OCS Study BOEMRE 2010-028

Modeling of Circulation in the North Aleutian Basin

Department of the Interior, Bureau of Ocean Energy Management,

Regulation and Enforcement Program Number M07PC13368

Enrique N. Curchitser

Institute of Marine and Coastal Sciences, Rutgers University

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OCS Study BOEMRE 2010-028

Modeling of Circulation in the North Aleutian Basin

Department of the Interior, Bureau of Ocean Energy Management,

Regulation and Enforcement Program Number M07PC13368

Enrique N. Curchitser

Institute of Marine and Coastal Sciences, Rutgers University

71 Dudley Rd. New Brunswick, NJ 08901

Kate Hedstrom Seth Danielson Tom Weingartner

University of Alaska, Fairbanks

October, 2010

This study was funded by the U.S. Department of the Interior, Bureau of Ocean Energy Management, Regulation and Enforcement (BOEMRE), Alaska Outer Continental Shelf Region, Anchorage

Alaska, under Contract No. M07PC13368, as part of the BOEMRE Environmental Studies Program.

This report has been reviewed by the Bureau of Ocean Energy Management, Regulation and Enforcement and approved for publication. Approval does not signify that the contents necessarily

reflect the views and policies of the Service, nor does mention of trade names or commercial products

constitute endorsement or recommendation for use.

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OCS Study BOEMRE 2010-028

Abstract

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

Energy Management, Regulation and Enforcement (BOEMRE) program M07PC13368,

Modeling of Circulation in the North Aleutian Basin. The primary aim of the work

done under this award was to use a state-of-the-art coupled circulation-sea ice numerical

ocean model to simulate several decades of the currents in the North Aleutian Basin

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 models and

their implementation for this particular problem, and describes the extensive modeldata comparisons that have been performed as part of the current award.

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CONTENTS OCS Study BOEMRE 2010-028

Contents

1 Introduction 9

1.1 Background: The Eastern Bering Sea . . . . . . . . . . . . . . . . . . . . . . 9

2 Scientific and technical approaches 12

2.1 The coupled ocean-sea ice model . . . . . . . . . . . . . . . . . . . . . . . . 12

2.2 Bering Sea Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

3 Model-data comparisons 19

3.1 Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19

3.2 Model Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

3.2.1 Tides . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

3.2.2 Velocities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

3.2.3 Sea-ice . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

3.2.4 Transects . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

3.2.5 Timeseries at M2 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46

3.2.6 Shelf-wide T and S . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

4 Summary remarks 62

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LIST OF FIGURES OCS Study BOEMRE 2010-028

List of Figures

1 Schematic of the circulation in the Bering Sea . . . . . . . . . . . . . . . . . 10

2 A suite of nested models in the North Pacific. The models relevant to the

Bering Sea are (1) The North Pacific (NPac 0.18 deg.), (2) The Northeast

Pacfic (NEP at 10 km) and (5) BERING, currently . . . . . . . . . . . . . . 16

3 Soundings used to generate custom bathymetry for the Bering Sea and Gulf

of Alaska. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18

4 Model domain in native projection and resulting bathymetry. . . . . . . . . . 18

5 Map showing the location of observational data available for model comparisons. The NAB planning area is shaded light blue. Small grey dots within

the NAB indicate CTD hydrographic samples locations from the UAF-IMS

and the NODC-WOD databases. Samples along the Cape Newenham to M2

transect (thick black line) will form the primary section for hydrographic

comparisons of fronts and stratification locations and strengths. Multi-year

temperature, salinity and current measurements are made on moorings at site

M2 (star), Bering Strait (black solid circle) and Unimak Pass (black solid

square). Red open circles denote tidal elevation harmonics stations and blue

plus signs denote tidal current ellipse harmonics stations. The light purple

shaded area indicates the approximate domain of the 2002 surface drifter tracks. 20

6 Model co-tidal map for the Bering Sea (top) and a zoom of the southeastern

Bering region (bottom). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23

7 Depth profiles for K1 tidal component. . . . . . . . . . . . . . . . . . . . . . 24

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LIST OF FIGURES OCS Study BOEMRE 2010-028

8 Time evolution of K1 tidal component . . . . . . . . . . . . . . . . . . . . . 24

9 Depth profiles for M2 tidal component. . . . . . . . . . . . . . . . . . . . . . 25

10 Time evolution of M2 tidal component . . . . . . . . . . . . . . . . . . . . . 25

11 Mean profiles for M2 and K1 tidal components . . . . . . . . . . . . . . . . . 26

12 Model versus observations scatter plots of elevation amplitude and phase and

ellipse phases for the M2 (top) and K1 (bottom) tidal components. . . . . . 27

13 Seasonal surface and bottom currents. . . . . . . . . . . . . . . . . . . . . . 29

14 Monthly averaged surface and bottom currents (January and February). . . . 30

15 Monthly averaged surface and bottom currents (March and April). . . . . . . 31

16 Monthly averaged surface and bottom currents (May and June). . . . . . . . 32

17 Monthly averaged surface and bottom currents (July and August). . . . . . . 33

18 Monthly averaged surface and bottom currents (September and October). . . 34

19 Monthly averaged surface and bottom currents (November and December). . 35

20 Comparison of model (blue) and drifter (red) velocities. . . . . . . . . . . . . 36

21 Comparison of model (blue) and drifter (red) velocities. . . . . . . . . . . . . 36

22 Time series of bulk sea ice area (top) and volume (bottom). Blue is model,

red SSM/I satelite data, when available. . . . . . . . . . . . . . . . . . . . . 38

23 Monthly mean observed and modeled ice concentration differences. . . . . . . 40

24 Monthly mean observed and modeled ice concentration root mean squared

error. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

25 Monthly mean observed and modeled ice concentration standard deviation. . 41

26 Monthly mean observed and modeled ice concentration cross-correlation. . . 41

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LIST OF FIGURES OCS Study BOEMRE 2010-028

27 April temperature and salinity observed minus model differences along Cape

Newenham transect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

28 April Density and density gradient comparisons along Cape Newenham transect. 43

29 June temperature and salinity observed minus model differences along Cape

Newenham transect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44

30 June Density and density gradient comparisons along Cape Newenham transect. 44

31 September temperature and salinity observed minus model differences along

Cape Newenham transect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

32 September density and density gradient comparisons along Cape Newenham

transect. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45

33 Power spectra at 10 m depth at mooring site M2. Model (red) and observations (blue). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

34 Power spectra at 50 m depth at mooring site M2. Model (red) and observations (blue). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

35 Site M2: Monthly mean and +/-1 standard deviation for temperature and

salinity at 10m and 60m. Observations are in red, model in blue. . . . . . . . 48

36 Site M2: Temperature time series comparison at 10m and 60m. . . . . . . . 54

37 Site M2: Salinity time series comparison at 10m and 60m. . . . . . . . . . . 55

38 Site M2: Timeseries of density at 10m and 60m. . . . . . . . . . . . . . . . . 56

39 Site M2: Monthly mean density gradient (60 to 10m). . . . . . . . . . . . . . 56

40 Site M2: Monthly mean temperature and salinity scatter plots for 10m and

60m . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

41 Model vs. BASIS surface temperature and salinity comparisons for 2002. . . 58

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LIST OF TABLES OCS Study BOEMRE 2010-028

42 Model vs. BASIS near bottom temperature and salinity comparisons for 2002. 58

43 Model vs. BASIS surface temperature and salinity comparisons for 2003. . . 59

44 Model vs. BASIS near bottom temperature and salinity comparisons for 2003. 59

45 Model vs. BASIS surface temperature and salinity comparisons for 2004. . . 60

46 Model vs. BASIS near bottom temperature and salinity comparisons for 2004. 60

47 Model vs. BASIS surface temperature and salinity comparisons for 2005. . . 61

48 Model vs. BASIS near bottom temperature and salinity comparisons for 2005. 61

List of Tables

1 Temperature statistics at 10m for model and data . . . . . . . . . . . . . . . 50

2 Temperature statistics at 60m for model and data . . . . . . . . . . . . . . . 51

3 Salinity statistics at 10m for model and data . . . . . . . . . . . . . . . . . . 52

4 Salinity statistics at 60m for model and data . . . . . . . . . . . . . . . . . . 53

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1 INTRODUCTION OCS Study BOEMRE 2010-028

1 Introduction

1.1 Background: The Eastern Bering Sea

The physical oceanography of the eastern Bering Sea exhibits complex phenomena over a

broad range of spatial and temporal scales, including tidal mixing, topographically steered

flows, shelf-slope exchanges through canyons, mesoscale eddy mixing, flows through narrow

passes and the yearly formation, drift, and melting of sea ice (Schumacher et al. 2003).

A description of the current systems in the Bering Sea, schematically illustrated in Figure

1, can be found in Stabeno et al. (1999). Inflow to the Bering Sea occurs through the Aleutian

Passes, predominantly Amukta and Amchitka Passes and Near Strait. The principle outflow

is through Kamchatka Strait to the west. While transport through the Bering Strait is small

( 0.8 Sv; Roach et al. 1995), the flow from the Bering to the Arctic strongly dominates the

flow patterns of the northern Bering Sea shelf.

The eastern Bering Sea has the following primary physical features: 1. Principal Currents: The principal currents in the eastern Bering Sea basin are the Aleutian North Slope

Current (ANSC, Stabeno and Reed 1994; Stabeno et al. 1999) and the Bering Slope Current

(BSC; Schumacher and Reed 1992). The ANSC flows along the northern side of the Aleutian

Islands. The source of these waters is predominately through Amchitka and Amukta Pass.

The ANSC is the source of the BSC, which is the eastern boundary current of the cyclonic

circulation of the Bering Sea basin (Stabeno and Reed 1994). 2. Tidal influence: The eastern

Bering Sea (EBS) is strongly influenced by tidal mixing, as well as subtidal flows driven by

wind and buoyancy. Subtidal flows interact with tides through the density field. The intense

tidal and wind mixing, coupled with stratification by solar heating, leads to three domains

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1 INTRODUCTION OCS Study BOEMRE 2010-028

Figure 1: Schematic of the circulation in the Bering Sea

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1 INTRODUCTION OCS Study BOEMRE 2010-028

on the shelf (Coachman 1986; Coachman 1982; Schumacher and Stabeno 1998). The coastal

domain (0-50 m bottom depth) is well mixed or weakly stratified. The middle domain (50-

100 m) is characterized by a two-layer system. The outer domain (> 100m), more oceanic in

character, exhibits mixed layers at the surface and bottom with a stratified layer between.

The importance of including both tidal and subtidal flows in numerical simulations of the

southeastern Bering Sea shelf was shown in Hermann et al., (2002). 3. Shelf-slope exchange:

Exchange between shelf and slope waters provides for a vigorous tracer exchange but the

magnitude of this exchange is not well measured. Flow onto the shelf is a combination of

currents interacting with topography (e.g. canyons) and instabilities of shelf break flows

(Stabeno and Van Meurs 1999). 4. Meanders and eddies: Meanders and eddies are common

over the southeast corner of the basin (Schumacher and Stabeno 1994). Altimetric, SST and

drifter data have revealed mesoscale features on 20-200 km scales. 5. Ice physics and the

cold pool: Ice forms in the the northern Bering Sea during winter. This sea ice is advected

south into the SEBS, where the leading edge melts. This is one of the main mechanisms

contributing to the heat and salt content and density stratification and vertical mixing of

shelf waters. A persistent pool of cold water (< 2

oC) forms over the northern shelf each

year and over the southern shelf in years of extensive ice cover. Fresh water from melting

ice plays an important role in the spring setup of the 2-layer system (Stabeno et al. 2001).

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2 SCIENTIFIC AND TECHNICAL APPROACHES OCS Study BOEMRE 2010-028

2 Scientific and technical approaches

2.1 The coupled ocean-sea ice model

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

based on the Regional Ocean Modeling System (ROMS). ROMS is a free-surface, hydrostatic primitive equation ocean circulation model whose core was developed at Rutgers University and UCLA with significant contributions from a large community of users. ROMS

is a terrain-following, finite difference (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., 2004), atmosphere-ocean flux computations based on the ocean model prognostic variables using bulk-formulae (Fairall et al., 1996) 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 elasticviscous-plastic (EVP) rheology (Hunke and Dukowicz, 1997) 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 model also includes frazil ice

growth in the ocean being passed to the ice (Steele et al., 1989). It currently follows a single

ice category, which exhibits accurate results in a marginal ice zone such as the Bering Sea.

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The principal attributes of the model 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 oats.

(f) Option for point sources and sinks.

2. Horizontal

(a) Orthogonal-curvilinear coordinates.

(b) Arakawa C grid.

(c) Closed basin, 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.

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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.

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., 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.

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(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.

Exhaustive details of the model are provided in the accompanying manual which is part

of the deliverables for this contract (Hestrom, 2010, MMS 2009-062).

2.2 Bering Sea Implementation

Figure 2 shows the set of nested model domains currently implemented in the north Pacific

ocean, ranging from 4 km in the Bering Sea (BERING) to 10 km in the Northeast Pacific

(NEP) to 0.18 deg North Pacific (NPac) grids. Since the current contract called for a multidecadal integration, we settled for the 10 km horizontal resolution NEP domain (domain 2

in figure 2). This domain captures the circulation both in the Bering Sea and the Gulf of

Alaska which is crucial to the physics of the North Aleutian Basin and is easily affordable

for the required timescales.

The NEP model extends from approximately 20 N to 71 N. It extends about 2250 km

offshore from the North America west coast at a nominal horizontal resolution of 10 km

and 60 terrain-following vertical levels stretched towards the surface boundary. The terrainfollowing coordinate system has distinct advantages for modeling shelf regions; The full

vertical coordinate system is compressed over the shelf resulting in high vertical resolution

in the region of interest (e.g., 60 layers over the whole Bering Sea shelf in the current NEP

implementation) and as a consequence no computations are wasted in the bathymetry. The

grid (a rectangle in a Lambert Conical projection) is rotated relative to lines of constant

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2 SCIENTIFIC AND TECHNICAL APPROACHES OCS Study BOEMRE 2010-028

Figure 2: A suite of nested models in the North Pacific. The models relevant to the Bering

Sea are (1) The North Pacific (NPac 0.18 deg.), (2) The Northeast Pacfic (NEP at 10 km)

and (5) BERING, currently

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longitude so as to minimize computations over land. The coupled ocean/sea ice model has

been integrated for the period of 1958 through 2004. The surface forcing for the NEP model

is derived from the datasets for Common Ocean-ice Reference Experiments version 2 (CORE

v2; Large and Yeager, 2004), which consists of 6-hourly winds, air temperatures, sea level

pressure and specific humidity, daily short-wave and downwelling long-wave radiation, and

monthly precipitation. The air-sea fluxes are computed using bulk formulae (Fairall et al.,

1996). The oceanic surface boundary layer is computed using the k-profile parameterization

(Large et al., 1994). Riverine inputs are implemented as a surface fresh water flux which

guarantees global conservation over long-timescales. Boundary and initial conditions for this

domain were derived from the Simple Ocean Data Assimilation (SODA 2.0.2; Carton et

al., 2000a,b) which is an ocean reanalysis product for the 1958-2006 period. The nominal

resolution of SODA is 0.5 degrees and we were able to obtain 5-day averages for the boundary

conditions. The northern boundary has a sink term which is set to enforce a 0.8 Sv northward

transport through the Bering Strait.

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

and semi-diurnal components derived from the Oregon State Tidal Prediction Software (Egbert and Erofeeva, 2001) (OTPS;

http://www.coas.oregonstate.edu/research/po/research/tide/global.html).

One significant aspect to simulating tides well is a good representation of the bathymetry.

To that end, we used a newly available Alaska region bathymetric dataset that was constructed solely from observational soundings (figure 3) blended with a conventional ETOPO

dataset for the regions south of 45N. The resulting model bathymetry is shown in figure 4.

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Figure 3: Soundings used to generate custom bathymetry for the Bering Sea and Gulf of

Alaska.

Figure 4: Model domain in native projection and resulting bathymetry.

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3 Model-data comparisons

An integral part of this project is model skill assessment. We will rely on historical observational datasets for model validation. Although not all of the datasets are located strictly

within the North Aleutian Basin (NAB) domain, model-data comparisons in the NAB and

on the greater Bering Sea shelf will provide the most robust evaluations of model performance in both regions. We first describe the data that we have selected for comparisons and

then show results.

3.1 Datasets

Oceanographic mooring data from two primary sites are used to evaluate the bulk statistical

properties of the temperature, salinity and current fields (Figure 4). The comparisons include

evaluation of interannual, seasonal , synoptic and tidal variability. Multi-year time series are

available from the NOAA site M2 within the NAB (1995-present, 56 36.20’ N; 164 38.00’

W; P. Stabeno, unpubl. data). We also use data from a mooring (F8) deployed just south

of St. Lawrence Island from September 1998 to September 1999. The M2 mooring has

temperature and salinity measurements at various depths and a profiling current meter. M2

is used to assess both the local stratification and currents in the model. The F8 mooring data

is used to assess the vertical structure of tidal currents. Mean current speed and direction

from mooring data in the published literature base are used to make point comparisons

of velocity to modeled currents. Other datasets used are SSM/I satellite data for ice cover,

drifter data for surface velocity comparisons, CTD hydrographic samples from the UAF-IMS

and the NODC-WOD databases, and tidal elevation stations throughout the Bering Sea.

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Figure 5: Map showing the location of observational data available for model comparisons.

The NAB planning area is shaded light blue. Small grey dots within the NAB indicate

CTD hydrographic samples locations from the UAF-IMS and the NODC-WOD databases.

Samples along the Cape Newenham to M2 transect (thick black line) will form the primary

section for hydrographic comparisons of fronts and stratification locations and strengths.

Multi-year temperature, salinity and current measurements are made on moorings at site

M2 (star), Bering Strait (black solid circle) and Unimak Pass (black solid square). Red open

circles denote tidal elevation harmonics stations and blue plus signs denote tidal current

ellipse harmonics stations. The light purple shaded area indicates the approximate domain

of the 2002 surface drifter tracks.

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3.2 Model Evaluation

The following are the model evaluation metrics that are being carried out as part of this

project:

1. Tidal heights and currents

(a) Amplitudes and Phases

(b) Current ellipse parameters

(c) Vertical tidal current structure

2. Sub-tidal currents

(a) Spectral comparisons

(b) Mean flow comparisons

3. T and S variability

(a) Monthly means and anomalies over the shelf at mooring M2

(b) Across the NAB, the whole Bering shelf and the Bering basin

4. Density gradients

(a) Cross-isobath structure

(b) Frontal locations

(c) Frontal strength

(d) Vertical density gradients: magnitude and location within water column

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5. Ice cover

(a) Spatial/point time series

(b) EOFs

We now proceed to present some of these comparisons:

3.2.1 Tides

The cotidal maps (Figure 6) are compared to those of Kowalik [1999]. The overall pattern of

the co-phase lines is very similar and typically within 15-30 in the deep basins. Amphridromic

points in the Gulf of Anadyr, Norton Sound, near St. Lawrence Island, near Nunivak Island

and near Cape Newenham are reproduced. Co-amplitudes lines also exhibit similar patterns

and magnitudes.

Examination of the tidal ellipse parameters for the M2 and K1 constituents (Figures

7-11) shows that the model captures the basic behavior of the tidal bottom boundary layer

(BBL) effects. These include the following. 1) The semidiurnal (M2) BBL extends farther

from the seafloor than the diurnal (K1) BBL. 2) Stratification effects appear to impact the

M2 BBL, resulting in enhanced M2 currents during the late summer and early fall when the

vertical density gradient is at its annual maximum. M2 tidal current can exhibit a subsurface

maximum.

The M2 amplitude comparison presented in Figure 12 (top left panel), shows that on

average the model slightly overestimates the observed M2 elevations although the slope of

the fit is close to 1:1. The elevation phases (lower left panel) shows an offset of approximately

60 degrees for many stations and scatter on the order of another 60 degrees. The closely

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Figure 6: Model co-tidal map for the Bering Sea (top) and a zoom of the southeastern Bering

region (bottom).

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Figure 7: Depth profiles for K1 tidal component.

Figure 8: Time evolution of K1 tidal component

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Figure 9: Depth profiles for M2 tidal component.

Figure 10: Time evolution of M2 tidal component

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Figure 11: Mean profiles for M2 and K1 tidal components

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Figure 12: Model versus observations scatter plots of elevation amplitude and phase and

ellipse phases for the M2 (top) and K1 (bottom) tidal components.

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clustered stations that lie on the 1:1 line between 240 and 360 degrees are comprised of

stations that are found mostly in deep waters in the northern and western Gulf of Alaska.

This shows that the tidal phasing is correct as it propagates northward and that the tidal

elevation wave is retarded in the model with respect to the observations. It is likely that this

phase change occurs as the tidal wave crosses the Aleutian Island chain or when it impinges

upon the shelf.

Because the velocity field is closely tied to the sea surface elevation slope, it is often more

difficult to simulate. The M2 currents show more relative scatter than the amplitudes but

the fit also closely follows the 1:1 line. Phasing of the tidal ellipses are also nearly 60 degrees

offset from that observed. Note that within tidal ellipses, there can exist an ambiguity of

180 degrees so the scatter shown in the lower right panel over-emphasizes the actual scatter.

3.2.2 Velocities

Because of a lag in obtaining forcing data, the model was not run for 2008 and 2009 - when

most of our drifter data was collected - so we make seasonally averaged comparisons rather

than direct time-matched comparisons. These are presented in figures 13-21. The drifter data

suggest a seasonal reorganization of the surface currents, with the nearshore region exhibiting

mostly along-shore motion during the summer accompanied by little offshore spreading. In

the fall and winter months, the drifters show a pronounced westward motion. These two

behaviors appear to be captured by the model.

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Figure 13: Seasonal surface and bottom currents.

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Figure 14: Monthly averaged surface and bottom currents (January and February).

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Figure 15: Monthly averaged surface and bottom currents (March and April).

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Figure 16: Monthly averaged surface and bottom currents (May and June).

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Figure 17: Monthly averaged surface and bottom currents (July and August).

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Figure 18: Monthly averaged surface and bottom currents (September and October).

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Figure 19: Monthly averaged surface and bottom currents (November and December).

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Figure 20: Comparison of model (blue) and drifter (red) velocities.

Figure 21: Comparison of model (blue) and drifter (red) velocities.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

3.2.3 Sea-ice

Figure 22 (top), presents the model and satellite-derived ice extent for all the years of the

model run. The model clearly shows skill in reproducing both the magnitude and interanual

variability in this field. The bottom of the figure is the model derived ice thickness, for which

there is no significant time series available in the Bering sea.

The following figures (23-26) present spatial maps of the sea ice fields. The blue contours

(Figure 23) show the mean monthly observed ice concentrations (10% concentration contour

interval). The colors show the difference between the model and the observation, with differences contoured at the 10% concentration level. Warm (red) colors indicate the model

concentration is greater than those observed; cool (blue) colors indicate the model concentrations are less than those observed. We find that the model is typically within 10-20% of

that observed, underestimating the ice concentrations in and south of the Gulf of Anadyr in

the mid to late winter and overestimating concentration in the late spring/early summer in

the northern central region.

The blue contours in Figure 24 show the monthly mean observed ice concentrations (10%

concentration contour interval). The RMSE is typically between 10 and 30%. The largest

errors appear in the western Gulf of Anadyr during the spring and early summer months.

In Figure 25, the color contours show the difference, in standard deviation between the

model and the observations. Differences in standard deviation are typically less than +/-

20%. Ice variation is typically greater in the observed fields in the mid and late winter

whereas the model shows slightly greater variation in the late spring and early summer.

In Figure 26, the blue contours show the mean monthly observed ice concentrations

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 22: Time series of bulk sea ice area (top) and volume (bottom). Blue is model, red

SSM/I satelite data, when available.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

(10% concentration contour interval). Yellow contours denote the region that is significant

at the 95% confidence interval. We find that extensive regions beyond the low mean ice

concentration of 10% are significantly correlated, which indicates that years of extensive ice

are well captured by the model. The Gulf of Anadyr, Anadyr Strait and Chirikov Basin

regions have the most consistently non-correlated grid points. This may be due to the

boundary conditions forcing flow through the Bering Strait rather than a result of model

dynamics. Because the northward transport through Bering Strait is prescribed, the flow

field in Anadyr Strait is not tightly coupled to the regional wind field, which has been shown

to strongly impact the instantaneous transport through Bering Strait. Shpanberg Strait is

not impacted to the same degree because a much greater proportion of the Bering Strait

throughflow comes from the western side of St. Lawrence Island rather than the eastern

side.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 23: Monthly mean observed and modeled ice concentration differences.

Figure 24: Monthly mean observed and modeled ice concentration root mean squared error.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 25: Monthly mean observed and modeled ice concentration standard deviation.

Figure 26: Monthly mean observed and modeled ice concentration cross-correlation.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

3.2.4 Transects

Figures 27-32 show model/data comparisons along a transect off Cape Newenham. The

difference fields show that the temperature and density offsets seen at mooring M2 extend

across the shelf and last through the spring, summer and fall seasons. Positive differences

near 25m of depth reflect biases in the model mixed layer depth relative to observations.

The mean density and the horizontal gradient panels show that the model generally captures the proper location and magnitude of the two major frontal features (shelf break and

nearshore). The offset at the shelf break is likely due to differences between the real and

model bathymeteries. Nearshore differences may attributed to the model approximation of

river discharge. The vertical density gradient shows that the model exhibits a shallow, strong

pycnocline. This feature, like the temperature offset, is linked to the model albedo setting.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 27: April temperature and salinity observed minus model differences along Cape

Newenham transect.

Figure 28: April Density and density gradient comparisons along Cape Newenham transect.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 29: June temperature and salinity observed minus model differences along Cape

Newenham transect.

Figure 30: June Density and density gradient comparisons along Cape Newenham transect.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 31: September temperature and salinity observed minus model differences along Cape

Newenham transect.

Figure 32: September density and density gradient comparisons along Cape Newenham

transect.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

3.2.5 Timeseries at M2

The spectra show that in the low frequency band, the model has approximately the right

amount of energy (within the 95% confidence bounds) for the near-surface currents. Thus,

the current fluctuations at periods between 100 and 1000 hours (4 days to 1.3 months)

predicted by the model are statistically similar to those observed at mooring M2. However,

at higher frequencies (periods of 10 hours to 100 hours), the model is biased toward the

lower 95% limit of the observations, indicating that slightly less kinetic energy is predicted

than that observed. The currents found at 50m depth also show no statistically significant

difference between the spectra of those predicted and those observed for the 100-1000 hour

periods. Likewise, the higher frequency band (10-100 hours) is biased low in the model output

at 50 m depth but less so than for the 10 m depth currents. One reason for these low-biased

discrepancies might be found in the use of 6-hourly winds to force the ocean surface: 6-

hourly winds do not contain the same high frequency spectral content as that observed and

the near-surface currents are generally sensitive to wind forcing. This hypothesis is supported

by the lack of a distinct inertial peak in the near-surface model CW spectrum. Alternately,

it is possible that the model damps the higher frequency motions more strongly than lowfrequency motions; the degree of frictional seabed coupling could be partly responsible.

Similarly, differences in nonlinear current generation due to turbulence could also partly

account for disparity between the observed and modeled currents.

Near the long-term NOAA mooring M2 located in 70m water depth on the Bering Sea

shelf, we find that the model captures 40-50% of the monthly temperature variability and

20% of the monthly salinity variability after the mean monthly climatology has been removed

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 33: Power spectra at 10 m depth at mooring site M2. Model (red) and observations

(blue).

Figure 34: Power spectra at 50 m depth at mooring site M2. Model (red) and observations

(blue).

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 35: Site M2: Monthly mean and +/-1 standard deviation for temperature and salinity

at 10m and 60m. Observations are in red, model in blue.

from the moored T/S records. The simulated T and S fields exhibit mean monthly offsets

of -0.3 to +2.6 C and -0.9 to -0.3 PSU respectively. Excluding November, December and

January for lack of data, the observed vs. simulated density gradient between the 10 m

to 60 m depth levels agrees to within 0.15 kg m-3 in all months except July, August and

September where the difference is between 0.3 and 0.4 kg m-3. The model under-estimates

the magnitude of this gradient in all months except May.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

The following four tables give additional details on the model and data computed statistics:

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

10m Temperature: 1995 to 2006 Site M2 comparison of monthly data

N Mean Std Dev Maximum Minimum Corr. RMSD

Model Obs. Model Obs. Model Obs. Model Obs.

January 9 3.12 2.47 0.86 1.32 4.78 4.35 1.99 0.44 0.90 0.75

February 11 2.56 1.01 1.22 1.89 4.89 3.60 1.17 -1.44 0.90 0.89

March 11 2.71 1.17 0.92 1.52 4.47 3.09 1.22 -1.03 0.75 0.90

April 11 3.29 1.10 0.73 1.12 4.29 2.49 1.98 -1.00 0.43 0.90

May 12 4.81 2.50 0.74 1.20 5.88 4.41 3.48 0.05 0.73 0.79

June 12 7.26 5.83 1.03 1.32 8.87 7.74 5.91 3.83 0.64 0.88

July 11 9.41 9.70 1.06 1.26 11.19 11.51 8.14 8.09 -0.10 1.19

August 11 11.04 10.76 0.94 1.44 12.39 12.58 9.52 8.48 0.49 1.01

September 11 10.09 9.49 0.65 1.28 11.48 11.56 8.92 7.97 0.82 0.83

October 9 7.66 6.67 0.52 0.88 8.32 7.83 6.78 5.74 0.34 0.84

November 9 5.63 4.83 0.44 0.88 6.17 6.22 4.86 3.56 0.72 0.74

December 9 4.12 3.60 0.56 1.09 5.01 5.39 3.21 2.35 0.83 0.74

Mean 11 5.98 4.93 0.81 1.27 7.31 6.73 4.77 3.09 0.62 0.87

Table 1: Temperature statistics at 10m for model and data

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

60m Temperature: 1995 to 2006 Site M2 comparison of monthly data

N Mean Std Dev Maximum Minimum Corr. RMSD

Model Obs. Model Obs. Model Obs. Model Obs.

January 7 3.08 2.54 0.91 1.19 4.79 4.35 1.99 1.00 0.86 0.67

February 8 2.61 1.50 1.11 1.41 4.88 3.47 1.51 -0.50 0.84 0.77

March 8 2.88 1.39 0.90 1.32 4.47 3.05 1.77 -0.44 0.64 0.86

April 10 3.07 0.94 0.85 1.05 4.29 2.34 1.67 -0.75 0.50 0.85

May 10 3.88 1.26 0.82 1.27 5.36 3.10 2.57 -0.59 0.60 0.89

June 10 4.40 1.94 0.85 1.17 5.73 3.44 3.12 0.21 0.81 0.68

July 10 4.97 2.53 0.76 1.04 6.02 3.71 3.60 0.99 0.81 0.67

August 10 5.45 2.88 0.71 0.93 6.33 3.92 3.98 1.52 0.84 0.60

October 9 6.19 3.90 0.54 1.02 7.30 6.13 5.26 2.22 0.95 0.60

November 7 5.80 4.61 0.42 0.78 6.31 5.54 5.16 3.56 -0.06 0.83

December 7 4.19 3.50 0.55 0.95 5.02 4.75 3.33 2.37 0.86 0.63

Mean 9 4.36 2.52 0.76 1.09 5.58 4.07 3.19 0.96 0.70 0.73

Table 2: Temperature statistics at 60m for model and data

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10m Salinity: 1995 to 2006 Site M2 comparison of monthly data

N Mean Std Dev Maximum Minimum Corr. RMSD

Model Obs. Model Obs. Model Obs. Model Obs.

January 9 31.53 31.99 0.21 0.20 31.80 32.23 31.15 31.69 0.58 0.39

February 11 31.64 31.87 0.28 0.22 32.01 32.22 31.09 31.53 0.46 0.44

March 11 31.66 31.92 0.26 0.16 32.07 32.14 31.27 31.62 0.24 0.46

April 11 31.63 31.91 0.30 0.23 32.10 32.12 31.01 31.27 0.61 0.43

May 12 31.55 31.83 0.33 0.23 31.95 32.11 31.01 31.28 0.48 0.50

June 12 31.51 31.84 0.27 0.19 31.97 32.15 31.07 31.41 0.52 0.44

July 11 31.45 31.77 0.28 0.20 31.93 32.02 31.00 31.28 0.48 0.43

August 11 31.24 31.72 0.27 0.21 31.80 32.01 30.86 31.24 0.58 0.42

September 10 31.02 31.68 0.26 0.16 31.35 31.92 30.40 31.37 0.30 0.44

October 9 31.00 31.80 0.41 0.12 31.63 31.99 30.45 31.60 0.35 0.55

November 9 31.26 31.89 0.29 0.14 31.62 32.20 30.63 31.75 0.56 0.40

December 9 31.48 31.96 0.19 0.18 31.75 32.33 31.13 31.74 0.39 0.38

Mean 10 31.41 31.85 0.28 0.19 31.83 32.12 30.92 31.48 0.46 0.44

Table 3: Salinity statistics at 10m for model and data

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60m Salinity: 1995 to 2006 Site M2 comparison of monthly data

N Mean Std Dev Maximum Minimum Corr. RMSD

Model Obs. Model Obs. Model Obs. Model Obs.

January 0

February 4 31.57 31.96 0.30 0.21 32.01 32.17 31.34 31.70 0.83 0.36

March 4 31.59 31.90 0.33 0.20 32.07 32.11 31.33 31.67 0.60 0.43

April 9 31.60 31.94 0.33 0.16 32.10 32.09 31.01 31.60 0.55 0.48

May 10 31.61 31.90 0.30 0.16 31.95 32.08 31.19 31.53 0.45 0.46

June 10 31.54 31.90 0.29 0.20 31.97 32.15 31.07 31.45 0.40 0.46

July 10 31.46 31.90 0.29 0.18 31.93 32.11 31.00 31.51 0.37 0.46

August 10 31.25 31.91 0.28 0.15 31.80 32.08 30.86 31.63 0.37 0.43

September 10 31.05 31.91 0.29 0.11 31.41 32.08 30.40 31.72 0.55 0.43

October 3 31.17 31.95 0.51 0.12 31.63 32.07 30.62 31.83 -0.66 0.67

November 0

December 0

Mean 8 31.43 31.92 0.32 0.17 31.87 32.10 30.98 31.63 0.38 0.46

Table 4: Salinity statistics at 60m for model and data

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The time series plots (figures 36-38) show the temporal distribution of moored temperature and salinity data. Data collection began in 1995 and continues through the present. We

observe a net offset between the model and data, with the bias more pronounced at depth.

This bias is also seen in the scatter plots (figure 40).

Figure 36: Site M2: Temperature time series comparison at 10m and 60m.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 37: Site M2: Salinity time series comparison at 10m and 60m.

3.2.6 Shelf-wide T and S

Point-to-point evaluations are often difficult for a model since it is not expected that a

model reproduce turbulent variability precisely as in nature. Therefore a planar view can be

instructive for overall model performance. Figures 41-48 compare model temperature and

salinity for the Bering shelf and show that the model reproduces with appreciable skill the

structure and variability of these fields. In these figures, the model results and observational

data have been re-gridded to an identical mesh so that direct comparisons can be made

between the two. In all cases, the color scales are the same for the two output fields and the

spatial mean has been subtracted everywhere to eliminate the bias of constant offsets.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 38: Site M2: Timeseries of density at 10m and 60m.

Figure 39: Site M2: Monthly mean density gradient (60 to 10m).

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Figure 40: Site M2: Monthly mean temperature and salinity scatter plots for 10m and 60m

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Figure 41: Model vs. BASIS surface temperature and salinity comparisons for 2002.

Figure 42: Model vs. BASIS near bottom temperature and salinity comparisons for 2002.

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Figure 43: Model vs. BASIS surface temperature and salinity comparisons for 2003.

Figure 44: Model vs. BASIS near bottom temperature and salinity comparisons for 2003.

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Figure 45: Model vs. BASIS surface temperature and salinity comparisons for 2004.

Figure 46: Model vs. BASIS near bottom temperature and salinity comparisons for 2004.

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3 MODEL-DATA COMPARISONS OCS Study BOEMRE 2010-028

Figure 47: Model vs. BASIS surface temperature and salinity comparisons for 2005.

Figure 48: Model vs. BASIS near bottom temperature and salinity comparisons for 2005.

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4 SUMMARY REMARKS OCS Study BOEMRE 2010-028

4 Summary remarks

We presented results from a regional coupled circulation-sea ice model of the Bering Sea

which was run in hindcast mode for the 1985-2005 period. A significant effort was spent

on model evaluation. Though the focus of the study was on the North Aleutian Basin

(NAB), in order to achieve a more comprehensive evaluation of model performance datasets

in the Bering Sea beyond the NAB were also used. The data we used was a combination of

multi-year timeseries from moorings, drifter data, satellite and ship transect data.

As a general statement, the model exhibits predictive skill in this region. However, the

skill is not uniform in all variables or on all scales. Sea ice cover is generally well represented

in the model. Model kinetic energy is similar to observations at frequencies lower than three

days and at tidal frequencies, but the inertial peak is missing in the model and the noise

floor between 10 and 100 hours is biased low. We speculate that this is primarily due to the

wind forcing dataset which itself does not resolve the inertial frequency. The seasonal and

interannual temperature and salinity variability in the model matches well with time series

observations at NOAA mooring M2. A comparison with transect data shows the model

reproduces the seasonal transitions in the water column from a well-mixed shelf regime, to

a highly-stratified two-layer system during the summer at the mid-shelf, although the depth

and strength of stratification are still difficult parameters to resolve. The near-shore remains

well mixed throughout the year both in the data and the model results; energy for mixing is

supplied from both wind and tide forcing. Particular care was taken in the implementation

of the tides in this model. We deemed this to be important because tides are known to

represent a significant amount of the total kinetic energy of the system. As a whole, tidal

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4 SUMMARY REMARKS OCS Study BOEMRE 2010-028

amplitudes and phases are well represented in the model. However, in particular places

where coastal geometry is significant (e.g, Cook Inlet) the model does exhibit errors.

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REFERENCES OCS Study BOEMRE 2010-028

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