OCS Study

BOEM 2018-018

Development of a Very High-Resolution
 

Regional Circulation Model of Beaufort Sea
 

Nearshore Areas
 

U.S. Department of the Interior

Bureau of Ocean Energy Management

Headquarters, Sterling, VA

www.boem.gov

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OCS Study

BOEM 2018-018

Development of a Very High-Resolution

Regional Circulation Model of Beaufort Sea

Nearshore Areas

Enrique N. Curchitser, Rutgers University, New Brunswick, NJ

Kate Hedstrom, University of Alaska, Fairbanks, AK

Seth Danielson, University of Alaska, Fairbanks, AK

Jeremy Kasper, University of Alaska, Fairbanks, AK

Prepared under BOEM Cooperative Agreement M15AC00011

by

Rutgers University

14 College Farm Road

New Brunswick, NJ 08901

Published by

U.S. Department of the Interior

Bureau of Ocean Energy Management

Headquarters, Environmental Studies Program

Sterling, VA

May, 2018

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OCS Study

BOEM 2018-018

DISCLAIMER
 

This report was prepared under cooperative agreement between the Bureau of Ocean Energy

Management (BOEM) and Rutgers University. This report has been technically reviewed

by BOEM, and it has been approved for publication. Approval does not signify that the

contents necessarily reflect the views and policies of BOEM, nor does mention of trade names

or commercial products constitute endorsement or recommendation for use. It is, however,

exempt from review and compliance with BOEM editorial standards.

REPORT AVAILABILITY

To download a PDF file of this Environmental Studies Program report, go to the U.S.

Department of the Interior, Bureau of Ocean Energy Management, Environmental Studies

Program Information System website (https://www.boem.gov/espis/) and search on OCS

Study BOEM 2018-018. Or contact the BOEM Alaska OCS Region office at the address

listed below.

Bureau of Ocean Energy Management

Environmental Sciences Management Section

3801 Centerpoint Drive, Suite 500 l Anchorage, AK 99503

Phone: (907) 334-5200

Email: alaska.studies@boem.gov

CITATION

Curchitser, E.N., K. Hedstrom, S. Danielson and J. Kasper. 2017. 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.

About the cover: Beaufort Shelf on June 23, 2016. LANCE-MODIS image from https://lancemodis.eosdis.nasa.gov/imagery/subsets/?area=global. We acknowledge the use of Rapid Response imagery from the Land, Atmosphere Near real-time Capability for EOS (LANCE)

system operated by the NASA/GSFC/Earth Science Data and Information System (ESDIS)

with funding provided by NASA/HQ.

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BOEM 2018-018

Abstract
 

This document is the final report for the U.S. Department of the Interior, Bureau of

Ocean Energy Management (BOEM) Cooperative Agreement M15AC00011, Development of a Very High-Resolution Regional Circulation Model of Beaufort Sea Nearshore

Areas. The primary aim of the work done under this award was to use a state-of-thescience coupled circulation sea ice numerical ocean model to simulate at least a decade

of the currents sea ice and winds in the Beaufort Seas nearshore areas, which can be

used as inputs to oil-spill models. This document reviews the basic oceanography of

the region of interest, describes the relevant details of the circulation and ice models and their implementation for this particular problem, and describes the numerical

model-data comparisons that have been performed as part of this current award.

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CONTENTS BOEM

OCS Study
 

2018-018
 

Contents

Acronyms and abbreviations xi
 

1 Introduction 1
 

1.1 Background: The Beaufort Sea . . . . . . . . . . . . . . . . . . . . . . . . . 1
 

2 Scientific and technical approaches 4
 

2.1 The coupled ocean-sea ice model . . . . . . . . . . . . . . . . . . . . . . . . 4
 

3 Model Implementation 7
 

3.1 Pan-Arctic Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
 

3.1.1 Atmospheric forcing . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
 

3.1.2 Fresh water fluxes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
 

3.1.3 Initial and boundary conditions . . . . . . . . . . . . . . . . . . . . . 11
 

3.1.4 Climatology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
 

3.1.5 Albedo . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
 

3.1.6 Other model details . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
 

3.2 Beaufort shelf models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12
 

3.3 The model runs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
 

3.3.1 Pan-Arctic . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
 

3.3.2 Beaufort Shelf . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
 

4 Model Evaluation 15
 

4.1 The data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
 

4.1.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
 

4.1.2 Model evaluation overview . . . . . . . . . . . . . . . . . . . . . . . . 18
 

4.2 Model-data comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32
 

5 Summary remarks 64
 

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LIST OF FIGURES BOEM 2018-018

List of Figures
 

1 Idealized schematic of the circulation in the Chukchi and Beaufort seas. Nominal flow pathways are colored based on water properties, origin and/or function. 2

2 Wind speed and directions as measured by the QuickScat satellite on 29

September (left) and 2 October (right) 2004. The images show mesoscale

wind field shears and divergences that can affect the ocean circulation. . . . 3

3 Cartoon of an ice floe with a deep keel. Variables are defined in table 2 . . . 7

4 Bathymetry (meters) of the Pan-Arctic domain. . . . . . . . . . . . . . . . . 9

5 Grid spacing (km) of the Pan-Arctic domain. . . . . . . . . . . . . . . . . . 10

6 Bathymetry (meters) of the larger Beaufort Sea domain. . . . . . . . . . . . 13

7 Bathymetry (meters) of the smaller Beaufort Sea domain. . . . . . . . . . . . 13

8 Tide gauges, current meter and temperature/salinity time series mooring sites

selected for comparisons. Description of corresponding data is given in table 4. 15

9 Monthly climatology from Utqia ̇gvik (formerly Barrow) of hourly meteorological data over 1940-2014. Top row: air temperature and sea level pressure.

Middle row: wind speed magnitude and direction to which the wind blows.

Bottom row: U (east-west) and V (north-south) wind vector components. . . 17

10 Pan-Arctic model climatological sea surface temperature (oC) for April, June,

September and December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

11 Beaufort 2 model climatological sea surface temperature (oC) for April, June,

September and December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

12 Beaufort 3 model climatological sea surface temperature (oC) for April, June,

September and December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

13 Pan-Arctic model climatological ice extent for April, June, September and

December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

14 Beaufort 2 model climatological ice extent for April, June, September and

December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

15 Beaufort 3 model climatological ice extent for April, June, September and

December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

16 Pan-Arctic model climatological ice thickness (meters) for April, June, September and December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

17 Beaufort 2 model climatological ice thickness (meters) for April, June, September and December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

18 Beaufort 3 model climatological ice thickness (meters) for April, June, September and December. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27

19 Pan-Arctic model climatological ice speed for April, June, September and

December. Contour level (0.0005 m/s) chosen to identify the landfast ice. . . 29

20 Beaufort 2 model climatological ice speed for April, June, September and

December. Contour level (0.0005 m/s) chosen to identify the land-fast ice. . 30

21 Beaufort 3 model climatological ice speed for April, June, September and

December. Contour level (0.0005 m/s) chosen to identify the land-fast ice. . 31

22 Coamplitude elevation in meters (color shading) and cophase in degrees (black

line) contours for semidiurnal tidal constituent M2. . . . . . . . . . . . . . . 32

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LIST OF FIGURES BOEM 2018-018

23 Coamplitude elevation in meters (color shading) and cophase in degrees (black

line) contours for semidiurnal tidal constituent S2. . . . . . . . . . . . . . . . 33

24 Coamplitude elevation in meters (color shading) and cophase in degrees (black

line) contours for diurnal tidal constituent K1. . . . . . . . . . . . . . . . . . 34

25 Coamplitude elevation in meters (color shading) and cophase in degrees (black

line) contours for diurnal tidal constituent O1. . . . . . . . . . . . . . . . . . 35

26	 Observed (red) and modeled (blue) sea surface elevations for the NOAACOOPS tidal station in Prudhoe Bay (top), Red Dog Dock (middle) and Nome

(bottom). All fields have been smoothed with a 35-hour low-pass Butterworth

filter to remove tides and other high-frequency fluctuations. All model fields

are from the Arctic2 model. . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

27	 Comparison of observed sea level fluctuations in Prudhoe Bay (top row and all

red traces) to modeled sea level fluctuations (blue) for the Beaufort2 (second

row), Beaufort3 (third row) and Arctic2 (bottom row) models. All fields have

been smoothed with a 35-hour low-pass Butterworth filter to remove tides and

other high-frequency fluctuations. . . . . . . . . . . . . . . . . . . . . . . . . 38

28	 Comparison of the observed (blue) and modeled (red) along-coast sea surface

elevation gradient magnitude between Nome and Red Dog Dock (top) and

between Red Dog Dock and Prudhoe Bay (bottom). All fields have been

smoothed with a 35-hour low-pass Butterworth filter to remove tides and

other high-frequency fluctuations. . . . . . . . . . . . . . . . . . . . . . . . . 39

29	 Subtidal comparison of the Arctic2 model with observations in Bering Strait.

The top row shows the along-strait velocity (positive nominally northward),

the second row show the cross-strait velocity (positive nominally eastward),

the third row is temperature and the bottom panel shows modeled salinity.

The observed fields are shown with red lines (data collected approximately 10

m above the seafloor). The modeled fields are shown at the surface (black) and

at the seafloor (blue). All fields have been smoothed with a 35-hour low-pass

Butterworth filter to remove tides and other high-frequency fluctuations. . . 40

30	 Comparison of annually averaged volume, heat, fresh water and ice freshwater transports (ordered from top to bottom, respectively) as depicted by the

PAROMS model and observations. Time series are shown at the left over 1983­

2015; scatterplots of the volume, heat and fresh water transports are shown

in the right-hand column along with the regression equations and correlation

coefficients. Abbreviation: WWL 2012 refers to Woodgate et al. [2012]. . . . 41

31	 Comparison of observed (red, all rows) and modeled salinity at the Camden

Bay mooring site. Model traces show salinity at the sea surface (black) and at

the seafloor (blue). The observed salinity is measured within one meter of the

seafloor. All fields have been smoothed with a 35-hour low-pass Butterworth

filter to remove tides and other high-frequency fluctuations. . . . . . . . . . . 42

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LIST OF FIGURES BOEM 2018-018

32	 Comparison of observed (red, all rows) and modeled temperature at the Camden Bay mooring site. Model traces show temperature at the sea surface

(black) and at the seafloor (blue) for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. The observed temperature is

measured within one meter of the seafloor. All fields have been smoothed with

a 35-hour low-pass Butterworth filter to remove tides and other high-frequency

fluctuations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

33	 Comparison of observed (red, all rows) and modeled (blue) water column

averaged along-shelf currents at the Camden Bay mooring site for the Beaufort2 (second row), Beaufort3 (third row) and Arctic2 (bottom row) models.

All fields have been smoothed with a 35-hour low-pass Butterworth filter to

remove tides and other high-frequency fluctuations. . . . . . . . . . . . . . . 44

34	 Comparison of observed (red, all rows) and modeled (blue) water column

averaged cross-shelf currents at the Camden Bay mooring site for the Beaufort2 (second row), Beaufort3 (third row) and Arctic2 (bottom row) models.

All fields have been smoothed with a 35-hour low-pass Butterworth filter to

remove tides and other high-frequency fluctuations. . . . . . . . . . . . . . . 45

35	 Comparison of observed (red, all rows) and modeled salinity at the Dinkum

mooring site. Model traces show salinity at the sea surface (black) and at the

seafloor (blue). The observed salinity is measured within one meter of the

seafloor. All fields have been smoothed with a 35-hour low-pass Butterworth

filter to remove tides and other high-frequency fluctuations. . . . . . . . . . . 46

36	 Comparison of observed (red, all rows) and modeled temperature at the

Dinkum mooring site. Model traces show temperature at the sea surface

(black) and at the seafloor (blue) for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. The observed temperature is

measured within one meter of the seafloor. All fields have been smoothed with

a 35-hour low-pass Butterworth filter to remove tides and other high-frequency

fluctuations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

37	 Comparison of observed (red, all rows) and modeled (blue) water column

averaged along-shelf currents at the Dinkum mooring site for the Beaufort2

(second row), Beaufort3 (third row) and Arctic2 (bottom row) models. All

fields have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other high-frequency fluctuations. . . . . . . . . . . . . . . . 48

38	 Comparison of observed (red, all rows) and modeled (blue) water column averaged cross-shelf currents at the Dinkum mooring site for the Beaufort2 (second

row), Beaufort3 (third row) and Arctic2 (bottom row) models. All fields have

been smoothed with a 35-hour low-pass Butterworth filter to remove tides and

other high-frequency fluctuations. . . . . . . . . . . . . . . . . . . . . . . . . 49

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LIST OF FIGURES BOEM 2018-018

39	 Comparison of observed (red, all rows) and modeled temperature at the Reindeer mooring site. Model traces show temperature at the sea surface (black)

and at the seafloor (blue) for the Beaufort2 (second row), Beaufort3 (third

row) and Arctic2 (bottom row) models. The observed temperature is measured within one meter of the seafloor. All fields have been smoothed with a

35-hour low-pass Butterworth filter to remove tides and other high-frequency

fluctuations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

40	 Comparison of observed (red, all rows) and modeled (blue) water column averaged along-shelf currents at the Reindeer mooring site for the Beaufort2

(second row), Beaufort3 (third row) and Arctic2 (bottom row) models. All

fields have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other high-frequency fluctuations. . . . . . . . . . . . . . . . 51

41	 Comparison of observed (red, all rows) and modeled (blue) water column

averaged cross-shelf currents at the Reindeer mooring site for the Beaufort2

(second row), Beaufort3 (third row) and Arctic2 (bottom row) models. All

fields have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other high-frequency fluctuations. . . . . . . . . . . . . . . . 52

42	 Comparison of observed (red, all rows) and modeled temperature at the Smith

Bay mooring site. Model traces show temperature at the sea surface (black)

and at the seafloor (blue) for the Beaufort2 (second row), Beaufort3 (third

row) and Arctic2 (bottom row) models. The observed temperature is measured within one meter of the seafloor. All fields have been smoothed with a

35-hour low-pass Butterworth filter to remove tides and other high-frequency

fluctuations. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

43	 Comparison of observed (red, all rows) and modeled (blue) water column averaged along-shelf currents at the Smith Bay mooring site for the Beaufort2

(second row), Beaufort3 (third row) and Arctic2 (bottom row) models. All

fields have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other high-frequency fluctuations. . . . . . . . . . . . . . . . 54

44	 Comparison of observed (red, all rows) and modeled (blue) water column averaged cross-shelf currents at the Smith Bay mooring site for the Beaufort2

(second row), Beaufort3 (third row) and Arctic2 (bottom row) models. All

fields have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other high-frequency fluctuations. . . . . . . . . . . . . . . . 55

45	 Comparison of ship observations (top row) of temperature to modeled ocean

temperature at the sea surface (left column) and the seafloor (right column).

The Beaufort2, Beaufort3 and Arctic2 models are in rows 2, 3 and 4, respectively. Circles in the top row panels denote CTD cast locations. . . . . . . . 57

46	 Comparison of ship observations (top row) of salinity to modeled ocean salinity at the sea surface (left column) and the seafloor (right column). The

Beaufort2, Beaufort3 and Arctic2 models are in rows 2, 3 and 4, respectively.

Circles in the top row panels denote CTD cast locations. . . . . . . . . . . . 58

47	 Time series of the Arctic2 (top), Beaufort2 (middle) and Beaufort3 (bottom)

models in reproducing the mean ice area anomalies for each of their respective

grids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60

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LIST OF TABLES BOEM 2018-018

48	 Correlation matrix of ice area anomalies for each of the three model for the

Pan-Arctic model domain (top), the Beaufort2 model domain (middle) and

Beaufort3 model domain (bottom). The model data that comprise the comparisons are the coarse Pan-Arctic model (left), the medium-resolution Beaufort2 model (center) and the fine-resolution Beaufort3 model (right). . . . . . 61

49	 Comparison of modeled (left) and observed (right) ice concentration for the

coarse (top), medium-resolution (middle) and fine-resolution (bottom) models. 62

50	 Thermal satellite image from 14 November 2012. Dark colors represent relatively cold temperatures, white colors show relatively warm (e.g., ocean water). The figure extends from Banks Island in the upper right to St Lawrence

Island in the lower left. Leads in the ice pack show as thin bright lines between

the light grey ice pack floes. Fog and clouds are dark grey smears that extend

across land/ocean boundaries. . . . . . . . . . . . . . . . . . . . . . . . . . . 63

List of Tables

2 Variables used in the landfast ice parameterization (see Fig. 3). . . . . . . . 7

3 Snow and ice albedo values. . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

4 Tide gauges, current meter and temperature/salinity time series mooring sites

selected for comparisons with model output. . . . . . . . . . . . . . . . . . . 16

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ACRONYMS AND ABBREVIATIONS BOEM 2018-018

Acronyms and abbreviations

ARDAT Arctic River Discharge and Temperature data set

BOEM Bureau of Ocean Energy Management

CICE Community Ice Model

COOPS Center for Operational Oceanographic Products and Services

EVP Elastic Viscous Plastic [a rheology]

GLORYS Global Ocean Reanalysis

GLS Generic Length Scale

HYCOM Hybrid Coordinate Ocean Model

KE Kinetic Energy

MEKE Mean Eddy Kinetic Energy

MERRA Modern Era Reanalysis

MKE Mean Kinetic Energy

MKS Meter, Kilogram, Second

MPI Message Passing Interface

NetCDF Network Common Data Format

NOAA National Oceanographic and Atmospheric Administration

NSIDC National Snow and Ice Data Center

PA Principal Axis

RCM Rotary Current Meter

RMSE Root Mean Square Error

ROMS Regional Ocean Modeling System

S Salinity

SSH Sea Surface Height

SODA Simple Ocean Data Assimilation

SSM/I Special Sensor Microwave/Imager

STD Standard Deviation

Sv Sverdrup (106m3/s)

T Temperature ◦T degrees True. Degrees clockwise from due North

UAF University of Alaska Fairbanks

UW University of Washington

U Zonal velocity

Ur Along-shelf velocity; aligns with the principal axis of variation

V Meridional velocity

Vr Cross-shelf velocity; orthogonal to the principal axis of variation

θ Principal axis orientation

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1. INTRODUCTION BOEM 2018-018

1 Introduction

1.1 Background: The Beaufort Sea

Beaufort Sea Oceanography

The Alaskan Beaufort Sea shelf (Figure 1) extends approximately 500 km eastward from

Point Barrow to the Canadian border, abutting the Mackenzie. The shelf width is approximately 80 km as measured from the coast to the 200 m isobath. Shelf depths grade smoothly

offshore with bottom slopes typically being about 10−3 inshore of the 100 m isobath. Sea

ice can cover the shelf year-round, although more typically the inner shelf (and in recent

years the entire shelf) is ice-free during the summer months. Landfast ice begins to form

in October and extends 20-40 km offshore through mid-June so that it covers nearly 25%

of the shelf area (Barnes et al. [1984]) through most of the year although lockup events can

extend far past the shelf break for as long as ten or twenty days (Mahoney et al. [2014]). The

duration of the landfast ice season has decreased in the Beaufort Sea by up to two months

since the 1970s (Mahoney et al. [2014]). The landfast ice is relatively smooth adjacent to

the coast, but is increasingly deformed offshore. Currents under the landfast ice are small

and uncorrelated with the local winds whereas currents in the presence of freely drifting ice

during breakup and open water (summer-fall months) are highly coherent with the winds

(Weingartner et al. [2009, 2017]).

The Beaufort’s oceanic circulation and ice deformation are related to the seasonally varying winds. These we summarized in the form of monthly statistics using the archived National

Weather Service wind record in Barrow from 1949–2005. The alongshore component of the

winds accounts for most of the variance in the winds and are primarily responsible for forcing

shelf circulations. In general, winds blowing from the northeast prevail throughout the year.

On a monthly basis the majority of the alongshore winds are westward (upwelling favorable) and westward winds are, on average, stronger than eastward (downwelling-favorable)

winds. There are, however, substantial seasonal differences. Westward winds are strongest

in late fall and early winter and occur most frequently in October, November, and March.

Westward winds are only slightly more frequent than eastward winds in July and August,

although westward winds are stronger in these months. Thus, on average, upwelling favorable conditions prevail throughout the year. Although the alongshelf wind stress component

is important in the ocean circulation, the north-south component plays an important role in

ice dynamics. In particular, winter winds are primarily onshore (southward) and thus force

pack ice onshore and deform the landfast ice edge. Less frequent offshore winds can result in

detachment of the landfast ice (breakouts). These seasonal variations are primarily related

to the deep high pressure cell centered over the Arctic Ocean in winter. However, the high

pressure system weakens in summer and fall, when low-pressure systems invade the Beaufort

Sea from the North Pacific (Maslanik et al. [1999]).

Seasonally varying mesoscale winds may substantially alter the synoptic wind field in

the nearshore zone. For example, a persistent summer sea breeze results in mean westward

1
 
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1. INTRODUCTION BOEM 2018-018
 

Figure 1: Idealized schematic of the circulation in the Chukchi and Beaufort seas. Nominal

flow pathways are colored based on water properties, origin and/or function.

2
 
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1. INTRODUCTION BOEM 2018-018

Figure 2: Wind speed and directions as measured by the QuickScat satellite on 29 September (left) and 2 October (right) 2004. The images show mesoscale wind field shears and

divergences that can affect the ocean circulation.

winds within 25 km of the coast (Kozo [1982a,b]). Small-scale structure in the wind field

can be discerned from satellite-derived winds (Figure 2). During the landfast ice period

from October through April mountain barrier baroclinicity (Kozo [1980, 1984]) can produce

along-shore divergence in the wind field. This effect occurs when the southward flow of

low-level cold air from the Arctic Ocean is blocked along the northern flank of the Brooks

Range. The resulting isopycnal slopes induce eastward surface winds of about 15 m/s over

a horizontal width scale of 200–300 km. The western Beaufort coast is rarely influenced by

the mountain barrier effect because it lies more than 300 km north of the Brooks Range,

but the eastern Beaufort coast lies within 60 km of the mountains. Consequently, winds can

be westward over the western Beaufort coast but eastward along the eastern coast. Kozo

[1984] estimated that the mountain barrier baroclinicity effect occurs approximately 20% of

the time during winter.

Three distinct oceanic regimes bound the Alaskan Beaufort Sea. To the west, variability

in Barrow Canyon outflow is large, especially in fall and winter, and mainly due to fluctuations in the regional winds (Weingartner et al. [1998]; Weingartner et al. [2005]; Woodgate

et al. [2005]). Some of the Barrow Canyon outflow continues eastward as a subsurface current

(or slope undercurrent) along the Beaufort shelfbreak and slope where it forms the upper

halocline waters of the Canada Basin (Mountain et al. [1976]; Aagaard [1984]; Pickart [2004];

Pickart et al. [2005]; Nikolopoulos et al. [2009]). Under weak westward winds or eastward

winds some of the water exiting Barrow Canyon rounds Pt. Barrow and continues onto the

inner portion of the western Beaufort shelf (Okkonen, pers. comm.).

The outer shelf and continental slope provide the offshore boundary for the Alaskan

Beaufort Sea. In the upper 50 m or so the flow is westward and part of the southern limb of

the wind-driven Beaufort Gyre. This flow can occasionally be reversed by strong eastward

winds and/or by occasional shelfbreak upwelling that advects eastward momentum from the

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2. SCIENTIFIC AND TECHNICAL APPROACHES BOEM 2018-018

slope undercurrent onto the shelf at least as far inshore as the 50 m isobath (Aagaard [1984];

Pickart [2004]; Nikolopoulos et al. [2009]).

The Mackenzie shelf joins the Alaskan Beaufort shelf to the east and likely the yearround discharge from the Mackenzie River influences the eastern Beaufort shelf (Carmack

et al. [1989]; Macdonald et al. [1989]; Macdonald and Carmack [1991]). Mackenzie shelf

water has been detected throughout much of the Canada basin, including the continental

slope of the Chukchi and western Beaufort Sea as far as 160W longitude (Guay and Falkner

[1998]; Macdonald et al. [1999]). Conceivably, wind-driven currents transport Mackenzie shelf

waters onto the Alaskan Beaufort shelf as well; observations of satellite imagery suggest that

the summer melt in the Alaskan Beaufort is strongly dependent upon the Mackenzie River

plume (Weingartner et al. [2009]). In addition to the Mackenzie River, a large number of

smaller rivers discharge into the Alaskan Beaufort Sea. These are asymmetrically distributed

with most of them discharging into the central and eastern portions of the shelf.

2 Scientific and technical approaches

In this section, we describe the modeling framework, its components, and the details of the

specific model implementations for this project, forcing, boundary conditions, and the various

model integrations performed. Further details on the numerics of coupled circulation-sea ice

model are given in the manual provided as part of this project (Hedstrom [2018]).

2.1 The coupled ocean-sea ice model

The main research tool used for this work is a state-of-the-science coupled ocean/sea ice

model based on the Regional Ocean Modeling System (ROMS) (Shchepetkin and McWilliams

[2005]). ROMS is a free-surface, hydrostatic primitive equation ocean circulation model

whose core was developed at Rutgers University and University of California, Los Angeles

with significant contributions from a large community of users. ROMS is a terrain-following,

finite volume (Arakawa C-grid) model with the following advanced features: extensive restructuring for sustained performance on multi-processor computing platforms (using MPI);

high-order, weakly dissipative algorithms for tracer advection; a unified treatment of surface and bottom boundary layers (e.g., K-Profile Parameterization; Large et al. [1994]),

atmosphere-ocean flux computations based on the ocean model prognostic variables using

bulk-formulae (Fairall et al. [2003] or Large and Yeager [2009]) and an integrated set of procedures for data assimilation (e.g., optimal interpolation and adjoint-based methods; Moore

et al. [2004]). ROMS also has an integrated float tracking capability. The vertical discretization is based on a terrain-following coordinate system with the ability to increase the

resolution near the surface and bottom boundary layers.

ROMS has been coupled to a sea-ice model (Budgell [2005]) consisting of the elastic4
 
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viscous-plastic (EVP) rheology (Hunke and Dukowicz [1997], Hunke [2001]) and the Mellor

and Kantha [1989] thermodynamics. It is fully explicit and implemented on the ROMS

Arakawa C-grid and is therefore fully parallel using MPI, just as ROMS is. The sea ice

model also includes frazil ice growth in the ocean being passed to the ice (Steele et al.

[1989]). It currently follows a single thermodynamic ice category, which exhibits accurate

results in a marginal ice zone such as the Bering Sea (Danielson et al. [2011]).

The principal attributes of the ocean and sea ice models are:

1. General

(a) Primitive equations with potential temperature, salinity, and an equation of state.

(b) Hydrostatic and Boussinesq approximations.

(c) Optional third-order upwind advection scheme.

(d) Optional Smolarkiewicz advection scheme for tracers (potential temperature, salinity, etc.).

(e) Optional Lagrangian floats.

(f) Option for point sources and sinks.

2. Horizontal

(a) Orthogonal-curvilinear coordinates.

(b) Arakawa C grid.

(c) Choice of closed, periodic, prescribed, radiation, and gradient open	 boundary

conditions.

(d) Masking of land areas.

3. Vertical

(a) sigma (terrain-following) coordinate.

(b) Free surface.

(c) Tridiagonal solver with implicit treatment of vertical viscosity and diffusivity.

4. Ice

(a) Hunke and Dukowicz elastic-viscous-plastic dynamics.

(b) Mellor-Kantha thermodynamics.

(c) Orthogonal-curvilinear coordinates.

(d) Arakawa C grid.

(e) Smolarkiewicz advection of tracers.

(f) Lemieux landfast ice parameterization.

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5. Mixing options

(a) Horizontal Laplacian and biharmonic diffusion along constant s, z or density surfaces.

(b) Horizontal Laplacian and biharmonic viscosity along constant s or z surfaces.

(c) Optional Smagorinsky horizontal viscosity and diffusion (but not recommended

for diffusion).

(d) Horizontal free-slip or no-slip boundaries.

(e) Vertical harmonic viscosity and diffusion with a spatially variable coefficient, with

options to compute the coefficients with Large et al. [1994], Mellor-Yamada, or

generic length scale (GLS) mixing schemes.

6. Implementation

(a) Dimensional in meter, kilogram, second (MKS) units.
 

(b) Fortran 90.
 

(c) Runs under UNIX, requires the C preprocessor, gnu make, and Perl.

(d) All input and output is done in NetCDF (Network Common Data Format), requires the NetCDF library.

(e) Options include serial, parallel with MPI, and parallel with OpenMP.

The above list, describing the dynamical core, hasn’t evolved significantly over the fifteen

years since the early development of ROMS. However, many of the numerical details have

changed a great deal. Examples include consistent temporal averaging of the barotropic

mode to guarantee both exact conservation and constancy preservation properties for tracers;

redefined barotropic pressure-gradient terms to account for local variations in the density

field; vertical interpolation performed using conservative parabolic splines; and higher-order,

quasi-monotone advection algorithms. In addition, many capabilities have been added to the

basic model including data assimilation (both 4DVar and Ensemble Kalman Filter), several

biogeochemical models, and on- and off-line particle tracking.

Of particular importance to this project is a new implementation of landfast ice in the

coupled ocean-sea ice model. The Arctic ocean has many shallow shelves, the Beaufort being

one of them, on which landfast ice can form every winter. For this project, we implemented

the parameterization described in Lemieux et al. [2015]. Basal stress terms τb are added to

sea ice momentum equations representing a bottom drag applied to the deepest ice keels.

Included is a parameterization of the ice thickness distribution so that even a single icecategory model can use the landfast ice option. Figure 3 shows a cartoon of an ice floe with

a keel.

Exhaustive details of the landfast ice implementation, the coupled circulation/sea ice

model, timestepping and parameterization scheme, boundary conditions and more are provided in the accompanying manual which is part of the deliverables for this cooperative

agreement (Hedstrom [2018]).

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Δx

li

hrb

hd

hw

hl hrt

βli

Figure 3: Cartoon of an ice floe with a deep keel. Variables are defined in table 2
 

Variable Value Description

hw

hrt

hrb

hd

li

Δx

β

k1

k2

u0

Cb

O(0.01)

8.0

15.0

5.0 × 10−5

20

water depth

ridge height above level ice

draft of keels below level ice bottom

draft of ice below sea surface

length of ice floe within grid cell

x-dimension of grid cell

ridged fraction of floe

tunable parameter

tunable parameter

small velocity

ice strength parameter

Table 2: Variables used in the landfast ice parameterization (see Fig. 3).

3 Model Implementation

One of the main challenges of this project was to be able to run a very high-resolution model

of the Beaufort shelf for at least a decade. The main difficulty is in generating ocean-ice

boundary conditions to drive the regional coastal domains. Indeed, few examples exist of this

type of implementation. A full treatment of the limited-area boundary condition problem

in ocean models is beyond the scope of this report. After significant experimentation, we

settled on a three-domain nested solution. Our implementation begins with a moderate

resolution Pan-Arctic domain, where no sea-ice traverses the boundaries. We then downscale

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this solution to a Chukchi-Beaufort Sea higher resolution domain. Finally, this solution is

further downscaled to a sub-kilometer resolution Beaufort shelf model. By closely controlling

the downscaling of boundary information from the Pan-Arctic to the Beaufort domain, we

were able to run the very high-resolution Beaufort model for the period of January 1999 to

September 2015, exceeding the period required by the project. Some implementation details

are common to the three domains.

3.1 Pan-Arctic Implementation

We developed a Pan-arctic domain that focuses the resolution down to roughly 6 km over the

Chukchi and Beaufort seas and coarsens away from the region of interest. Figure 4 shows the

bathymetry and the extent of this domain. The grid is non-uniform, having more resolution

close to Alaska—a measure of grid spacing is shown in Figure 5. The bathymetry came from

ETOPO1 with a cutoff of 10 m depth (the next generation Arctic runs will have a better

bathymetry as well as wider passages through the Canadian Archipelago).

The overall model extent was determined after extensive testing of the coupled oceansea ice model. Though we have extensive experience with open boundary conditions for

multi-decadal integrations of regional ocean models, the open sea ice boundaries proved

to be challenging. The final configuration was chosen so as to minimize errors in both the

circulation and the sea ice distributions in the region of interest, while allowing us to maintain

high resolution there.

3.1.1 Atmospheric forcing

We are using the MERRA forcing [Rienecker et al., 2011] and computing the momentum, heat

and salt fluxes from the atmospheric conditions and the each model’s surface temperature.

The turbulent fluxes are computed using bulk flux formulae. We have Python scripts for

downloading MERRA so that the MERRA files can be used as is, on their native grid,

then interpolated by ROMS internally to the domain at hand. Because of the mis-match in

resolution between the MERRA and ROMS grids, we take particular care to only interpolate

atmospheric values over the ocean to the ROMS grid. We achieve this with a careful preprocessing of the atmospheric fields before they are read by the ROMS code.

3.1.2 Fresh water fluxes

For fresh water input from land, we have the ARDAT fields for the Arctic ocean [Whitefield

et al., 2015], which includes monthly river temperature. These are applied as point sources

to include the effects of bringing in warm water into the Pan-Arctic model domain. For the

rest of the domain, we are applying Dai et al. [2009] as the RUNOFF field.

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Figure 4: Bathymetry (meters) of the Pan-Arctic domain.
 

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Figure 5: Grid spacing (km) of the Pan-Arctic domain.
 

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3.1.3 Initial and boundary conditions

Initial and boundary conditions were derived from the Simple Ocean Data Assimilation

(SODA), a reanalysis by Carton et al. [2005]. SODA fields are available through 2008.

There are global HYCOM fields for the latter years. There are Python scripts to create

initial, climatology and boundary conditions forcing fields from the SODA and HYCOM

files. Ice initial thickness and concentration are extracted from a GLORYS simulation while

the other initial ice conditions are set to uniform values where the SST is cold, zero otherwise.

Ice boundary conditions are assumed to be not needed with the boundaries so far from the

pack ice.

The side known as “South” is across the North Pacific ocean. The sides known as

“East” and “West” are open on the Pacific sides, in the Gulf of Alaska and near Kamchatka,

respectively. The northern Hudson Bay is masked out in this version. The last boundary is

“North”, on the Atlantic side. All use a combination of radiation and nudging as suggested

by Marchesiello et al. [2001]. The nudging timescales are 3 days on inflow, 360 days on

outflow, values we have found to be well-behaved in prior simulations. The idea is that on

outflow, the boundary conditions should be dominated by a radiation condition, allowing

signals to exit the domain. On inflow, the boundary conditions should be more strongly

influenced by conditions from the global model.

3.1.4 Climatology

Other forcing includes a 360-day nudging to a monthly sea surface salinity climatology.

There’s an option to turn this off under the sea ice, which we have done.

Finally, there’s a nudging to 2-d (for ice) and 3-d (for the ocean) climatology. This last

comes from SODA/HYCOM for the ocean and includes interannual variability, but has been

compiled monthly instead of more frequently as used by the boundary conditions. This

nudging is applied in a band of 20 grid points around all open boundaries, ramping up from

zero in the interior to a timescale of 30 days right on the boundary. The ice nudging is turned

off in the Arctic, but was needed for the Beaufort domains, with an even shorter timescale,

while the ocean nudging was not needed for the Beaufort domains.

3.1.5 Albedo

The default ice albedo is chosen based on the presence or absence of snow and on the surface

temperature. Temperatures below −1◦ C are assumed to be dry while temperatures between

−1◦ C and 0◦ C are a linear combination of the wet and dry albedoes shown in Table 3. The

ocean albedo is assumed to depend on latitude θ as:

1.0 − [0.069 − 0.11 cos(2θ)] (1)

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Table 3: Snow and ice albedo values.
 

Wet ice 0.60 Snow is absent, temperature is at 0.0 C

Dry ice 0.65 Snow is absent, temperature is below -1.0 C

Wet snow 0.72 Snow is present, temperature is at 0.0 C

Dry snow 0.85 Snow is present, temperature is below -1.0 C

3.1.6 Other model details

Tidal forcing is implemented through the boundaries using the eight dominant diurnal

and semi-diurnal components derived from the Oregon State TOPEX/Poseidon Global Inverse Solution (TPXO) (Egbert and Erofeeva [2002]) (http://www.coas.oregonstate.edu/

research/po/research/tide/global.html). For landfast ice, we use the parameterization

from Lemieux et al. [2015].

Some choices are made via C preprocessor flags when compiling ROMS. These choices

include masking, salinity, sea ice and the non-linear equation of state. We also use Laplacian

viscosity on σ-surfaces, diffusion along constant z-surfaces and the full non-linear, curvilinear

momentum equations. We are applying the tides, including the forcing provided by the tidal

potential. The linear bottom drag coefficient varies spatially and is prescribed as a function

of the water depth. The circulation model was unstable with the GLS vertical mixing scheme

(Umlauf and Burchard [2003] and Warner et al. [2005]), so we switched back to the k-profile

parameterization vertical mixing (Large et al. [1994]) which proved to be more well-behaved

once a bug was fixed.

In the rest of this section, we describe the particular implementation of each of the model

domains.

3.2 Beaufort shelf models

The Beaufort Sea domains are set up much like the Arctic, described above. We implemented

two domains, a roughly 3 km curvilinear domain (Beaufort2), and a rectangular domain with

uniform 0.5 km resolution over the coastal Beaufort (Beaufort3), as shown in Fig. 6–7.

The bathymetry is from the Alaska Regional Digital Elevation Model (ARDEM) version

2 [Danielson et al., 2016], which in the region of the Beaufort Sea is derived from the International Bathymetric Chart of the Arctic Ocean (IBCAO; Jakobsson et al. [2012]). The

bathymetry was clipped to 3 m and 2 m depth for the two domains, respectively. The wetting and drying algorithm was turned on later for the small domain after an enclosed bay

became unstable several years into the run.

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Figure 6: Bathymetry (meters) of the larger Beaufort Sea domain.
 

Figure 7: Bathymetry (meters) of the smaller Beaufort Sea domain.
 

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3.3 The model runs

3.3.1 Pan-Arctic

The Pan-Arctic model simulation delivered to BOEM are:

•	 PAN-ARCTIC:

–	 Pan-Arctic domain at 6–9 km resolution

–	 Three-hourly Modern-Era Retrospective Analysis for Research and Applications

(MERRA) atmospheric fields and radiation forcing

–	 Boundary conditions and climatology from SODA and HYCOM

–	 Albedo as described in §3.1.5

–	 Years simulated: 1999–2015

3.3.2 Beaufort Shelf

The two simulations on the Beaufort shelf delivered to BOEM are:

•	 BEAUFORT2:

–	 Chukchi-Beaufort domain at 3 km resolution.

–	 Three-hourly Modern-Era Retrospective Analysis for Research and Applications

(MERRA) atmospheric fields and radiation forcing

–	 Ocean and ice boundary conditions and climatologies coming from the Pan-Arctic

model simulation

–	 Albedo as described in §3.1.5

–	 Years simulated: 1999–2015

•	 BEAUFORT3:

–	 Beaufort shelf domain at 0.5 km resolution.

–	 Three-hourly Modern-Era Retrospective Analysis for Research and Applications

(MERRA) atmospheric fields and radiation forcing.

–	 Ocean and ice boundary conditions and climatologies coming from the BEAUFORT2 model simulation

–	 Albedo as described in §3.1.5

–	 Years simulated: 1999–2015

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4 Model Evaluation

4.1 The data

4.1.1 Background

Publicly available in situ and remotely sensed datasets comprise the basis for model evaluation and validation. The moored records come from the University of Alaska Fairbanks

(UAF) and University of Washington (UW) field campaigns during 1999-2006 in the Beaufort Sea and Bering Strait (Figure 8). These data are described in agency reports (see

http://psc.apl.washington.edu/HLD/Bstrait/bstrait.html and http://www.ims.uaf.edu/beaufort/)

and peer review publications including (Weingartner et al. [2017]; Woodgate [2017]).

Figure 8: Tide gauges, current meter and temperature/salinity time series mooring sites

selected for comparisons. Description of corresponding data is given in table 4.

Tide gauge data from Nome, the dock at Red Dog mine and Prudhoe Bay were downloaded from the National Oceanographic and Atmospheric Administration’s (NOAA’s) Cen15
 
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Station

Name

Station Type Latitude

(◦N)

Longitude

(◦W)

Date Range

A3

Camden

Dinkum

Reindeer

Smith

Prudhoe

Red Dog

Nome

PABR

Oceanographic Mooring

Oceanographic Mooring

Oceanographic Mooring

Oceanographic Mooring

Oceanographic Mooring

Tide Gauge

Tide Gauge

Tide Gauge

Airport Weather Station

66.33

70.03

70.41

70.51

70.99

70.41

67.58

64.49

71.28

168.95

144.94

147.89

148.32

154.03

148.53

164.06

165.44

156.77

01/2005–12/2006

09/2004–10/2006

09/2004–10/2006

08/2005–08/2006

08/2004–17/2005

10/2004–11/2004

10/2004–11/2004

10/2004–11/2004

01/1940–12/2014

Table 4: Tide gauges, current meter and temperature/salinity time series mooring sites

selected for comparisons with model output.

ter for Operational Oceanographic Products and Services (CO-OPS) Tides and Current

web archive https://tidesandcurrents.noaa.gov/. Tide gauge data are used to evaluate the

model’s response to wind-driven sea level setup and setdown.

Summary meteorological data that represent the regional wind and temperature fields

come from the long-term weather station PABR located at the Utqia ̇gvik Airport (formerly

Barrow Airport), and were downloaded from NOAA’s National Centers for Environmental

Information (NCEI) at https://www.ncdc.noaa.gov/, formerly the National Climate Data

Center (NCDC). These we summarized in the form of monthly statistics using the archived

National Weather Service wind record in Barrow from 1940-2014 (Figure 9).

Remotely sensed ice concentration data comes from the National Snow and Ice Data Center (NSIDC) archives of the passive microwave SSM/I satellite measurements and is available

on a daily or bi-daily basis for the entire model integration period. Data were collected by

the Nimbus-7 Scanning Multichannel Microwave Radiometer (SMMR), the Defense Meteorological Satellite Program (DMSP) -F8, -F11 and -F13 Special Sensor Microwave/Imagers

(SSM/Is), and the DMSP-F17 Special Sensor Microwave Imager/Sounder (SSMIS). We use

data processed with the Goddard Flight Space Center’s NASA team algorithm, version 1.1,

downloaded January 2018. We note that ice area is a computation that scales each grid cell’s

area by its fractional ice cover, with resulting units of km2. Ice area differs from the other

common satellite-based ice cover metric, ice extent, which computes the total area bounded

by the ice edge contour, also reported with units of km2. The latter metric ignores leads and

features of partial ice cover that lie within the greater ice pack. The ice area metric provides

a more realistic measure of the fraction of ocean that is covered by ice and consequently may

provide a better measure of the amount of surface that can be characterized by high and low

albedos that are associated with ice and ocean surfaces, respectively. However, the satellite

data is known to be sensitive to ice and snow conditions and interpretation of the data must

be done with care.

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Figure 9: Monthly climatology from Utqia ̇gvik (formerly Barrow) of hourly meteorological
 

data over 1940-2014. Top row: air temperature and sea level pressure. Middle row: wind
 

speed magnitude and direction to which the wind blows. Bottom row: U (east-west) and V
 

(north-south) wind vector components.
 

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4.1.2 Model evaluation overview

To show the seasonal progression of the various models’ behavior for surface thermal and ice

fields over the full dynamic range of conditions, we graph summary monthly mean model

fields from April, June, September, and December in Figures 10 through 21. These include

temperature from the Pan-Arctic model (Figure 10), the Beaufort 2 model (Figure 11) and

the Beaufort 3 model (Figure 12).

We save the ocean and sea ice fields for the duration of the model simulation. All output

is in NetCDF format. Restart, daily averages, three-hourly averaged surface fields, stations

and floats. The three-hourly surface data was delivered to BOEM as required. We present

here a series of figures of climatological values of sea surface temperature, ice extent, ice

thickness and ice velocity (as a proxy for land-fast ice) for four different months, for each of

the three model simulations described above.

Sea surface temperature climatology for the three runs is shown in Figures 10–12. Areas

covered by sea ice are colder than −1◦C, including the central Arctic all year round. Waters

on the Pacific and Atlantic sides of the Arctic show a clear annual cycle of summer warming

and winter cooling. The open boundaries appear to be well-behaved except possibly to the

east of Greenland, where cold outflow from the Arctic meets a warm band of water being

nudged to SODA fields. For the Beaufort Sea domains, the SST remains cool year-round

except in the location of warm river inflows.

The ice concentration climatology for the three domains is shown in Figures 13–15. The

ice extent is at a minimum in September, when it retreats completely from the Beaufort3

domain. Quick comparisons with the satellite ice extent show that we’ve got a reasonable

seasonal cycle, better than in previous iterations of the Pan-Arctic model. A more quantitative comparison will follow. The nested domains show very little boundary effects from the

nesting, though the Beaufort3 domain shows some artifacts from mismatch with the values

being nudged to on the western part of the boundary, especially in April and June.

The ice thickness climatology for the three domains is shown in Figures 16–18. There

is less thickness than ice concentration data for model evaluation. Typically, the model

ice tends to be thinner than observations. The bias could be due to having a single ice

category in our model. Experiments with the Community Ice Model (CICE) coupled to

ROMS develop a thicker ice climatology using five ice thickness categories.

The Beaufort2 domain shows no obvious boundary effects in the thickness fields, while

the Beaufort3 domain shows hints of disagreement with the nudging from the Beaufort2 grid

along the western boundary, especially in April and June. The sea ice is quite dynamic on

the outer boundary of the Beaufort3 domain; perhaps this wasn’t the wisest location for a

grid open boundary.

Figures 19–21 show the ice speed, with the 5 × 10−4 m/s contour showing the edge of

the landfast ice. The Pan-Arctic domain has landfast ice in April and June in the Canadian

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Figure 10: Pan-Arctic model climatological sea surface temperature (oC) for April, June,

September and December.

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Figure 11: Beaufort 2 model climatological sea surface temperature (oC) for April, June,

September and December.

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Figure 12: Beaufort 3 model climatological sea surface temperature (oC) for April, June,

September and December.

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Figure 13: Pan-Arctic model climatological ice extent for April, June, September and December.

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Figure 14: Beaufort 2 model climatological ice extent for April, June, September and December.

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Figure 15: Beaufort 3 model climatological ice extent for April, June, September and December.

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Figure 16: Pan-Arctic model climatological ice thickness (meters) for April, June, September

and December.

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Figure 17: Beaufort 2 model climatological ice thickness (meters) for April, June, September

and December.

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Figure 18: Beaufort 3 model climatological ice thickness (meters) for April, June, September

and December.

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Archipelago as well as along the Siberian coast. This version of the landfast ice parameterization is only active in shallow water. Lemieux (personal communication) has since adjusted

the sea-ice rheology to allow for better agreement with landfast ice observations, which he

tested in the Kara Sea. In particular, the new formulation allows for some small tensile

strength in the ice that helps the representation of the landfast ice.

The Beaufort 2 and Beaufort 3 domains give a better view of where the model is developing landfast ice. It is at the maximum in April, where it develops along much of the Beaufort

coast. It extends farthest offshore at the Mackenzie delta, where the water is shallow. It also

extends to the boundary nudging band in the Beaufort3 domain on the western edge. It is

clear from these figures that we turned off the fastice parameterization within the boundary

nudging band in order to avoid excessive ice locking there.

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Figure 19: Pan-Arctic model climatological ice speed for April, June, September and December. Contour level (0.0005 m/s) chosen to identify the landfast ice.

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Figure 20: Beaufort 2 model climatological ice speed for April, June, September and December. Contour level (0.0005 m/s) chosen to identify the land-fast ice.

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Figure 21: Beaufort 3 model climatological ice speed for April, June, September and December. Contour level (0.0005 m/s) chosen to identify the land-fast ice.

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Figure 22: Coamplitude elevation in meters (color shading) and cophase in degrees (black

line) contours for semidiurnal tidal constituent M2.

4.2 Model-data comparisons

Figures 22–25 are cotidal charts for the Arctic2 model semidiurnal constituents M2 and S2

and the diurnal constituents K1 and O2, respectively. The location of amphridromes and the

magnitude of the elevations compare favorably with tidal models of the Bering Sea, Gulf of

Alaska, and Arctic Ocean tides (e.g., Kowalik and Proshutinsky, 1993; Padman and Erofeeva,

2004). Achieving phasing that matches the phase of the observed tides is somewhat more

difficult than achieving proper harmonic amplitudes as the tidal phase can change under

different stratification and ice cover conditions. For the case of the tidal analyses shown

here, the analysis was based on one month of mid-winter (full ice cover and seasonally low

stratification) model results.

Sea surface height (SSH) fluctuations are surface adjustments to oceanic flows, which

can be driven by winds, tides, and other surface and internal forces. SSH fluctuations

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Figure 23: Coamplitude elevation in meters (color shading) and cophase in degrees (black

line) contours for semidiurnal tidal constituent S2.

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Figure 24: Coamplitude elevation in meters (color shading) and cophase in degrees (black

line) contours for diurnal tidal constituent K1.

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Figure 25: Coamplitude elevation in meters (color shading) and cophase in degrees (black

line) contours for diurnal tidal constituent O1.

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can be the result of local forcing, such as wind stress directly applied to the local ocean

surface, or the result of disturbances that propagate into a region from a distance, such

as progressive tides or continental shelf waves. Tidally filtered SSH fields from the Arctic2

version of the PAROMS model and from coastal tide gauge stations observations at Nome,

Red Dog and Prudhoe Bay (Figure 8) are similar in character, although the model slightly

underestimates the magnitude of the fluctuations at Prudhoe slightly more than for the

other two stations (Figure 26). The two Beaufort sea nested models exhibit greater fidelity

to the observations (Figure 27), suggesting that the PAROMS ETOPO digital elevation

model bathymetry in the Beaufort region is markedly inferior to the IBCAO bathymetry

that comprises the Beaufort2 and Beaufort3 models. Deviations of the nested grid model

results from the observed elevations hint at inadequacies in the surface wind field. Lack of

weather stations across the Arctic as a whole limit the ability of atmospheric reanalysis and

hindcast models to reproduce high-latitude atmospheric pressure and wind systems with the

fidelity achieved in lower latitudes.

Along-shelf pressure gradients that drive shelf flows can be assessed by computing the

along-shelf SSH gradient between adjacent stations. This computation shows that the model

tends to slightly underestimate the SSH gradient (Figure 28), suggesting that the model will

also tend to underestimate the current velocity magnitude. We examine shelf currents in

detail in the sections below.

Bering Strait is the single choke-point for flows connecting the North Pacific and Arctic

Oceans. Hindcasting a realistic flow field here is critical for accurate reproductions of the

entire Chukchi shelf flow field. In turn, the Chukchi flow field helps drive communication

between the Chukchi and Beaufort seas. The Beaufort2 nested model grid encapsulates the

northeast Chukchi Sea and Barrow Canyon, and this region directly communicates with the

western Beaufort. Hence, the long-term monitoring sites in Bering Strait (e.g., Woodgate

et al., 2005; Woodgate et al., 2012) provide an important comparison opportunity against

observational data for evaluation of the model’s performance outside of the Beaufort Sea

focus domain. Observations made at Bering Strait’s long-term monitoring station A3 in

2005 and 2006 show that the model captures the majority of the principal axis variance

observed at A3 and with similar magnitude (Figure 29). On an annual basis (Figure 30),

the volume transport (r= 0.68), heat transport (r = 0.83), fresh water transport (r= 0.63)

and ice transport of the model is consistent with the values as reported by Woodgate et al.

[2012]. These correlations are all significant at the 95% confidence level and explain 46%,

69% and 40% of the observed variance, respectively.

Figures 31 through 44 show comparisons of model-predicted along-shelf (Ur) and crossshelf (Vr) velocities, temperature, and salinity for moorings at the Smith, Reindeer, Dinkum,

and Camden sites. The length of valid salinity records from the Smith and Reindeer moorings

was quite short (approximately one month long) and so we do not show those records here.

The salinity comparisons (Figures 31 and 35) show that the Beaufort2 and Beaufort3

nested models reproduce the observed annual cycle with approximately the right phasing

and amplitude. Some individual events are not well reproduced; we believe that many

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Figure 26: Observed (red) and modeled (blue) sea surface elevations for the NOAA-COOPS

tidal station in Prudhoe Bay (top), Red Dog Dock (middle) and Nome (bottom). All fields

have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other

high-frequency fluctuations. All model fields are from the Arctic2 model.

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Figure 27: Comparison of observed sea level fluctuations in Prudhoe Bay (top row and all

red traces) to modeled sea level fluctuations (blue) for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. All fields have been smoothed with a 35-hour

low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 28: Comparison of the observed (blue) and modeled (red) along-coast sea surface

elevation gradient magnitude between Nome and Red Dog Dock (top) and between Red Dog

Dock and Prudhoe Bay (bottom). All fields have been smoothed with a 35-hour low-pass

Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 29: Subtidal comparison of the Arctic2 model with observations in Bering Strait.

The top row shows the along-strait velocity (positive nominally northward), the second row

show the cross-strait velocity (positive nominally eastward), the third row is temperature

and the bottom panel shows modeled salinity. The observed fields are shown with red lines

(data collected approximately 10 m above the seafloor). The modeled fields are shown at

the surface (black) and at the seafloor (blue). All fields have been smoothed with a 35-hour

low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 30: Comparison of annually averaged volume, heat, fresh water and ice freshwater

transports (ordered from top to bottom, respectively) as depicted by the PAROMS model

and observations. Time series are shown at the left over 1983-2015; scatterplots of the

volume, heat and fresh water transports are shown in the right-hand column along with

the regression equations and correlation coefficients. Abbreviation: WWL 2012 refers to

Woodgate et al. [2012].

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Figure 31: Comparison of observed (red, all rows) and modeled salinity at the Camden

Bay mooring site. Model traces show salinity at the sea surface (black) and at the seafloor

(blue). The observed salinity is measured within one meter of the seafloor. All fields have

been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other highfrequency fluctuations.

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Figure 32: Comparison of observed (red, all rows) and modeled temperature at the Camden

Bay mooring site. Model traces show temperature at the sea surface (black) and at the

seafloor (blue) for the Beaufort2 (second row), Beaufort3 (third row) and Arctic2 (bottom

row) models. The observed temperature is measured within one meter of the seafloor. All

fields have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and

other high-frequency fluctuations.

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Figure 33: Comparison of observed (red, all rows) and modeled (blue) water column averaged along-shelf currents at the Camden Bay mooring site for the Beaufort2 (second row),

Beaufort3 (third row) and Arctic2 (bottom row) models. All fields have been smoothed with

a 35-hour low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 34: Comparison of observed (red, all rows) and modeled (blue) water column averaged cross-shelf currents at the Camden Bay mooring site for the Beaufort2 (second row),

Beaufort3 (third row) and Arctic2 (bottom row) models. All fields have been smoothed with

a 35-hour low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 35: Comparison of observed (red, all rows) and modeled salinity at the Dinkum

mooring site. Model traces show salinity at the sea surface (black) and at the seafloor

(blue). The observed salinity is measured within one meter of the seafloor. All fields have

been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other highfrequency fluctuations.

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Figure 36: Comparison of observed (red, all rows) and modeled temperature at the Dinkum

mooring site. Model traces show temperature at the sea surface (black) and at the seafloor

(blue) for the Beaufort2 (second row), Beaufort3 (third row) and Arctic2 (bottom row)

models. The observed temperature is measured within one meter of the seafloor. All fields

have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other

high-frequency fluctuations.

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Figure 37: Comparison of observed (red, all rows) and modeled (blue) water column averaged

along-shelf currents at the Dinkum mooring site for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. All fields have been smoothed with a 35-hour

low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 38: Comparison of observed (red, all rows) and modeled (blue) water column averaged

cross-shelf currents at the Dinkum mooring site for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. All fields have been smoothed with a 35-hour

low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 39: Comparison of observed (red, all rows) and modeled temperature at the Reindeer

mooring site. Model traces show temperature at the sea surface (black) and at the seafloor

(blue) for the Beaufort2 (second row), Beaufort3 (third row) and Arctic2 (bottom row)

models. The observed temperature is measured within one meter of the seafloor. All fields

have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and other

high-frequency fluctuations.

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Figure 40: Comparison of observed (red, all rows) and modeled (blue) water column averaged

along-shelf currents at the Reindeer mooring site for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. All fields have been smoothed with a 35-hour

low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 41: Comparison of observed (red, all rows) and modeled (blue) water column averaged

cross-shelf currents at the Reindeer mooring site for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. All fields have been smoothed with a 35-hour

low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 42: Comparison of observed (red, all rows) and modeled temperature at the Smith

Bay mooring site. Model traces show temperature at the sea surface (black) and at the

seafloor (blue) for the Beaufort2 (second row), Beaufort3 (third row) and Arctic2 (bottom

row) models. The observed temperature is measured within one meter of the seafloor. All

fields have been smoothed with a 35-hour low-pass Butterworth filter to remove tides and

other high-frequency fluctuations.

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Figure 43: Comparison of observed (red, all rows) and modeled (blue) water column averaged

along-shelf currents at the Smith Bay mooring site for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. All fields have been smoothed with a 35-hour

low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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Figure 44: Comparison of observed (red, all rows) and modeled (blue) water column averaged

cross-shelf currents at the Smith Bay mooring site for the Beaufort2 (second row), Beaufort3

(third row) and Arctic2 (bottom row) models. All fields have been smoothed with a 35-hour

low-pass Butterworth filter to remove tides and other high-frequency fluctuations.

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of these could be related to features such as river plumes, which the model is capable of

reproducing only in a statistical sense as the river forcing fields provide a climatology and

not an inter-annually varying discharge field. The PAROMS model provides a salinity time

series at the mooring sites that approximates the annual average, but the amplitude of the

this model’s salinity is appreciably reduced in comparison to the observations and the two

nested models. The model’s surface salinity shows freshening events during summer and fall

months that are much fresher than the near-bottom salinity measurements reveal; we lack

suitable time series of salinity at the surface to adequately evaluate the model’s behavior

for this field. The temperature comparisons (Figures 32, 36, 42 and 39) show an observed

annual cycle that is tracked relatively closely by all of the models, though like the salinity

field, the higher-resolution nested models perform better than the coarser Pan-Arctic model.

The observed water column cooling that occurs each fall is reproduced with a synchronous

decline of temperature in each of the models, although the Pan-Arctic model for the farther

offshore mooring (Reindeer) remains somewhat stratified whereas the observations and the

nested models suggest an isothermal water column at the freezing point. This behavior of

the Pan-Arctic model is directly related to the less accurate bathymetry employed by this

model’s configuration.

Examination of the velocity figures (Figures 33, 34, 37, 38, 40, 41, 43, nad 44) reveals that

the nested models produce a materially useful improvement of the predictions in comparison

to the Pan-Arctic model, but in general the velocity magnitudes are slightly underestimated

by all of the models. Like the observations, the models reveal a strongly two-season character

of the flow field: weak currents during winter months when landfast ice is present, and

energetic with relatively large amplitude current fluctuations when the landfast ice is absent.

Ship-based hydrography from the BOEM-funded 2011 Beaufish study provides data for

comparing the temperature and salinity fields at the surface and near the seafloor. Upwellingfavorable winds during this month may have been responsible for relatively cool and salty

water observed on the eastern and inshore side of the observed stations.

The models used in this project tend to compute temperatures that are similar to those

observed within the bounds of the data collected (Figure 45). Close to shore and outside of

the station grid of the ship samples, the models all exhibit temperatures at the surface and

the seafloor that are warmer than farther offshore. In this region the higher-resolution models

(Beaufort 2 and 3) depict temperatures that are appreciably warmer than those computed by

the Pan-Arctic model. We note that some broad-scale features seen in the data also appear

to be represented in the modeled fields. For example, the surface temperatures are cooler

closer to the coast and in the eastern portion of the domain. This feature also appears in

the model, although with a diminished offshore extent. The warmest waters observed, and

modeled, are in the far west of the domain near Barrow Canyon,

The near-bottom salinities on the shelf as depicted by the models are a bit saltier than

those observed (Figure 46). In the case of the Pan-Arctic model, the on-coast displacement

of the shelf break is partly responsible for the mis-match. In the case of the nested models, it

appears that some freshwater is held close to the coast by a frontal system. At the surface, the

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Figure 45: Comparison of ship observations (top row) of temperature to modeled ocean

temperature at the sea surface (left column) and the seafloor (right column). The Beaufort2,

Beaufort3 and Arctic2 models are in rows 2, 3 and 4, respectively. Circles in the top row

panels denote CTD cast locations.

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Figure 46: Comparison of ship observations (top row) of salinity to modeled ocean salinity

at the sea surface (left column) and the seafloor (right column). The Beaufort2, Beaufort3

and Arctic2 models are in rows 2, 3 and 4, respectively. Circles in the top row panels denote

CTD cast locations.

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modeled salinities are extremely close to those observed, especially in the eastern portion of

the domain (including the upwelling signal noted above). The models predict fresher waters

offshore, and a hint of such waters are visible in the western portion of the observed grid.

Comparison of observed and modeled sea ice concentrations show that the Arctic2 PanArctic model (Figures 47 and 48) reproduces 58% of the ice area variance across the PanArctic domain as a whole, 88% of the variance in the Beaufort2 domain, and 64% in the

nearshore Beaufort3 domain. In contrast, the Beaufort2 model captures 61% of the variance

of the ice area variance across its entire domain and the Beaufort3 model captures 44% of

the variance. We note that the resolution of the satellite data (25 km) is coarser than any

of the models and this characteristic of the passive satellite ice concentration measurements

provides a limit to our ability to assess the fidelity of the model’s ice field. This latter point

is illustrated by the panels shown in Figure 49, which clearly shows the difference between

the pixel size of the observations in relation to the ice model resolution. Figure 50 provides a

high-resolution thermal satellite image in the Chukchi-Beaufort region in order to illustrate

some of the fine-scale ice features that are fully unresolved by the passive microwave sensor.

The newly implemented landfast ice parameterization provides a means by which the

models can dynamically grow and destroy extensive regions of landfast ice. Initial comparisons show that the models can reproduce the timing and magnitude of landfast ice extent.

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Figure 47: Time series of the Arctic2 (top), Beaufort2 (middle) and Beaufort3 (bottom)

models in reproducing the mean ice area anomalies for each of their respective grids.

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Figure 48: Correlation matrix of ice area anomalies for each of the three model for the

Pan-Arctic model domain (top), the Beaufort2 model domain (middle) and Beaufort3 model

domain (bottom). The model data that comprise the comparisons are the coarse PanArctic model (left), the medium-resolution Beaufort2 model (center) and the fine-resolution

Beaufort3 model (right).

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Figure 49: Comparison of modeled (left) and observed (right) ice concentration for the coarse

(top), medium-resolution (middle) and fine-resolution (bottom) models.

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Figure 50: Thermal satellite image from 14 November 2012. Dark colors represent relatively

cold temperatures, white colors show relatively warm (e.g., ocean water). The figure extends

from Banks Island in the upper right to St Lawrence Island in the lower left. Leads in the

ice pack show as thin bright lines between the light grey ice pack floes. Fog and clouds are

dark grey smears that extend across land/ocean boundaries.

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5. SUMMARY REMARKS	 BOEM 2018-018

5 Summary remarks

This project has advanced our ROMS modeling capabilities in the Beaufort Sea, and more

broadly, across the entire Arctic. Particular achievements include:

•	 Success at nesting high-resolution grids into a coarser model domain for a model setup

that includes sea ice crossing the model interface boundary.

•	 Incorporation of a landfast ice field that grows and contracts with changing wind, ice,

and oceanographic conditions.

•	 Forcing of the terrestrial river discharges through the sidewall boundaries with full

momentum, heat, tracer and freshwater fluxes.

Evaluation of the models’ performance show that the nested grids can provide an appreciably more accurate depiction than the coarser Pan-Arctic model, although even the

broad-scale model is able to provide statistically significant predictive capacities for the sea

surface elevation and ice fields and the heat, volume and freshwater fluxes. Performance of

the highest-resolution model may be hampered by the lack of an accurate bathymetric elevation model for the nearshore Beaufort Sea. However, we note that even at 500 m resolution

many of the barrier islands are not sufficiently resolved. The temporal and spatial resolution

of datasets that are available for making robust model-data comparisons continues to be a

limiting factor in assessing all aspects of the model’s fidelity to the real ocean.

The results of this study suggest a number of avenues for further improving our ability to

model the Beaufort shelf and for improving our understanding of the ocean currents and sea

ice here. Recommendations for future advances for the circulation modeling of the Arctic

seas include:

•	 Development of a high-resolution coastal discharge forcing field that specifies the terrestrial discharge at sub-km resolution in space and at daily or hourly resolution in

time. Present options for coastal runoff forcing lag behind our ability to incorporate

such datastreams in a realistic fashion.

•	 Collection of shelf-wide high-resolution bathymetric data for improving the underlying

digital elevation model. Data in waters shoreward of the 20m isobaths, particularly

those in and around the barrier islands should be the highest priorities.

•	 Additional experiments with the landfast ice algorithms are warranted in order to

further optimize the suite of settings that result in the most realistic ice field.

•	 Despite the number and location limitations of available datasets for model evaluation,

there are always additional analyses that hold promise for revealing physical processes

that are important to a full understanding of the Beaufort shelf circulation. We recognize extensive opportunities for case-study investigations of individual events (e.g.

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5. SUMMARY REMARKS BOEM 2018-018

discrete storms) and processes (e.g., coastal upwelling) that would benefit from focused

study.

Finally, we highlight the approach taken here, namely that of multiple nests starting with

the relatively coarse basin scale model and ending in a very high-resolutoin coastal domain.

This first clear advantage of this approach is the ability to integrate the circulation and

sea ice dynamics in the region of interest at a very high-resolution for decadal timescales.

This is simply not feasible for extended domains. Furthermore, by integrating the physics

at different resolutions, this methodology, in effect, concentrates the effort and cost of the

simulation where it is needed for particular objectives. The Pan-Arctic model is useful to

generate the large-scale context–from storms to interannual and decadal variability–which

is then successively passed on to the more regional models through the boundaries. At

the higher resolutions, local details of the coastline and circulation (e.g., barrier islands,

ocean turbulence) can then be integrated in the context provided by the large-scale model.

The challenge is always the treatment of the dynamics at the model interfaces allowing

the propagation of information, from shelf waves to moving ice. In this project, we made

significant advances for nested modeling of coupled circulation-sea ice systems, which creates

opportunity for the investigation of the physics and the ecosystems in this region.

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REFERENCES BOEM 2018-018

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