ACCESS NUMBER: TO-13212

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STUDY TITLE: Physical–Biological Numerical Modeling on Alaskan Arctic Shelves

REPORT TITLE: Physical–Biological Numerical Modeling on Alaskan Arctic Shelves

CONTRACT NUMBER: 14-35-001-30661; TASK ORDER (TO): 13212

SPONSORING OCS REGION: Alaska

APPLICABLE PLANNING AREA: Beaufort Sea, Chukchi Sea, Hope Basin; Norton Basin

FISCAL YEAR OF PROJECT FUNDING: FY 1996

COMPLETION DATE OF REPORT: June 2000

COST: FY 1996: $96,270; CUMULATIVE PROJECT COST: $96,270

PROJECT MANAGER: Henry J. Niebauer

AFFILIATION: University of Alaska Fairbanks

ADDRESS: Institute of Marine Science, University of Alaska Fairbanks, Fairbanks, AK 99775-7220

PRINCIPAL INVESTIGATOR*: Henry J. Niebauer

KEY WORDS: Arctic shelf, sea ice, numerical modeling, biology, flux, convection

BACKGROUND:

The Arctic shelves north of the Bering Strait and north coast Alaska are regions of important benthic and

marine mammal resources, and are areas that will be impacted by offshore oil, gas and mining

operations. These shelves are also regions of seasonal sea ice cover that is mobile and restless. Physical

(including ice), biological and chemical oceanographic conditions in both summer and winter are such

that several types of both natural and man-derived “particles” (e.g., primary production or phytoplankton,

detritus, petroleum, mining, radionuclides etc.) may interact, aggregate and ultimately be transported

deeper into the ocean, into the Arctic halocline, onto the shelf bottoms, and into the benthic and marine

mammal ecosystems. In this study, we numerically modeled the generation, flux and fate of the natural

primary production phytoplankton particles, and their interaction with each other and with physical

processes. At least two main pathways or processes for this flux are brine rejection from ice formation in

fall and winter, and primary production blooms in spring and summer.

OBJECTIVES:

Our main goal was to use numerical modeling, in conjunction with existing data, to understand and

predict the interaction of the atmosphere, ocean and ice environment on the Alaskan Arctic shelves and

the role of this physical environment in driving and modulating primary production, including interaction

and aggregation of particles. The specific objectives were: 1) to modify an existing numerical model of

Arctic waters, which includes sea ice as well as biological and nutrient chemistry interactions, to

realistically model the vertical flux of phytoplankton as aggregates; 2) to model Arctic shelf spring

phytoplankton blooms, aggregation and vertical transport of chlorophyll/phytoplankton as a function of

physical processes of melt water and/or solar heated surface stratification, wind mixing and wind-driven

coastal upwelling of nutrients into the photic zone; 3) to model the vertically downward transport of water,

both in conjunction with and as a separate process from the flux of natural/primary production particles

and particle aggregation. This physical emphasis includes modeling higher density surface water

formation and deep convection through processes such as ocean surface cooling, ice formation and brine

rejection, and possible interaction with eddies; and 4) to interact with field/data groups and individuals, as

recommended by the National Academy of Science reports.
ACCESS NUMBER: TO-13212

DESCRIPTION:

A cross-sectional, multi-layered, time-dependent, finite difference numerical model composed of physical,

biological and sea ice submodels was applied to the region off north coast Alaska. The air–ice–ocean

interaction was numerically simulated using physical forcing of winds as well as heating and cooling of the

ocean to melt or make ice. The modeling simultaneously simulated physical interaction with both primary

production and the aggregation of phytoplankton. Available hydrographic, satellite etc. data were used for

initial and boundary conditions and for verification.

Four sets of experiments were conducted: simulation of 1) aggregation of primary production

particles or chlorophyll — the aggregation modeling was used in the rest of the modeling; 2) ice edge melt

experiments with and without ice melt, with and without deep stratification; 3) coastal upwelling with a

mobile ice cover with and without winds, with and without ice cover; and 4) experiments simulating deep

convection from brine rejection after wind-driven preconditioning with and without density as a function of

pressure, and with and without deeper stratification.

SIGNIFICANT CONCLUSIONS:

The numerical results suggest that phytoplankton particle interactions (aggregation) and subsequent

sinking provide a dominant pathway for the removal of natural particles, nutrients and carbon from the

surface layer. This material goes to the Arctic halocline, the shelf bottom, or the deep Arctic basin. In the

experiments, this aggregation and flux is modulated by physical processes of melt water induced

stratification leading to spring blooms at ice edges, and by coastal upwelling after winds cleared ice off

the ocean surface. Much stronger flux was generated when ice formation generated enough salt brine to

cause deep convection carrying material to the benthos. This convection was short-lived (~1 day) but

generated vertical speeds of ~100s m day–1. When pressure was included in calculating density, vertical

speeds doubled. This process is sensitive to vertical stratification such that strong convection was not

generated when using initial conditions from field data. More work is needed to further explore this aspect

of the modeling. A problem is that relatively little field data are available for verification. Those data

available suggested that the model primary productivity, production and the natural flux values (but not

necessarily the convective flux values) were reasonable.

STUDY RESULTS:

The modeling appears to reasonably portray some of the complex physical and biological mesoscale

processes and their interaction in Arctic waters. These processes included particle aggregation and flux

through the water column as forced by ice edge melt water stratification, coastal upwelling in ice infested

waters, and by wind forcing that cleared ice off the ocean followed by freezing conditions that generated

ice leading to brine rejection and vertical convection. Some of the vertical convection experiments are

really not verifiable yet as data do not exist on vertical flux due to convection. By running controlled

experiments and simulations in which physical, biological, nutrient chemical and ice conditions were

carefully forced and varied, progress was made in understanding how the processes work in nature.

Reasonable agreement was obtained in comparison of model results with existing data suggesting insight

was gained into these poorly understood air–ice–ocean processes of the Arctic.

STUDY PRODUCTS:

Niebauer, H.J. 1997. Physical–biological numerical modeling on Alaskan Arctic shelves. Presentation at

CMI Annual Research Review, February 1997, University of Alaska Fairbanks.

Niebauer, H.J. 1998. Physical–biological numerical modeling on Alaskan Arctic shelves, p. 27–38. In

University of Alaska Coastal Marine Institute Annual Report No. 4.

Niebauer, H.J. 2000. Physical–biological numerical modeling on Alaskan Arctic shelves. Final report,

University of Alaska Coastal Marine Institute. 84 p.

*P.I.’s affiliation may be different than that listed for Project Manager(s).
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Focus area for Physical–Biological Numerical Modeling on Alaskan Arctic Shelves, especially in the

Chukchi and Beaufort Seas region near the Barrow Canyon.