.

Proceedings of a Workshop

on

Small-Scale Sea-Ice and Ocean Modeling (SIOM)

in the Nearshore Beaufort and Chukchi Seas

Jia Wang, Principal Investigator

University of Alaska Fairbanks

Final Report

July 2003

OCS Study MMS 2003-043
This study was funded in part by the U.S. Department of the Interior, Minerals Management Service

(MMS), through Cooperative Agreement No. 1435-01-98-CA-30909, Task Order No. 85238, between

the MMS, Alaska Outer Continental Shelf Region, and the University of Alaska Fairbanks.

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 Minerals Management Service, nor does

mention of trade names of commercial products constitute endorsement or recommendation for use

by the Federal Government.
Jia Wang

Principal Investigator

Frontier Research System for Global Change

International Arctic Research Center

University of Alaska Fairbanks

Fairbanks, AK 99775-7335

E-mail: jwangiarc.uaf.edu

July 2003
Contact information

e-mail: cmi@sfos.uaf.edu

phone: 907.474.7707

fax: 907.474.7204

postal: Coastal Marine Institute

School of Fisheries and Ocean Sciences

University of Alaska Fairbanks

Fairbanks, AK 99775-7220
Theme 3 9

Theme4 10

Acknowledgements 11

References 11

Appendix 1: Workshop Agenda 13

Appendix 2: Workshop Abstracts 17

Theme 1 17

Theme 2 22

Theme 3 29

Theme 4 45

Appendix 3: SIOM Workshop Participants 53

111
(higher concentration and thickness) is produced 5

iv
community.

Jntroduction

In general, most sea-ice models on basin scales use relatively smooth plastic yield surfaces combined

with thickness distributions based largely on an aggregate statistical representation of ridging processes.

Variable thickness multi-level sea-ice models are typically based on this paradigm, which requires

considerable amounts of simultaneous ridging and open water creation to occur under deformation states,

involving large amounts of shear but little divergence. While sufficient for pack-ice simulation in the

Arctic Ocean for climate study purposes, it is still unclear how to treat and redistribute the spectrum of

ice thickness from thin, new ice to thick, ridged ice, and to landfast ice in most of the present models. To

accommodate the spectrum of ice thickness, the redistribution is typically carried out in some statistical

manner commensurate with the aggregate arguments. These models have not, however, typically

considered landfast ice, which observations have shown does evolve in a manner incorporating ridging

as it strengthens and extends. Also, a precise manner in which ice is redistributed and what ice classes

are used likely needs to be specifically tailored to small-scale models.

Recent studies on small-scale modeling approaches have typically made greater use of fracture-based

modeling (including anisotropic models) where theologies incorporating fracture-based yield curves

have been bound to fail with the characteristic formation of intersecting cracks. Aggregate failure

characteristics still may be smooth once the initial failure has occurred. Additional issues in small-scale

models are rapid kinematic wave propagation (coupling between relative-steady state momentum

1
The major obstacle in development of small-scale and regional sea-ice-ocean models is still a lack of

proper verification data. Present satellite data are providing sea-ice velocity fields in high resolution and

those products can be used extensively for a verification of ice dynamics. The modeled evolution of seaice thickness distribution is practically impossible to verify. However, the forthcoming polar satellites

(IceSat, CryoSat) may provide an accurate measurement of ice thickness over a large scale. In reviewing

existing data of sea-ice conditions in Arctic coastal regions and future data collection activities, workshop

participants identified potential short- and long-term strategies in improving modeling, in particular of

coastal landfast ice.

Coupled ice-ocean modeling

Small-scale ice-ocean modeling can capture rich information in the Beaufort-Chukchi seas. Recent

simulations using a nested coupled ice-ocean model with an eddy-admitting resolution (37 km) in the

Beaufort-Chukchi seas [Wang et al. 2002] indicate the necessity of usmg a fine-resolution model for

understanding coastal processes. Figure 2 shows the mtercomparison between the simulated sea-ice flow

(left) and satellite-observed water color (right) in the study region in August. Mesoscale eddies are clearly

captured by both the model and satellite measurement, which cannot be detected in the basin-scale models

with coarse horizontal resolution. Furthermore, this model can also capture wintertime Arctic halocline

ventilation along the Beaufort-Chukchi coast, probably induced by oceanic upwelling that is driven by

the anticyclonic Beaufort Gyre.

2
140

120

100

20 40 60 80 100 120 140

J::)II

Figure 2. Model-simulated sea-ice velocity (left panel) and satellite-measured water color (right, courtesy

of Tom Weingartner, originated from JPL) in the BeaufortChukchi seas on 18 August 2000.

Mesoscale eddies are visible from the observations, while the model captures an eastward

coastal current along the Alaska coast and an offshore westward current. Off the shelfbreak,

mesoscale eddies of sea ice are active, similar to the satellite measurement. On the left panel,

the axes are labeled by model gridpoint (3.7 1cm) numbers, so the distance of the x-axis (south

to north) is 520 km, while the y-axis (from east to west) distance is 700 km.

3
iceocean modeling and observations over the next 510 years can be summarized in the following in

order of themes:

THEME 1: Sea-Ice Mechanics and Modeling

At spatial scales of less than 10 km, satellite observations [Kwok 2001] and theory

[Coon et al. 1998] show sea ice to behave in a strongly anisotropic fashion, while most

models used to date are isotropic. Thus, anisotropic models should be developed to better

capture sea-ice properties such as sea-ice fractures, stresses, ridging, rafting, etc. The

discontinuities in the RGPS data should be sorted by type, e.g., shear, shear with opening,

or shear with closing. It is useful to use Kwok's data to determine if isotropic models

reproduce types of features.

Landfast ice is crucial to coastal processes. Thus, landfast ice models including scouring

and anchoring processes should be developed. At the same time, parameterization of

landfast ice in a coupled ice-ocean model may be the initial step. The processing of the

RGPS data should be extended to shore for use in the validation of fast-ice models.

For climate atmosphere-sea-ice-ocean models with grid sizes larger than 10 km. a

parametenzation of processes at the 10-km scale is necessary to capture anisotropic

behavior. At 100-rn resolution, continuum models are unlikely to work; thus, discrete

(Lagrangian) models are needed. Fine resolution continuum models (-1 km) should be

used.

4
-200. -100. 0. 100. 200.

-200. -100. 0. 100. 200. -200. -100. 0.

-200. -100. 0. 100. 200. -200. -100. 0.

100. 200. -200. -100. 0.

100. 200.

100. 200.

Figure 3. The oceanic heat flux anomaly without tides (the solution wlth no tides minus the one with

tides) in Hudson Bay, simulated by a coupled ice-ocean model. Units are in W m2. A positive

value indicates stronger heat flux released by tidal mixing and a negative value indicates less

heat flux exchange between the water column and sea ice because more sea ice (higher

concentration and thickness) is produced.

5
p

be implemented in small-scale ice-ocean models with tidal forcing.

Ocean model resolution must be eddy-admitting (-1 km) to resolve mesoscale eddies,

coastal upwellingldownwelling, dense water formation [Wang et al. 2002],

mixing/entrainment and arctic halocline ventilation.

Because atmospheric forcing is usually taken from coarse-resolution reanalysis such as

NCEP (National Centers for Environmental Prediction) and ECMWF (European Centre

for Medium-Range Weather Forecasts) reanalysis products, for small-scale ice-ocean

modeling purposes, high-resolution observations and reanalysis products or MM5

simulation/forecast atmospheric forcing fields are needed. An observational plan for

forcing, validation, and assimilation should be well integrated with the modeling plan.

THEME 4: Sea-ice and Ocean Observations

Observational studies should be conducted to collect data in:

Regional atmosphere parameters

Regional meteorological analyses (data assimilation)

Surface energy balance

Melt pond convergence

Albedo

Snow accumulation

6
SIOM Working Group Recommendations (recorded by rapporteurs)

THEME 1: Sea-Ice Mechanics and Modeling

Landfast ice

The following issues need to be addressed. Where does oil in ice go? Where does ice go? What are the

time scales of breakup, from hours to days? What are the relationships between breakup and cracks, and

river runoff?

Pack ice

Pack ice can push fast ice into shore. Thus, pack ice can provide boundary conditions for

landfast ice models/calculations.

What is pack ice modeling scale? If at 10 km, Ron Kwok's data shows many inactive

regions, which allows resolution of ngid and defomiing regions and is good for BCs.

What is the timescale for a pack model? If using a 6-hr atmospheric forcing or coupling

timescale (e.g., NCEP reanalysis), it is not interested in a 1 0-mm timescale of lead

fonnation. So, the wind forcing (hourly wind can resolve sea breezes) decouples with

lead formation of timescale of 10 mm (i e, sea-ice internal mechanics determines its lead

and cracks, given hourly wind and thermodynamical forcing.

7
Tides (3 hours)

Because breakout is important for oil transport and pack ice (arching) thickness

Force and deformation data are needed on lOOm

Lengthscales to test/calibrate models

Does oil industry have these fme-resolution data? Is it easy to get?

Extent of landfast ice from LandsatIAVMRR imagery

Atmospheres/ocean forcing resolution for ice models?

Pack forces > atmosphere forces

Thermal forces great

Thermodynamic considerations are more important

THEME 2: Sea-Ice Dynamics and Modeling

The following issues should be addressed:

Formation of fast ice (ridges, gouging, destroying pipelines)

Tidal currents, fast ice can affect motion, erosion, deposition, sediment transport,

break away of fast ice, particularly during spring timehigh tide.

Strengthening of ridges (diffusion process, salinity increase)

Coupling effects of current with fast and pack ice: formation and breakout

River outflow on breakup

8
Major model components need to be added for waves, surges and tides.

Models should pay more attention to propagation of errors in forcing or

parameters onto P.D.F. solutions (ensemble runs, etc.)

The knowledge of forcing is more limiting than the knowledge of the physics

Bi-directional nesting with 0 (1 kin) will include eddies, thin layers,

coastal currents, mixing and entrainment

An observational plan for setup, forcing, validation, and assimilation should

be well integrated with the modeling plan.

Assimilation methods not violating conservation laws.

Observational requirements:

1) Model setup, 2) Forcing, 3) Validation, 4) Assimilation, and

5) Empirical studies to sub-grid parameterizations

Topography < 1km in the horizontal, -10 cm in the vertical, repeated over time.

Further analysis for evolution (scour, sediment transport, grain size).

Combine atmospheric data with large-scale re-analysis (MM5, downscaling) including

higher moments. May need new data? Precipitation? Winds over sea? Runoff, roughness.

9
Coastal ocean

Sediments/entrainment/transport

- association with storms

River input (overflow, flux)

Hydrography (fronts, current, ocean structure etc)

Sea level

Bathymetry

Coastal ocean

Fast ice - stability, coverage

Fast ice thickness profile (laser profiler)

Fast ice deformation (radar interferometry)

- Model-based interpretation

Ice motion (RADARSAT, others)

- Local smaller scale observations

Sediments/contamination (Modis, Landsat)

- Coverage

Local sea-ice thickness distribution

Recommendations

Coastal RADAR!! - Connections to synoptic and basin scale dynamics

Identification of critical parameters

Repository/archive for science dataset for coastal ocean

10
Saucier, F.J., F. Roy, D. Gilbert, P. Pellerin and H. Ritchie. In press. The formation and circulation

processes of water masses in the Gulf of St. Lawrence. J. Geophys. Res.

Wang, J., M. Ikeda and FJ. Saucier. 2003. A theoretical, two-layer, reduced-gravity model for

descending dense water flow on continental slopes. J. Geophys. Res. 108(C5),

10.1029/2000JC000517

Wang, J., Q. Liu and M. Jin. 2002. A nowcastlforecast model for the Beaufort Sea iceoceanoil spill

system (NFM-B SIOS), p. 80-94. In University of Alaska Coastal Marine Institute Annual Report No.8.

OCS Study MMS 2002-001, University of Alaska Fairbanks and USD01, MMS, Alaska OCS Region.

11

(g)

(invited: 30 mlns, others: 20 mins, posters: 5 mins)

9:50-10:40: Presentations from Theme 1

Jan Haapala: "Open Questions of Calculation of Redistribution of Sea-Ice" (invited)

Max Coon: "Pack Ice Anisotrophic Constitutive Law

10:40-11:00: Coffee break

11:00-12:50: Presentations from Theme 1 (continued)

Mark Hopkins: "High-resolution Arctic Basin Sea Ice Modeling"

H.L. Schreyer: An Application of the Matenal Point Method to Oriented

Failure of Sea Ice"

Daniel Feltham: A Continuum Rheology of Anisotrophic Sea Ice"

Robert Pritchard: "Fast Ice Dynamics and Anisotropic Plasticity"

Bifl Hibler "On Simulating Oriented Failure Zones in Sea Ice Velocity

Fields" (poster presentation, 5 mins)

12:50-1:00: Group Photo

Front steps of IARC Building

1:00-2:00: Lunch, IARC 5th floor

2:00-3:00: Discussion of Theme 1

Rapporteur: Max Coon

13
8:309:00: Continental breakfast, IARC 401

Theme 3 Chair: Jan Haapala (Small-Scale and Basin-Scale Coupled Ice-Ocean Modeling)

9:00-10:50: Presentations

Moto lkeda:

+Quizheng Liu

William Garwood:

Jia Wang:

Keguang Wang:

10:50-11:10: Coffee break

from Theme 3

"Parameterization of Thin Ice in a Coupled Ice-Ocean Model: Application

to the Seasonal Ice Cover in the Sea of Okhotsk" (invited)

"Sea Ice Modeling for the Bohai Sea of China"

"Thermodynamics Critical to Arctic Mixed Layer Systems"

"A Nested Coupled Ice-Ocean Model for the Beaufort Sea"

"A Numerical Investigation of Sea Ice Dynamics in the

Gulf of Riga" (poster introduction, 5 mins)

11:10-12:30: Presentations from Theme 3 (continued)

Francois Saucier: "Modelling the Formation and Circulation Processes of

Water Masses and Sea Ice in the Gulf of St. Lawrence, Canada (invited)

Xiangdong Zhang: "Coordinated Changes of Sea-ice and Ocean Properties in the Beaufort

and Chukchi Seas"

Wieslaw Maslowski: "Modeling the Arctic Ocean and its Sea Ice: From a Local

to Basin Scale"

Meibing Jin: "A Coupled Ice-Ocean Model in the Pan Arctic and North Atlantic

Ocean: Seasonal Cycle"

14
Information on Ice over State" (poster presentation, 5 mins)

7:00 p.m. Bus transportation to Tanana Valley Fair. Meet in front of Natural Science Building.

Return transportation will be from main fairground entrance at 9:00 p.m. and 9:30 p.m.

Friday, August 9

8:30-9:00: Continental breakfast, IARC 401

Theme 4 Chair: Ron Kwok (continued)

9:00-10:00: Presentations from Theme 4 (continued)

Hajo Eicken: "Observations and Modeling of Small-Scale Thermodynamic Ice

Growth and Decay Processes in Coastal Arctic Seas" (invited)

Aleksey Marchenko: "On the Excitation of Natural Shelf Modes due to Self- Induced

Oscillations of Ice Floes by Ridges Buildup"

Kazutaka Tateyama: "Introduction of Sea Ice Monitoring Radar Network on the

Okhotsk Coast of Hokkaido"

15
16

6:10-9:30 p.m.: Workshop Dinner, River's Edge Resort

6:10 p.m.: Bus departs from Natural Sciences Building

6:30 - 7:30 p.m.: Appetizers and No Host Bar

7:00-7:30 p.m.: Invited speech: The Mystery of the Aurora" by Dr. Syun Akasofu,

chaired by Dr. Non Tanaka, IARCIFRSGC, Acting Program Director

7:30-9:30 p.m.: Dinner

9:30 p.m.: Bus transportation to Cutler Apartments
Pack Ice Anisotropic Constitutive Law

MAX CooN

The two striking facts about present ice dynamics models are that they do not: 1) reproduce the oriented

fracture patterns of openings and closings in the pack, and 2) accurately model the effects of frazil/pancake

ice formation in the ice margin. These are areas that produce the most ice growth, the most turbulent heat

flux to the atmosphere, the most salt flux to the ocean, and the most energy dissipation due to slippage,

ridging, and rafting. The first shortcomings will be addressed here. Existing sea ice models have shown

limited success for predicting the degree to which any given lead will open for prescribed or even observed

forcing conditions.

An anisotropic plastic rheology introduced by Coon et al. [1992,1998] described the opening of leads

associated with traction free lines that can admit discontinuities in displacement. Anisotropic constitutive

law for use in the sea ice dynamics model is presented here. This accounts directly for refrozen lead

systems in the pack ice strength (with anisotropic failure surface) and in the ice thickness distribution (with

an oriented thickness distribution). The lower limit (5 km) of the model resolution is controlled by the

fracture spacing of old, thicker ice and the maximum lead width. The upper limit of the rnodel resolution

(100 km) is controlled by curvature in the lead directions and variations in lead width. These, in turn, are

controlled by the variations in internal ice stress due to driving forces (wind and currents), which set the

time resolution. In addition, the principal stress normal to a new lead must be zero as it happens. This

17
jy pp

together to form pressure ridges. Pressure ridging is incorporated via a parameterization based on

discrete element simulations of the ridging process. At the end of each 24-hour period the mode! pack is

reinitialized. Reinitialization begins by p'acing an undeformed configuration over the deformed pack. The

thickness distribution in the deformed pack is passed to the undeformed pack. New ice is grown in leads.

The undeformed pack is run for one hour to create a new aggregate fracture pattern and to equilibrate

the stress and velocity fields to the new daily wind field. The model is used to run simulations of several

months duration. The simulation results are compared to the maps of Unear Kinematic Features (LKF5)

produced by the Radarsat Geophysical Processor System (RGPS). The output of the model pack is run

through a virtual RGPS to generate similar maps of LKFs to use in the comparison. Regional variation of

the basic floe size permits high resolution (-1 km) simulation of areas of interest.

An Application of the Material Point Method to Oriented Failure of Sea Ice

H.L. SCHREYER' AND D.L. SuisKY

1Department of Mechanical Engineenng, Department of Mathematics and Statistics, University of New

Mexico, Albuquerque, NM 87131

Failure of sea ice, as exhibited through the formation of leads, is one of the dominant modes of behavior

that must be captured if the motion of sea ice is to be modeled successfully. The use of a discrete

constitutive equation to represent the evolution of decohesion is conceptually much simpler than using

fracture rnechanics or failure critena based on continuum constitutive equations. Criteria for failure initiation

can be proposed and checked directly instead of using an indirect approach involving large deformations in

18
A Continuum Rheology of Anisotropic Sea Ice

ALEXANDER WILCHLNSKY AND DANIEL FELTRAM

Centre for Polar Observation and Modelling, Dept. Space and Climate Physics, UCL UK

email: daniel.feftham©cpom.ucLac.uk

Sea ice dynamicists during AIDJEX' , and most subsequent researchers, have postulated that, in the

inelastic regime, the internal ice stress tensor s be a function solely of the velocity deformation tensor

au/ax and scalars. The requirement of material frame indifference2 then implies that

o .o +&j & (1)

where = + au/ax1) is the strain rate; i, ç are shear and

bulk viscosities and Pis ice strength; , ç and Pare functions of the

strain rate invanants and scalars. Equation (1) is the general ReinerRivIin constitutive relation, Which is isotropic; in sea ice models, y is

set to zero. This general form atlows plastic or viscous behaviour

depending upon the functional form of the viscosities, as in Hibler's

well-known, and widely implemented, viscous-plastic rheology3. The

isotropy of this rheology is generally justified where one resolves

length scales of order 100 km and greater as the disfribution of

oriented features (e.g. cracks, leads, ridges) on this scale is, on

average, isotropic. We, however, are interested in resolving physical

processes on length scales of order 1-100 km so that we can develop a rheology more appropriate for

smalter-scale, potentially anisotropic sea ice dynamics. We allow the stress tensor to depend upon the

19
lceCasting, Inc., 20 Wilson Court, San Rafael, CA 94901-1230

email: pritchardrasme.org, http://www.icecasting.com, teL/fax: (415) 454-9899

My goal in participating in the SIOM workshop is to learn how fracture mechanics can improve modeling

of ice behavior. As part of this learning process, I wish to review some of the processes that dominate and

control ice motion. Ice fracture may describe some of these processes, but it does not descilbe others.

Ridging is the essential process controlling ice motion. Fracture mechanics should help us to descibe the

initiation of ridging better, but after it begins, fracture is not a key part of the behavior. The continuation of

ridging is descilbed well by plasticity models, even down to the evolution of a single ridge.

At smaller space scales (as small as a single ridge system), an anisotropic plasticity constitutive law

appears to descibe the process of ridging. I will present an anisotropic elastic-plastic law (AEP) and an

oriented thickness distribution that describe the changing ice conditions during ridging. I will show several

simple examples of how the AEP law desci-ibes the stress and deformations.

I hope to learn how fracture mechanics can help extend our understanding of srnall scale ice behavior.

Can it help define the tensile properties of the AEP yield surface? Its flow rule? Can it help to descilbe

the initial formation of a ridge? Plasticity behavior is unstable if a tensile failure occurs 'instantly'. Are there

ways to modify this behavior? Is there a slower' time scale that descilbes this failure? Can a decohesion

law describe it? The Aidjex plasticity model accomplished much of this because the redisthbution process

occurred slowly. Is there a comparable description at smafler scales? Or can fracture and plasticity

mechanics be melded to describe both the formation of a lead, and its evolution into a ridge? Should we

develop better numerical solution schemes that explicitly describe discontinuities in solutions? I would

hope to ask these questions in the workshop, and to stimulate discussions that will help improve our

understanding of ice behavior.

20
21
shows that coupling of kinematic waves in pack ice with ice mechanics is likely the main source for this

phenomenon which will be increasingly important on the small scale. In addition the problem of kinematic

waves in pack ice with some analytic development and available physical examples.

Modelling of Superimposed Ice Freezing and Sub-Surface Melting Based on Basis98

and Basis99 Data

B1r4 CRENG, JOUKO LAUNIANEN AND TIMO VIIIMA

Finnish Institute of Maa'ine Research P.O. Box 33 FIN-00931 Helsink4 Finland

INTRODUCTION

The Baltic Air-Sea-Ice Study (BASIS) project, which belongs to the GEWEX-BALTEX program, was

carried out in 1998-2000. The objective of BASIS was to create and analyse an experimental data set

for optimization and verification of coupled atmosphere-ice-ocean models (Launiainen, 1999). A field

experiment organized in winter 1998 in the northern Baltic Sea formed the central element of BASIS.

Another ice expedition was carried out in the next winter in 1999. The locations of the two field

experiments were quite close to each other. Table I gives some basic information about the expeditions.

Measurements during the BASIS-98 field experiment indicated that the ice surface was quiet variable.

Snowfall was occasionally observed and snowdrift largely occurred due to frequently strong wind. On

the contrary, the ice bottom showed little change indicating a thermal equilibrium stage. Observations

indicated snow to ice transformation at the snow-ice interface. Numerical modelling suggested that the

re-freezing of the surface melt water was a primarily source for snow-ice formation (Cheng, et al., 2001).

22
In order to study superimposed ice formation, we first need to estimate how much melting can be supplied.

There are two important processes of metting:

For a surface layer with its temperature larger than the melting point, the metting is calculated from the

surface heat balance.

The sub-surface melting happened mainly due to the effect of absorbed short-wave radiation below the

surface. When the ice temperature for a sub-surface layer tends to be larger than the melting point, it is

restored to the melting point, and the heat flux balance between this isothermal sub-surface layer and

adjacent upper and lower snow/ice will be used for metting calculation. This procedure satisfies the

continuity of conductive heat flux in snowfice and the conservation of energy.

RESULTS AND ANALYSIS

The BASIS-98 field measurements indicated some 39% of the data with air temperature above freezing.

The model run in Cheng et al, (2001) produced indeed a total surface melting of 12 cm snow. Taking into

account sub-surface melting, re-run model produces about 0.5 cm (4%) more melting. The difference is

actuatly not much. During BASIS-98, the surface melting largely occurred when air temperature was higher

than the freezing point. On the other hand, downward solar radiation in February at 63°N is still quite

limited and not strong enough to generate large amounts of sub-surface melting. The BASIS-99 showed

only 20% data with air temperature above freezing (Table 1). However, the observed daytime average

solar radiation was 35% larger than that in BASIS-98 and sub-surface melting could start. Figure 1

displays the modelled total surface snow melting. The mode! resutt indicated a total 4.5 cm melting in

which about 20 % were accounted by the sub-surface metting. This simulation was analogous to that

presented in Cheng et al., (2001), i.e., the extemal forcing was based on the weather mast measurement

and the model parameters were largely based on the field measurement. Ice salinity was from 0.38 to 0.65

ppt. Freezing temperature at ice bottom was -0.2 °C. According to in situ measurements, the snow was

23
During BASIS-99, however, the weather conditions were quite mild and not favourable for the

superimposed freezing. Instead the percolated melt water may form a slash layer above ociginal ice layer.

The observed and modelled snow and ice temperature fields are given in Figure 2. Both measurement and

calculation indicated quite high sub-surface temperature. Unfortunately, the uppermost thermistor recorded

unreliably high snow temperature and thus is not shown here. Radiative heating of the uppermost sensors

in snow or ice on dear days can lead to incorrect temperature measurement (Brandt and Warren, 1993).

Results showed that the sub-surface metting located within 10 cm below the surface. The model results

dearly showed the diurnal variation of snow temperature, while measurements did not, except for the first

day after deployment of the thermistor stick. This maybe due to the effect of wetness of the snow layer.

The snowfall was seen occasionally mixed with rainfall since the second day of the expedition. The rainfafi

together with surface melt water may temporarily exist and full the porosity of the snow layer before they

percolate to the snow-ice interface forming the slash layer. In addition, due to the heavy loading of snow,

the saline water from the ocean may seep up to the snow-ice interface. The latent heat of the water

fraction in snow can damp the daily variation of temperature of the snow layer. These effects were,

however, not taken into account in the model. Further studies of those effects on superimposed ice

formation are needed.

The model results of the increased sub-surface temperature effect have been difficult to verify

experimentally due to the difficulties involved in measunng the temperature profile using thermal sensors

embedded in snow/ice (Koh and Jordan, 1995). To verify such modelling resutts, more work, both

theoretical consideration and field observations, is needed.

24
80 82 84

Julian day (1999)

86

Figure 1. Snow and ice temperature (°C) during BASIS-99 experiment. a) Observed snow and ice

temperature by thermistor strings. The symbols o and give the locations of sensors in snow

and ice, respectively. b) Modelled snow and ice temperature.

Table 1. The basic weather and ice information during field experiments.

25

50

78 80 82 84

Julian day (1999) - BASIS-98 (18 days) BASIS-99 (7 days)

Time l6Feb.-7March l9Mar.-26March

Location 63,08.12N 21,14.67E 63,55.05N 22,5672E

Weather conditions(average):

wind speed Va(lOm)

air temperature Ta (lOm);

relative humidity Rh (4.5m)

Ta > 0.0 °C (portion of data)

downward solar radiation Q5

(daytime average)

mean surface albedo (as)

7.6 ms1

43 ec

78 %

39 %

139 Wm2

0.73

4.7 ms

-1.6°C

84 %

20 %

188 Wm2

0.81

Average ice thickness 38.6 cm 446 cm

Snow properties:

soft snow (5)

hard snow (h)

average snow thickness -

-4.3cm

9cm

18 cm

23.1 cm

Mean water level relative to the ice surface -1.4 cm +7.9 cm
py

secretariat. (Publication 14).

Launiainen, J. and B. Cheng. 1998. Modelling of ice thermodynamics in natural water bodies. Cold Reg.

Sd. TechnoL. 27(3), 153-1 78.

Lepparamta, lvi. & Seinã A. 1982: Statistics of fast ice thickness along the Finnish coast, - Finnish Mar.

Res. 249: 62-71.

Lepparanta, M. 1983: A growth model for black ice, snow ice and snow thickness in subarctic basins. -

Nordic Hydral. 14: 59-70.

Liston, G.E., J.G. Winther, 0. Bruland, H. Elvehoy and K Sand. 1999. Below-surface ice melt on the

coastal Antarctic ice sheet J. GlacioL, 45(150), 273-285.

Maykut, G.A. and N. Untersteiner. 1971; Some results from a time dependent thermodynamic model of sea

ice. J. Geophys. Res., 76(6). 1550-1575.

Perovich, D.K 1996. The optical properties of sea ice. CRREL Rep. 96-1.

Saloranta, T. 2000: Modelling the evolution of snow, snow ice and ice in the Baltic Sea, - Tellus 52.4: 93-

108.

Sturm, M., J. Holmgren, M. Konig and K Morris. 1997. The thermal conductivity of seasonal snow. J.

GlacoiL, 43(143) 26-40.

26
internal stress field. An idealized experiment is performed to highlight the difference in evolution of the sea

ice cover in the circumstance of a relatively coarse-resolution grid versus that of a fine-resolution one. The

experiment shows that the ubiquitous presence of instabikties in the near-surface ocean flow field as seen

on a fine-resolution grid, effectively leads to a sink of sea-ice areal coverage that does not occur when

such flow instabilities are absent, as on a coarse-resolution grid. This result also implies that a fineresolution grid may have a more efficient atmosphere-sea-ice-ocean thermodynamic exchange than

a coarse one. This sink of sea-ice areal coverage arises because the sea-ice undergoes sporadic,

irreversible plastic failure on a fine-resolution grid that, by contrast, does not occur on a coarse-resolution

grid.

Applications of a Marginal Ice Zone Model

LEIF TOUDAL

Technical University of Denmark

An ice and salt flux analysis model for the marginal ice zone has been developed. It is forced by daily

average winds and daily satellite ice observations. The measure of producing ice in the model is obtained

from a comparison between the advected ice field from the previous time step and the actual satellite ice

observations. The model has been applied to the Greenland Sea, the Bering Sea and the Beaufort and

Chukchi Seas. Results of mode! now-casts will be presented and discussed.

27
Mechanical Simulation of Shore Fast Ice Break-Up: Kara And Laptev Seas

D. ZYRYAN0v', D. DivllE', ArJ R. Koiss2

1Norsk Polarinstitutt, Troms, Hjalmar Johansensgate 14, Norway; email: denis.zyryanov@npolar.no

2Norwegian Defense Research Establishment (FF1), DWision of Electronics, Post Box 25, NO-2027, Kjeller,

Norway; email: reinert.komes@ffi.no

The mechanical stresses caused by wind and ocean currents play an important role in ice cover break up.

In our work we focus on the fast (attached to a shore) ice development in the Kara and Laptev Sea basins.

During winter, these areas are partly covered by ice, whose destruction and development is subjected to

strong offshore winds. The geomechanical model applied in the work is based on the Particle Flow Code

(PFC) that is widely being used recently in numerous mechanic applications. The PFC deals with circular

partides-disks. which can be bonded together to simulate rigid bodies and continuous media. At the start

of the modeling the disks fill all the free space in the model domain. Then an external load is applied,

simulating wind drag force. It leads to the mechanical break up of the ice sheet and causes ice drift.

The simulation results revealed areas sensitive to the wind stress fluctuations and sea ice physical

propertiesareas of local mechanical unstability. One such area was discovered in vicinity of Sverdrup

island in the Kara Sea basin. Finally, we compare the simulation results with in-situ observations (AARI

dataset) of the fast ice in the Kara and Laptev seas. The results demonstrate a coincidence of the areas of

mechanical instability with observed annual variations in fast ice edge, confirming that shore geometry play

a significant role in ice cover spreading and ultimately defines its outhne and development.

28
complex in the Bohai Sea. Exploratory drifling and planning for production and transportation for oil and

gas in the Bohal Sea during winter have raised great demand for numerical forecast of sea ice. A dynamicthermodynamic ice model with three levels (level ice, rubble and open water) for simulating the ice growth,

decay and drift in the Bohai Sea is developed on the basis of a review of the climate and ice conditions in

the Bohai Sea and the earlier sea ice modeling studies. The routine satellite remote sensing data opened

up possibilities for operational numerical sea ice forecast. The output of T106L19 atmospheric model from

National Meteorological Center of China was used for the operational numerical ice prediction of the Bohal

Sea and the northern Yellow Sea.

The ice model was coupled with Blumberg's ECOM-si model, a three-dimension, primary equation and

semi-implicit ocean model, for studying the ice-sea interaction of the shallow water and modelling the

variation of ice conditions of the Bohai Sea. It is showed from simulation that oceanic effects on ice are

important, especially, the tide driven effect is more important in the Bohai Sea. Therefore, the effect of tidal

current must be considered in forecasting the local sea ice of the Bohai Sea. And as a result of the joint

effects of wind and tidal current, the pile-up of sea ice is greater than that affected by wind stress only. The

coupled model considers the dynamic and thermodynamic effects at the interface of sea/ice, sea/air and

ice/air. The ocean heat flux is a very important factor for the simulation of growth and decay of ice. An

empirical formula is used to calculate the ocean heat flux in the Bohai Sea. The two cases of ice growth

and decay processes are hindcasted and the results are satisfied.

The ice model is also coupled with the Princeton Ocean Model (POM ) to study the relation of ice cover

variation with the heat budget at the interface of ice/sea, air/ice and air/sea in the Bohai Sea duling the

ice growth period. The case studies show that the thermodynamic effects from atmosphere and ocean are

very important to the Bohai Sea ice, especially during the period of freeze-up, when the ice edge can often

extend tens of miles in several days. Cloud is a sensible element to the heat budget at the surface of ice

and water.

29
some mesoscale eddies in the fine model. Neither the slope current nor the mesoscale eddies are captured

in the coarse model. Thus, this fine nested model preliminarily captures some dynamical features in the

Beaufort Sea and its shelves.

Furthermore, upwelling along in the Beaufort Sea in winter can be detected due to the strong Beaufort

High-driven anticydonic circulation, which produces an offshore Ekman drift and favors a upwelling of the

Atlantic Water in the lower layer. As a result, sea ice thickness and concentration near the coast reduce

due to the warming of the Atlantic Water.

Thermodynamics Critical to Arctic Mixed Layer Systems

R0LANI) WILLIAM GARWOOD, JR.

Naval Postgraduate School, Department of Oceanography, Monterey, CA 93943

- email: garwood@nps.navy.mil

The thermodynamics of seawater at near-freezing temperatures is critical for modeling mixed layer

entrainment of heat that determines ice thickness, polynya formation, and the location of MIZs. This

thermodynamics is related to nonlinearities in the equation of state for seawater, which are greatly

augmented wherever the sea surface temperature approaches freezing. Particularly important is

1hermobancity, the dependence of thermal expansion a on temperature and pressure. This phenomenon

is not resolved in coarse-grid hydrostatic models, but considerable insight has been provided by highresolution nonhydrostatic large-eddy simulation (LES), which resolves turbulence, internal waves,

30
Figure 1. Thermobanc process important to the coupled

air-sea-ice system.

A Numerical Investigation of Sea Ice Dynamics in the Gulf of Riga

KEGUANG WANG1, MATE I LEPPARANTA' AN1) TARMO KOuTs2

Department of Physical Sciences, University of Helsinki, Helsinki, Finland

Department of Marine Physics, Estonia Marine Institute, Tallinn, Estonia

The Gulf of Riga is a rather small semi-enclosed water body in the eastern part of the Baltic Sea bordenng

on Estonia and Latvia. It only covers -16400 km2 with a volume of -240 km3 and a quite square shape. In

normal and severe winters, the gulf is often covered by ice, while in mild winters it is often ice-free. Based

on the ice charts from the Finnish Institute of Marine Research, the ice cover in the gulf shows the following

31
only few storms during late December and early January can trigger highly different responses in sea ice

production over the following months. Finally, we examine the sea ice cover variability over synoptic to

seasonal time scales when comparing the use of an elastic-viscous-plastic rheology (e.g., Hunke and

Ducowicz, 1997), a simpler cavitating fluid rheology (e.g., Flato, 1994), and ARGOS and Radarsat-derived

sea ice dynamics.

Flato, G. M., & W. D. Hibler, Modeling pack ice as a cavitating fluid, J. Phys. Oceariogr., 22, 626-650,

1992.

Hunke, E. C., & J. K Dukowicz, An elastic-viscous-plastic model for sea ice dynamics. J. Phys. Oceano.,

27, 1849-1867, 1997.

Saucier, F. J, F. Roy, D. Gilbert, P. Pellerin, & H. Ritchie, The formation and circulation processes of water

masses in the Gulf of St Lawrence. J. Geophys. Res., in press.

Coordinated Changes of Sea-ice and Ocean Properties in the Beaufort and Chukchi

Seas

XIANGDONG ZHANG AND MoTo IKEDA

Frontier Research System for Climate Change/International Arctic Research Center, University of Alaska

Fairbanks, AK99775, USA; email: xdz@iarc.uaf.edu

During recent decades, dramatic changes have been observed in the Arctic atmosphere, sea-ice and

ocean. For example, the sea level pressure shows unprecedented persistent and low values; ocean part

32
ggg y

eliminate singularity problem at the North Pole. The model domain extends from the North Pacific (--30°N),

over the Arctic Ocean, the Canadian Archipelago and the Nordic Seas, to the North Atlantic (to -45°N).

This approach allows accounting for all the northern latitude ice covered regions at a high and neatly

uniform resolution. This research is an extension of our previous modeling work (e.g. Maslowski et al.,

2000, Maslowski et al., 2001).

Model improvements have been so far tested in two steps. The first test was designed to examine the

effects of higher resolution on the existing ice model coupled to an ocean model with more realistic

bathymetry. A 60-year integration of the coupled ice-ocean model has been completed. This model run

has been forced with daily-averaged realistic atmospheric fields from the European Centre for Mediumrange Weather Forecasts (ECMWF) 1979-1993 reanalysis. The coupled model adapts the Los Alamos

National Laboratory (LANL) Parallel Ocean Program (POP) ocean model with a free surface. The sea ice

model uses a viscous-plastic rheology, the zero-layer approximation of heat conduction through ice and

a simplified surface energy budget. Many improvements, such as the ice edge position in the Nordic and

Benng/Chukchi Seas, are results of more realistic modeling of the upper ocean currents and hydrography.

More results from this experiment are available at <www.oc.nps.navy.mil/-pips3>.

The second test has focused on evaluating the new sea ice model ability to predict leads and polynyas. A

20-year integration of the stand-alone 9-km sea ice model with a mixed ocean layer has been completed.

This model uses the most recent version of sea ice model (CICE 3.0) developed at LANL. It contains

an improved calculation of ice growth/decay based on work of Bitz and Lipscomb (1999). It has been

configured to use five sea ice categories with 4 layers per category, a snow layer in each category, the

EVP rheology (Hunke and Dukowicz, 1997), the remapping of sea ice transport (Lipscomb and Hunke,

2002) and ice thickness (Lipscomb, 2001). The realistic daily-averaged 1979-1988 ECMWF atmospheric

data has been used to force the model. Mostly output from this second test will be discussed. Results on

model representation of leads and polynyas over the Chukchi/Beaufort Sea region will be presented,

33
forcings using the NCEP/NCAR reanalysis, restored to the surface monthly climatologicat temperature and

salinity. The simulated total transport of the Labrador Sea is consistent with historical observation. The

western North Atlantic is dominated by the cyclonic Labrador Current system, while the eastern North

Atlantic is dominated by the anticydonic gyres. Greenland Basin and Noweigian Basin both have a cydonic

gyre.

In Fram Strait, the model simulated an outflow of Arctic surface water from the Greenland side and inflow

of the North Atiantic Water from the eastern side. The simulated temperature and salinity in Fram Strait

indicate the intrusion of the Atlantic Water and the outflow of the Arctic surface water. In the Arctic Basin,

the simulated total transport is cyclonic in both the Eurasian Basin and the Canadian Basin although the

surface ocean current is anticydonic.

The observed sea ice concentration, thickness, heat flux, and freshwater thickness are used to validate the

model simulations. The consistency and difference are discussed.

34
The sea ice model

The ice model presented by Launiainen and Cheng (1998) is used in this study. The basic model equations

read:

The ice temperature is governed by the heat conduction equation (1), while Eq. (2) and (3) represent the

boundary conditions for the temperature, and the heat and mass balance at the surface and bottom.

In Eq. (1) T is the temperature, p is density, c is specific heat, k is thermal conductivity, q(z,t) is the amount

of solar radiation penetrating below the surface, t is time, and z is the vertical coordinate below the surface

(positive downwards). Subscripts i and s denote ice and snow, respectively. The thermal conductivity

and heat capacity of sea ice are given as: k = k + s/(T -273.15) and (pc) = poco + ys/(T1 273.15)2,

following Maykut and Untersteiner (1971), where k, Po and c0 are the thermal conductivity, the density

and the specific heat of pure ice, respectively, and s is the ice salinity, and y are constants.

35

,s(z,) a (k a75(z,t) aq15(z,t)

0

(1)

(2)

(3a)

(3b)

,S at - az (1-at)Q '0 d _Qb(Tfc - _p1Lj dII,/dt = -(k, aT,/az)bOl

az

+ F

Qh(Tc)+Qle(T*)+F'c(Tfc)_F'm

az = r(T*)
In Eqs. (3a) and (3b) the ice bottom temperature (T) is constrained to be the freezing temperature and F

is the oceanic heat flux, assumed constant.

An implicit 6-point symmetrical CN scheme, which was derived on the basis of the numerical integral

interpolation method (Cheng. 1996), is used to solve the heat conduction equation. The scheme derived

by this method has been mathematically proven to be conservative (U and Feng, 1980). The model

parameters are listed in Table 1. The sea ice properties are based on average values from the literature

and fie!d measurements in the Baltic Sea.

Penetrating solar radiation in ice and snow

The solar radiation penetrating into sea ice depends strongly on the wavelength of the irradiance, on its

angle of incidence, on the structure of the sea ice and on sky conditions. For ice modelling purposes,

however, simple parametenzations based on the Bouguer-Lambert law are often used (e.g., Untersteiner,

1964, Maykut and Untersteiner, 1971, Grenfell and Maykut, 1977). In these studies

q1(z,t) = io(1-cz)Q ez_, z > zi, is described as an exponential decay through the ice depth, where

i0 = F(C, Ci) is defined as the fraction of the wavelength-integrated incident irradiance transmitted through

the top zi = 0.1 m of the ice, and parametenzed according to the sky conditions (C) and sea ice colour (Ci),

for example, I = 0.18(1-C) + 0.35C for white ice, and = 0.43(1-C) + 0.63C for blue ice (Grenfell and

Maykut, 1977; Perovich, 1996), and Ki is the bulk extinction coefficient below zi varying from 1.1 to 1.5m1

(Untersteiner, 1961). Near the surface, Ki can be one or two orders of magnitude larger than 1.5 m1

(Grenfell and Maykut, 1977). Accordingly, a two-layer parameterization scheme for q1(z,t) is assumed in our

ice model. In the top 0.1 m of the ice, we applied q(z,t) = (1-<z)Q ez, 0 < z <zi. The extinction coefficient

KI is calculated as KI = -10 x In (i0), i.e., KI is valid for the very uppermost layer by fitting the values of i0

of Grenfell and Maykut (1977) observed for the 0.1 m level in the ice. For example, KI = 17 m1 for dear

sky (C=0) and white ice conditions. Below 0.1 m in the ice, we used ci(z,t) = i,(1-czJQ z zi,

according to Maykut and Untersteiner (1971), where K2 = 1.5 m'. A two-layer scheme for q(z,t) has been

36
at Kemi, a meteorological station in the northern Baltic Sea (latitude 65.6 N) were used with fixed values.

Each parameter is simply averaged over the three winters and for each month. The model runs are

accordingly made for December, March and April corresponding to the ice-freezing, ice-thermal equilibrium

and ice warm-up seasons. The solar radiation is incorporated as a monthly average diurnal cyde.

For the ice growth season, the initial ice thickness is taken to be 0.05m without snowfall. For the ice

equilibrium stage, the initial snow and ice thicknesses are 0.3 m and 0.6 m respectively, in accordance with

the monthly average from IDA. The spatial resolution of the ice layer is defined as Ah1 = HKJNI, where H is

the initial ice thickness and N1 is the number of grid points. In the simulations we let N1 take every integer

from 3 to 10, and then even integers from 12 to 32, i.e., a total of 19 spatial resolutions is used. The spatial

resolution of the snow layer is defined as Ah8 = HSNS, where H is the initial snow thickness; we let N

vary from 3 to 15, while the snow thickness is assumed to remain constant. For the warm-up season, only

bare ice with an initial thickness Hi0 = 0.7 m is considered. The time step (t) we used here is from 600s up

to 6 hours.

The effect of the numerical resolution on model results can be summarized as follows:

(1) During the freezing season, the model yields quite accurate results compared with analytical solutions,

indicating that the dassical linear ice temperature model is still valid with reasonable accuracy. Figure 3

shows that the sensitivity of the results to numerical resolution occurs for large values of the ratio of the

time step (s) to the grid size (m). This sensitivity can be decreased by using a fully implicit scheme or by

reducing the value of this ratio. Resolution tends more to affect model results for short-term predictions.

After adjustment to the initial conditions, the ice growth rate converges with decreasing grid size and

resolution has no significant influence on model predictions. A large time step should be avoided under

conditions of a rapid ice growth.

37
gggggpppy p 

and a spatial resolution of 2- 5 cm in ice or snow, if possible. For climatological studies, an relaxation of

resolution is certainly needed, but the ratio of time step to grid size should remain small.

38

E

:10.2

Figure 2. Vertical temperature profile in ice (a) and

snow (b), obtained by the analytic solution

(solid line) and the a numerical model

using as its spatial resolution = 3

(broken line), 10 (dot-dashed line) and 30

(dotted line). The boundary condition is of

the Neumann type.

Figure 1. The vertical ice temperature profile obtained

by an analytic solution (solid line) and the

numerical model using spatial resolutions of

N, = 3 (broken line). 10 (dot-dashed tine)

and 30 (dotted line). The boundary

conditions are of the Dinchiet (a) and

Neumann (b) type. Each ice temperature

profile corresponds to a time near mid-day.

15.10 -5 0 5

Snow temperature (C)

-10-8-6-4-2 0

Ice temperature (C)

.8.5.4 -5 -4.6 -4.2

Ice temperature (C)

.5.4 -5 -4.6

Ice temperature (C)
39

Figure 4. (a) Modelled daily variation of surface temperature in March (on the 5th day modelled) versus

spatial resolution. The stars indicate the daily maximum (upper) and minimum (lower) surface

temperatures, while the circles give the average daily surface temperature. (b) Modelled daily

average surface heat fluxes versus model spatial resolution. Each symbol refers to a term in the

surface heat flux: net long-wave radiation flux (*), sensible heat flux (x), latent heat flux (+),

absorbed solar radiation in the surface layer (o), conductive heat flux (.), and net surface heat flux

(s). Calculations are made for bare ice.
Boggild, C.E., J.G. Winther, K Sand and H.E!vehoy. 1995. Sub-surface melting in blue-ice fields in

Dronning Maud Land, Antarctica: Obseivation and modelling. Ann. GlacioL, 21. 162 - 68.

Cheng B. 1996. The conseivative difference scheme and numerical simulation of a one-dimensional

thermodynamic sea ice model, M. Sd. Bull., 15(4), 8--IS (in Chinese).

Ebert, E.E. and J.A. Curry. 1993. An intermediate one-dimensional thermodynamic sea ice model for

investigating ice-atmosphere interaction. J. Goophys. Res., 98(C6), 10,085-10,109.

Efimova, N.A. 1961. On methods of calculating monthly values of net long-wave radiation. MeteoroL

Gidrol., 10, 28-33.

Gabison R. 1987. A thermodynamic model of the formation, growth, and decay of first-year sea ice. J.

Glaciol., 33(113), 105-109.

Grenfell, T. C. and G.A. Maykut. 1977. The optical properties of ice and snow in the Arctic Basin. J. GlacioL,

18(80), 445-463.

Haapala, J. et aL 1996. Ice data bank for Baltic Sea climate studies. Report Series in Geophysics, 35.

Dept. of Geophysics, University of Helsinki.

Hoitslag, A. A. M and H. A. R. de Bruin. 1988. Applied modelling of the nighttime surface energy balance

over land. J. AppL Meteorol., 37, 689-704.

Högström, U. 1988. Non-dimensional wind and temperature profiles in the atmospheric surface layer: a reevaluation. Boundary-Layer MeteoroL, 42, 55-78.

Jacobs, J.D. 1978. Radiation climate of Broughton Island. In: Barry, R.G. and Jacobs, J.D. (eds): Energy

budget studies in relation to fast-ice breakup processes in the Davis Strait. Occas. Pap. 26: 105-120. INst.

of Arctic and Alp. Res., Univ. of Cob., Boulder.

Launiainen, J and B. Cheng. 1998. Modelling of ice thermodynamics in natural water bodies. Cold Reg.

Sci. Technol. 27(3), 153-178.

40
MARKUS MEIER

One of the goals of the Swedish Regional Climate Modeling Program (SWECLIM) is to simulate long-term

changes and natural variability of the Baltic Sea. AN essential feature of the Baltic is the seasonal ice

cover. Normally, the ice season lasts 5-7 months, from November to May. Interannual variability of the ice

extent is large. Dwing a mild winter, ice occurs only in the northernmost sub-basin, the Bothnian Bay, while

in a cold winter the whole Baltic Sea can become ice-covered. As surface albedo changes drastically with

ice conditions, sea ice in the Baltic is regarded as a key element in the North-European climate system. In

addition. Baltic Sea ice extent and mean winter temperature in North and Central Europe are strongly

correlated. The ice extent is fairly well correlated to the North Atlantic Oscillation (NAO) index during winter.

However, the relalionship is not stationary over time.

In this study, Baltic Sea ice variability in changing climate is investigated using a 3D coupled iceocean

model, the Rossby Centre Ocean model (RCO). RCO is a further development of the Ocean Circulation

Climate Advanced Modeling Project model (OCCAM). As the OCCAM project focuses on global scales it

was necessary to add parameterizations important for the Baltic Sea. A two-equation turbulence closure

scheme, open boundary conditions, and a sea ice model were main features that had to be added for our

purposes.

The ocean model in RCO is coupled with a Hibler-type two-level (open water and ice) dynamicthermodynamic sea ice model. An extension of the widely used viscous-plastic rheology with an elastic

component leads to a fully explicit numerical scheme that improves computational efficiency, particularly

on tiigti resolution grids, and adapts easily to parallel computer architectures. The ice thermodynamics are

based on Semtner's layer models for thick ice/snow (multiple layers) and thin ice/snow ('zero'-Iayer) using

tharactetistic discrimination thicknesses for ice and snow. In RCO thick ice consists of one or two ice

layers and thick snow consists of one snow layer. Snow is converted to snow ice if flooding as calculated

41
Towards Accurate Sea-Ice Modeling in a Coupled MM5/Sea-Ice Model: Treatment of

the Ocean

JING ZBANG', JEFFREY S. TILLEY' ANI) XLANGDONG ZHANG2

1Geophysical Institute, University ofAlaska, Fairbanks, AK 99775

2lntemat!onal Arctic Research Center, University of Alaska, Fairbanks, AK 99775

Recent evidence of sea-ice concentration and thickness decreases as well as warming of oceanic upperand mid-levels has been hypothesized to be caused by amplified global warming within the higher latitudes

of the Northern Hemisphere. As such, the proper specification of sea-ice and ocean properties has become

a topic of considerable importance in model evaluation and climate change assessment.

A coupled MM5/Sea-ice model has been developed and applied to the Pan-Arctic region in which a

thermodynamic sea ice model is induded to replace the simple treatment of sea ice in the standard MM5

model. In the standard MM5 model, sea ice concentration is treated as a binary function (either 100% or

0 over a grid cell). Our coupled system allows sea-ice concentration to range from 0-100% and to be a

prognostic quantity. A comparison of the simulations with the standard MM5 and the coupled system over

the Pan-Arctic region shows substantial sensitivity in the resulting atmospheric simulation (e.g., surface air

temperature, sensible and latent heat fluxes, boundary-layer structure) to the sea ice treatment at daily and

bi-weekty time scales.

However, coupled simulations are not without their limitations. In particular, ocean temperature is

presci-ibed with sea surface temperature (SST); it can either be held constant at the initial values or vary

on a daily timescale as presciibed from NCARJNCEP reanalysis data. This approach, while common for

regional operational or research models where the simulation length is less than 5 days, has some

42
pp

the sea ice thermodynamics and air-ice coupling. Spatial attention is paid to the different meteorological

conditions with respect to the radiative forcing: cases of clear and overcast skies during the polar night and

spring time are studied. These cases are simulated with a wide range of atmospheric pressure gradients,

i.e. the geostrophic wind driving the ABL flow varied from 2 to 24 m in various simulations. In addition,

we study the effect of the snow cover. The structure of the model is shown in Figure 1.

-1°C

GI G G20

1 Oceanpart

4

Warm air mass

001 >

Os O1( t)

Os

II

Cout)led model

Tair(z)

Ice

Tair(z)

Tice

Figure 1. Structure of the coupled ABL-sea ice model. The first 20 grid cells from the inflow

boundary represent open sea with a fixed surface temperature and a fixed inflow

temperature profile (aT/az = -6.5 K km). The rest of the grid cells (72) represent sea

ice with or without a snow cover.

43
44
gypp

far-reaching, affecting nearshore and shelf circulation as well as ice-ocean interaction.

As a component of the ocean, fast ice acts as a hindrance to navigation and as a conduit to transmit stress

over large distances, making it an essential consideration for shipping and nearshore development in the

Arctic. Acting as an extension of the land, it serves to protect the coast from erosive forces of ocean and

ice. In addition the ice is used as a habitat by Arctic wildlife and for subsistence hunting activities by native

peoples.

Ice-ocean interaction within the nearshore zone is strongly affected by the fast ice, which diminishes

momentum transfer between the atmosphere and ocean and represents an important constraint for the

nearshore tidal components. Beneath the fast ice, therefore, there can exist a large volume of water

isolated from the atmosphere and experiencing little thermohaline driving due to reduced bottom freezing

rates. Thus at the fast ice edge, which is the site of significant new-ice formation, there can be complex

thermohaline circulation.

Given the concentration of population, industry and infrastructure at the coast in the Arctic, improved

modeling of the nearshore zone would be of benefit for many future activities. More research is required to

allow sea ice-ocean models of higher spatial resolution to adequately represent the processes governing

the cycle of fast ice establishment and decay. In the meantime, however, observations of fast-ice

distribution and evolution may help in improving model performance in the coastal zone and over the

nan-ow parts of the Beaufort and Chukchi shelves.

Though a primitive fast ice climatology can be derived from observations, the state of the fast ice is

governed by a number of covarying dynamic and thermodynamic processes leading to a complex evolution

through the ice year and great interannual and spatial variability. For the fast ice to be incorporated as a

45
p(y, y , ) 

The efficiency of convection in the freshwater melt pond, originated due to upper surface heating or shortwave radiation absorption, in the bottom melting is studied within the framework of the modified RayleighBenard problem for two layer binary fluid.

The Importance of the Interactions Between Landfast Ice and Pack Ice for Models of

the Nearshore Chukchi and Beaufort Seas

LEWIS SHAPIRO, HAJO EIcIEN AN!) ANDREW MAHONEY

Geophysical Institute, University of Alaska Fairbanks

For the foreseeable future, it is likely that most impact in the Arctic related to exploration and production of

resources and expanded surface transportation will take place within about 200 km or less from the nearest

coastline. That area includes (1) the landfast ice zone, (2) the shear zone where deformation between the

landfast ice and the pack ice takes place, (3) part of the pack ice zone, and, (4) the area occupied by

stable extensions of the landfast ice which may extend that zone seaward for various time periods.

The purpose of this paper is to describe some properties of the ice, and patterns and modes of deformation

that are characteristic of those areas, and to explain their significance to developing models of processes in

the nearshore Chukchi and Beaufort Seas.

46
In 1999, there was distinctive intrusion of warm water around 20-40 m depth was found in further north

of Chukchi Sea. The water ;s betieved to be Pacific in origin based on stable isotope systematics and N**

values. In 2000, the intrusion of the water mass in subsurface layer was less pronounced.

Stable isotope of seawater is being used for differentiating water masses in Arctic Ocean. Over 1200

seawater samples were analyzed for stable oxygen isotope ratio for this study. Sainity-616O retationship

revealed distinctive Atlantic, Pacific seawater end-member points, and also mixing with fresh water (liver

and ice melt) and brine formed during sea ice formation. Startiingly, water mass with the öO and salinity

values accepted for typical Pacific water in Arctic Ocean could not be found. It could jeopardy the feasibility

of the estimation of pacific water fraction in the previous studies.

Barium (one of alkali-earth elements) has been used for American river input in Arctic without checking

its conservativeness for tracing water masses. In order to evaluate biological alternation of barium, its

contents in surface layer water after river water mixing with high barium concentration (600 nmol/kg for

Mckenzie water) is obtained based on liver water fraction of seawater estimated by 6180 systematics. By

companng with actually observed concentration, as great as 50% of barium reveals to be removed from

seawater, presumably by biological uptake. Because of the substantial atteration, barium should be used

as a water mass tracer after appropriate correction for biological effects or under oceanographic conditions

in which biological processes for barium is not active such as under thick multi-year ice zone.

47
jy py 

OBSERVATIONS ON ICE STATE

On the whole, ice cover is a very complicated media consisting of numerous constituent elements. The

term Ice state" implies a wide set of parameters characterizing properties of actual ice cover. A description

of ice state can be done for the local area, for the entire sea or any of its parts. In principal, ice conditions

can be called ice state or ice regime for any scale.

We believe that it is not reasonable to try to develop a comprehensive definition of ice state, because ice

cover causes different influence on different activities in ice-infested waters. Therefore, support of those

activities with ice information must meet different requirements and deal with various properties and

features of ice cover.

For many tasks, the complete set of required information on ice cover state could not be obtained without

field observations on mesoscale inhomogeneity of ice parameters, forms of floating ice, ridge configuration,

and physical properties of ice.

Field observations play an important role in the development of models and as ground truth. Therefore

close interrelation between environmental observers and authors of numerical methods is a necessary

condition to increase the accuracy and quality of ice support.

It is necessary to emphasize that numerous interesting datasets were collected by industry and could be

very useful for development and testing numerical ice models. Among such data are hourly observations

from oil rigs on ice drift and parameters of ice cover state. Another example indudes frequent maps of ice

distribution accompanied by photos of ice conditions collected during air reconnaissance in support of

testing contingency plans for events of an oil spill in ice covered waters.

48
near a coastal line (bamer islands).Allowing for this layer gives a more precise description of spatial

changes in ice velocity near a coastal line. It significantly increases accuracy of simulating ice deformation

and corresponding processes of ,idging in the vicinity of a coast.

Another subgrid descnption is app'ied to the simulation of ice edge location. Knowledge on exact

configuration of ice edge is meaningful for various practical offshore works. The information on ice edge

location is used in the model to improve mathematical descnption of dynamic and thermodynamic processes.

Original algorithm of calculating advective changes in ice parameters is based on allowing for local changes

in parameters within grid cells. The approach suppresses undesirable influence of computational viscosity.

RESULTS

Results of using discussed model underwent detailed considerations. Numerical methods created of

the basis of the model have successfully found wide application in calculations of current and future ice

conditions. To support users, a three-to-four-day forecast is considered as a pnmary planning tool and an

one-to-two-day forecast allows us to take into account more detailed infonnation about extreme events

such as hazardous pressure in ice cover.

The model allows reliable simulation of redistribution of ice cover for the entire summer period without

accumulation of significant errors and development of long-term ice forecasts for several months in

advance. We would like to emphasize that the quality of ice simulations does not depend upon sign or

magnitude of ice anomaly in every specific year. It has been shown that the method has a high quality

of mod&ing interannual changes in ice concentration for seas with different ice regime.

A similar method is also used for short-term (several days) ice forecasts of ice edge position, thickness,

concentration (including partial), and the location of compaction areas. Observations on oil ligs

49
Observations and Modeling of Small-Scale Thermodynamic Ice Growth and Decay

Processes in Coastal Arctic Seas

H. EIcKEN', D. K. PER0VIcH2, T. C. GRENFELL3, K. FREY', L. H. SHAPIRo', A. MAHONEY'

tGeophysical Institute, University of Alaska Fairbanks, Fairbanks, AK, USA

2Id Regions Research and Engineering Laboratoiy, Hanover, NH, USA

3Depaitment of Atmospheric Science, University of Washington, Seattle, WA, USA

In discussing the present status and future directions of ice-ocean modefling and in particular the adequate

representation of smaller-scale processes and phenomena, the issue of predicting the thermodynamic (i.e.,

static) growth and melt of sea ice is typically considered a problem not as pressing as those re'ated to ice

dynarnics. This is well justified by the excellent performance of even simpler ice-growth models (such as

0+ layer model by Semtner [19761 and recent improvements such as those of Bitz and Lipscomb [1999])

in predicting the thickening of level ice. The inverse square-root dependence of growth rate on thickness

dependence furthermore helps to dampen errors in initial or boundary conditions when predicting ice

growth. Nevertheless, as will be shown in exemplary ice-mass balance data obtained near Barrow, Alaska,

the key role of the snow cover in controlling heat transfer through the ice slab still represents a formidable

challenge insofar as the snow cover is highly vanable both in space and time.

However, the key challenge facing the entire suite of ice-growth/decay models remains the prediction of

summer ablation and complete ice decay. In this presentation, by drawing on data coflected at the Surface

Heat Budget of the Arctic Ocean (SHEBA) ice camp and in first-year fast ice off Barrow in 1998 to 2001, we

50
2lntemational Arctic Research Center, University of Alaska Fairbanks, Fairbanks, AK 99775-7340

3Geophysical Institute, University of Alaska Fairbanks, Fairbanks, AK 99775-7335

Field observations under the Iandfast sea ice near Point Barrow Alaska, recorded vertical displacements of

several centimeters amplitude and of an order of 600's period (Bates and Shapiro, 1980). Assuming that

typical period of irregular displacements of ice floes due to pressure ridge buildup between them has an

order of ten minutes (Marchenko and Makshtas, 2001), we speculate that gravity waves of such penod

could be excited by the transfer of ice floes impulse in the water.

In the accordance with this hypothesis the dynamic problem on the compression of circular floes under

the influence of onshore wind is considered. It is shown that the compression reatized by irregular

displacements of the floes in onshore direction even exist in the cases when wind velocity is a constant

Therefore these oscil!ations can be called as se'f-induced oscillations. Two types of setf-induced

oscillations are found. Oscillations with penods of several tens of minutes are excited due to the interaction

between the ridging and continuous ice, which is accumulated at the seaward boundary of continuous ice.

Oscillations with periods of several minutes and less are excited due to the interaction of a few ridge build

up events between the floes.

Then we analyze the frequencies of natural modes of water oscillations on shetf region near Point Barrow.

It is shown that second shelf mode, whose length in normal direction to the coastline is two times smaller

than the shelf width, has period of the order 600 s. Thus, self-induced oscillations of the floes producing

impulse transfer in the water can be the source of effective excitation of the natural shelf modes.

Bates, H.F., and LH. Shapim, 1980. Long-period gravity waves in ice-covered sea. J. Geophys. Res., Vol.

85, NC2, 1095-1100.

51
gjy

3lntemational Arctic Research Center, Fairbanks, Alaska, USA; email: makshtas@iam.uaf.edu

The temporal and spatial variation of shore-fast ice extent in the northeastern part of the Kara Sea during

1953-1999 is investigated and its sensitivity to interannual vanability of regional dimate is estimated.

Regular observations by the Arctic and Antarctic Research Institute (AARI) during 1953-1990 and SSM/I

surface bnghtness temperatures during 1987-2000 provide data on the fast ice extent. Monthly means air

temperature, snow depth and precipitation rate recorded by meteorological polar stations located in the

study area are used to assess an influence of atmosphenc conditions on the fast ice extent. Wind velocity

and direction measured on the Dikson Island are used to estimate an influence of the wind stress on the

fast ice extent Monthly means of the Ob and Enissey discharge in 1950-1987 are used as well.

Data analysis shows a significant correlation (about -50%) between mean winter temperature anomalies

and the average fast ice area in May for all meteorological stations located in the study area. No significant

correlations were found between fast ice extent and cumulative winter precipitation as well as with average

snow depth dunng winter. This differs from results reported for the Canadian Arctic fast ice. Analysis of the

sensitivity of fast ice extent to the wind direction shows significant role that the strong eastward and southeastward winds play in fast ice formation during a winter season. This is indicated by correlation of the same

order (-50%) as for average winter temperature anomalies. Wind data analysis also indicates that winds

from northeast during June-July increase fast ice destruction while winds from northwest tends to restrain

it. No correlations were found between the fast ice extent and the preceding summer river outflow rate.

The area of fast ice in spring months shows dear bimodal distribution. This indicates the existence of two

different regimes of fast ice formation driven by prevailing winds. Time series records of shore fast ice area

show a steady decrease in 1953-1990. The deciease is most pronounced in May. The shore fast ice in the

northeastern part of Kara Sea seems to break up eaily in the period 1980-1990 compared to 1953-1960.

52
Fax: (907)474-7290

E-mail: hajo.eickengi.alaska.edu

Website: www.gi.alaska.edu/-eicken

Roland W. (Bill) GARWOOD, Jr.

Department of Oceanography

Naval Postgraduate School

833 Dyer Road, Room 328

Monterey, California 93943-5193

Phone: (831) 656-2673

Fax: (831) 656-2712

E-mail: garwoodnps.navy.mil

Website: www.oc.nps.navy.mil/opbl

Kate HEDSTROM

Arctic Region Super Computing

University Of Alaska Fairbanks

910 Yukon Drive

Fairbanks, Alaska 99775

Phone: (907) 474-7896

Fax: (906) 474-5494

E-mail: kate@arsc.edu

Website: www.arsc.edu

53

Fax: +44 (0) 20 7679 7883

E-mail: daniel.fettham@cpom.ud.ac.uk

Website: www.cpom.org

Jan HAAPALA

Department of Physical Sciences

University of Helsinki

P.O. Box 64

Helsinki, Finland FIN-00014

Phone: +358-9-1915 1015

Fax: +358-9-1915 1000

E-mail: Jan.J.Haapala@helsinkifi

Website: www.physics.helsinki.fi

William D. (Bill) HIBLER Ill

International Arctic Research Center

Frontier Group

P.O. Box 757335 / 930 Koyukuk Dr.

Fairbanks, Alaska 99775-7335

Phone: (907)474-7254

Fax: (907)474-2643

E-mail: billh@iarc.uaf.edu

Website: www.frontier.iarc.uaf.edu
300-235 4800 Oak Grove Drive

Pasadena, California 91109

Phone: (818) 354-5614

Fax: (818) 393-3077

E-mail: ron.kwok@jpinasa.gov

Website: www-radar.jpl.nasa.gov/rgps

Qinzheng UU

National Marine Environment Forecast Center

8, Dahuisi Road, Haidian District

Beijing, 100081, China

Phone: (8610) 62173322 Ext. 146 or 150

Fax: (8610)62173620

E-mail: qzIivaxp800.nmefc.gov.cn

Aleksey MARCHENKO

General Physics Institute

Russian Academy of Sciences

Kantemirovskays Street, 18/2-363

115409 Moscow, Russia

Phone: 7-095-320-8710

Fax: 7-095-135-0270

E-mail: arnarch@orc.ru or amarch©kapella.gpi.ru

54

381 Elden Street

Hemdon. Virginia 20170-4817

Phone: (703) 787-1714

Fax: (703) 787-1053

E-mail: ronald.Iai©mms.gov

Website: www.mrns.gov/eppd/sciences/osmpfindex.htrn

Andrew MAHONEY

Geophysical Institute

University of Alaska, Fairbanks

P.O. Box 757320 / 903 Koyukuk Dr.

Fairbanks, Alaska 99775-7320

Phone: (907) 474-5648

Fax: (907)474-7290

E-mail: mahoneygi.aIaska.edu

Website: www.gi.alaska.edu/-mahoney

Wieslaw MASLOWSKI

Oceanography Department

Naval Postgraduate School

Monterey, California 93943

Phone: (831)656-3162

Fax: (831)656-2712

E-mail: maslowsk©oc.nps.navy.mil

Website: www.oc.nps.navy.miI/-'pips3
Phone: (907) 258-3446

Fax: (907) 279-5740

E-mail: chuck@fairweather.com

Howard L (Buck) SCHREYER

Department of Mechanical Engineering

University of New Mexico

Albuquerque, New Mexico 87131

Phone: (505) 299-3102

Fax: (505) 277-1571

E-mail: schreyerme.unm.edu

Caryn SMITH

Minerals Management Service

949 East 36th Avenue

Anchorage, Alaska 99508-4363

Phone: (907)345-4082

Fax: (907) 345-6805

E-mail: caryn.smfth©mms.gov

Website: www.mms.gov

55

Mont-Joli, Quebec, Canada G5H 3Z4

Phone: (418)775-0791

Fax: (418)755-0542

Email: saucierf©dfo-mpo.gc.ca

Website: www.qc.dfo-mpo.gc.ca/imL/en/scientists/sauciertj

Website: www.qc.dfo-mpo.gc.ca/imt/fr/chercheurs/saucierfj

Lewis SHAPIRO

Geophysical Institute

University of Alaska, Fairbanks

P.O. Box 757320 /903 Koyukuk Dr.

Fairbanks, Alaska 99775-7320

Phone: (907) 474-7196

Fax: (907) 474-7290

E-mail: lewsgi.aIaska.edu

Webstte: www.gi.alaska.edu

Deborah SULSKY

Department of Mathematics&Statistics

University of New Mexico

472 Humanities Building

Albuquerque, New Mexico 87131

Phone: (505) 277-7425

Fax: (505) 277-5505

E-mail: sulsky©math.unm.edu

Website: www.math.unm.edu/-sulsky
Keguang WANG

University of Helsinki, Division of Geophysics

Gustaf Haltsrominkata 2

P.O. Box 64

Helsinki, Finland FIN-00014

Phone: 358-9-19151002

Fax: 358-9-19151000

E-mail: keguang.wang@helsinki.fi

Website: http://geophysics.helsinki.fi

Xiangdong ZHANG

International Arctic Research Center

Frontier Group

University of Alaska Fairbanks

P.O. Box 757335 / 930 Koyukuk Dr.

Fairbanks, Alaska 99775-7335

Phone: (907) 474-2675

Fax: (907)474-2643

E-mail: xdz@iarc.uaf.edu

56

Jing ZHANG

Geophysical Institute

903 North Koyukuk Drive

P.O. Box 757320

University of Alaska Fairbanks

Fairbanks. Alaska 99775-7320

Phone: (907) 474-1539

Fax: (907)474-7290

E-mail: jin@rathlin.iarc.uaf.edu

Website: www.gi.alaska.edu

Denis ZYRYANOV

Norwegian Polar Institute

The Polar Environmental Centre

N-9296 Tromso, Norway

Phone: +47 7775 05 43 or +4777750500

Fax: +477775 05 01

E-mail: denis.zyryanov@npctar.no

Website: www.npolar.no -
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