year. An analysis identified per calendar year the annual
maximum wave height of the year and associated storm date. The result is a
list of 41 annual maximum wave heights and associated storm dates per calendar
year. In 1979, the average storm date occurred on 24 September (day 269); in
2019 this has increased to 15 October (day 289). This shifts the average
storm date 20 d later in the calendar year and results in storms with
generally higher wind speeds on top of the general decreasing IC
(Fig. 13b).
Increasing occurrences of high wave events, τro, are also
identified. Within the simulated 41 years, a statistically significant trend
of 0.24±0.10 d yr-1 (or +4.0±1.7 % yr-1) was
determined. This equates to an increase from 1.5 to 13.1 d each year with
high wave events in the offshore region. These rough days mainly occur
during the fall months of September and October (see also Table 3).
Wave periods and steepness
Figure 11e and f present the median Tm and computed Sen's trend for the fall
months. The median wave period varies slightly from offshore to nearshore,
with offshore values reaching as high as 4.7 s and nearshore values being as
low as 3.1 s. The wave period tends to increase over the analyzed
period, and the trend is statistically significant. The increase in Tm
varies spatially, with little increase in the shallow areas of FIB up to an
increase of 0.03 s yr-1 in the deeper offshore parts of Beaufort Sea. This increase in period is relatively small compared to the
median value (i.e., increase of +0.51±0.13 % yr-1). The
increase in wave period is most likely related to the increase in fetch
length of the larger domain, which allows for more wave development. On the
other hand, the median wave steepness of 0.0536 varies slightly in the
cross-shore direction (not shown). Sen's trends of the wave steepness
are all statistically insignificant and minor (-0.15 % to +0.23 % yr-1 for 97.5 % and 2.5 % exceedance). Therefore, based on the model
results, the wave period increases proportionally with the wave height while
maintaining similar wave steepness.
Time series of the daily median significant wave height
(Hs50) for 41 years of wave data across Foggy Island Bay as simulated
by the intermediate SWAN domain (a) and daily median ice concentration (b). Time series are smoothed by applying a moving weekly filter. The
median estimate for 1979 and 2019 is based on a linear fit per day of the
individual years.
Wave direction
Figure 11g and h present the annual median Dm and computed
Sen's trend. Offshore waves have a mean incident wave direction of
70–75∘ (nautical convention, clockwise from geographic north;
i.e., traveling from northeast towards the southwest) near the 100 m
isobath. This is (unsurprisingly) identical to the ERA5 wave rose of Fig. 2. Hence, incident wave directions in the offshore region strongly reflect
the boundary conditions. In shallower waters approaching the shore, the
waves refract towards the coastline, resulting in a mean wave direction of
48–54∘ (25th–75th percentile) around the 10 m isobath.
Computed Sen's trends show counterclockwise rotation up to 0.39∘ yr-1. These trends are larger closer to the shore in shallower water and
statistically significant. Closer to the offshore boundary, the trends are
closer to 0∘ yr-1 but are statistically insignificant. Table 4
presents the breakdown of the median wave direction over all the different
months and time periods. The median wave direction hardly changes for any of
the analyzed months. However, for the seasons and yearly median wave
direction, there is a statistically significant negative trend.
(a) Time series of the annual maximum wave height
(Hs,max) over the last
40 years as simulated with SWAN for the intermediate domain with an
estimate of day number of the associated peak. The range represents 1 standard deviation (SD) based on spatial variability within the domain. The
solid line is Sen's slope including the dashed uncertainty range for an
alpha of 0.05 (dashed lines). (b) Ice concentration and wind speed
during the storm based on ERA5 (circles) including linear fit (dashed
lines).
Median over 41 years of monthly cumulative wave power (P) along a
transect (see Fig. 4) from nearshore (left) to offshore (right). Different
colors represent different months and are cumulative (a). Associated
bathymetry and water depth (MSL: mean sea level) (b). The green line in the lower panel
marks the location of the Liberty Prospect project.
Wave power
Figure 14 presents the cumulative yearly wave power per month averaged over
all the years simulated. Wave power is highest offshore in deep water and
reduces closer to the shoreline. At the 10 m depth contour, the average
cumulative yearly wave power is ∼70 % of the offshore wave
power. At the 2 m depth contour, this decreases to ∼25 %. Preliminary analysis suggests that refraction on the shelf,
dissipation (whitecapping and bottom friction), and blocking of wave energy
due to the barrier islands all play a role. Figure 15 presents the
cumulative wave power at Liberty Prospect in FIB; 5-year smoothed values
for cumulative power and mean ice concentration in the shallow FIB have a
strong inverse correlation of -0.986. The yearly cumulative wave power
increased 5-fold over the 41 years analyzed. Also, the computed trend
reveals a statistically significant increase in the wave power which is in
absolute terms the largest offshore and less in the shallow parts of FIB.
However, in relative terms, the increase in wave power is almost constant
across the domain. In particular, a statistically significant value of Sen's trend of
3.9±0.2 % yr-1 is computed for the offshore compared to 3.8±0.2 % yr-1 at the 10 m depth contour. Table 4 presents
the breakdown of the mean wave power over all the different months and time
periods. Average wave power is small and hardly changes for the months
of December–June. For the months of July–November there is a statistically significant
increasing trend in wave power with the maximum increase occurring in
October. Similar trends emerge with the dominant months of July, August, September,
October, and November, explaining 93 % of the wave power together, and
this importance hardly varies in the cross-shore direction or with time.
Yearly cumulative wave power (P) smoothed over 5-year moving
windows at Liberty Prospect in Foggy Island Bay. Different colors
represent different months. The black line is the yearly mean 5-year
smoothed ice concentration.
Trend analysis of the median ice concentration (IC50),
90th percentile wave height (Hs,90), median wave period
(Tm,50), mean wave power (P), median wave direction (Dm,50), and
the number of rough days (τro) based on values of the
intermediate domain. Median and mean values computed over the entire 40 years of simulated data. Computed Sen's trends where the majority of grid
cells show a statistically significant trend (assuming alpha of 0.05) are
depicted in bold; otherwise, the trend is shown in the normal black color. DJF: December, January, and February. MAM: March, April, and May. JJA: June, July, and August. SON: September, October, and November.
The validation presented here shows that the constructed SWAN model can
reproduce waves during the open-water and MIZ seasons. This reproductive
skill has been achieved by forcing the model with ERA5 meteorology and with
the inclusion of air–sea temperature differences (Le Roux, 2009) and new
formulations by Rogers (2019) that account for the effect of ice on
(reduced) wave growth and dissipation. An efficient and accurate model-based
approach allowed for continuous 41-year simulations of waves across Alaska's
central Beaufort Sea coast and the detailed quantification of changes in the
wave climate across the seasons in shallower water than previous studies
analyzed.
In the current literature, there is a consensus that larger ice-free areas,
which persist longer into the fall, force higher sea states across
Beaufort Sea (e.g., Thomson et al., 2016; Liu et al., 2016; Stopa et
al., 2016). To our knowledge, no previous study has rigorously quantified
how wave patterns vary within the near- and inshore regions of the central
Alaska Beaufort Sea and across different seasons over the 41-year simulation
period. Within the Beaufort–Chukchi Sea domain, Thomson et al. (2016) found that
altimeter-derived measurements of wave energy increased between 2007 and
2014 and that modeled wave heights increased by 1 cm yr-1. Stopa et al. (2016) estimated an increase in wave heights up to 1 % yr-1 between
1992 and 2014. Findings of listed authors contrast with this study which
suggests larger increases in wave heights over time. In particular,
Hs50 increased by 6 %, and Hs90 and Hs,max increased up to
3 % and 1 %, respectively, over the 41-year hindcast period. We hypothesize
that trends are strongly influenced by specifics of the analysis method,
different wind and ice boundary conditions, locations and spatial extents,
and the timeframe considered, and therefore different studies should be
compared qualitatively instead of quantitatively.
Nearshore wave hindcasting is sensitive to wind forcing,
dissipative/restrictive effects by ice, and boundary conditions from
larger-scale models. Similar to several previous studies (e.g., Overeem et
al., 2011; Barnhart et al., 2014; Stopa et al., 2016; Erikson et al., 2020),
this study found that sea ice minimum now occurs later in fall, when the
wind speeds also increase, which creates more favorable conditions for wave
development. However, wind speed magnitudes and thus wave heights might be
underestimated due to known biases in extreme wind speeds. For example, Liu
et al. (2016) found underestimations of ERA-Interim for the Arctic Ocean.
Moreover, validation of ERA5 wind speeds at Prudhoe Bay shows an
underestimation during storm conditions (see Supplement). In
contrast, wave energy may be overestimated during breakup and freeze-up
due to poorly resolved ice concentrations within the nearshore (e.g., ERA5
ice concentrations used in this study are on a scale of ∼50 km, compared to the intermediate model domain of ∼250 km ×∼100 km). For example, Hošeková et al. (2021) found that while ERA5
reproduces the annual ice cycle well, this reanalysis product does not
resolve landfast ice. The relatively coarse resolution of ERA5 in general is
a limitation of this study, since small-scale wind variations and air–sea
temperature gradient in the MIZ are not resolved. Model skills for the ice
season (IC >5 %) could only be assessed with offshore field
measurements. The nearshore validations showed good skill (RMSE <15 cm) but were only available during the open-water season. Therefore, skill
in the nearshore region during the ice season is unknown and most likely
overestimated given the missing landfast ice and other unresolved processes
in ERA5. Usage of dynamically downscaled atmospheric and oceanographic
conditions will likely improve skill in the nearshore. Moreover, IC less
than 100 % for January–May is arguably due to ERA5 reanalysis uncertainties,
since these conflict with in situ observations.
The depth-induced saturation limit of wave heights around 10 m in the
shallow waters of FIB appears to be a result of the combination of
refraction and dissipation (depth-induced breaking, bottom friction, and
whitecapping) during the open-water season and sea ice concentrations during
breakup and refreeze and is sensitive to specific numerical settings used
in the model. In this study, default values in SWAN for whitecapping via
ST6 physics and depth-induced breaking in combination with calibrated bottom
friction and empirical ice coefficients were used. Further validation and
calibration of in situ measurements of wave extremes (in the presence of floating
ice) will provide invaluable insights into wave physics. More information on
nearshore waves, combined with more reliable data on open-water conditions
for wind and ice, is vital in understanding these complicated air–sea
interactions and feedback processes. For example, Thomson et al. (2016)
suggested that waves may be an important mechanism in the refreezing of ice
in the fall.
Our results suggest that wave heights and wave power increased significantly
over the past 41 years; however, only minor trends in median wave period and
wave steepness were found. Thomson and Rogers (2014) discussed the
emergence of swell in the Beaufort–Chukchi Sea domain. Thomson et al. (2016) showed with a local wind hindcast that for recent years (2004, 2006,
2012, and 2014) the wave periods are still short relative to other oceans,
which indicates that the sea state of any given ice-free location in the
domain is still dominated by local wind waves. Also, a wave model hindcast
by the same authors showed a statistically significant trend of 0.04 s
for the peak wave period over the years 1992–2014. This trend is comparable
to the trend found in these results of Tm50 of 0.03 s over the
period 1979–2019 for the fall. Moreover, in this study the computed
counterclockwise change in wave direction was also reported by others (e.g.,
Erikson et al., 2016, 2020).
Climate-change-induced trends of increasing temperatures and decreasing ice
concentrations and extents are expected to continue based on the latest
global climate models (e.g., Notz and SIMIP Community, 2020; Zanowski et al., 2021). It is thus
expected that the decreasing ice concentrations will result in a further
increase in wave heights, periods, and yearly cumulative wave power for
Alaska's central Beaufort Sea coast. It is unclear how extremes will change,
since storms are driven by the combined effect of ice and wind. Continued
changes in the wave climate will also likely accelerate historical trends in
changes to barrier islands and spits.
The present modeling approach neither allows for coupling with water levels
and currents nor includes wave processes such as wave
setup and swash. Wave processes at the coastline could be important for
estimating flood hazard and risk, especially given the increase in the
offshore annual extreme wave height and number of rough days per year
described herein. Further investigation into hydrodynamic-wave coupling and
the quantification of potential water-level changes with climate change will
provide value insights to support resource decisions.
Conclusions
A high-resolution SWAN (Simulating Waves Nearshore; Booij et al., 1999) wave
model, forced with ERA5 winds and waves, is calibrated and validated against
in situ offshore and nearshore wave measurements. The model includes formulations
that describe wind-wave growth due to air–sea temperature differences (Le
Roux, 2009) and new formulations (Rogers, 2019) to account for limited wave
growth and increased energy dissipation within the marginal ice zone (MIZ).
The inclusion of air–sea temperature differences influenced the wind to sea
drag coefficient by ±20 %. Empirical ice coefficients that are
typical of pancake and frazil ice resulted in the best model skill.
Sensitivity analyses showed that the friction formulation of Collins
(Collins-BFF; Collins, 1972) with a coefficient of 0.020 resulted in the
best fit compared to observations. The model validation reveals acceptable
skill in reproducing over 10 000 in situ time point observations over a 13-year
time period. Overall, wave conditions along the central Beaufort Sea coast
and in the shallow Foggy Island Bay are strongly modulated by the breakup
and freeze-up of sea ice.
A 41-year hindcast simulation was done to estimate changes in the wave
climate. Over the analyzed time period of 1979 through 2019, large changes
in the ice concentration (IC) were found. In particular, the open-water
season has, on average, increased from just a few weeks a year in 1979 to
more than 3 months (110 d) in 2019. The Mann–Kendall test reveals a
statistically significant trend of decreasing IC50 of -1.3 % yr-1 and -1.7 % yr-1 for the summer and fall seasons, respectively. Over the same time
period, no statistically significant trends in wind speed were found.
Model simulations show a 5-fold increase in the yearly cumulative wave
power over the 41-year analysis period, which has a strong inverse
correlation with IC50 (r=-0.986). Median wave heights (Hs50)
during the fall months (September, October, and November; SON) increased
approximately 6 % yr-1, and high wave heights (Hs90) increased
with a slightly lower rate of around 3 % and show an even stronger
negative correlation with IC50. Wave periods tended to increase as
well, albeit while maintaining a constant steepness. A counterclockwise
change in mean wave direction up to 0.39∘ yr-1 was found over the
analyzed time period. The months of July, August, September, October, and
November account for 93 % of the average yearly cumulative wave power and
also have a strong negative correlation with IC.
Annual extreme wave heights were found to increase over time. Model
simulations show an increase in average annual Hs,max from 2.90 m
in 1979 to 4.62 m in 2019. These modeling results equate to an increase
of 4 cm yr-1 or +1 % yr-1 and increases the number of rough days
offshore from 1.5 to 13.1 d. These increases in the highest wave height
occur due to later freeze-up in the fall. The shift in average storm date is
20 d from 1979 to 2019. Storms tend to have higher wind speeds and lower
IC. For the highest waves, the offshore trends deviate from the pattern that
emerges in the shallow parts of FIB. In particular, a depth-induced
saturation that corresponds to a γ of 0.4 shows that part of the
increase in energy is dissipated before reaching the shore. The importance
of dissipation is also found for the wave power where at the 10 m depth
contour, the average cumulative yearly wave power is ±70 % of the
offshore wave power, which decreases further to 25 % at the 2 m depth
contour.
Code and data availability
Data produced are available on ScienceBase at
10.5066/P990NDMQ (Engelstad et al., 2021). Wave observation data are available at
http://ndbc.noaa.gov (last access: 8 July 2021) using the keyword search term “Shell Arctic Buoy”, with some of the
proprietary deep-water data and all the nearshore data to be made available
at http://www.aoos.org (last access: 8 July 2021).
The supplement related to this article is available online at: https://doi.org/10.5194/tc-16-1609-2022-supplement.
Author contributions
All co-authors contributed to the initial framework and methodology. KN
performed the simulations and analysis. LE wrote the Introduction, and the
rest of the manuscript was written by KN. All co-authors contributed by
discussing, editing, and improving the paper.
Competing interests
The contact author has declared that neither they nor their co-authors have any competing interests.
Disclaimer
Any use of trade, firm, or product
names is for descriptive purposes only and does not imply endorsement by the
U.S. Government.Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
The authors thank Bjorn Robke for Fig. 1.
Financial support
Funding for this research was provided by the U.S. Bureau of Ocean Energy Management (cooperative agreement no. M17AC00020; UAF) and an interagency agreement (grant no. M17PG00046; USGS) for the project titled “Wave and Hydrodynamic Modeling Within the Nearshore Beaufort Sea”. Additional financial support was provided by the U.S. Geological Survey Coastal and Marine Hazards and Resources Program (Li Erikson and Anita Engelstad) and the University of Alaska Fairbanks (Jeremy Kasper and Peter Bieniek).
Review statement
This paper was edited by Bin Cheng and reviewed by Jim Thomson and one anonymous referee.
ReferencesAksenov, Y., Popova, E. E., Yool, A., Nurser, A. J. G., Williams, T. D.,
Bertino, L., and Bergh, J.: On the future navigability of Arctic sea routes:
High-resolution projections of the Arctic Ocean and sea ice, Mar. Policy,
75, 300–317, 10.1016/j.marpol.2015.12.027, 2017.Barnhart, K. R., Overeem, I., and Anderson, R. S.: The effect of changing sea ice on the physical vulnerability of Arctic coasts, The Cryosphere, 8, 1777–1799, 10.5194/tc-8-1777-2014, 2014.Booij, N.,
Ris, R. C., and Holthuijsen, L. H.: A third-generation wave model for
coastal regions. I- Model description and validation, J. Geophys. Res., 104,
7649–7666, 10.1029/98jc02622, 1999.Casas-Prat, M. and Wang, X. L.: Projections of Extreme Ocean Waves in the
Arctic and Potential Implications for Coastal Inundation and Erosion, J.
Geophys. Res.-Ocean., 125, e2019JC015745, 10.1029/2019JC015745, 2020.Casas-Prat, M., Wang, X. L., and Swart, N.: CMIP5-based global wave climate
projections including the entire Arctic Ocean, Ocean Model., 123, 66–85,
10.1016/j.ocemod.2017.12.003, 2018.Collins, J. I.: Prediction
of shallow-water spectra, J. Geophys. Res., 77, 2693–2707,
10.1029/JC077i015p02693, 1972.Collins, C. O. and Rogers, W. E.: A Source Term for Wave Attenuation by Sea
Ice in WAVEWATCH III®: IC4, 2017.
Curchitser, E. N., Hedstrom, K., Danielson, S., and Kasper, J.: Development of a Very High-Resolution Regional Circulation Model of Beaufort Sea Nearshore Areas, U.S. Dept. of the Interior, Bureau of Ocean Energy Management, Alaska OCS Region, Anchorage, AK, OCS Study BOEM 2018-018, 81 pp., 2017.
Dmitrenko, I., Gribanov, V. A., Volkov, D. L., Kassens, H., and Eicken, H.:
Impact of river discharge on the fast ice extension in the Russian Arctic
shelf area, Proc. 15th Int. Conf. Port Ocean Eng. under Arct. Cond.
(POAC99), Helsinki, 23–27 August 1999, 1, 311–321, 1999.Dumont, D., Kohout, A., and Bertino, L.: A wave-based model for the marginal
ice zone including a floe breaking parameterization, J. Geophys. Res.-Ocean., 116, 1–12, 10.1029/2010JC006682, 2011.
Dunton, K. H., Reimnitz, E., and Schonberg, S.: An arctic kelp community in the Alaskan Beaufort Sea KH Dunton, ERK Reimnitz, Arctic, 35, 465–484, 1982.Engelstad, A. C., Nederhoff, K., and Erikson, L. E.: Wave model results of the central Beaufort Sea coast, Alaska, U.S. Geological Survey data release [data set], 10.5066/P990NDMQ, 2021.Erikson, L. H., McCall, R. T., van Rooijen, A., and Norris, B.:, Hindcast storm events in the Bering Sea for the St. Lawrence Island and Unalakleet regions, Alaska, U.S. Geological Survey Open-File Report 2015‒1193, 47 p., 10.3133/ofr20151193, 2015.Erikson, L. H., Hegermiller, C. E., Barnard, P., and Storlazzi, C. D.: Wave
Projections for United States Mainland Coasts, U.S. Geological Survey summary of methods to accompany data release, 10.5066/F7D798GR, 2016.Erikson, L. H., Gibbs, A. E., Richmond, B. M., Storlazzi, C. D., Jones, B.
M., and Ohman, K. A.: Changing Storm Conditions in Response to Projected
21st Century Climate Change and the Potential Impact on an Arctic Barrier
Island – Lagoon System – A Pilot Study for Arey Island and Lagoon ,
Eastern Arctic Alaska, USGS Open-File Rep.,
10.3133/ofr20151193, 2020.Francis, O. P., Panteleev, G. G., and Atkinson, D. E.: Ocean wave conditions
in the Chukchi Sea from satellite and in situ observations, Geophys. Res.
Lett., 38, 1–5, 10.1029/2011GL049839, 2011.Frey, K., Moore, G. W. K., Cooper, L., and Grebmeier, J.: Divergent Patterns
of Recent Sea Ice Cover across the Bering, Chukchi, and Beaufort Seas of the
Pacific Arctic Region, Prog. Oceanogr., 136, 32–49,
10.1016/j.pocean.2015.05.009, 2015.
Gallaway, B. J. and Britch, R. P.: Environmental summer studies (1982) for
the Endicott development. LGL Alaska Research Associates Northern Technical
Services., and Sohio Alaska Petroleum Company, Fairbanks, Alaska.,
1983.Goda, Y.: Random Seas and Design of Maritime Structures, 3rd ed., WORLD
SCIENTIFIC, 10.1142/7425, 2010.Graham, R. M., Hudson, S. R., and Maturilli, M.: Improved Performance of
ERA5 in Arctic Gateway Relative to Four Global Atmospheric Reanalyses,
Geophys. Res. Lett., 46, 6138–6147, 10.1029/2019GL082781,
2019.
Hasselmann, K., Barnett, T. P., Bouws, E., Carlson, H., Cartwright, D. E.,
Enke, K., Erwing, J. A., Gienapp, H., Hasselmann, D. E., Kruseman, P.,
Meerburg, A., Muller, P., Ollbers, D., Richter, K., Sell, W., and Walden,
H.: Erganzungsheft zur Deutschen Hydrographischen Zeitschrift, Coast. Eng.,
7, 399–404, 1973.Hersbach, H., Bell, B., Berrisford, P., Hirahara, S.,
Horányi, A., Muñoz-Sabater, J., Nicolas, J., Peubey, C., Radu, R.,
Schepers, D., Simmons, A., Soci, C., Abdalla, S., Abellan, X., Balsamo, G.,
Bechtold, P., Biavati, G., Bidlot, J., Bonavita, M., De Chiara, G.,
Dahlgren, P., Dee, D., Diamantakis, M., Dragani, R., Flemming, J., Forbes,
R., Fuentes, M., Geer, A., Haimberger, L., Healy, S., Hogan, R. J.,
Hólm, E., Janisková, M., Keeley, S., Laloyaux, P., Lopez, P., Lupu,
C., Radnoti, G., de Rosnay, P., Rozum, I., Vamborg, F., Villaume, S., and
Thépaut, J. N.: The ERA5 global reanalysis, Q. J. Roy. Meteor. Soc.,
1–51, 10.1002/qj.3803, 2020.Hošeková, L., Eidam, E., Panteleev, G., Rainville, L., Rogers, W.
E., and Thomson, J.: Landfast Ice and Coastal Wave Exposure in Northern
Alaska, Geophys. Res. Lett., 48, 1–11,
10.1029/2021GL095103, 2021.
Kendall, M.: Rank Correlation Methods, 4th Ed. Charles Griffin, London,
1975.Kuik, A. J., van Vledder, G. P., and Holthuijsen, L. H.: A Method for
the Routine Analysis of Pitch-and-Roll Buoy Wave Data, J. Phys. Oceanogr.,
18, 1020–1034, 10.1175/1520-0485(1988)018<1020:AMFTRA>2.0.CO;2, 1988.Le Roux, J. P.: Characteristics of developing waves as a function of
atmospheric conditions, water properties, fetch and duration, Coast. Eng.,
56, 479–483, 10.1016/j.coastaleng.2008.10.007, 2009.Liu, Q., Babanin, A. V., Zieger, S., Young, I. R., and Guan, C.: Wind and
wave climate in the Arctic Ocean as observed by altimeters, J. Climate, 29,
7957–7975, 10.1175/JCLI-D-16-0219.1, 2016.
Madsen, O. S., Poon, Y. K., and Graber, H. C.: Spectral wave attenuation by
bottom friction: theory, in: Twenty First Coastal Eng Conf, Twenty-First
Coastal Engineering Conference, 20–25 June 1988, 492–504, 1988.Mahoney, A., Eicken, H., Gaylord, A. G., and Shapiro, L.: Alaska landfast
sea ice: Links with bathymetry and atmospheric circulation, J. Geophys. Res.-Ocean., 112, C02001, 10.1029/2006JC003559, 2007.Mahoney, A. R., Eicken, H., Gaylord, A. G., and Gens, R.: Landfast sea ice
extent in the Chukchi and Beaufort Seas: The annual cycle and decadal
variability, Cold Reg. Sci. Technol., 103, 41–56,
10.1016/j.coldregions.2014.03.003, 2014.Mahoney, A. R., Hutchings, J. K., Eicken, H., and Haas, C.: Changes in the
Thickness and Circulation of Multiyear Ice in the Beaufort Gyre Determined
From Pseudo-Lagrangian Methods from 2003–2015, J. Geophys. Res.-Ocean.,
124, 5618–5633, 10.1029/2018JC014911, 2019.Mann, H. B.: Nonparametric Tests Against Trend, 13, 245,
10.2307/1907187, 1945.Meylan, M. H., Bennetts, L. G., and Kohout, A. L.: In situ measurements and
analysis of ocean waves in the Antarctic marginal ice zone, Geophys. Res.
Lett., 41, 5046–5051, 10.1002/2014GL060809, 2014.Navarro, J., Varma, V., Riipinen, I., Seland, Ø., Kirkevåg, A.,
Struthers, H., Iversen, T., Hansson, H.-C., and Ekman, A.: Amplification of
Arctic warming by past air pollution reductions in Europe, Nat. Geosci., 9, 277–281,
10.1038/ngeo2673, 2016.Notz, D. and SIMIP Community: Arctic Sea Ice in CMIP6, Geophys. Res. Lett.,
47, e2019GL086749, 10.1029/2019GL086749, 2020.
O'Rourke, R., Comay, L. B., Folger, P., Frittelli, J., Humphries, M.,
Leggett, J. A., Ramseur, J. L., Sheikh, P. A., and Upton, H. F.: Changes in
the Arctic: Background and issues for congress (updated), Key Congr. Reports
Sept. 2019 Part I, 89–243, 2020.Overeem, I., Anderson, R. S., Wobus, C. W., Clow, G. D., Urban, F. E., and Matell, N.: Sea ice loss enhances wave action at the Arctic coast, Geophys. Res. Lett., 38, L17503, 10.1029/2011GL048681, 2011.Overland, J. E.:
Meteorology of the beaufort sea, J. Geophys. Res.-Ocean., 114, 1–10,
10.1029/2008JC004861, 2009.Overland, J. E.: A difficult Arctic science issue: Midlatitude weather
linkages, Polar Sci., 10, 210–216,
10.1016/j.polar.2016.04.011, 2016.Perrie, W., Gerdes, R., Hunke, E., and Treguier, A.-M.: Preface to the Arctic Ocean special issue, Ocean Model., 71, 1, 10.1016/j.ocemod.2013.08.005, 2013.Pisaric, M. F. J., Thienpont, J. R., Kokelj, S. V., Nesbitt, H., Lantz, T.
C., Solomon, S., and Smol, J. P.: Impacts of a recent storm surge on an
Arctic delta ecosystem examined in the context of the last millennium, P.
Natl. Acad. Sci. USA, 108, 8960–8965,
10.1073/pnas.1018527108, 2011.Raghukumar, K., Chang, G., Spada, F., Jones, C., Janssen, T., and Gans, A.:
Performance characteristics of “spotter,”' a newly developed real-time
wave measurement buoy, J. Atmos. Ocean. Technol., 36, 1127–1141,
10.1175/JTECH-D-18-0151.1, 2019.
Raubenheimer, B., Guza, R. T., and Elgar, S.: Wave Transformation across the
inner surf zone, J. Geophys. Res., 101, 589–597, 1996.Reguero, B. G., Losada, I. J., and Méndez, F. J.: A recent increase in
global wave power as a consequence of oceanic warming, Nat. Commun., 10,
1–14, 10.1038/s41467-018-08066-0, 2019.
Rogers, W. E.: Implementation of Sea Ice in the Wave Model SWAN, Technical Report, Naval Research Lab., Washington, D.C., https://www7320.nrlssc.navy.mil/pubs/2019/rogers2-2019.pdf (last access: 4 November 2021),
USA, 2019.Rogers, W. E., Babanin, A. V., and Wang, D. W.: Observation-consistent
input and whitecapping dissipation in a model for wind-generated surface
waves: Description and simple calculations, J. Atmos. Ocean. Technol., 29,
1329–1346, 10.1175/JTECH-D-11-00092.1, 2012.Sen, P. K.: Estimates of the Regression Coefficient Based on Kendall's Tau,
J. Am. Stat. Assoc., 63, 1379–1389,
10.1080/01621459.1968.10480934, 1968.Stopa, J. E., Ardhuin, F., and Girard-Ardhuin, F.: Wave climate in the Arctic 1992–2014: seasonality and trends, The Cryosphere, 10, 1605–1629, 10.5194/tc-10-1605-2016, 2016.Stroeve, J. and Notz, D.: Changing state of Arctic sea ice across all
seasons, Environ. Res. Lett., 13, 103001, 10.1088/1748-9326/aade56,
2018.Thomson, J. and Rogers, W. E.: Swell and sea in the emerging Arctic Ocean,
Geophys. Res. Lett., 41, 3136–3140, 10.1002/2014GL059983,
2014.Thomson, J., Fan, Y., Stammerjohn, S., Stopa, J., Rogers, W. E.,
Girard-Ardhuin, F., Ardhuin, F., Shen, H., Perrie, W., Shen, H., Ackley, S.,
Babanin, A., Liu, Q., Guest, P., Maksym, T., Wadhams, P., Fairall, C.,
Persson, O., Doble, M., Graber, H., Lund, B., Squire, V., Gemmrich, J.,
Lehner, S., Holt, B., Meylan, M., Brozena, J., and Bidlot, J. R.: Emerging
trends in the sea state of the Beaufort and Chukchi seas, Ocean Model., 105,
1–12, 10.1016/j.ocemod.2016.02.009, 2016.Wang, M. and Overland, J.: Projected Future Duration of the Sea-Ice-Free
Season in the Alaskan Arctic, Prog. Oceanogr., 136, 50–59,
10.1016/j.pocean.2015.01.001, 2015.Wang, X. and Swail, V.: Changes of Extreme
Wave Heights in Northern Hemisphere Oceans and Related Atmospheric
Circulation Regimes, J. Climate, 14, 2204–2221,
10.1175/1520-0442(2001)014<2204:COEWHI>2.0.CO;2, 2001.WCRP: World Climate Research Programme, Expert Team on Climate Change Detection, https://www.wcrp-climate.org/data-etccdi (last access: 4 November 2021), 2020.Zanowski, H., Jahn, A., and Holland, M. M.: Arctic Ocean Freshwater in CMIP6
Ensembles: Declining Sea Ice, Increasing Ocean Storage, and Export, J.
Geophys. Res.-Ocean., 126, e2020JC016930, 10.1029/2020JC016930, 2021.