Rutgers University

October 2001

This study was funded by the Alaska Outer Continental Shelf Region of the Minerals Management Service, U.S. Department of the Interior, Anchorage, Alaska, through Contract 14-35-01-

96-CT-30818 with Rutgers University, Institute of Marine and Coastal Sciences.

The opinions, findings, conclusions, or recommendations expressed in this report or product are

those of the authors and do not necessarily reflect the views of the U.S. Department of the Interior,

nor does mention of trade names or commercial products constitute endorsement or recommendation for use by the Federal Government.
UNIX is a registered trademark of the Open Group.

Sun is a trademark of Sun Microsystems, Inc.

SGI is a trademark of Silicon Graphics, Inc.

This is Contribution #2000-07 of the Institute of Marine and Coastal Sciences, Rutgers University.
I
5.2 Ice concentration 41

5.3 Ice thickness 44

5.4 Ice motion 44

6 Assessment and discussion 58

7 References 59

'I
derived fields, (right) Path-P fields. The color contours are those used in Fig. llb,d. 33

14 The numerical curvilinear grid used for the simulation. The resulting horizontal grid

spacing fields (in km) are shown at the bottom 37.

15 Model bathymetry (km): (upper) original ETOPO5 bathymetry, (lower) clipped and

smoothed bathymetry 38

16 Derived streamfunction (Sv) for September (Mean, 1982-1996) 42

17 Time-mean (1982-1996) ice concentration in the Arctic: (a) March (observed), (b)

September (observed), (c) March (model), (d) September (model). 45

18 Time-mean (1982-1996) ice concentration in the Western Arctic: (a) March (observed), (b) September (observed), (c) March (model), (d) September (model). . . 46

19 Ice concentration in the western Arctic Ocean for September: (left) observed, (right)

model hindcast. The color contours are those used in Fig. 18 47

20 Model ice thickness in the western Arctic Ocean for March 50

21 Model ice thickness in the western Arctic Ocean for September 52

22 Ice motion (cm/s) for the month of September (1982-1996): (left) buoy-derived

fields, (right) model hindcast.

List of Tables

1 The variables used in the description of the ocean model 7

2 The variables used in the vertical boundary conditions for the ocean model

III

55
iv
no evidence for a significant seasonal variability in the subsurface circulation in the Beaufort Sea

(Aagaard [4]). However, the wind forcing influence, and therefore the seasonal variability, is much

stronger near the surface where as much as 50% of the flow variability is wind related.

Björk [7] provides a concise review of the vertical structure in the Arctic Ocean compiled from

Threshnikov and Baranov [53]. The upper layer of the Arctic Ocean is characterized by a mixed

layer of thickness between 25 and 50 m and temperature near the freezing point. The salinity

increases horizontally from about 30 PSU in the Beaufort Sea, to about 32 PSU near the Pram

Strait. Below the mixed layer is a halocline where the salinity increases to about 34 PSU at 100 m

depth, while the temperature is still near the freezing point. Below the halocline the temperature

increases and attains a maximum with temperatures above 0 C. The corresponding layer is called

the Atlantic layer, and has a salinity of about 34.8 PSU. The upper boundary of the Atlantic Layer

is situated approximately at 300 m in the Beaufort Sea and rises to near 200 m near Greenland.

The maximum temperature is between 0.4 and 1 C over most parts of the basin with the warmest

water near the Fram Strait. The thickness of the Atlantic layer ranges between 500 and 800 m.

Below the Atlantic layer the water is relatively homogeneous with temperature between 0 and

0.9 C, and salinity in the range of 34.90 and 34.95 PSU.

1.2 Sea ice

The study of the seasonal and interannual variability of sea ice in the Arctic Ocean and adjacent

seas began in the early 1970's when hemispheric-scale observations of sea ice cover became possible.

All records prior to that time are spatially very incomplete. Parkinson [40], using passive microwave

2
- ligurs are estiITaLed jr

million m3 per second

Figure 1: Overview of the Arctic circulation.

50-200 rn

surlace watei

circulation

river ml low
lengthened since 1979, perhaps owing to an increase in net surface radiation. At present the cause

of these apparent trends is unknown, but a change in the advected heat into the Arctic is a likely

candidate.

1.3 The role of the Arctic atmosphere

The Arctic Oscillation (AO) has recently been identified as a fundamental mode of variability of

the Arctic atmosphere (Thompson and Wallace [51]). However, it is unclear how variations in the

AO affect regional conditions in the Arctic. Poleward transport of heat and moisture constitutes

the primary link between the Arctic and the global circulation system (e.g., Nakamura and Oort

[38]), thus we would expect to find strong linkages between the AO and patterns of advection into

and within the Arctic, as well as consequent interactions with cloud and.surface properties (e.g.,

sea ice). While global circulation models agree that variability is enhanced in the polar regions,

their ability to replicate present climate patterns varies widely, particularly at high latitudes. One

of the primary causes of these differences is believed to be unrealistic representations of energy

exchange processes between the atmosphere and sea ice.

Dramatic changes in a wide variety of atmospheric, cryospheric, oceanic, and biological variables

have been observed during this period by numerous investigators (e.g., Dickson [15]; Serreze et

al. [48]). For example, the AO exhibits a significant jump from a negative to a positive regime

near 1989 (Overland et al. [39]). An accompanying change in the low-level flow field is reported

by Walsh et al. ([55]), which is believed to be related to observed changes in sea ice extent

(Cavalieri et al. [11], Wang and Ikeda [58]) and sea ice motion fields (Kwok [31]). In the past

4
Fleming and Semtner [18] conducted a fully prognostic study of the Arctic ice cover using a

coupled ice-ocean model (horizontal resolution of 100 km) described by Fleming [17]. The coupled

model system consists of the Semtner [2] ocean model combined with the three-layer thermodynamic

ice model (Semtner [1]) and a modified Hibler [27] dynamic ice model. The study shows that

the vertical ocean heat flux appears to be the dominant mechanism controlling localized ice area

anomalies and the overall ice concentration. The model produces very reasonable simulations of

the ice edge position, particularly in the Barents and Greenland seas. However, consistent errors

in simulated ice concentration are identified in the numerical study, including an exaggerated meltfreeze cycle and insufficient ice thickness. The authors point out that the lack of a mixed layer

formulation in the model dynamics, and the coarse vertical resolution (20 to 30 m in the upper

layers for these experiments), are inadequate to properly represent the processes related to the

ocean's heat flux (e.g, vertical mixing, stratification and diffusion).

Piacsek et al. [42] studied the Arctic ice cover and upper ocean with a coupled ice-ocean model.

The model uses the Hibler ice model coupled to a three-dimensional ocean model that consists of a

turbulent mixed layer (Mellor and Yamada [37]) and an inverse geostrophic model. The horizontal

grid resolution adopted is approximately 127 km, and the vertical grid resolution ranges from

2.5 m near the surface to 1700 m near the bottom. The model is forced with 12-hourly GCMderived (Naval Polar Oceanography Center) atmospheric fluxes for the year 1986, for which good

quality ice edge analyses and observed Arctic buoy tracks (Colony and Rigor [14]) were available

for comparisons. In general, the computed ice edge positions from the model have comparable

accuracy to previous three-dimensional coupled ice-ocean studies, with too much ice growth during

5
growth and retreat - were also reproduced well by the coupled model to the degree that available

observations allowed model evaluation. Some systematic tendency for faster-than-observed melting

in the spring and summer months was seen in the model along the Siberian continental shelf. The

poorest model performance was obtained in the comparison between the model-inferred ice floe

("float") trajectories and the observed trajectories of surface floats released in the Arctic in 1983.

Mean motion of the simulated drifters was found to be comparable to those observed; however,

little agreement between individual sets of trajectories was obtained.

1.5 Organization of this report

The remainder of this report is organized as follows. Section 2 provides a brief overview of the

formulation of the coupled circulation / sea ice model. (The reader is referred to the accompanying

technical manual - Hedström [251 - for a complete description of the coupled system.) Section

3 discusses the properties of the new atmospheric forcing dataset being used for the first time in

this study. The configuration and parametric features of the 15-year retrospective simulation are

described in Section 4. Results of the simulation are discussed and compared with available contemporaneous observations in Section 5. Finally, a concluding Section summarizes model performance

and offers some suggestions on where to look for further improvements in the future.

6
Table 1: The variables used in the description of the ocean model

7

Variable Value (if constant) Description

g 9.81 m acceleration of gravity

D,DV,DT,VS

3, .Fv, 1T, .Fs

f(x, y)

h(x, y)

ii, ic

Km, KT, K

P

4(x, y, z, t)

Po + p(x, y, z, t)

S(x, y, z, t)

t

T(x, y, z, t)

u, v, w

x, y

z

(x, y, t)

diffusive terms

forcing terms

Coriolis parameter

bottom depth

horizontal viscosity and diffusivity

vertical viscosity and diffusivity

total pressure P p0gz

dynamic pressure = (P/p0)

total in situ density

salinity

time

potential temperature

the (x, y, z) components of vector velocity ii

horizontal coordinates

vertical coordinate

the surface elevation
Table 2: The variables used in the vertical boundary conditions for the ocean model

The surface boundary condition variables are defined in Table 2. On the variable bottom, z

h(x, y), the horizontal velocity components are constrained to accommodate a prescribed bottom

stress which is a sum of linear and quadratic terms:

Tb = ('yi + + v2)tz

= (' + + v2)v

The vertical heat and salt flux may also be prescribed at the bottom, although here they are set

to zero.

2.2 Ice Model Formulation

Hibler [27] has described a model for the simulation of sea ice circulation and thickness. Although

no longer the only sea ice model in existence, it is nonetheless still the standard approach used for

coupled simulations on the scales addressed here. Our version of the Hibler model has been rewritten

8

71,72 

, 

q

E - P

QT

rsx,

r, r

evaporation minus precipitation

surface heat flux

surface wind stress

bottom stress
as a viscous-plastic fluid was largely motivated by the desire to avoid the complexities associated

with elastic-plastic behavior under flow. The stress-strain relationship is given by

9

P = 2,, + (; - - i-ojj. (13)

The viscous-plastic terms (15) and (16) are found by taking the divergence of the stress tensor:

(F,F)=Vo (14)

with the result that

0 1 fth av\] (15)

0 F (3u Ov\l

(16)

where the nonlinear viscosities are given by

P

(17)

2 {( + f)(1 + l/e2) + 4e2f?2 + 2C11f22(1 - 1/e2)]11'2

and

(18)
Table 3: Variables used in the ice momentum equations

10

M(x, y, t)

P(x, y, t)

(Sh, S3, SA)

Oj (x, y, t)

(u, v)

(V10, i)

(x, y, t)

, (x, y, t)

ice mass (density times thickness)

ice pressure or strength

thermodynamic terms

stress tensor

air stress

water stress

the (x, y) components of ice velocity iT

10 meter air and surface water velocities

nonlinear bulk viscosity

height of the ocean surface
OAh

&

+ O(uAhi) +

0(vAhj)

Ox ày

+ O(uA) + O(vA)

01 Ox ày

Po [A(W W)+(1 A)Wao+Wjr] Pi

[(1 - A)Wao + (1 - A)wiI ph

11

Wao0

Wao <0

gpy gg

(A) should be chosen so that it is near zero until the ice concentration is large enough that ridging

is expected to occur, then should increase smoothly to one.

2.2.2 Thermodynamics

The thermodynamics used is based on the algorithm described in Mellor and Kantha [36], who

have a useful description of the various melting and freezing processes, plus the coupling to a full

three-dimensional ocean. Their form of equations (20) and (22) is:

(24)

0A1. (25)

Here, the W variables are the freeze or melt rates as shown in Fig. 2 and Table 4. The frazil ice

growth Wj will be discussed further in §2.4note that it contributes to changes in A as well as to

changes in hj. The other term that contributes to A is Wa,. This term includes a factor 4' which

Mellor and Kantha set to different values depending on whether ice is melting or freezing:

(26)

(27)
Table 4: Variables used in the ice thermodynamics

12

Qai

Qi2

Q20

Q8

r

SW4.

T0

T,

T2

T3

TmeiLi

W01

W00

WIT

W10

W0

m52

W01

heat flux out of the snow/ice surface

heat flux out of the ocean surface

heat flux up out of the ice

heat flux up into the ice

heat flux up through the snow

brine fraction in ice

incoming shortwave radiation

temperature of the bottom of the ice

temperature of the interior of the ice

temperature at the upper surface of the ice

temperature at the upper surface of the snow

freezing temperature

melting temperature of ice

melt rate on the upper ice/snow surface

freeze rate at the air/water interface

rate of frazil ice growth

freeze rate at the ice/water interface

rate of run-off of surface melt water
Figure 3 shows the locations of the ice and snow temperatures and the heat fluxes. The temperature profile is assumed to be linear between adjacent temperature points. The interior of the

ice contains "brine pockets", leading to a prognostic equation for the temperature T1.

The surface flux to the air is:

Qai = H4. LE4. 3LW4. (1 - cx3)SW4. +f3o(T3 + 273). (28)

The formulas for sensible heat, latent heat, and incoming longwave and shortwave radiation fluxes

are the same as in Parkinson and Washington [41]. The sensible heat is a function of T3, as is the

heat flux through the snow Q3. Setting Qaj = Q3, we can solve for T3 by setting r' = T + ET3

and linearizing in AT3. The temperature T3 is found by an iterative solution of the surface heat

flux balance (using the previous value of T1 in equation 36). As in Parkinson and Washington, If

T3 is found to be above the melting temperature, it is set to Tmeit and the extra energy goes into

melting the snow or ice:

Waj Qal - Q

p0L3

[E(T3, 1) - E(T1,R1)]

Note that L3 = (1 - r)L1 plus a small sensible heat correction. We are not storing water on

the surface in melt pools, so everything melted at the surface is assumed to flow into the ocean

(W0 Wai).

13
j

Q8 Q12 h (1 + Ck)

(36)

Note that in the absence of snow, Ck becomes zero and we recover the formula for the no-snow

case in which T3 = T2.

At the bottom of the ice, we have

2k Q,o=---(ToTi).

'Ii

The difference between Qio and Q12 goes into the enthalpy of the ice:

=QIoQ12.

We can use the chain rule to obtain an equation for timestepping T1:

lIE lOT1

p1h1 [--+iT. VT1] =QJoQ12

where

_{rIb T1) (T1T3)] QJoQ12 -

hj l+Ck

2ki[ T3(2+Ck)Tl1 =--(To+ l-fCk .1

14
a vertical temperature gradient for the upper ocean to use in the heat flux formula:

FT --CT(ToT) z-+0 (42)

pocpw

where

CTZ

'Jr (43) Pk ln(z/zo) + BT

BT b (Z' 1/2

Pr213 . (44)

Once we have a value for FT, we can use it to find the ice growth rates:

W20 =!(Q,o_FT)

p0L0

W = --(Qt2, - FT) (46) p0L0

(47)

where

L0 [E(To,1) - E(Ti,ri)] (48)

The ocean model receives the following heat and salt fluxes:

FTAQ+(1A)QWOLO (49)

= (W0 - AWr0)(Si - So) + (1 - A)S0(1 - E)W0 AW,o + (1 A)W (50)

15

(45)
to:

T2=Ti+{__+Ti(1_2P!.)} (54) Gino cpw

S2=S,(1+y). (55)

We also want the final temperature and salinity to be on the freezing line, which we approximate

as:

Tj=mS+nz. (56)

We can then solve for 'y:

7= /

. (57) -T, + mS, + nz

-+T,(1 -----mS,

PU; V CPU;,

The ocean is checked at each depth k and at each timestep for supercooling. If the water is below

freezing, the temperature and salinity are adjusted as in equations (54) and (55) and the ice above

is thickened by the amount: h=tzk. (58)

2.5 Horizontal boundary condition issues

Initial conditions at all points, and ice velocities at the boundaries thereafter, are required to initiate

the integration of the system of equations forward in time. The most natural boundary condition

16
17
pypy pg

extended clear-sky calms, which are rare. Retrieved skin temperatures agree surprisingly well

with observed 2-meter air temperatures, particularly given that clouds may interfere with surface

temperature retrievals. We know of no other surface temperature fields with this time and space.

resolution that have better accuracy in the Arctic. The comparison of retrieved total precipitable

water vapor to rawinsonde values is also encouraging (Fig. 7). The computed errors within each of

the five Path-P layers - which are bounded by the pressure levels: surface, 850, 700, 500, 400, and

300 hPa - are comparable with those estimated for the rawinsonde measurements themselves in

these extremely cold temperatures, and errors in nearly all layers and in all seasons are well below

the corresponding natural variability.

3.2 Interannual variations in surface forcing

Among the variables contained within the Path-P dataset are several which directly enter the

surface boundary conditions for the ocean and sea ice, and which presumably control to a large

degree their evolution. Of particular importance are sea level pressure (SLP), which dictates the

direction and magnitude of surface winds and hence surface stress, and surface air temperature

(SAT), which enters importantly into the surface heat budget.

Figures 8-13 show the time-mean and year-by-year average fields of SLP and SAT for the

months of March and September. The former month is chosen as typical of late winter conditions;

the latter, as noted above, has been shown to demonstrate the greatest interannual variations in sea

ice properties. To get an idea of the magnitude of the variations between independent atmospheric

18
-20

0I -40

-50

-50 -40 -30 -20 -10

Rdksonc Temperature [C]

Figtire 5: Temperature at 500 hPa: TOVS versus independent observations The black line shows

perfect (1:1) correspondence, while the green line indicates the best least-square fit (RMS error =

2.31 K).

19
25

- 5

20

Figure 7: Total precipitable water vapor: TOVS versus independent observations. The black line - shows perfect (1:1) correspondence, while the green line indicates the best least-square fit (RMS

error = 2.2 mm).

5 10 15 20 25 30

Radic,sonde [mini
and/or less well organized pressure patterns (1986, 1993, 1995). Note the especially rapid change

from strong cyclonic to strong anti-cyclonic circulation between 1988 and 1990, corresponding to

the AO regime shift reported by Overland et al. [39}. Local SLP gradients - related via geostrophy

to the strength of surface winds - are particularly variable in the Western Arctic, suggesting that

we should expect similar variability in the strength and direction of sea ice motion over this time

period.

Surface air temperature patterns undergo similar large year-to-year variations (Fig. 11-13). As

for SLP, maximum variability occurs in September, with much year-to-year change in the Western

Arctic. Although the early 1980's seem to suggest a cycle in which warmer- and colder-thanaverage years occur with equal regularity, the latter half of the 1980's and the 1990's suggest an

increasing frequency of warmer-than-average years. September 1995 is the warmest of any of the 15

years. This suggested trend towards warmer September SAT is consistent with the many reports

of systematically reduced ice volumes over the past several decades.

21
(c)

IIIlIIUhIIIUlilBU

(d)

r uiiiiiuiunmu...i

984 1033 996 1024

Figure 8: Fifteen-year-average (1982-96) sea level pre.ssiire (Jifla): (a) March (buoy data), (1))

September (buoy data), (c) March (Path_P/NCEP), and (d) September (Path-P/NCEP)

22
1985

1986

Figure 9: Sea level pressure (hPa) for the inonth of March (1982 -1996): (lefl) buoy-derived 1ields

(rigli) Path-P fields. The color conLours are Lhose used in Fig. 8a,c.

23

\

N N

/

-.--

lro '\

:

±

U / / . //\
1990

1991

Figure 9 continued.

24

r ey rf*

ww_

- 

N/ -4 -.-

q '1*

k4-

-49

y.
1995

1996

Figure 9 conLinued.

25

.1%'1

a A
1985

1986 ti

Figure 10: Sea level pressure (hPa) for the month of September (19821996): (left) buoy-derived

fields, (right) Path-P fields. The color contours are those used in Fig. 8h,d.

26

-, t

J

, l
1990

1991

Figure 10 continued.

27

k$ . I -m

4,

I,

!(II4

- .-.
1995

1996

Figure 10 continued.

28

r)S474!$V

25' \3

r44*i. Iriti)*I)1tvI

'"i ' .4z#

1.
w 1

Figure 11: Fifteen-year-average (1982-96) surlace air temperature (C): (a) March (buoy data), (b)

September (buoy data), (c) Mardi (Path-P), and (d) September (Path-P)

29

(c) (d)

-38.0 9.0 -18.0 13.0
.;pr

1985

1986

Figure 12: Surface air teinperature (C) for (lie month of March (1982-1996): (left) buoy-derived

fields, (right) Path-P fields. The color contours are those used in Fig. lla,c.

30
1990

1991

Figure 12 continued.

31

41

Iü ii'4 I iwc Jq!

1

'i -.'.1P4

,tt4I $b:,
1995

1996

Figure 12 conLinued.

32
1985

1986

Figure 13: Surface air leinpei-alure (C) for the month of September (1982-1996): (left) buoy-derived

fields, (right) Path-P fields. rflw cOlor contours are those use(1 in Fig. I lb,d.

33
.t3'pI VI_S!VftV

h :%

d

1990

1991

Figure 13 conl;inued.

34
1995

1996

Figure 13 continued.

35
bilinearly interpolated to the grid (Fig. 15, upper). This was done with the bathtub program

in Gridpak (Wilkin and Hedstrom [47]). Second, the bathymetry was clipped to lie between 50

and 5000 m in depth. Finally, the bathymetry was smoothed with a filter designed to reduce the

fractional change in depth from one gridpoint to the next, resulting in the bathymetry shown in the

lower half of Fig. 15. In the unfiltered bathymetry field, it can be seen that there are places where

the depth goes from 100 m to 1000 m between adjacent gridpoints. The vertical sigma coordinate

system in the model follows the bottom contours, but this is only sensible when the bathymetry is

horizontally resolved by the grid.

4.2 Surface forcing

As discussed above, the Arctic is forced primarily by atmospheric fluxes of momentum, heat and

moisture, with additional forcing arising from coastal buoyancy inputs (fresh water runoff) and

net exchanges between the Arctic and both the Bering Sea and the Atlantic Ocean. Of these

three forcing mechanisms, only the first is incorporated explicitly here. Direct exchanges (mass inflow/outflow) with the Bering Sea and the Atlantic Ocean are not included, although weak nudging

bands on the tracer fields are used to crudely approximate the effects of such mass exchanges.

The model is forced at the surface via imposed fluxes of momentum and heat, as described

above, using the Path-P atmospheric datasets. The forcing fields are interpolated in time from

the daily datasets to the required model timestep, and are bilinearly interpolated in space onto

the model grid. Within the model, the wind and water stresses are applied directly to the ice

momentum equations. For the water momentum equations, the applied atmospheric and sea ice

36
Figure 14: The numerical curvilinear grid used for ftc simulation. The resulting hori',ontal grid

spacing fields (in kin) are shown at the botLomu.

37

I I I 1 I 20 30 40 50 60 70 80 20 30 40 50 60 70 80 90 100 110 120
400 1200 2000 2800 3600 4400

Figure 15: Model bathymetry (km): (tipper) original ETOPO5 bathynietry, (lower) clipped and

smoothed bathymetry.

38
initial ice thickness is set to a uniform value of 1.65 in, the thickness of 1.5 m of water. This is not

a realistic value, but it was assumed that the thickness would reach an equilibrium after several

years of integration. Initial conditions for ocean temperature and salinity fields are also required.

These were obtained from the Enviromnental Working Group (EWG) Joint U.S.-Russian Atlas of

the Arctic Ocean (winter period; Arctic Climatology Project [54]).

The early tests were used to assess our ability to run with the features required. For instance,

we turned on the advection of snow and had to improve the melting of snow - e.g., if ice melts

laterally, the snow should also melt laterally. The early changes also brought "hakkis" more in

line with the model description in Mellor and Kantha (MK89). The hardest part of the model to

adjust turned out to be the treatment of vanishingly thin ice. If it isn't treated in a consistent

manner, the marginal ice zone can contain tall thin ice floes. These floes can be covered with tall,

thin drifts of snow as well. We adjusted the limiters on ice and snow volume and concentration

to obtain sensible results, but the whole business is rather unsatisfactory. One has to set the ice

concentration to the minimum when the ice volume gets to its minimum and vice versa. We also

had to add other limiters: the brine fraction can't get higher than 20% and the ice temperature

solver needed to be kept in bounds. We also solved for the ice temperature iteratively, borrowing

ideas from the CICE model (Los Alamos).

We tried both MPDATA and third-order upwind advections schemes, both in the ice and the

ocean. Neither scheme allows oscillations in the tracer field such as commonly occurs with secondorder centered schemes. The differences between the two schemes was negligible; however, the ice

ridging scheme was easier to implement with the third-order upwind advection in the ice.

39
40
5.2 Ice concentration

Although available oceanic observations preclude reliable multi-year estimates of net circulation

(nearly anywhere) in the Arctic Ocean, we do have basin-wide estimates for many years of ice

parameters such as ice concentration and ice motion. Some additional estimates of ice thickness

are also becoming available. The best observed of these fields is ice concentration which for many

yeaxs has been remotely observed from satellites. A summary of these observed fields, as obtained

from NSIDC (Cavalieri [12]), along with the corresponding model products, is shown in Fig. 17-19.

The time-mean (1982-1996) sea ice concentration for March and September is shown in Fig. 17.

In an average March, as expected, sea ice concentration is 100% nearly everywhere in the Arctic

in both observations and in the model hindcast. Although realistic in its sea ice concentration

estimates over much of the basin, the model does show two unrealistic features: a tendency for

reduced ice concentration near to some coastlines (e.g., the Western Arctic) and too much ice in

the extreme Eastern Arctic near the paths of connection to the Atlantic Ocean. These erroneous

features are in all likelihood due, in the former case, to the omission in our ice model of "fast

ice" which acts to retain ice in regions of shallow water, and in the latter to the particular open

boundary condition treatments in use along the edges which communicate with the Atlantic. The

introduction of more effective means of exchanging properties with the Atlantic would limit ice

concentration in the Eastern Arctic, as is observed.

The mean ice concentration in September is also reproduced by the model with considerable

skill (Fig. 17b,d). With the exception of some particular smaller-scale features on the Siberian

41
Fi

-15 -10 -5 0 5 10 15 20 25 30 35 40 45 50 55

Figure 16: Derived st,reanifuiicLioii (Sv) for SepLember (Mean, 1982- 1996)

42
0 5 10 15 20 25 30 35 40 45 50 55 Figure 16 conLinuecl.

43

-15 -10 -5
5.3 Ice thickness

Given the accompanying evidence from observations of a systematic thinning of the Arctic ice cover

over the past three decades, it is not surprizing that our 15-year retrospective simulation also shows

progressive loss of ice thickness with time (Fig. 20, 21). The simulated ice thickness fields for March

and September both show such a trend. In March, for example, typical values of ice thickness in

the early 1980's for the central Arctic tend to be about 3-4 meters, with values up to six or more

meters along the Canadian/Greenland margin in some years. These modeled values of thickness are

typical of those reported prior to the mid-1980's (e.g., Hibler [28]). By the early 1990's, however,

these values have fallen in the model hindcast to 1-2 meters and 3-4 meters, respectively. This loss

of ice thickness is consistent with, though perhaps somewhat faster than, reported observations

(e.g., Rothrock et aL [45]). Similar losses are seen in September.

5.4 Ice motion

Two estimates of ice motion for the 15 Septembers encompassed in our hindcast - one from observations of buoy motion (Rigor et al. [44]), and that reproduced by the coupled model - are shown

in Fig. 22. Note that a different color bar has been used for each; the range of speeds covered by

the observed ice motion is one-half that of the range of speeds for the modeled ice motion. This

mode of presentation was chosen to make both sets of vectors equally visible, since the optimally

interpolated fields of observed ice (buoy) motion tend to be smaller than those modeled by about

a factor of two. This is in part a reflection of the smoothing of the observed motion fields as a

consequence of the optimal interpolation process, which tends to weaken any strong local flows.

44
(C) (d)

0 0.1 0.2 0.3 04 0.5 0.6 0.7 0.8 0.9 10

Figure 17: r[jIIefleatI (1982-1996) ice coiicentralion in the Arctic: (a) March (observed), (B)

September (observed), (c) March (model), (d) September (model).

45
46

(d)

]

0 0.1 0.2 0.3 0 4 0.5 0.6 0.7 0.8 0.9 1 0

Figure 18: Tiine-ixiean (1982-1996) ice concentration in the Western Arctic: (a) March (observed),

(b) September (observed), (c) March (model), (d) September (model).
C) t)

U

r-

CC

C

C4-,
1990

1991

Figure 19 continued.

48
1995

1996

Figure 19 conLinued.

49
LI

3 4 5 6 7 8 9 I0

Figure 20: Model ice 1;hickness in the western Arctic Ocean for March.

50
IAH I I

Figure 20 continued.

51
2 3 4 5 6 7 8 9 10

Figure 21: Model ice thickness in the western Arctic Ocean for September.

52
2 3 4 S 6 7 8 9 I()

Figure 21 continued.

53

uuuu iva IL
54
/ 1 / _1_j

I H'.

-1 -

"lull

0 5 10

1986

Figure 22: Ice motion (cm/s) for the month of September (1982-1996): (left) buoy-derived fields,

(right) model hindcast.

55

iiiin;

0 10 20
v__&.. vrn

LI

1991

\\

1990 -'.;t:- i:

\ / 7----i.-----

LIH1IIUH

5 10 0 10 20

Figure 22 continued.

56
I

"Hilt

1995

1996

5 10

"II"

10 20

Figure 22 continued.

57
ppyg p

fected since version 2 of our coupled model was implemented. These newer versions have more

efficient time-stepping, better quasi-montone advection schemes, more accurate pressure gradients with associated improvements in treatment of topography, and additional capabilities

for inclusion of (e.g.) riverine sources and tides.

Highly parallel physics: The latest circulation models are highly parallel, allowing efficient

computation of large problems.

Enhanced resolution [0(5 km)] and steeper, less smoothed bathymetry: Both of these features are now possible, given the preceding improvements in model software and continuing

enhancements in available computational resources.

Ice thermodynamics with more ice categories and more vertical levels of ice temperature.

Improved open boundary conditions: Although still a matter more of art than science, there

now exist better inflow/outflow treatments that should improve exchanges with (e.g.) the

Atlantic Ocean and Bering Sea.

Better ridging function that is consistent with the ice rheology.

As we note, progress on models and methods that has occurred in the past few years now make

many of these improvements possible. A newest coupled circulation/sea ice model incorporating

all of these items has in fact been recently implemented, with a first application to the Southern

Ocean.

58
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