Ecosystem analysis of the bering/chukchi seas using a coupled time-dependent physical/biological simulation model

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Ecosystem analysis of the bering/chukchi seas using a coupled time-dependent physical/biological simulation model

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Title:
Ecosystem analysis of the bering/chukchi seas using a coupled time-dependent physical/biological simulation model
Creator:
Shuert, Paul G.
Place of Publication:
Tampa, Florida
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University of South Florida
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English
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xiii, 189 leaves : ill. ; 29 cm

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Subjects / Keywords:
Biotic communities -- Simulation methods ( lcsh )
Biotic communities -- Bering Straits ( lcsh )
Dissertations, Academic -- Marine science -- Doctoral -- USF ( FTS )

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General Note:
Thesis (Ph. D.)--University of South Florida, 1990. Includes bibliographical references (leaves 181-189).

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University of South Florida
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University of South Florida
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All applicable rights reserved by the source institution and holding location.
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028967950 ( ALEPH )
26506476 ( OCLC )
F51-00176 ( USFLDC DOI )
f51.176 ( USFLDC Handle )

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ECOSYSTEM ANALYSIS OF THE BERING/CHUKCHI SEAS USING A COUPLED TIME-DEPENDENT PHYSICAL/BIOLOGICAL SIMULATION MODEL by Paul G. Shuert A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Marine Science in the University of South Florida April, 1990 Major Professor: John J. Walsh, Ph.D.

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Graduate Council University of South Florida 'l'ampa, Florida CERTIFICATE OF APPROVAL Ph. D. Dissertation This is to certify that the Ph.D. Dissertation of Paul G. Shuert with a major in the Department of Marine Science has been approved by the Examining Committee on November 13, 1989 as satisfactory for the dissertation requirement for degree. Examining Committee: Maj}?ry;>otEtisor: John J. Walsh, Ph. D. Membei = ibrma:tl:j : -Jh'ke. Ph.D. Member: K. Coachman, Ph.D. Member: :PB3mas Hopkins, Ph.D. Mdmber: Gabriel A. Ph.D.

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COPYRIGHT Paul Shuert 1990 All Rights Reserved

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ACKNOWLEDGEMENTS I would like to acknowledge my wife Ginger for her patience and encouragement towards finishing this manuscript, and my parents for their support over the years while I worked towards this degree. I would also like to thank J.J. Walsh for his guidance and support in the completion of this project. Additionally, I would like to thank my committee, especially N.J. Blake for their comments on the research. others who should be mentioned for their various encouragements and help in providing data are C.D. Wirick, T.E. Whitledge, all of the ISHTAR researchers, D.A. Dieterle, and W.G. Gregg. This work was funded through the National Science Foundation, Division of Polar Programs (NSF Grant # DPP-8605659).

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TABLE OF CONTENTS LIST OF TABLES LIST OF FIGURES ABSTRACT 1.0 INTRODUCTION 2.0 PHYSICAL SUBMODEL 2.1 Background 2.2 Methods 2.2.1 Model Domain 2.2.2 Physical Equations 2.3. Validation Data 2.4 Output 2.5 Discussion 3.0 BIOLOGICAL SUBMODEL 3.1 Background 3.1.1. 3.1.2. Nutrients and Chlorophyll Zooplankton ii iv v xi 1 6 6 15 15 17 33 34 61 67 67 69 79

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3.1.3. Benthos . . . . . . . 83 3.2 Biological Equations . . . . 88 3 3 Validation Data . . . . 105 3.3.1 Fluorometry . . . . 105 3.3.2 Shipboard Observations . 107 3.3.3 Satellite Observations . . . 109 4.0 RESULTS OF THE COUPLED PHYSICAL/BIOLOGICAL MODEL . 111 4.1 Horizontal Fluxes . . . . . 111 4.2 Productivity of Phytoplankton . . . 121 4.3 Temporal Comparisons . . . . . 126 4.4 Spatial Comparisons . . . . 134 4.5 Benthic Fluxes . . . . . . 158 5 .0. CONCLUSIONS . . . . . . . . . . 167 6 0 REFERENCES . . . . . . . . . . . 181 iii

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LIST OF TABLES Table 1. Average daily transport through Anadyr, Shpanberg and Bering Straits. 35 Table 2. Transport volume statistics. 38 Table 3. Parameters used in the cases run for the biological sub-model. 112 Table 4. Total fluxes of nitrate for the 81 day simulation of the biological sub-model. 113 Table 5. Total fluxes of ammonium for the 81 day simulation of the biological sub-model. 117 Table 6. Total fluxes of chlorophyll for the 81 day simulation of the biological sub-model. 119 Table 7. Productivities within the model domain for the 12 cases of the biological sub-model. 123 Table 8. Average daily flux of carbon into the sediments. 161 Table 9. Carbon budget for case (2) of the Bering/Chukchi Sea model. 174 iv

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Figure 1. Figure 2. Figure 3. Figure 4. Figure 5. Figure 6. Figure 7. Figure 8. Figure 9. LIST OF FIGURES The Bering and Chukchi Seas showing the major circulation. ACW is Alaskan Coastal Water, AS is the Alaskan Stream, EKW is the East Kamchatka current, BSC is the Bering Slope current, and AC is the Anadyr current. The model grid. Grid represents 10 kilometer spacing in the x and y direction. Daily averaged wind velocity at Nome, Alaska, for 1985 used in the calculation of the flow field and in the calculation of the mixing coefficients for the biological submodel. FNOC wind model predictions for Julian Day 52, 1982 used as an example of this wind parameter input into the model. (A) Bathymetry, and (B) Mooring locations during the 1985 ISHTAR field experiment. CM designate current meters, FL designate fluorometers. Relationship between measured velocities at the current meters and transport at Anadyr and Shpanberg straits. Daily transports through Anadyr and Shpanberg Straits and the total incoming transports (A+S) for the 1985 simulation period. Percent of the total transports for Anadyr and Shpanberg straits assigned to each grid point in the model domain. Comparison of current meter data with no wind, Nome wind, and FNOC wind simulations for mooring 8. {See Figure 5 for the location of this mooring in relation to v 8 16 21 22 27 28 30 31

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the model domain). Figure 10. Comparison of current meter data with no wind, Nome wind, and FNOC wind simulations for mooring 9. (See Figure 5 for the location of this mooring in relation to 40 the model domain). 41 Figure 11. Comparison of current meter data with no wind, Nome wind, and FNOC wind simulations for mooring 10. (See Figure 5 for the location of this mooring in relation to the model domain). 42 Figure 12. current meter comparisons with modeled data, Nome wind forcing case for Julian Days 190 -195. 44 Figure 13. (A) Velocity field and (B) sea surface elevation at Julian Day 214, using the Nome wind forcing case. 47 Figure 14. (A) Velocity field and (B) sea surface elevation at Julian Day 226, using the Nome wind forcing case. 50 Figure 15. (A) Velocity field and (B) sea surface elevation at Julian Day 227, using the Nome wind forcing case. 51 Figure 16. (A) Velocity field and (B) sea surface elevation at Julian Day 228, using the Nome wind forcing case. 52 Figure 17. (A) Velocity field and (B) sea surface elevation at Julian Day 229, using the Nome wind forcing case. 53 Figure 18. (A) Velocity field and (B) sea surface elevation at Julian Day 230, using the Nome wind forcing case. 54 Figure 19. (A) Velocity field and (B) sea surface elevation at Julian Day 226, using the Nome wind forcing case and a constant boundary transport of 1.2 Sv. 55 Figure 20. (A) Velocity fieldiand (B) sea surface elevation at Julian Day 227, using the Nome wind forcing case and a constant boundary transport of 1.2 Sv. 56

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Figure 21. Figure 22. Figure 23. Figure 24. Figure 25. Figure 26. Figure 27. Figure 28. Figure 29. Figure 30. Figure 31. (A) Velocity field and (B) sea surface elevation at Julian Day 228, using the Nome wind forcing case and a constant boundary transport of 1.2 sv. (A) Velocity field and (B) sea surface elevation at Julian Day 229, using the Nome wind forcing case and a constant boundary transport of 1.2 sv. (A) Velocity field and (B) sea surface elevation at Julian Day 230, using the Nome wind forcing case and a constant boundary transport of 1.2 sv. (A) Velocity field and (B) sea surface elevation at Julian Day 241, using the Nome wind forcing case. Pathways of material described by the state equations of the biological submodel. The (A) surface and (B) near-bottom distributions of nitrate in August 1988 (from Walsh etA!., 1989) The (A) surface and (B) near-bottom distributions of ammonium in August 1988 (from Walsh et AI., 1989) AVHRR derived surface temperature on August 3, 1985 (from K. Dean, University of Alaska). Chlorophyll concentrations on July 18, 1980 as seen by the Coastal Zone Color Scanner, (from F. Muller-Karger, USF). A chlorophyll (J.'g 11 ) composite of the surface distribution of phytoplankton biomass within the Bering/Chukchi Seas during June-August 1978-88, (from Walsh et al., 1989) The distribution of composite organic carbon (% dw) within surficial sediments in the Bering/Chukchi seas (from Walsh, al., 1989) Figure 32. Surface incident radiation (cal cm2 hr-1 ) calculated (Gregg and Carder, submitted) over the simulation period and used in the vii 57 58 59 64 68 71 73 74 75 76 87

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Figure 33. Figure 34. Figure 35. Figure 36. Figure 37. Figure 38. Figure 39. Figure 40. Figure 41. Figure 42. Figure 43. biological submodel. Average daily productivity (g c m"2 day"1 ) calculated over the model domain for cases (1) -(12). Comparison of moored fluorometers with model output for the same location over the simulation period for cases (2), (11), and (12). The depth integrated, horizontal distribution of chlorophyll (mg Chl m"2 ) for case (2) at (A) Julian day 225, (B) Julian day 227, (C) Julian day 229, and (D) Julian day 231. Vertical profile of chlorophyll (mg Chl m"3 ) south of mooring !10 from the convention line east approximately 70 km. A) observed, and B) predicted. Vertical profile of ammonium NH4 m"3 ) south of mooring !10 from the convention line east approximately 70 km. A) observed, and B) predicted. Horizontal distribution of integrated nitrate (mg-at N03 m ) used as initial conditions for the coupled model. Horizontal of integrated ammonium (mg-at NH4 m ) used as initial conditions for the coupled model Horizontal distribution of integrated chlorophyll (mg Chl m"2 ) used as initial conditions for the coupled model The depth-integrated (A) nitrate (mg-at N03 m"2 ) and (B) chlorophyll (mg. Chl m"2 ) stocks in the Bering/Chukchi Seas during August 1988 (from al., 1989). The depth integrated, horizontal distribution of nitrate (mg-at N03 m"2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260. The depth integrated, horizontal distribution of ammonium (mg -at NH4 m"2 ) viii 99 125 127 130 132 133 135 136 137 139 140

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Fiqure 44. Fiqure 45. Fiqure 46. Fiqure 47. Figure 48. Fiqure 49. Fiqure 50. for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260. 142 The depth integrated, horizontal distribution of chlorophyll (mg Chl m 2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260. Horizontal distribution of average daily productivity (g C m2 day-1 ) over the model domain for case (2), for (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260. The depth integrated, horizontal distribution of nitrate (mg-at N03 m-2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260, for 1988 constant boundary conditions. The depth integrated, horizontal distribution of ammonium (mg-at NH4 m2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260, for 1988 constant boundary conditions. Horizontal distribution of average daily productivity (g C m2 day-1 ) over the model domain for the 1988 boundary condition, for (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260. The depth integrated, horizontal distribution of chlorophyll (mg Chl m2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260, for 1988 constant boundary conditions. Average net benthic flux over the model domain. Negative values indicate a net flux out of the benthos, positive values are a net flux into the benthos. This figure is a comparison of the sinking rate specification (cases (2), (6), and (7)). 145 147 150 151 152 154 159 Figure 51. Horizontal distribution of carbon in the ix

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Figure 52. Figure 53. benthos (g C m"2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260. Horizontal dfstribution of nitrate (J.'g-at NO 1 ) in bottom waters of the Chukchi, East Siberian and Laptev Seas, (from Codispoti, 1965). Average daily productivities (g c m"2 day1 ) calculated over the model domain for the 1988 constant boundary condition case. X 160 171 175

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ECOSYSTEM ANALYSIS OF THE BERING/CHUKCHI SEAS USING A COUPLED TIME-DEPENDENT PHYSICAL/BIOLOGICAL SIMULATION MODEL by Paul G. Shuert AN ABSTRACT A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Marine Science in the University of South Florida April, 1990 Major Professor: John J. Walsh, Ph.D. xi

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A coupled, time-dependant, 3-dimensional, physical/biological mathematical model was constructed to simulate some of the important biological and physical processes of the northern Bering and southern Chukchi Sea ecosystem. This model simulates the time-dependant changes in nutrient and chlorophyll fields on a broad ecosystem scale to examine the importance of physical forcing and chemical conditions on biological rates, particularly primary production. Some of the important results from the model follow. The distribution of chlorophyll and productivity in the northern Bering Sea is determined primarily by the strength of the advection and secondarily by the nutrient and chlorophyll distributions at the southern boundaries of the model domain. Ammonium concentrations are spatially determined by both benthic regeneration and by the advective field. Approximately 60% of the nitrate entering the model remains unutilized, to be exported at the model's downstream boundary within -200 Km of the Siberian coast. This unutilized nitrate presumably is available to enrich the nutrient concentrations of the East Siberian Sea waters at least as far west as Wrangel Island. xii

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Declining light availability from mid-summer on, in shorter daylengths and lower sun angles, leads to a decline in productivity in spite of ample nutrients. Zooplankton are an unimportant influence on chlorophyll, productivity and nutrient distributions, consuming only -1% of the daily productivity. Benthic respiration of carbon, and concomitant regeneration of nitrogen, constitute important sources of dissolved carbon and nitrogen to the model domain, without which, productivity is significantly curtailed. Approximately 3. 6 g c m-2 yr-1 (calculated over 150 days), is buried in the Bering/Chukchi Sea sediments, or -1% of the annual productivity. Abstract approved: __ John J. Walsh, Ph.D. Department of Marine Science Date of Approval xiii

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1 1.0 INTRODUCTION The estimation of fluxes of nutrients and,phytoplankton through the northern Bering Sea and the southern Chukchi sea can be used to determine the amount of carbon that will be available for, (1) higher trophic levels of this ecosystem, (2) burial within the underlying sediments, and (3) export into the Arctic Ocean. The rates of biological processes within this ecosystem can be shown to have a direct and measurable effect on the amount of un-utilized and recycled nutrients available for export to the Arctic Ocean. The goal of this research is to construct a coupled, time-dependant, 3-dimensional, physical/biological mathematical model to simulate some of the important biological and physical processes of this ecosystem. This model will use synoptic ship samples, satellite data, and moored time series data as boundary and initial values, and as verification data for the model. The aim of simulation modeling in the marine ecosystem is to represent first order physical, chemical and biological processes in simple mathematical expressions that are easily calculable, such that they can be predictive of future events. Our ability to predict the distribution in time and space of any variable, be it biological, chemical, or physical in

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2 nature, is only limited by our understanding of the processes which affect those variables, by our ability to accurately measure those processes and variables initially, i.e., avoiding chaotic behavior, and by our ability to compute these processes in a reasonable time frame. When our complete understanding of ecological processes is achieved we will be able to predict, with simulation models, how man s activities will impact the environment before he must live with the consequences of these activities. As an example, even a crude working simulation model would have been an effective tool in predicting the progress and potential effects of the recent EXXON VALDEZ oil spill in Prince William Sound, Alaska as the events progressed. Spill management decisions might have been more effective with some predictive capabilities that present state-of-the-art models can provide. In addition to examining the fluxes of nutrients and carbon fixation by phytoplankton in the Bering and Chukchi Sea ecosystem, the present simulation model provides a mechanism to examine hypotheses concerning individual processes over a broad ecosystem scale. Often equations are devised to describe biological processes in the environment based on laboratory experiments andjor small scale sampling efforts within shipboard containers. Large scale models, such as the one presented here, provide a means to examine the relevance of the rate measurements and can provide insights towards

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3 improving hypotheses such as the effects of nutrient concentrations and light availability on phytoplankton growth, and the large scale impact of zooplankton on a phytoplankton population. This model assumes that this ecosystem is driven by the physical laws of nature as we understand them, and the results will either confirm our suppositions, or hopefully, provide some indication of the deficiencies of these formulations and assumptions. In this context, simulation modelling can be considered a valid scientific method of hypothesis testing. Specifically, this model will estimate the amount of nitrate, ammonium, and chlorophyll that remains unutilized and is exported into the northern Chukchi Sea and the East Siberian Sea. Additional estimates regarding the amount of carbon deposited to the sediments and available to the macrofauna within the northern Bering and southern Chukchi seas will be made. Estimates of the amount of carbon available to higher trophic levels will also be made. The model provides a mechanism to examine this ecosystem on both a broad spatial scale of 10 kilometers resolution, and simultaneously over a broad temporal scale with "observations" every 15 minutes for 81 days, (the model can actually 'sample' 30810 points of the environment in approximately 3 minutes). In this model two types of data sets are utilized. First, ship samples of the important biological, chemical, and physical variables were compiled to use as boundary conditions

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4 and initial conditions for the model, and as a verification for the model results. Secondly, time series of current velocities and directions as well as a time series of chlorophyll were also employed to construct boundary conditions and as verification of the model results. Each of these data sets, although rigorously collected, has some deficiencies when a spatial/temporal view of the ecosystem is desired. Ship data is most often aliased in both time and space, being but a few samples over a narrow spatial and temporal domain, while moored time series give a good temporal coverage of but single points. Simulation models can make use of both of these data sets to provide a realistic assessment of the environment over a broad spatial. and temporal domain, thereby improving the worth of both data types. A barotropic physical submodel was constructed and used to approximate the flow field on a daily basis during July through September of 1985. The values of current flow from the physical submodel were used in the biological submodel to simulate the advective fluxes of chlorophyll, nitrate, and ammonium Diffusive fluxes were estimated in the vertical using a wind driven mixing parameter. Ship data and fluorometer time series from points along the southern edge of the model domain were used to construct a boundary data set for the model.

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5 The model provides a means to examine the time-dependent changes in chlorophyll fields measured with fluorometers at a few locations within the domain. It also provides a means to examine the importance of physical forcing and chemical conditions on biological rates, particularly primary productivity, on an ecosystem scale. The resulting analysis improves our knowledge as to the fate of organic carbon in the Bering-Chukchi Sea, and leads to a better understanding of continental shelf processes and of the global carbon cycle. The model will also be useful for guiding future field sampling strategies and to answer other important questions about the system.

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6 2.0 PHYSICAL SUBMODEL 2.1 Background The Bering Sea had been considered an undistinguished sea of the Pacific Ocean (Ohtani, 1973) until recently when its importance to fisheries and oceanography, with its high biological productivity (-170 g C m2 yr1 Walsh, 1988) and nutrient concentrations, and its influence on Atlantic deep water formation, was noted al., 1988; Worthington, 1970). The southern part of the Bering Sea is the source of the southward flowing Oyashio CUrrent which is cold, rich in nutrients and oxygen, and high in biological productivity as well (Ohtani, 1973). At the northern edge, the Anadyr Current flowing north through Bering Strait is the only connection between the Pacific Ocean and the Arctic Ocean and as such has some influence on the chemical and physical properties of the North Atlantic Ocean. The deep Aleutian Basin (depth >3500 meters) constitutes approximately 40% of the geographic area of the Bering Sea. Another 20% of the area is continental slope. The remaining 40% of the area is continental shelf (depth <200 meters), extending northwestward from Fox Island, Alaska in the Aleutian Islands to near Cape Navarin, U.S.S.R. on the Siberian coast. The shelf extends north through the narrow (85 Km width) Bering Strait across the Chukchi Sea and into

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the Arctic Basin at approximately 75 N. 7 Taken south to north, the combined shelf of the two seas is the widest (-1600 Km width) in the world ocean, the only comparable shelves are the North Sea Shelf, the East China Sea Shelf, and the Falkland Shelf (Coachman, 1986). Seventy-five percent of the source water to the Bering Sea comes from the Pacific Ocean by way of the Alaskan Current (Alaskan Stream) flowing westward along the Aleutian Chain. This water enters the Bering Sea mainly through Amchitka strait (1155 meters sill depth) and through Near Pass to the west of Attu Island (2000 meters sill depth; Ohtani, 1973). The remaining 25% of the water has its source in the Western Subarctic Gyre, a cyclonic gyre situated between the East Kamchatka current, the Alaskan CUrrent, and the western end of the Aleutian Chain (Ohtani, 1970; 1973; coachman, 1986). Net transport into the Bering Sea has been estimated at 11.0 sv, of which approximately half (5.5 6.0 Sv) enters through Amchitka Pass. Of the 11.0 sv that enter the Bering Sea, approximately 10.0 Sv eventually leaves the area as the East Kamchatka CUrrent. The currents are described in Figure 1. A generally cyclonic circulation in the Aleutian Basin has been recognized by many authors (Dodimead, et al., 1963; Favorite, 1966; and, Arsen'ev, 1967). current velocities in the basin are sluggish, usually only 1-3 em sec-1 except for the areas along the Kamchatka coast and along the continental slope where velocities can reach 10-15 em sec-1

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160E USSR 50 170E 1iOE 180 G. WRANQB..IS. 180 8 170W 70 AlA aKA 10 50 170W Figure 1 . The Bering and Chukchi Seas showing the major circulation. ACW is Alaskan Coastal Water, AS is the Alaskan Stream, EKW i s the East Kamchatka current, BSC is the Bering Slope current, and AC is the Anadyr current.

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9 The Bering Slope Current flows northwest along the eastern edge of the Aleutian Basin from the Aleutian Islands to near Cape Navar in, with most of the flow ( 4. 0-4. 5 Sv) turning south to flow along the Siberian coast where it contributes to the East Kamchatka current. The remainder of the flow (0.5-1.0 Sv) crosses the shelf at Cape Navarin and flows north along the Siberian Coast as a topographic boundary current (Kinder, gt 1986). The current in flowing north across the shelf encounters shoaling depths. The vorticity induced by topographic change (f/H) is at least an order of magnitude greater than the change in planetary which concentrates the flow as a boundary current; the direction of the flow is such that intensification is to the left when facing up slope. Once across the shelf break, the flow continues around the Gulf of Anadyr through Anadyr Strait, providing some of the source water to the study area. The physical features of the southeast Bering Sea Shelf have been characterized by Coachman (1986) in which he proposed three distinct domains based on water mass properties and physical processes. An outer domain along the shelfbreak is dominated by a general northwest advection of water at 1-5 em sec"1 with a cross-shelf exchange on the order of 1 em sec1 (Kinder and Schumacher, 1981; Coachman, 1986). This exchange is enhanced by tidal flow. The total kinetic energy in the southeastern Bering Sea is 90% tidal energy, contrasted

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10 to only 10-20% of the total kinetic energy in the northern Bering Sea in the region south of st. Lawrence Island. An inner (coastal) domain also exhibits a distinct northward flow, somewhat slower than the outer domain, with a flushing time of approximately 6 months (compared to 4 months for the outer domain). The northward advection in the coastal domain is driven in part by the general downward sloping sea surface between the Bering Sea and the Arctic Ocean of approximately o. 5 m (Stigebrandt, 1984) which continuously removes water from the region; and is also strongly baroclinic. Around Bristol Bay there are four small rivers, the largest two (Kvichak and Nushagak) discharge a maximum of -1. o x 103 m3 sec"1 during summer. The Kuskokwim River further north discharges a mean 3. 0 x 103 m3 sec"1 in May (Coachman, 1986). The removal of coastal water is therefore seasonally offset by a large freshwater supply from the Alaskan rivers during the summer melt, which accumulates in the domain until approximately September. Vertical fluxes are relatively rapid in the coastal domain because of the lack of any significant vertical structure, and as such, freshwater and heat are rapidly mixed throughout the domain. The domain is shallow and the tidal currents are strong (approximately 30 em sec"1 ) and the mixing from the tidal shear at the bottom is sufficient to maintain vertical homogeneity in temperature and salinity. This mixing is governed primarily by the amount of turbulent energy

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11 introduced by the wind shear at the surface and tidal shear at the bottom. The accumulation of runoff in the coastal domain is completely flushed by winter. The coastal domain contributes to the flow into the northern Bering Sea study area through the easternmost side of Shpanberg Strait. The central domain, between the inner and outer domains, is extremely sluggish in terms of flushing, and diffusive fluxes are of primary importance to its exchange with water masses on either side. The study region comprises the area of the northern Bering Sea shelf bounded on the southern edge by St. Lawrence Island, and includes the southern Chukchi Sea shelf extending north to approximately 11 N (Figure 1). The predominately northward flow enters through the -75 Km wide Anadyr Strait on the western side of st. Lawrence Island and through the much wider (-190 Km) Shpanberg Strait on the eastern side of st. Lawrence Island. Water transiting Anadyr Strait is relatively warm (1-2c) and saline (>33 ojoo) originating further south as part of the Bering Slope current. This current, here designated Anadyr Stream, mixes slightly in the Gulf of Anadyr with central shelf derived water (Coachman, 1986) before it is finally advected through Anadyr Strait with an average summer velocity of 15 em sec1 Shelfwater frequently flows through the eastern part of Anadyr Strait. This is also Bering Shelf Water.

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12 Two water masses flow through Shpanberg Strait. A low salinity (<31.8 ojoo) coastal water mass, termed Alaskan coastal Water, has its origin as coastal domain water in the southeastern Bering Sea (Coachman, 1986), and flows through the eastern side of the Strait. This water mass is also modified by freshwater input from the Yukon River, with a mean discharge of approximately 1. 5 x 105 m3 sec1 A second water mass, termed Bering Shelf Water has its origin as central domain and outer domain water that has resided for some time south of st. Lawrence Island, flowing primarily through the western side of Shpanberg Strait. Bering Shelf Water most of the time also flows north around the western end of St. Lawrence Island, occupying the easternmost side of Anadyr Strait. The principal driving mechanism of the northward transport through the Bering Strait is a sea surface sloping downward toward the north, of steric origin (Coachman et al., 1975; Stigebrandt, 1984). A mean sea level difference of 0.65 meters separates the North Pacific and Atlantic Oceans (Reid, 1961; Stigebrandt, 1984) with 0.50 meters of this gradient between the Pacific and Arctic Oceans. Annual mean transport estimates for the Bering Strait have varied from 0.56 Sv (Aagaard, gt al. 1985) to 0.95 sv (Fedorova and Yankina, 1964). Recently Coachman and Aagaard (1988) re-examined 40 years of estimates of Bering Strait transports and revised the mean transport estimate to 0 .78

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13 sv. Periodicities in transport are evident at very long periods of 50-60 years, long mesoscale periodicities of 14-20 years, short mesoscale periodicities of 6-8 years, and short 1-2 year periodicities. These periodicities are thought to be associated with long term global climatic changes that effect the steric difference between the North Pacific and Atlantic Oceans. Although a hydraulic head drives the long term transport, the regional wind forcing is a primary source of the short term variability of water motion. For example, there is a reasonable correlation (r2 = 0.49) between northward transport through the Bering Strait and the east-west atmospheric pressure gradient across the Strait (Coachman and Aagaard, 1981). The orientation and magnitude of the pressure gradient is determined by the positioning of the Siberian High pressure cell and the Alaskan Low pressure cell. Generally, when atmospheric pressure is higher on the west side of the Bering Strait, air flow and water transport are towards the south. The regional wind distribution can act in two ways to modify the transport in and around Bering strait. First, it acts directly as a stress on the sea surface, contributing 0.5 sv for each dyne cm"2 of sectional mean wind stress (Coachman, et al., 1975). Secondly, the wind acts indirectly by modifying the sloping sea surface between St. Lawrence Island and the area to the north of the Bering strait. These changes in sea surface height are directly translatable to altering

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14 the pressure head, thus initiating a barotropic response to the setup or setdown of the sea surface. Changes in the wind field can cause changes in transport from 1-2 sv northward to more than 3 sv southward (Coachman and Aagaard, 1981), and these changes can occur on time scales as short as 3-5 days (Coachman and Aagaard, 1988). Generally, a strong acceleration of the wind to the southwest causes slowing and reversal of the flow through Bering Strait. This can have major consequences for the spatial displacement of water mass boundaries south and north of the Strait. For example, if the waters in only Bering and Shpanberg Straits reverse, Anadyr water will penetrate much further to the east. Thus, the relative amounts of each water mass flowing in or out of the basin will determine their east-west extent within the Basin (Coachman, unpublished). Relaxation of wind, or a change in current direction towards the north restores normal northward flow within 4-7 days. The goal of the physical submodel was to construct a simple barotropic simulation of the gross features of the flow field to be used to drive a biological submodel. This was accomplished by using estimates of the transports within Anadyr and Shpanberg Straits as boundary values for a depthintegrated time dependant solution of the flow field. The results of the simulation were compared to current meter observations within the interior of the model domain.

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15 2.2 Methods 2.2.1 Model Domain A polar stereographic projection of a 810 x 810 kilometer grid with a 10 kilometer spacing was overlaid on a map of the northern Bering and Chukchi Seas to be used as the model area. The grid was rotated 37.50 clockwise from a point located at 64.28.15' Nand 173.82.10' w. This placement allowed for the maximum number of grid points across Anadyr and Shpanberg Straits, where boundary conditions of transport, nutrients, chlorophyll and zooplankton were repeatedly measured with moored instruments and shipboard sampling during the 1985 ISHTAR field experiment. This grid is shown in Figure 2. The depth of the water column at each grid point was obtained from bathymetry digitized from u.s. Department of Commerce charts, data from the Defense Mapping Agency, and data from the Geophysical Institute of the University of Alaska. Resolution of the bathymetric coverage over the grid (i.e., how close each depth value used is to each grid point) was never greater than 2.0 kilometers. Grid points in areas of less than 10 meters depth were set to 10 meters to limit the mixing time in the biological submodel. This is discussed later. For the biological submodel, the spatial domain was divided into 10 layers such that the depth of each layer was

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MODI!!L.. QR%0 SO K%L..OMETER SPAC%NB %N X AND Y D%RECT%0N Figure 2. The model grid. Grid represents 10 kilometer spacing in the x and y direction. 16

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17 calculated as H/10, where H is the digitized bathymetry at each grid point. The left-hand cartesian coordinate system used throughout both submodels was such that x(i) is positive (1 to 81) to the east, y(j) is positive (1 to 81) to the north and z(k) is positive (1 to 10) from surface to the bottom. 2.2.2 Physical Equations A depth-integrated barotropic model of the flow was constructed to simulate the flow characteristics of the physical habitat. A linear form of the Navier-Stokes and continuity equations was used such that dU &e {1) = -gH +r -Bx +fV dt &x dV &e (2) = -gH + Fy -By -fU dt &y de &U &V (3) = -+ dt &x &y where U and V are the depth-integrated horizontal velocities in the x and y direction, respectively. H is the depth of the water column entered into the model as a digitized bottom

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18 topography. The g is the acceleration due to gravity, e is the elevation of the sea surface, and, and FY are the wind stress components of the horizontal frictional force. Bx and sY are the bottom stress components of the horizontal frictional force. The f is the Coriolis parameterization given by the formula (4) f = 2 n sin 4> where n is the Earth's rotation rate of 0.73 x 10-4 rotations sec-1 and 4> is latitude in degrees. Initially the Coriolis parameter was calculated for each grid point in the model and used in the calculations from an array. The gradient in the Coriolis parameter introduced numerical waves in the results when boundary values at the straits were changed (changing the boundary values at each successive day introduces a perturbation in the numerical scheme) These numerical waves resembled long Rossby-like waves and generated small numerical errors in the mass balance of the model. In order to achieve a steady state solution a more simplistic way of parameterizing the Coriolis force from a single latitude ( 66 00. 0' N) was subsequently employed, (i.e. an f-plane was used) and f = 1. 4 x 10-4 sec-1 was used for the remainder of the calculations.

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19 At the bottom boundary, Bx and sY were calculated using a mean linear bottom stress over two adjacent grid points such that (5) (6) y _, ( ) B f,J = (2aC) (H1,J + H1 ,1 _1 ) V1,J where c is a dimensionless drag coefficient of 2 x 103 and a is assumed to be a constant of 50 em sec1 In this case a high value of the a is used to dampen the response of the transports to changing transport conditions imposed at the downstream boundaries so that large overshoots in the U and V components were eliminated and the system achieved a steady state much faster. (See equations (14) -(17)). Wind stress was incorporated in the form of the quadratic drag law such that (7) (8) = c10 w wY Where r and are the X and y components of the wind stress in cm2 sec2 respectively. W is the total wind velocity in em sec"1 and Wx and WY are the x and y components of the wind velocity, respectively, in m sec1 C10 is a dimensionless drag coefficient that varies with W such that at W 7.0 m

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20 -3 C10 = 1. 6 X 10 1 and at W C!: 10. o m sec1 C10 = 2. 5 x 103 At 7. 0 > W < 10.0 m sec1 a linear relation between C10 and wind speed is assumed such that (9) c10 = 0. 0003 (W -7. 0) + 0. 0016 Three separate cases of wind speed and direction were tested in the physical submodel. In the first case, wind stress was taken to be spatially constant. 3-hour observations at Nome, Alaska obtained from the National Weather Service for 1985 were averaged over a 24 hour period and entered into the wind stress term in this case. Figure 3 plots the daily averaged velocity and direction of the wind at Nome, Alaska used in this simulation. A second case made use of an array of spatially varying winds, derived from the Fleet Numerical Oceanographic Center (FNOC) weather model predictions (Mikoh and Kaitala, 1976). The model predicts the global winds at 19.5 meters above sea level on a 2.5 degree square grid at 6 hour intervals. The FNOC data was scaled to a 10 meter height using the 1/7 power law assumption (Spaulding, Esaj i, Mendelsohn, and Turner, 1987). The observed winds at Tin City, Alaska were shown to compare well with the FNOC model predictions (Spaulding, Esaji, Mendelsohn, and Turner, 1987). Figure 4 shows a

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-E CIO -E CIO -E 10 5 0 5 10 10 5 0 5 10 N t 190 200 210 JULY 220 230 240 AUGUST SEPTEMBER Figure 3. Daily averaged wind velocity at Nome, 21 Alaska, for 1985 used in the calculation of the flow field and i n the calculation of the mixing coefficients for the biological submodel.

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M-82 l7t 1 ll2 1.,. 1M 1M 114. 1 lfZ 1 1 T-, 1 r T- <2-SCALE (m/sec) .. .. l .1 10 -t> Figure 4. FNOC wind model predictions for Julian Day 52, 1982 used as an example of this wind parameter input into the model. 22

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23 typical FNOC wind prediction for Julian Day 54, 1982. FNOC winds were averaged daily and the closest observations were matched to each grid point. A third case was run without wind stress for comparison with the results of the two cases with wind. The numerical integration of equations ( 1) -(3) was carried out using a forward differencing scheme in time and space such that 'these equations now become (10) un+ 1= u" + 0.25( t) (v" + v" + v" + v" ) (f) i,j i,j f,j f,j+1 i-1,j f-1,j+1 -( t) (F" .) f,J c11) vn+ 1= vn + o.2sc t> ct.r1 + un+ 1 + un+ 1 + un+ 1 > Cf> i,j i,j 1,j i+1,j i,j-1 i+1,j-1 (12) en+1 f,j g( t) (H . + H . _1 ) (en . en . _1 ) -( t) (BY 1 ) 2 y l,J l,J 1,J l,J 1, -( t) (Fy .) f,J t = en -cun+1 f,j X f+1,j t un+,; -cvn+, f, y f,j+1 vn+1) f,j

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24 The time step t satisfies the Courant-Freidricks-Lewy condition (Lax, 1967) of numerical stability for explicit time difference algorithms such that ( 13) t < X/ ( 2gH111811) -Yt Assuming H.u = 50 meters in the Bering Sea, g equal to 10 meters sec-2 and x equal to 10 kilometers, the maximum limit of t is 5.3 minutes. A time step of 3 minutes was chosen to calculate the flow, this being well within the limits of the stability criterion. At the land boundaries, the condition of U = V = e = 0 was invoked such that only g rid squares over water were calculated. At the downstream open boundaries in the Chukchi Sea, a "sponge" with an absorbing boundary (Israeli and Orszag, 1981) was invoked in the numerical calculation. This condition allows only outward energy flux. The "sponge" works by increasing bottom friction incrementally in the last ten grid points in the positive V and the negative U direction to the end of the model domain, at which point the open boundary condition (OBC) is applied. At the I= 1 line (the west side of the model domain), V is set to zero and the open boundary condition is applied to calculate uf, J such that X [ 1 -B ] n+1 n i j t X \ (14) u = u ---(gH ) i,j i,j X 2 X i j H i,j

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where n -f t (V i,j H + H n + v ) CJ i, j+1 X i,j i+1,j (15) H = ------i, j 2 t X e + F X i, j 25 At the j = 81 line (the north edge of the model domain), uf,j is set to zero and the open boundary condition is applied to calculate vi,j such that n+1 (16) v = i,j where y (17) H i,j n v i,j y [ B i,j 1 -Hy i,j 2 y t y \ ] (gH ) i,j n n t y -f t (U + U ) g e + F i,j i,j-1 y i,j H + H i,j i,j-1 = -------2

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26 Simulation of the time-dependant flow pattern was run using the southern boundary values of transport at Anadyr and Shpanberg Straits, while wind speed and direction were updated every 24 hours of simulated time. The boundary transports within Anadyr and Shpanberg Straits were calculated from current meter data (see Figure 5 for the locations of the instrument arrays in relation to the model domain), and from the relationship between the measured component of the northward flow and the calculated transport obtained from sections of anchored stations and flows measured from the ship for 5 cases in each strait (Figure 6). Equations (18) (20) were taken from Coachman {ISHTAR, 1985 Data Report). For example, in Anadyr Strait moorings 6 and 7 were used to calculate the transport (T) through this strait from, (18) T = 0.033 V 0.036 which has a correlation coefficient, r, of 0.94. V is the component of the northward velocity measured from current meters 6 and 7 such that (19) V moorfng 6 + V 11100dng 7 v = 2 Similarly for Shpanberg Strait, mooring 3 is used to calculate the transport from,

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174" 172" 170' .... ... 8 /,' , , , -IUIWTf1' / IIIODII., , 'COli ln. ...... oe tCIII --. CN-. : ,, ',''/ "" -a. t ... . ...,._.. I'TIIINT ', Ot tCM . / t .: f1' ... ,. --f7. 172" 110' ... Figure 5. (A) Bathymetry, and (B) Moorinq locations durinq the 1985 ISHTAR field experiment. CM desiqnate currentmeters, FL desiqnate fluorometer&. ... 112" .. "' -...J

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50 u 40 41 (/) e u 30 20 -10 .0 TRANSPORT CALIBRATION ANADYR AND SHPANBERG STRAITS ANADYR V TRANSPORT (Sv) ISH-6 + ISH-7 2 SHPANBERG V ISH-3 28 0 Figure 6 Relationship between measured velocities at the current meters and transport at Anadyr and Shpanberg straits.

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29 (20) T = 0. 03 V11100rfng 3 0. 08 with an r of 0.92, and where V is now the northward component of velocity measured at mooring 3. The velocities used in equations (18) (20) were entered as daily averages of each mooring, and transports at Anadyr and Shpanberg Straits were calculated from these. Figure 7 shows the variation in total transport at these straits over the 1985 field year. The distribution of total transport along the boundary grid points was derived from profiling current meter surveys of Anadyr and Shpanberg straits during the 1985 field season (ISHTAR 1986 Data Report). The proportion of the total transport at each grid point was derived from these profiles (Figure 8). The individual surveys show that there are times when the net transport across a strait is southward, (Figure 7), but this sum is comprised of some grid points with northward flow (Figure 8). Points of negative percent represent southward flow, such that summing the inflow and outflow across each strait gives the total transport. In the time domain, a linear interpolation was used to calculate the daily transport at each grid point between the observation times of Figure 8. Initial conditions of flow within the model domain were achieved by first starting the model with all u, v, and e

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> "1a:a: 1&10 CD D.. zen z o.. > !!! 1a: 0 a: D.. >en oz za: ..... > !!! c; + I a: -o D.. -lCIJ z 1- oa: ........ 30 MEASURED TRANSPORTS FROM CURRENT METERS (UJ88) II II I I I llllllllllllllllllllllllllllllllllllllllllllllllll I Ill I I I I II I Ill I I I I Ill I II 190 200 210 220 230 240 250 2eO 270 Figure 7. Daily transports through Anadyr and Shpanberg Straits and the total incoming transports (A+S) for the 1985 simulation period.

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45.0 40.0 35. 0 30. 0 25. 0 20. 0 15. 0 10.0 I 5 0 .0 -5.0 .... -10.0 ;l b .... 15 M PERCENT TOTAL 'T'RANSPOOT AT 6RID POINTS x july A )ull 13-15 0 sep 7-9 sept 17-19 31 Figure 8. Percent of the total transports for Anadyr and Shpanberg straits assigned to each grid point in the model.

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32 arrays set to zero. The boundary conditions for Julian day 190 (8 July 1985) were then entered and the model was run for 30 days of simulated time under the desired wind condition. The steady state solutions of these variables were used as their initial conditions. The two cases with wind and one without wind forcing were run from 08 July 1985 (Julian day 190) through 26 September 1985 (Julian day 270). The model took an average of 15 hours for each run of the 81 day simulation on a DEC MICROVAX III computer, using approximately 2.5 Megabytes of memory.

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33 2.3. Validation Data A total of thirteen current meters were deployed at 10 mooring locations within the Bering Sea during the 1985 Field season (Figure 5). The length of the current meter records ranged from 38 days (CM 2) to a maximum of 84 days. The sampling interval for all the current meters was 20 minutes. A simple daily average of the resultant speed and direction at moorings 8 10 were used for validation data. At moorings 3, 6, and 7, only the v-component of the velocity was used to calculate the transport through Anadyr and Shpanberg Straits from equations (18) (20). Mooring locations 7 and 10 each contained two current meters. In these cases, velocity and direction of the top and bottom current meters were averaged together. In both cases, the differences between the resultant velocity and direction from the top and bottom meters were very small, <5 em sec-1 and <1o. All of these current meter records were edited to include only Julian day 190 (08 July 1985) through Julian day 270 (26 Sept 1985).

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34 2.4 output The daily transports calculated from current meters using equations {18) (20) for Anadyr and Shpanberg Straits and the model derived transports for Bering Strait in the Nome Wind case are shown in Table 1. Total transport at the southern boundary (Anadyr + Shpanberg) ranges from a negative southward transport of 1.077 Sv on Julian day 259 to a maximum northward transport of 1.914 sv on Julian day 241, with a mean of 0.927 sv. The mean daily total transport for the 81 day simulation in Bering Strait is about the same at 0.919 Sv {Table 2). Walsh et al. {1989) estimated the mean transport from July through September 1985 to be 1.28 Sv, over a longer period. Transport statistics for Shpanberg, Anadyr and Bering Straits and for the Chukchi boundary are shown in Table 2. The Chukchi boundary refers here to a line from Ft. Hope southwest to near Cape Dezhneva (See Figure 5) (hereby referred to as Chukchi Strait). During the simulation, the volume fluxes through Anadyr, Shpanberg, Bering and Chukchi Straits were summed as a check of the mass balance of the model. The procedure insures that the numerical scheme accounts for all the mass. Mass balance was closely approximated when total inflow (Anadyr + Shpanberg) was balanced against either Bering or Chukchi fluxes. Exceptions to this occur when the transport changes dramatically between two days. In these situations

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35 TABLE 1: Average daily transport through Anadyr, Shpanberg and Bering Straits. Anadyr and Shpanberg straits are calculated from daily averages of the current meters in these straits. Total transport is the total amount entering or leaving the system through Anadyr and Shpanberg strait. Bering transport is calculated from the model. The difference in transport is the Total minus Bering transport. (Transports are in Sv). JULIAN ANADYR SHPANBERG TOTAL BERING DIF-DAY TRANSPORT TRANSPORT TRANSPORT TRANSPORT FERENCE (Sv) (Sv) (Sv) (Sv) (Sv) 190 0.964 0.257 1.221 ----------191 1.135 0.005 1.140 1.160 -0.020 192 1.068 0.122 1.190 1.190 o.ooo 193 1.103 0.227 1.330 1.300 0.030 194 1.082 0.241 1.323 1.330 -0.007 195 0.961 0.238 1.199 1.230 -0.031 196 0.943 0.293 1.236 1.220 0.016 197 1.077 0.285 1.362 1.330 0.032 198 1.170 0.231 1.401 1.390 0.011 199 1.029 0.427 1.456 1.450 0.006 200 1. 041 0.331 1.372 1.390 -0.018 201 1.054 0.202 1.256 1.280 -0.024 202 0.930 0.301 1.231 1.240 -0.009 203 0.735 0.438 1.173 1.190 -0.017 204 0.933 0 .379 1.312 1.270 0.042 205 0.992 0.299 1.291 1.290 0.001 206 1.036 0.290 1.326 1.290 0.036 207 0.989 0.212 1.201 1.200 0.001 208 0.815 -0.031 0.784 0.860 -0.076 209 0.779 -0.015 0.764 0.750 0.014 210 0.993 0.171 1.164 1.040 0.124 211 0.877 0.104 0.981 1.010 -0.029 212 0.800 0.091 0.891 0.890 0.001 213 0.656 0.091 0.747 0.760 -0.013 214 0.846 0.201 1.047 0.950 0.097 215 0.921 0.189 1.110 1.070 0.040 216 0.637 0.130 o. 767 0.830 -0.063 217 0.825 0.333 1.158 1.030 0 .128 218 0.771 0.314 1.085 1.080 0.005 219 0.954 0.546 1.500 1.360 0.140 220 1.022 0.172 1.194 1.230 -0.036 221 0.825 0.177 1. 002 1. 020 -0.018 222 0.682 0.363 1.045 1.010 0.035 223 0.893 0.359 1.252 1.260 0.008

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36 TABLE 1: Continued. JULIAN ANADYR SHPANBERG TOTAL BERING DIF-DAY TRANSPORT TRANSPORT TRANSPORT TRANSPORT FERENCE (Sv) (Sv) (Sv) (Sv) (sV) 224 1.120 0.263 1.383 1.310 0.073 225 1.233 0.312 1.545 1.530 0.015 226 1.282 0.290 1.572 1.530 0.042 227 0.901 -0.036 0.865 1.010 -0.145 228 0.225 -0.009 0.246 0.390 -0.144 229 0.176 -0.177 -0.001 0.050 -0.051 230 0.892 0.235 1.127 0.890 0.237 231 0.785 0 .180 0 .965 0.990 -0.025 232 0.519 0.090 0 .609 0.680 -0.071 233 0.751 0.484 1.235 1.040 0.195 234 0.531 0.113 0.644 0.750 -0.106 235 0.364 -0.045 0.319 0.370 -0.051 236 0.570 0.139 0.709 0.580 0.129 237 0.544 0.226 0.770 0.720 0.050 238 0.671 0.237 0.908 0.907 0.001 239 0.973 0.286 1.259 1.120 0.139 240 1.265 0.464 1.729 1.540 0.189 241 1.381 0.533 1.914 1.790 0.124 242 1.055 0.198 1.253 1.340 -0.087 243 0.732 0.047 0.779 0.840 -0.061 244 0.760 0.212 0.972 0.880 0.092 245 0.578 0.838 1.416 1.270 0.146 246 0.559 0.701 1.260 1.240 0.020 247 0.965 0.140 1.105 1.060 0.045 248 0.878 0.188 1.066 1.020 0.046 249 0.797 0.221 1.018 0.980 0.038 250 0.698 0.542 1.240 1.140 0.100 251 0.944 0.352 1.296 1.220 0.076 252 0.678 0.288 0.966 1.000 -0.034 253 -0.135 0.240 0.105 0.310 -0.205 254 0.475 0.181 0.656 0.470 0.186 255 0.117 0.071 0.188 0.290 -0.102 256 -0.018 0.513 0.495 0.410 0.085 257 -0.936 0.787 -0.149 0.030 -0.179 258 -1.086 0.788 -0.298 -0.280 -0.018 259 -0.874 -0.203 -1.077 -0.980 -0.097 260 0.241 -0.121 0.120 -0.210 0.330 261 0.444 -0.088 0.356 0.320 0.036

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37 TABLE 1: Continued. JULIAN ANADYR SHPANBERG TOTAL BERING DIF-DAY TRANSPORT TRANSPORT TRANSPORT TRANSPORT FERENCE (Sv) (Sv) (Sv) (Sv) (sV) 262 0.578 -0.087 0.491 0.480 0.011 263 0.292 -0.361 -0.069 0.060 -0.129 264 0.383 -0.099 0.284 0.210 0.074 265 0.351 -0.079 0.272 0.290 -0.018 266 0.684 -0.099 0.585 0.530 0.055 267 0.874 -0.054 0.820 0.800 0.020 268 0.651 0.284 0.935 0.970 -0.035 269 0.529 0.030 0.559 0.680 -0.121 270 0.581 0.622 1 .203 1.120 0.083

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TABLE 2: Transport statistics. Shpanberg and Anadyr transports were calculated from current meter data. Total transport is Anadyr + Shpanberg transport. Bering and chukchi transports are calculated by the model. Chukchi transports are normally negative (i.e. towards the west). (Transports in Sv). 38 81 DAY TOTAL TRANSPORT AVERAGE MINIMUM DAILY DAILY TRANSPORT TRANSPORT MAXIMUM DAILY TRANSPORT SHPANBERG STRAIT 17.610 0.217 -0.361 0.838 ANADYR STRAIT 57.546 0 .710 -1.086 1 .381 TOTAL TRANSPORT 75.156 0 .927 -1.077 1.914 BERING TRANSPORT 73.518 0.919 -0.980 1.790 CHUKCHI TRANSPORT -73.447 -0.918 0.836 -1.760

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39 a setup or setdown of the sea surface causes an imbalance in .the flow through the straits. As stated previously, three separate cases of wind forcing (no wind, Nome wind, and FNOC wind) were run and compared to data from current meters within the interior of the model domain. Figures 9-11 are stick diagrams of both the current meter vectors and the vectors of simulated currents under the three wind forcing cases of the model at moorings a, 9, and 10 (recall Figure 5). A visual examination of the model results at each of the mooring locations suggest that there is little difference in both magnitude and direction of the simulated flow under the three cases of wind forcing. However, some variation is seen when any case of wind forcing is compared to the current meter data. The model results at mooring 8 (Figure 9) show the best match to the current meter data, with speeds of the same relative direction and magnitude. The model output at mooring 9 (Figure 10) shows the same direction, however, the magnitude of the vectors is approximately one third less than the observed current. The simulated flow at mooring 10 (Figure 11) shows a nearly constant difference in direction of 30-40, shifted to the east, but with about the same speeds as measured, when compared to the current meter observations. Figures 9-11 indicate that the type of wind stress specified had very little effect on the velocities at

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1za: ww a:t a:w u 0 z HW ou z a z H en 0 z a z H en ou z IJ... I I I I rrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrrr riTTrrnnnnrnn T r r r 180 200 210 21!0 230 2) HO HO JULIAN DAY Figure 9. Comparison of current meter data with no wind, Nome wind, and FNOC wind simulations for mooring 8. (See Figure 5 for the location of this mooring in relation to the model domain). 2 7 0 clac L+SO c11/aac L-to cM/aac L+SO CM/aac L+so c/aec: 0

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1za: ww a:t a:w u 0 z HUJ 3:en au z 0 z H 3:W en 0 z 0 z H xw en au z u.. I I I I I I I I I I I II I I I I I I I I I I I I I II I II II I I II I I I I I I I Ill I I I Ill I I Ill I I II II I Ill I I II II I Ill II I SIMI 200 2SO 280 230 240 JULIAN DAY Figure 10. Comparison of current meter data with wind, and FNOC wind simulations for mooring 9. for the location of this mooring in relation domain). 2110 no no wind, Nome (See Figure 5 to the model 270 .+tO c/eec -so c/eec .+so c/eec -so c/eec .+SO c/eec -so c/eec +SO o/o -so c/eec ... t-J

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..... za: ww a:t a:w ::>L u 0 z HW 3:(1) c{ ou z 0 z H 3:W (/) Wet LU 0 z 0 z H 3:UJ (/) Uct ou z u. I I II I I I I I I I I I I I I I I I I I I II I I I Ill I 1111 Ill 111111111111 I I I Ill I Ill II II I I Ill I 11111111 Ill 180 200 IUO aao a30 o no aeo .JULIAN DAY Figure 11. Comparison of current meter data with no wind, Nome wind, and FNOC wind simulations for mooring 10. {See Figure 5 for the location of this mooring in relation to the model domain). 870 +tO c/eec -so +tO -so c/c: .+tO -to .+SO -so ,a:.. .....,

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43 these locations. This is most likely attributable to the specification of the boundary transports at Anadyr and Shpanberg Straits from observed. The principal driving mechanism of the transport is the sea level difference from the Pacific to the Arctic Ocean, with the winds acting only to modify the flow (Coachman and Aagaard, 1988). The observed current meter velocities, used to determine boundary transports, have already adjusted to the wind stress effects and thus the flow pattern already incorporates most of the natural response to the wind conditions. Some of the discrepancies in comparisons of the moored current meter data with the numerical model may also be the result of bottom topography. The bathymetry of the model domain is somewhat smoother, for numerical considerations, than the actual bottom topography. In general, the northern Bering Sea is shallow with a mean depth of only 50 meters, and strong bathymetric steering is expected. The knolls and shallow troughs of the area, though gentle, represent a substantial fraction of the total depth. This relief is noticeably absent in the model specification of bathymetry, contributing to the change in the fine scale steering of the flow that must be occurring at the selected moorings. A specific example is seen in Figure 12 where the measured vs. modelled vectors at the three

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z s w 0 a: w 1w 1z w a: a: ::J 0 C\1 < 0 ..... ..... < (J) ..... < a) C-CURRENT METER M-MODEL OUTPUT c c cr 20M/SEC c c M M 190 191 192 193 194 I JULIAN DAY ( 1985) ___ j -------------------Figure 12. current meter comparisons with modeled data, Nome wind forcing case for Julian Days 190 -195. 44

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45 interior mooring locations for Julian days 190 -195 are presented. At mooring 10, a nearly uniform -Jo offset in direction between the measured and modelled velocities is evident. This mooring is approximately 20 kilometers south of the Diomede Islands in Bering strait. Actually, these islands split the Strait into two channels, whereas the model grid is too coarse to reproduce these bathymetric details. Other smaller features in the real ocean bathymetry that are absent in the model specification would be expected to cause some differences between the in situ data and the model results. The model vectors shown in Figures 9-11 are most like the current meter vectors in cases where Anadyr and Shpanberg Strait transports are steady, strong and northward (i.e. Julian day 190-220 in Table 1). The model's response to abrupt changes in magnitude of the transport and direction of the flow is slow, especially when the system reverses. Part of this lag in response could be overcome by changing the specification of the boundary conditions used. For example, in cases of flow reversal, the specification of the transport at the downstream boundary in the Chukchi Sea could contribute to a faster response within the model grid points in the interior. The lack of any measurements in this region precluded the possibility to make these specifications.

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46 Considering the lack of any substantial difference in the results obtained from the three cases of the model, it is of marginal value to force the physical model with either Nome or FNOC winds. However, in the Chukchi sea, winds are important in reversals of flow, where the currents are weak (Figure 16a). A specification of the wind stress is also necessary for the biological submodel to parameterize the vertical mixing term. The simpler wind forcing of the Nome wind case was used in the following results, because the FNOC wind case required more memory and calculation time on the computer without providing more realistic results. A typical flow pattern is presented in Figure 13a where the velocity field for Julian day 214 is plotted. In this figure, the Bering strait transport of 0.95 Sv approximates the calculated 81 day mean of 0.92 Sv, and as such may be considered a snapshot of the summer average flow pattern. The general flow pattern can thus be described. A contour plot of sea surface elevations is also shown for this day (Figure 13b). Typically, water entering Anadyr strait migrates slightly eastward in the Chirikov Basin. Bathymetric steering and the hydrostatic pressure gradient force the flow to turn north through Bering Strait. The Shpanberg Strait flow follows the shallow bathymetry of the Alaskan coast before joining the Anadyr flow through Bering Strait. Only a small transport is computed within

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6 2 -A 6664 68-174 I ANADVfllt .ao cm/eec: 172 I ... 17B I \ \ : I VELOCXTY NOMK WXNOS CAK CAY as STRA%T O .De a v 't' '7" '12 8 68-66 64-ANAO.,,_ .Tf'Uio:IT ............. 62 SEA ELEVATXON (m) CAY % 8 lfj& STAAXT TAANSPOAT O .De Figure 13. (A) Velocity field and (B) sea surface elevation at Julian Day 214, using the Nome wind forcing case. lf' ""' ....a

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48 Norton Sound. Once through Bering strait, flows over most of the area slow due to the increase in cross-sectional area encountered past the narrow Bering Strait, with the majority of the water flowing northwestward across the Chukchi Shelf towards Wrangel Island. A small but distinct flow turns cyclonically north past Pt. Hope and appears to flow as a coastal current north and east along the Alaskan coast towards Point Barrow. Table 1 indicates four periods of reversal of the flow through Shpanberg Strait ranging from 1 to 9 days in duration. Anadyr Strait transport reversed for only 2 periods ranging from 1 to 4 days. The flow reversals are caused by a strong wind blowing from the northwest (Figure 3). In most cases the reversals in Shpanberg strait (3 out of 4) were small, amounting to a maximum of -0.17 sv (i.e. southerly) transport through this strait, (Julian days 208-209, 227-229, and 235). Each of these reversals were accompanied by a reduction of the flow through Anadyr Strait and a small decline of the Bering Strait transport. Figures 14a through 18a show the velocity fields for Julian days 226 through 230. As expected from the change in transport within Shpanberg strait, the flow field between Nome and st. Lawrence Island turns southeast by Julian day 229 (Figure 17a). As the winds turn again towards the north (Figure 3) and the northward transport through Shpanberg

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strait is restored, a portion of the flow appears to flush into Norton Sound (Figure 18a). 49 Contours of the sea surface elevation also illustrate this reversal (Figures 14b-18b). Julian Day 226 (Figure 14b) illustrates a typical set up of water along the eastern side of the system. As the flow through Shpanberg strait reverses (Figures 15b-17b), the set up diminishes on the eastern coast due to a removal of water from the system. Normal conditions of northward flow through Shpanberg strait are restored by Julian day 230 (Figure 18b) with the set up of water again on the eastern side of the Chirikov Basin. To test the relative strengths of the boundary transports and the wind in determining the flow field, another case of the physical model was run in which the boundary transports for Julian day 190 (Table 1) were held constant over the simulation period, only the wind stress was varied. Holding the transport constant and allowing the wind to act on the flow had no detectable effect on the flow field south of Bering Strait; similarly north of Bering Strait, flow reversals like those of Julian day 226 230 did not occur. The constant transport through Bering Strait (1.2 Sv) was high, and the changing wind stress did not impart enough energy to turn the flow (Figures 19a-23a). However, plots of the sea surface elevation (Figures 19b 23b) indicate that the wind is at least able to pile up water along the

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A 68-6664-ANA OVA 62:SO am/ec 1 74 172 179 I TAA%T J6s--' 166 L ...,., I , , I I I WXNOS CASE OAV aae BERXNa STRAXT TRANSPORT-S .e3 Sv 164 I 8 84-ANADYIIt .,. 62BEA (m) DAY XS aae BBRXNa aTRAXT TRANaPORT Figure 14. (A) Velocity field and (B) sea surface elevation at Julian Day 226, usinq the Nome wind forcinq case. VI 0

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A 6866-64ANADY,_. 62so cm/c 174 172 179 I .. ,1'ta--166 I ... t . VELOC%TV NOME W%N09 CASE OAY aa? BTRA%T TRANSPORT-1 .01 Bv 174 B 6866-64ANAOY,_ TAAaT ...,,.,,..... eT'f'A:IT 62SEA KLEVAT%0N (m) OAV %. 227 If' TRAXT TRAN.PORT-1 .01 Figure 15. (A) Velocity field and (B) sea surface elevation at Julian Day 227, using the Nome wind forcing case.

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A l71 1 72 179 Iff\ 1 ',I I I' I 'II 66 6'1-ANADV 62 30 CM/c TAA%T VELOCITY WZNOS CABC UUL%AN OAV 228 BERXNQ aTAA%T TRANSPORT 0.38 S v --I Ia 17" 172 !78 , 'q.& 66-6 1 ANAOV ... Tf!Uioa:T 62-_ _.. e.-n (m) OAV %8 aae TRAZT TRAN.POAT 0 .38 Figure 16. (A) Velocity field and (B) sea surface elevation at Julian Day 228, usinq the Nome wind forcinq case. l11 ""

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A 171 1]2 ly1 ,', ,/ .t' "' ,/ ,/ .. r/,/_,, ,/ ... ... B 174 172 '/ 11f8 Lo:'0 // / 68 _...,.. I I "' -. ,_ ..... y ,/ I I I '-.+-+-.-.t' I I I ,_.._._,..."' tl I ,......._.._.._.__,_,, \. l.._.., / /r 6862-66,, ...... ,......_ ...... .._.._ .... .,..,, .................... ._ ..... -", ..... ................. ... ' .... ... ... ... ' .... ' .... \ ' I f I ,'1' ... .. ' I .. , f I , .. .. ' ... , f ... # ____ .... 0 ,. ,. , \ . \ ..... .... \ ........ .... I' .r ............... \ ,. ANADY.. ...... -+ .... \ \ \ \ 0 eT .. 4ZT ..._ -... -.. '1. -. \ \ I 1 ... -"'"'"'''''' ctA"l>lioo..a 1 ...,. ..._ \ \ \ \ \ 64-... .... \ l I I \ I I -'\\II II -......l.\1 J I I I 1 1 1 I I -4o I I I ,/ /J I,. \\, I I I I I (I ofJI ( I ao om/eec .T,. I I .. AZT -' + I ol I, J ,'. VKLOCXTV -NOME WXNOa CASK OAV RR8 aTAAXT TRANSPORT O .Oe a v 6664. ANADV ... ..-n .............. 62SKA .L.VATXON (m) DAV za 228 j aT"AXT 0,08 Figure 17. (A) Velocity field and (B) sea surface elevation at Julian Day 229, usinq theNome-wind forcinq case. U1 w

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A 174 68172 178 I 6\ l 1)9 166 _, I I T ., J J , " .. r r .... --' lf4 q tp 17' lf' 8 l_0.1/ 68-66-66tt:'t . 64-ANADV .. -62-o om/a .... eT .. AZT VBL..OCl:TY NOMI!! Wl:NDS CASE DAY 230 SI!!Al:NQ e+AAl:T TRANSPORT 0 .83 Sv 61--.,....,._. IJTIII'A8T 62BIEA BL..IEVATl:ON (m) DAY l:S aao eAXNG STAAl:T TAANSPOAT o.ae Figure 18. (A) Velocity field and (B) sea surface elevation at Julian Day 230, usinq the Nome wind forcing case. l11

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A '7" tp ,.,. 686664-ANAOV ... -62ao oM/c vLOCXTV NOME WXND& DAY aae eRXN. aTAAXT TRANSPORT 'ta -'r I I I 'f" OP' NAL.a CONSTANT e c sv B '7" '72 '"t-L_../ 68 6664-...........,....,... ... ............ 62aKA L.VATXON OAV xa RRB eAXNg aTAAXT TAANaPOAT Figure 19. (A) Velocity field and (B) sea surface elevation at Julian Day 226, using the wind forcing case and a constant boundary transport of sv. U1 U1

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A 68-66171 tp 171 . .a_r; ,. )'J.,f\'f.CQAI'C 't, r' '/t1f' /"" ', t r' ,, '. . . M -"'"lt'7 t t \ ....... o ,. ... \ f ' v ... T .. AZ _. f f ... ......... ---''''''' ......... ... ,,, ... __ ,,,,, .. "" I t t t I t t I I I t f \ I t I ,._,,,. .... ... , If' ........... I t f 1 , I f f t ,,,,* ... eT .. A.ZT I 171 B 68-66-64-.... ............ .... 62ao 0 t ....... 0 .............. , 62V.LOCXTY W%N08 DAY RR? 8TRA% T TRAN8PORT S R Figure 20. (A) Velocity field and (B) at Julian Day 227, using the Nome wind constant boundary transport of 1.2 Sv. eA L.VATXON (MI CON8TAN DAY %8 88? eRXNG s a sea surface elevation forcing case and a VI 0\

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A IJ2 6866-M -ANAOV.. --re :: r .. , , , ' t , 'I -' , _., .. t , ','_,,. , , , ' I If' B 68-66-M-.............,. eTfi!I4T rz .................. aT .,.. .... .._ .._ eT .. AZT ',' t 62 ao o/o 62vLOCXTV NOM. WXN08 DAY aae TRAN8PORT a ... Figure 21. (A) Velocity field and (B) at Julian Day 228, usinq tne.Nome wind constant boundary transport of Sv. eA LVATXON (.,) DAY %8 aae A 8 sea surface elevation forcinq case and a lf& 1ft U1 ....,J

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A 17i 686664-AHAOV ... ..... 1]2 1]1 .... ,Ilia Ill& I... ... T-. .,. 174 tp lJI lf' 8 6866OP' ... ...._ 64-__....,_. _.......rr 62-ao CIM/eec 62NOM WXND. DAY aao "XNG eT"AXT TAANePOAT-S a ::NOTAN(. 0 Figure 22. (A) Velocity field and (B) at Julian Day 229, using the Noe wind constant boundary transport of 1.2 sv. (M) DAY x aae & sea surface elevation forcing case and a lf& .,. l11 (X)

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A 17 1 ]2 .1,. 1.,. lfi B 17'' 1]2 1.,. 1ft 68-11866-66&t&t-ANAD\'111 -, 62-T"AZT o a/eeo ' 62-.... .__.. ........ rY vLOCXTY NOM. WXNDe DAY eao "xN eT"AXT TAANePO"T Figure 23. (A) Velocity field and (B) at Julian Day 230, using wind constant boundary transport of 1.2 sv. eKA L.VATXON () DAY xe eao "XNe .T"AXT T"AN.PO"T sea surface elevation forcing case and a 196 1fi U1 \D

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60 coast during the period when currents reversed in the Nome Wind case (Julian day 226230). When the weaker timedependant transport is used as the boundary transport, the effects of the wind stress are more obvious during this period (Figures 14-18); the following results utilize the time-dependant boundary condition of transport. These current reversals may have an effect on the productivity of the system, especially further downstream of the Chirikov Basin. Slowing of the transport through the Bering Strait will increase the residence time of nutrientrich Anadyr water in the Chirikov Basin, thus reducing the delivery of nutrients to the Chukchi Sea. A substantial decrease in the productivity north of Bering Strait may result as the residence time of water in the Chirikov Basin south of Bering Strait increases and sedimentation of the overlying productivity increases.

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61 2.5 Discussion Several authors have modelled the transports of the northern Bering and southern Chukchi Seas (Walsh and Dieterle, 1986; Overland and Roach, 1987; and Spaulding, Isaji, Mendelsohn and Turner, 1987). Each employs similar 2-dimensional, barotropic solutions to estimate the flow fields and transports within Bering strait. overland and Roach (1987) found that in the presence of the winter seasonal wind stress from the northeast, a reduced sea level difference between the Pacific and Arctic Oceans of 0.4 meters produced a northward transport through Bering strait in good agreement with hydrographic estimates. Their results also indicate that the maximum flow through Bering Strait is geostrophically limited, not frictionally or inertially. Spaulding et al., (1987) employed a similar model to examine the influence of open boundary conditions, grid sizes, bottom friction coefficients, and wind forcing on current meter and sea level observations for both a winter and summer case. Both of these investigations point to the sea level difference between the Pacific and Arctic Oceans as the principal driving force of the northward transport, and that the variability of the wind stress in and around Bering Strait causes variations in the transport.

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Walsh and Dieterle (1986) constructed another depth integrated barotropic model and specified, for three different runs, the transport at Anadyr and Shpanberg Straits to be 0.6, 1.2, and 1.8 sv. Their purpose was to examine the effects of varying transports on the distributions of nutrients and chlorophyll in the Chirkov Basin under several environmental conditions. 62 Another approach was taken by Nihoul et (1986) in which a fully 3-dimensional baroclinic model was constructed to determine whether the residual circulation pattern in the northern Bering Sea were compatible with the observed nutrient and chlorophyll distributions. The velocity and buoyancy patterns obtained with a 1.8 sv transport through Bering Strait were visually compared to a six year composite of chlorophyll distribution in surface waters. The complexity of this model requires the use of a supercomputer (i.e. a Cray) to handle the calculations and is thus not available for most investigations. The model presented here, like the others, gives a representative picture of the gross features of the flow. Overland and Roach (1987) and Spaulding, et al., (1987) obtain a similar flow field as the present model, and northsouth sea level differences across Bering Strait are of the same approximate magnitude (0.3 0 4 meters) for each of the models. The more complex representation of Nihoul gt

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(1986) also gives a similar flow field, when similar cases of transport are compared. 63 transport through Bering Strait is directly related to the along axis (N-S) sea level difference (OVerland and Roach, 1987). Maximum transport through Bering Strait is geostrophically controlled (Overland and Roach, 1987) and as such, the results of the numerical model at Bering Strait must meet the transport criterion of Toulany and Garrett (1984). The limit of the transport for geostrophic control is given by (21) Q = g e H f-1 where Q is the maximum transport through the strait for geostrophic balance, e is the along-strait sea level difference, f is the Coriolis parameter (equation 4), and H is the average depth of the water column in the strait, (Toulany and Garrett, 1984). In Figure 24a and 24b for Julian day 241, when the maximum transport through Bering Strait during the simulation period was obtained, the velocity vectors and the lines of iso-height are nearly parallel through the strait, and in general parallel one another throughout the model domain. Maximum sea level elevation is on the southeastern side and minimum sea level elevation is on the northwestern side of the strait. This along-strait sea level difference of 0.5 meters (Figure 24b) and an average depth of 50 meters

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A 171 172 171 68-66 64-ANAOV'Iil 62eT"AXT o cm/eec VSLOCXTY NOM. W%NDB CABS DAY BSRXNm 8TAA%T TRAN8PORT-S ,78 8 v ........... B T 1'{2 171 6866-61- .,. ..... .,. ... .T .... aT 62BEA CLEVAT%0N ( m ) DAY xa BER%Nm aTAA%T TRANaPOAT S ,78 Figure 24 (A) Velocity field and (B) sea surface elevation at Julian Day 241, usinq theNome wind forcinq case. 1fj6 11(1 -0\

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65 sets the geostrophic limit of transport of 1.9 sv, only slightly greater than the 1.79 sv calculated from the numerical model. Thus Bering Strait transport calculated by the model appears to be geostrophically controlled and the principal driving mechanism of the transport is the sea level difference between the north and south basins (Toulany and Garrett, 1984; Overland and Roach, 1987). There is a normal distribution pattern of water mass properties (i.e. salinity and temperature) as a consequence of the usual northward advection within the Chirkov Basin (Coachman, unpublished). As seen in Figure (9), and again in Figures (24a) and (24b), the vectors of the general northward flow tend to follow the bathymetric contours through at least Bering Strait. Water mass properties tend to stay with this flow pattern such that the three water masses and their different nutrient contents exhibit lateral banding across the system, with no isopleths oriented in the east-west direction (Coachman, unpublished; Coachman, et gl., 1975). However, major changes of transport through the system can take place very rapidly, from 1 Sv northward to more than 2 Sv southward, in as short a time as 2 days (Coachman and Aagaard, 1981). These transport changes will lead to variations in the relative amounts of each water mass entering and/or leaving the system. If these transport changes are out of phase through the three straits, large

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66 scale displacements in the water mass boundaries would be expected. Such a change was evident in both the real system (Coachman, 1989) and in the numerical model (Table 1). Lateral displacement of water masses may be important to the biological behavior of the system, since changes in residence time of nutrients and chlorophyll within the Chirikov Basin will result. More importantly, however, is the aliasing of validation data by introduction of east-west variance, which the model is unable to resolve. Similarly, bottom resuspension of particles by current reversals is another complex property of the real world (Walsh, 1989), unresolved by the present physical model.

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67 3.0 BIOLOGICAL SUBMODEL 3.1 Background The biological submodel was constructed to simulate the flows of carbon and nitrogen through the major pathways of the food webs in the northern Bering and Chukchi Seas. The model balances the physical supply of nitrogen with the biological uptake andjor regeneration by phytoplankton, zooplankton and the benthos. Figure 25 shows the pathways for nitrogen considered in the model. Nitrogen assumes only two forms, nitrate and ammonium. Urea, a significant excretory product of zooplankton and macrofauna, is considered as ammonium and no mechanism is included for nitrification, such that the only form of regenerated nitrogen is ammonium. Zooplankton which are mostly copepods in this system, are explicitly represented only as grazers. Higher order consumers are neglected, except for a general predatory loss of herbivores. The benthic component includes all processes, in or on the sediments, which consume particulate nitrogen (carbon) and excrete ammonium. The various aspects of these designations and the assumptions of the groupings of these biological components, as they relate to the Bering and Chukchi Seas ecosystem, are described in detail within the following sections.

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68 PATHWAYS OF SUBMODEL NITRATE \ 17 CHLOROPHYLL \l7 AMMONIUM ZOOPLANKTON /'1 I'J L BENTHOS l/1 N \ l7 EXPORT Figure 25. Pathways of material accounted for in the state equations of the biological submodel.

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3.1.1. Nutrients and Chlorophyll Within the southeastern Bering Sea significant annual variation in the stocks of inorganic nitrogen occurs. 69 During winter, a vertically homogeneous content of -15 at N03 1-1 is present at the 60 meter isobath (Whitledge, gt 1986). By June, low concentrations of< 1.0 N03 1-1 extend to 2 5 meters depth where they are separated from the deeper waters by a strong pycnocline (Whitledge, et 1986). The depletion of nitrate in the upper layers is attributable to an extensive spring bloom across the shelf, amounting to approximately 170 g c m 2 yr1 of annual productivity. The spring bloom of the southeastern Bering Sea begins by about April over most of the shelf area, depending on the severity of the previous winter. Surface chlorophyll concentrations increase from < 2.0 mg m-3 to greater than 20.0 mg m 3 during the bloom (Smith and Vidal, 1984), and depth integrated stocks of chlorophyll can range from -25 mg m 2 to >400 mg m 2 over the shelf (Hansell, et al., 1989). Chlorophyll stocks continue to increase, until nutrient depletion occurs in the upper mixed layer; usually by June. Following the spring bloom, ammonium is produced in the bottom layers. This is most significant in the middle shelf domain where a maximum of 15 NH4 1-1 has been measured. The source of the ammonium is phytoplankton decomposition

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and benthic biological processes as evidenced by increases in urea 1986) 70 The seasonal depletion of nitrate by June in the southeastern Bering Sea is in sharp contrast to > 20 N03 1"1 in near-bottom water found along the Siberian Coast in the northern Bering Sea during late August (Walsh, t 1989). In fact high concentrations of nitrate> 500 m"2 are found in Anadyr Water along the Siberian Coast throughout the summer (Whitledge, ISHTAR 1985 Progress Report). Farther to the northeast in the Beaufort Sea, water column stocks of nitrate are consistently low, only 1-2 N03 1"1 were found under the ice at the 60 meter isobath in April 1987, and undetectable amounts occur in October (Aagaard, gt Al., 1988). In contrast, high nitrate concentrations (10 -15 N03 1"1 ) were found during August 1963 in the East Siberian Sea (Codispoti, 1965; Codispoti and Richards, 1968). Source nitrate, in concentrations of 25-30 N03 1"1 (Figure 26) is continuously transported onto the shelf upstream of Cape Navarin, where the Bering Slope current bifurcates (Hufford and Husby, 1970). By August, within most of the Gulf of Anadyr, nutrient uptake in the overlying, stratified water column has lead to depleted conditions at the surface of <1.0 N03 1"1 Near-bottom ammonium concentrations (the residual products of prior biological activity) in the Gulf of Anadyr

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Figure 26. The (A} surface and (B) near-bottom distributions of nitrate in August 1988 (from A1. 1989} -..J

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72 and south of St. Lawrence Island (Figure 27) can be >4 NH4 1-1 in August. More than 2 NH4 1-1 may at times be advected through Anadyr Strait. In contrast, nitrate concentrations appear to be barely utilized within Anadyr Strait and along the Siberian coast (Figure 26). High surface concentrations of nitrate are maintained by continuous upwelling and vertical mixing within Anadyr Strait to maintain >25 N03 1-1 in surface waters found within Anadyr water in the Chirikov Basin 1989). Evidence of this upwelling and mixing can be found in remote sensing imagery. A 1985 AVHRR image from August 3 (Figure 28) shows a surface temperature gradient away from Cape Chukotskiy from 3 to 8"C. Furthermore, a 1980 CZCS image of Figure 29 (Muller-Karger, unpublished), and a composite of surface chlorophyll distributions over the summer periods of 1978 through 1988 (Figure 30) show pigment concentrations increasing, from <0.5 to >10.0 Chl 1-1 away from the Cape. These spatial gradients of temperature and algal biomass are typical of upwelling regions (Walsh, 1988). Approximately 95% of the northward input of nitrate to the northern Bering/Chukchi Sea is injected through Anadyr Strait. .The mean cross-strait, depth integral of nitrate in Anadyr strait in July -September 1985 ranged from 467 to 610 mg-at N03 m-2 whereas an order of magnitude less input

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G.> & - I 0 & . '-. "r? A Ito" 17&" 110" 110" Figure 27. The (A) surface and (B) near-bottom distributions of ammonium in August 1988 (from gl., 1989) -..J w

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ISHTAR 1986 sr Figure 28. AVHRR derived surface temperature on August 3, 1985.

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67 63 JULY 18, 1980 185 ,, I .. '\ I .,. ____ _, ..... ""-...J 189 193 Figure 29. Chlorophyll concentrations on July 18, 1980 as seen by the Coastal Zone Color scanner, (from F. MullerKarqer, USF). ....... Ul

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-175E --... --, '2o0 "'... ( ... 76 Figure 30. A chlorophyll (JJ9 1"1 ) composite of the surface distribution of phytoplankton biomass within the Bering/Chukchi Seas during June-August 1978-88, (from Walsh et u., 1989)

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77 (40-70 mg-at N03m-2 ) was measured across Shpanberg Strait (Walsh, et al., 1989). Maximum flow of the Yukon river occurs in June when -1.5 x 105 m3 sec-1 is discharged into the eastern end of_Shpanberg Strait. This discharge rate is comparable to the annual mean rate of the Mississippi River (Livingston, 1963). Nitrate concentrations within the river are -10 N03 1-1 gl., 1989). However, barely 1.0 N03 1-1 of this nitrate signal is detectable in Alaskan Coastal water (Whitledge, ISHTAR 1985 Progress Report). There is thus a distinct east-west gradient of nitrate across both Anadyr and Shpanberg straits. Bottom nitrate concentrations range from 31.5 N03 1 -1 on the western side of Anadyr Strait to 6.5 N03 1-1 on its eastern side. Nitrate concentrations in Shpanberg Strait are generally uniformly low, usually about 1. o N03 1-1 (Whitledge, ISHTAR 1985 Progress Report). Periodically, Anadyr Water flows around the south side of st. Lawrence Island, bringing elevated concentrations of nitrate into the western side of Shpanberg Strait. The mean depth integrals of chlorophyll across both Anadyr and Shpanberg straits are similar during this same period, respectively -55 mg Chl m-2 and 49 mg Chl m-2 Fluorometer records and ship transects indicate that chlorophyll seed stocks enter both straits near st. Lawrence

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Island within near-bottom water. Concentrations of 2-4 Chl 1"1 are measured at times in these regions. 78 North of St. Lawrence Island, high algal biomass is concentrated in two areas of the Bering and Chukchi Seas, along the date line, extending west in the Chukchi Sea, and in the central area of the Chirikov Basin (Figure 29). These pools of high chlorophyll are connected by a narrow band which may represent convergence of water masses in Bering Strait. The apparent high chlorophyll concentrations depicted in Norton Sound (Figure 29) may reflect the influence of suspended organic matter of Yukon River origin. Depth integrated stocks of chlorophyll can reach 900 mg Chl m"2 within these areas, ten-fold the inputs through Anadyr and Shpanberg Straits. Outside these regions, the chlorophyll stocks are more typically 10 200 mg Chl m Within the areas of high algal biomass, photosynthetic carbon uptake ranges from 1 -16 g c m"2 day1 compared to usually less than 1. 0 g C m"2 day1 outside these regions (Springer, et 1989a). The phytoplankton community within the high biomass regions consists mainly of diatoms, dominated by Chaetoceros spp. and Thalassiosira spp Flagellates numerically dominate the lower biomass areas (Springer and McRoy, ISHTAR 1985 Progress Report).

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79 3.1.2. Zooplankton The relative distribution and abundances of zooplankton in the northern Bering Sea are related to the origins of the water masses and to the timing and magnitude of the spring bloom in the southeastern Bering Sea (Walsh t 1989). The outer shelf and slope of the southeast Bering Sea is dominated by the large-sized copepods Neocalanus plumchris, H cristatus, Metridia pacifica and Eucalanus bungii, along with various euphausiids, accounting for up to 95% of the biomass. The inner shelf is also dominated by euphausiids and copepods (Thysanoessa raschi and Calanus marshalae), accounting for about 90% of the biomass in this region (Vidal Smith, 1986). These species are advected north along with their respective water masses and enter the northern Bering Sea through Anadyr and Shpanberg Straits as previously described. In addition to this distinct separation of zooplankton groups associated with the water masses, it appears that the biomass of copepods entering these straits in 1985 is comparable to those populations in the southeastern Bering Sea. For example, Springer, et al., (1989b), found 9.0 g m-2 (dry weight) within Anadyr strait during July 1985. This value is comparable to peaks of 9.9 g m-2 found in June -August 1956-1970 by Ikeda and Motoda (1978). Cooney (1981) reported a standing stock of 8.4 g m-2 in May -June 1975-1979 (Cooney, 1981), while Vidal and Smith (1986) found

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80 10-14 g m2 in April 1980 over the continental slope and outer shelf region of the southeastern Bering sea. In the western section of Shpanberg.Strait (1989) reported values of 5.2 g m"2 in 1985, which is similar to the 3.9 g m2 measured during May-June by Cooney, (1981) and 2.5 6.0 g m2 during June-August by Motoda and Minoda (1974) in the middle domain of the southeastern Bering Sea. The only reported zooplankton biomass measurement in the coastal domain of the southeastern Bering Sea was by Cooney (1981) and showed a value of 1.3 g m2 {Cooney, 1981) which is comparable to 1.5 g m2 reported by Springer, et al., (1989b) for Alaskan Coastal Water. It is evident that the southern populations of zooplankton at least maintain their standing stocks, persisting in the same water masses advected from the south to the north. It is thus assumed that the metabolic parameters of these same species, such as ingestion and excretion rates measured in the southeastern Bering Sea {Dagg, et al., 1982), may be used in the model of the northern Bering/Chukchi Seas. There is a general decline of zooplankton biomass throughout the summer months in all three water mass types entering the two Straits. in the model area. Within Anadyr Strait, biomass declines from -5.0 g m2 in mid summer to 2.0 g m2 by early fall, while within Shpanberg Strait biomass declines from 1.0 g m2 to 0.2 0.5 g m2 during the

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81 same period AI., 1989b). Calculated fluxes of zooplankton, from the transport and biomass estimates, through Anadyr Strait decline from 45 x 109 g day1 in July, 9 _, 1985 to -11 x 10 g day by September of the same year. Some portion of this decline in zooplankton flux is attributable to a general decline in water transport through the Strait, but there is also a decline in standing stocks. Zooplankton fluxes in Shpanberg Strait showed less change over the summer. The seasonal patterns of zooplankton abundance reflect the prior changes in copepod abundance on the southeastern Bering Shelf. Mid-May peaks of zooplankton within the southeast Bering Sea (Cooney, 1981; Smith and Vidal, 1986) would propagate to Anadyr Strait in about 6 weeks based on an average transit time of this water mass (Springer, et al., 1989b). This is about early July, a time when the peaks of zooplankton biomass in Anadyr Strait are seen. The trophic importance of zooplankton may differ between the southeastern and northern Bering Sea ecosystems. The outer shelf of the southeastern Bering Sea supports one of the world's largest single-species fisheries for Theragra chalcogramma, Walleye pollack, with mean landings of well over 1 million metric tons annually (Smith, 1981; Bakkala, et al., 1981). They and planktivorous whales feed in this area on euphausiids and copepods (Frost and Lowry, 1981; Nasu, 1974). Thus, zooplankton are an important link in the

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transfer of primary production to apex predators in the southeastern Bering Sea. 82 In contrast to these fisheries of the southeastern Bering Sea, the northern Bering Sea appear to lack large pollack populations, as evidenced by the absence of any commercial fishing ventures. Large numbers of migrating gray whales, however, feed within the northern Bering/Chukchi Seas each summer. These whales are a dominant predator of the rich benthic amphipod communities of the area AI, 1977; Nerini, 1984}. The immense populations of planktivorous seabirds, particularly least and crested Auklets, depend on copepods, primarily Neocalanus, and euphausiids (Thysanoessa 2RR} for their diet (Bedard, 1969; Springer and Roseneau, 1985}, but their impact on the standing stock of zooplankton around St. Lawrence Island is extremely small, removing < 2.0% of the biomass (Springer and Roseneau, 1985}. Assuming a similar ingestion rate for these copepods as that measured within the southeastern Bering Sea (Cooney and Coyle, 1982; Dagg, et al., 1982), an algal grazing loss of only o. 3 g c m"2 day"1 might be expected. This is a small (-15%} portion of the mean daily productivity of -2.0 g c m2 day"1 within Anadyr waters. Thus, algal growth usually outstrips losses of grazing to the zooplankton. However the return of nitrogen to the water column by zooplankton excretion is important for downstream productivity.

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83 The fate of zooplankton north of Bering Strait in the Chukchi Sea is most likely death, with little reproductive success. Springer et al., {1989), estimate that 1.8 x 1012 g c of zooplankton could be exported to the Chukchi Sea and into the Arctic Basin during the summer of 1985. Johnson {1963) collected a few Neocalanus and Acartia at 75 N, but suggests that these are expatriots and most of the Bering Sea zooplankton have.died. Some Acartia and Oithona have been found in the Beaufort Sea along the coast. These species can survive and reproduce here, but do not penetrate further into the Arctic Ocean, nor do they penetrate into the Atlantic Ocean {Johnson, 1958; and 1963). It is probable that zooplankton are advected into the northern Chukchi Sea, and reside there for a brief period, during which they are either consumed, or expire, and fall to the bottom. None of the species present in ISHTAR catches were described by Hopkins {1969a, and 1969b) in the Arctic Ocean. 3.1.3. Benthos The organic carbon content of the surface sediments, the species composition, and the biomass of the macrobenthos in the Bering and Chukchi Seas have all remained unchanged for at least the previous 10 -15 years {Stoker, 1981; Grebmeier, et al., 1988). The benthic macrofauna appear to be stable and very diverse. Although faunal assemblages are strongly correlated with substrate type, it appears that

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84 this relationship is not causal. It merely reflects other environmental conditions, which dictate faunal and sediment distributions (Stoker, 1981). Generally, benthic diversity and standing stock increase from the southern Bering Sea north into the Chukchi sea. Mean standing stocks range from 3 g C m2 at 60. N to 23 g c m2 at 68 N. Predation appears to be the controlling mechanism in regulating benthic standing stocks of the southeastern Bering Sea. On the northern Bering and Chukchi sea shelves, physical and environmental variables, which regulate food supply to the region, are the controlling mechanism of standing stocks, although cetaceans may influence the abundance of benthic stocks in some areas (Grebmeier, 1988). The benthos underlying the Anadyr and Bering Shelf water masses is dominated by detritus-feeding amphipods (primarily Ampeliscidae and Isaeidae) and bivalves (primarily Nuculidae and Tellinidae). The largest reported macrofauna! standing stocks in the Bering Sea were 905 g wet wt -2 m found in the Chirikov Basin north of st. Lawrence Island (Alton, 1974), 482-631 g m2 in the northern Bering Sea and 635-673 g m2 in Bering strait (Stoker, 1978; Feder, et al., 1985; Grebmeier, 1987). In the southern Chukchi Sea, benthic standing stocks are also high, 465-591 g m-2 (Stoker, 1978; and Grebmeier, 1987). Considerably lower benthic standing stocks (55-482 g m-2 ) are found in regions

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85 underlying Alaskan Coastal water of both the northern Bering and the southern Chukchi Seas (Stoker, 1978; Feder, et al., 1985; Grebmeier, 1987). Mean benthic biomass expressed as carbon varied from.6.3 g C m-2 under Alaskan Coastal Water to 21. 5 g c m-2 under Anadyr Water Al., 1989) Generally, there are two distinct regions of high benthic biomass within the study area; one in the center of the Chirikov Basin, and the other approximately 100 kilometers north of the Bering Strait on the south Chukchi Sea shelf. Each of these biomass centers receive large inputs of detrital matter from the overlying water column. However, the characteristics of the benthos and the remineralization of organic matter within these depocenters differ. South of Bering Strait, the sediment has a very high sand content (>70% dw) and a low organic carbon content of 0.2-0.4% dw (Figure 31), while north of Bering Strait lower sand (>60% dw) and higher carbon (1.5 2.0 % dw) are found gl., 1989). The lack of accumulation of organic matter south of Bering Strait is due to the large bottom friction velocities of 1-2 m sec-1 high macrobenthic biomass, and a large remineralization rate by the macrofauna (50-60% of the total). North of the strait, where currents decrease in velocity, there is a greater dependency on microbial remineralization processes; only 5-50% of the remineralization is by macrofauna.

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86 In addition to a latitudinal gradient of benthic processes, east -west gradients of biomass and sediment organic matter are related to the overlying water masses. Mean benthic biomass under Alaskan Coastal water is only 6.3 g C m"2 for example, whereas under Bering Shelf/Anadyr water, the mean biomass is g c m"2 (Grebmeier, 1988). High C/N ratios of particulate matter within surface sediments under Alaskan Coastal water indicate the influence of riverine input of organic matter to the benthic community (Parker and Scanlan, 1987;.Grebmeier, et gl., 1988). Low C/N ratios in the surface sediments underneath Anadyr and Bering Shelf waters indicate that more nitrogen-rich food of planktonic origin reaches the benthos, supporting a more direct coupling between the benthos.and the overlying water column productivity (Grebmeier, et al., 1988). The release of recycled nitrogen from these sediments of the Chirikov Basin provides partial support for the continuing bloom of chlorophyll downstream in the Chukchi Sea. This will be evident in the results of.the model.

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87 Figure 31. The distribution of composite organic carbon (% dw) within surficial sediments in the Bering/Chukchi seas (from Walsh, et al., 1989)

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88 3.2 Biological Equations The biological submodel simulates the flows of nitrogen and carbon through the northern Bering and southern Chukchi Sea ecosystem. Figure 25 illustrates the major pathways of carbon and nitrogen between the components of the ecosystem that are considered. The flow of elements along these pathways can be quantified using a series of partial differential equations to describe the temporal and spatial distribution of the pools of nitrate, ammonium, chlorophyll, zooplankton, and benthos. The nitrogen equations consider just two forms of nitrogen; nitrate and ammonium. The state equations for nitrate and ammonium balance the inputs of dissolved nitrate and ammonium with the uptake of these nutrients by phytoplankton, and subsequent transfer in particulate form to the zooplankton and the benthos. An estimate of the amount of the unutilized nutrients available for export to the Arctic Ocean is also considered. The carbon budget is coupled to the nitrogen budget by using a conversion ratio of particulate carbon to nitrogen for the phytoplankton, zooplankton and the benthos. Phytoplankton are assumed to include all algae containing chlorophyll g, such that a comparison can be made between the time-dependant model results and several time series of

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89 moored fluorometer records obtained during the 1985 field season. The zooplankton component of the model explicitly considers only herbivores, mostly calanoid copepods and the appendicularian Oikopleura. Sagitta. an abundant member of the zooplankton community is the dominant consumer of copepods, and its trophic impact is implicitly considered as a predatory loss in equation (25) in the model. The benthos compartment concerns the storage, regeneration, and efflux of nitrogen from the faunal community of the sediments as a whole, with no partitioning among these processes. Only thebiological regeneration of nutrients from the macrofauna is considered, since no attempt was made to quantify nitrification by anaerobic bacteria. Phytoplankton were incorporated into the benthic compartment through sinking losses, while ammonification led to the only nitrogen product returned to the water column. In a similar way, zooplankton fecal material was added to the benthos, and ammonium was returned to the water.column after remineralization. A series of partial differential equations describe the temporal-spatial in the pools of nitrate, ammonium, chlorophyll, zooplankton and benthos. The state equation for nitrate N03 1-1 ) is,

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2 2 c5NO c5NO c5NO c5 NO c5 NO 3 3 3 3 3 (22) = -u -v --+ K + K X 2 y 2 eSt c5x c5y c5x c5y 2 c5 NO 3 + K a1P N z 2 c5z Similarly, the state equation for ammonium (J.'g-at NH4 1"1 ) is, (23) The (24) c5NH 3 = -u eSt + K z 2 c5NH c5NH c5NH 3 3 -v --+ K c5x 2 c5NH 3 2 c5z X 2 c5y c5x -a 1 1 P + ez +rB N 2 c5NH 3 3 + K y 2 c5y chlorophyll distribution (1-'9 Chl 1"1) is described by, c5P = eSt 2 c5P c5P c5 p -u -v -+ K + K X 2 y c5x c5y c5x 2 c5 p c5P + K + abP -gZ -s z 2 c5z c5z 2 c5 p 2 c5y 90

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For zooplankton (J,g c 1-1) the state equation is 2 2 cSZ cSZ cSZ cS z cS z (25) = -u -v + K --+ K X 2 y 2 eSt cSx cSy cSx cSy + gZ -ez -GZ -fZ. And finally for the benthos ( J,g c 1-, ) cSB cSP (26) = s -rB + fZ. eSt cSz The first four terms on the right hand side of equations (22) -(24) express the horizontal advection and diffusion of the state variables, where u and v are the time-dependant barotropic velocities obtained from the 91 physical sub-model, i.e. u=U/H and v=V/H. and Ky are the horizontal numerical diffusion coefficients in the x and y direction, respectively, and are implicit in the model. The fifth term is vertical diffusion; Kz is the vertical mixing coefficient in the z direction and is derived from the local wind stress (Csanady, 1976), at the surface such that, (27) K = z 200 f

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where Fx and FY are the x and y components of the wind stress, obtained from equations (7) and (8), and f is the Coriolis parameter from equation (4). 92 The terms a'PN and a''PN in equations (22) and (23) describe the uptake of nitrate and ammonium as a function of phytoplankton biomass in terms of particulate nitrogen -a more detailed description follows in Equations (31) -(37). eZ is the rate of excretion of ammonium by zooplankton which is a function of zooplankton biomass. r is the regeneration rate of phytoplankton nitrogen from the benthos. Three cases of r were run where 0%, 50%, and 80% day-1 of the benthic nitrogen (B) is returned to the water column as ammonium. r is essentially an indicator of trophic efficiency which regards the biological elements of the benthos as grazers of phytoplankton. No further trophic transfer within the benthos is parameterized. The term abP (equation 24) describes the growth of phytoplankton in units of mg chlorophyll m-3 day-1 where b is the conversion ratio of particulate nitrogen to chlorophyll, and a is defined in equation (31). gZ is the grazing loss of phytoplankton biomass expressed as a function only of zooplankton biomass, while GZ is the predatory loss of the herbivores. The last term of equation (24) quantifies the sinking loss of phytoplankton. Advection of each of the state variables was accomplished using a second upstream differencing method or

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93 "donor cell" method (Roach, 1982). For the simplest case of advection of a dissolved substance (N) in the horizontal directions, with no gain or loss terms (i.e. conservative), t+1 (28) N t = N [ u N RR t--u N L L X v N u u -v N ] D D y where u and v are the barotropic velocities, x and y are the horizontal distances and t is the time step. The barotropic velocities, u and v obtained from the physical submodel, are the same for all layers (k=1 to 10) of the biological model, i.e., no vertical shear of the flow field. The numerical technique involved in the advection calculation incorporates diffusion implicitly in the calculation sequence. This is accomplished by assuming that at the end of each time step, the pools of nitrate, ammonium, chlorophyll and zooplankton in each box become well mixed. Thus, no gradients in the state variables are maintained within individual boxes. Thus, and are implicit in the model, and in equations (22)-(25). In a way similar to advection, the vertical mixing flux of a dissolved substance (N) can be calculated as, t t t l t K N + N -2N t+1 t z i,j,k+1 i, j, k-1 i, j ,k (29) N = N + i, j ,k i, j, k z z

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94 where is the mixing coefficient. calculated from equation (27), z is the thickness of the layer at each grid point, and t is the time step of mixing. In order to test the importance of vertical mixingin the model, one simulation was run in which there was no mixing, i.e. K1 = o. Sinking of a particle, in this case phytoplankton cells and zooplankton fecal material, is calculated using another finite difference expression such that for any particle (N), (30) t+l t t s N = N + ----i,j,k i,j,k z t ] -N i,j,k where t is the time step, z is the thickness of the layer at that grid point, and s is the sinking rate of the particle. Three values of the sinking rate of phytoplankton were tested in the model (1.0, 2.5, and 5.0 m day-1). In equations (25) and (26) the term fZ accounts for the fecal pellet production as a function of zooplankton biomass. Fecal pellets are allowed to sink to the benthos at a rate of 50 m day1 Phytoplankton and fecal pellets become an undistinguished pool of organic material on the bottom, such that the entire pool of organic material becomes available to be recycled into ammonium. The losses of dissolved nitrate and ammonium (equations (22) and (23)) are coupled to the growth of phytoplankton (equation (24)) such that,

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95 (31) abP = a'P11 +a' 'P11 where a' is the nitrate-based growth rate, and a'' is the ammonium-based growth rate, and a is the total growth rate. b is the conversion ratio of particulate nitrogen (P11) to chlorophyll (P) which was taken as 0.5 in the model (Walsh and Dieterle, 1986). The growth rates a, a, and a'' are expressed as a function of light, nutrient concentration, and temperature (Steele, 1962) such that, for nitrate as a substrate, I a' = J1 I::J z max I (32) sat e and for ammonium as a substrate, I z t I NH z I 3 (33) a' = J1 sat max I e t sat 1.0 + NH 3 The last bracketed in equations (32) and (33) are the Michaelis-Menton uptake of nitrate and ammonium

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96 by a cell, where N03 and NH3 are the external concentrations of each nutrient at time t, 1.5 is the half-saturation constant for nitrate (Walsh, 1975), and 1.0 is the halfsaturation constant for ammonium A!., 1969). Michaelis-Menton uptake is a kinetic model for the action of an inducible enzyme on a substrate, such that the difference in the half-saturation constants tends to favor the uptake of ammonium. The calculation of the Michaelis-Menton expressions in the model further forces the preferential uptake of ammonium by first assuming each cell will take up the maximum amount of the available ammonium to fill the nitrogen requirements of growth. The maximum nitrogen requirement of phytoplankton growth is bounded by When ammonium is depleted, the model will fill the remainder of the nitrogen requirement using the available nitrate as a substrate such that a'+ a'' will never exceed 100% of the nitrogen required for growth. in equations (32) and (33) is the maximum specific growth rate of phytoplankton, using an extension of Malone (1982) and Eppley (1972): (34) = 0.85 (100.0275T) I where T is the temperature in c. Since the model did not have temperature as a state variable, an average summer temperature (3.C) for the Bering Sea was used. in this

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97 case is 1.03 doublings day1 Two other values of (0.52 and 0.25 doublings day-1 ) were also used in the model. I1 is the instantaneous irradiance at depth z and is a function of the total irradiance at the surface (I0). The model calculates a non-spectral irradiance (I1), (35) I = I k z z 0 where The attenuation coefficient can be divided into 3 components, (1) the diffuse attenuation coefficient of pure water, (2) k8 the diffuse attenuation coefficient of coastal water (Walsh, 1988), and (3) diffuse attenuation coefficient of phytoplankton expressed as a function of the chlorophyll concentration in the overlying water, (37) kp = O. 03 Chl (Smith and Baker, 1982). Using an average concentration of 3.0 mg Chl m3 and assuming that the euphotic zone extends to the 1% light level, then the depth of the euphotic zone is -27 meters. The total irradiance at the surface I0 is calculated at each time step using a very high spectral resolution model (Gregg and Carter, submitted). The model uses the mean

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98 extraterrestrial irradiance and accounts for Rayleigh scattering, ozone absorption, aerosol scattering and absorption, and surface reflectance. The total irradiance used in the model was integrated over the photosynthetically active portion of the spectrum (400-700 nm). Local weather conditions, such as fog, were estimated with the reported visibility from Nome, Alaska obtained from the National Weather Service Local Climatological Data base; they are entered in the model at three hour intervals. Visibility was reported in whole miles and ranged from 1 to 35 miles. Such visibility is for the horizontal direction, and does not include the impact of clouds. The model does not account for cloud cover, and therefore may overestimate the irradiance at the surface. Cloud cover can be extensive in the northern Bering Sea; to estimate possible effects, three cases of incident radiation were run (100%, 75% and 50% of I0). Figure 32 presents the total surface irradiance calculated by the light model for 1985 at 15 minute intervals. The maximum daily surface incident radiation (Local Noon) decreased each day of the 'simulation, from a maximum of -23 cal cm"2 hr"1 at Julian day 190 to a minimum of -11 cal cm"2 hr"1 at Julian day 270. It is also evident from this figure that the photoperiod decreases each day as well, from 23.15 to 11.88 hours. Phytoplankton growth is only allowed when light is available.

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z Q a: (1)0 .... z w 2 15 12 10 8 5 2 0 198 206 214 222 230 238 246 254 262 270 JUUAN DAY 99 Figure 32. surface incident radiation (cal cm"2 hr"1 ) calculated (Gregg and Carder, submitted) over the simulation period and used in the biological submodel.

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100 The exponential expression of equations (32) and (33) allows photoinhibition of growth at high light intensities, such that, if Iz is greater than I88t, a' and a'' become smaller, expressing an inhibition of photosynthesis. When Iz = Isat' and nutrients are not limiting, then a' = p.1118x and a''= Malone (1977) found light inhibition during incubation experiments at intensities of 3.6 and 4.8 cal cm"2 hr"1 Thus, Isat was set to 5. 0 cal cm"2 hr"1 The daily primary production was calculated by summing the increment of growth in phytoplankton stocks at each time step and converting to carbon using a carbon/chlorophyll ratio of 45/1. The terms ez in equation (23) and gZ in equation (24) couple the grazing of zooplankton on phytoplankton with the excretion of ammonium by zooplankton (equation 25). The consumption rate of herbivorous zooplankton is assumed to be 10% of the zooplankton biomass per day, based on feeding studies in the southeastern Bering Sea (Dagg, et al., 1982). Thus, the instantaneous feeding rate of zooplankton in units of carbon is 0.10 z (38) g = t 45.0 where 1/45 is the conversion ratio of chlorophyll to carbon. Assuming an intermediate assimilation efficiency of 51% for the herbivorous zooplankton, 49% of their ingestion would be

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101 unassimilated fecal pellets which are allowed to sink at a constant rate of 50 m day1 This fecal pellet flux appears as a source term for the benthos in equation (26). Of the assimilated food, 60% is assumed to be devoted to daily maintenance nitrogen demands; re; excretion amounts to -30% of the daily ingestion, gZ. When the population is in state, 6Z/6t=O, the other 40% of the food would be assimilated each time step in the model for growth, reproduction, and predatory losses of the zooplankton population. Respiration is a carbon loss only. Observations suggest, however, that the biomass of the herbivorous zooplankton population declined over time, from 5 g dw m 2 on July 21 to 2 g dw m "2 on August 24, such that the other losses exceeded 21% of the daily ingestion, gZ. The predation loss, GZ, was described in terms of zooplankton biomass rather than grazing stress, because predators eat individual herbivores. A predation rate in % per day was calculated based on the two values of zooplankton biomass, (39) 1 G = (ln z1 -ln z0 ) (t1 t0 ) 100 where z1 and z0 are the biomass at times t1 and t0 (Vidal and Smith, 1986). With z1 = 2.0 and Z0 = 5.0, and (t1 t0 ) = 33 days, we obtain a predation rate of -2.78% day1 To allow closure on the set of equations, this loss in biomass is summed during the model run and is considered a predatory

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102 loss (i.e. zooplankton being consumed by higher predators such as birds, chaetognaths, and fish). The term GZ in equation (25) represents this trophic loss and appears as "export" in Figure 25. The last term in equations (23) and (24) and the terms of equation (26) involve both the sinking of phytoplankton and fecal pellets to the benthos and the regeneration of ammonium and its release to the water column. In this case it is assumed that, of the phytoplankton and fecal pellets that reach the benthos and are consumed, 80% of the biomass is returned to the water column as ammonium. In addition to the specification of an 80% recycling by the benthos, two other cases were tested for this parameter. In one simulation all phytoplankton and fecal pellets that entered the bottom were retained, with no regeneration of ammonium. In another case, SO% of the detritus reaching the benthos was recycled as ammonium back into the water column. Equations (22) -(26) are for 10 layers over the entire horizontal grid using a forward differencing scheme. t was set to 15 minutes for all calculations except mixing. A split time mode was used for the mixing calculations such that, (41) t max = [ /] z

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103 where tNx is the maximum time step allowed to satisfy the Von Neuman condition of numerical stability for explicit time difference algorithms, z is the minimum of depth in the model domain (1.0 meters), is calculated from equation (27). In each case a smaller t than that calculated in equation (41) was used such that a whole number of mixing steps were executed for each pass of the remainder of the calculations. The 1985 boundary values of nitrate, chlorophyll and zooplankton were obtained from 4 separate ship transects across Anadyr and Shpanberg Straits. The 1988 boundary conditions were taken from one cruise of the Akademik Korolev as part of a joint u .s.-u. s s R expedition to the Bering and Chukchi Sea. The ship data were assigned to grid points in the y and z direction across the Straits at the locations of the samples. Linear interpolation was used to assign values to grid points along the first east-west model grid line for the 1985 data set. Nitrate, ammonium and zooplankton were assumed to change linearly between sampled dates during 1985 and held constant during 1988, thus two different daily pictures of the boundary conditions at Anadyr and Shpanberg Straits were constructed. The fluorometer records from 3 instruments moored in these Straits (Figure 5), as well as chlorophyll data, were used to construct boundary conditions of the 1985 flux of chlorophyll across these interfaces. The 1985

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104 boundary values at Anadyr and Shpanberg Straits were updated each day at 0000 hrs. Initial conditions of these state variables were established by setting the boundary conditions at Julian Day 190 and advecting the nutrients, chlorophyll and zooplankton for 150 simulated days at which time the concentrations at each grid point were unchanging. Initial conditions of the benthos were set at zero (i.e. no carbon had accumulated). The daily averaged u and v yelocities for each grid point used in equations (22) -(26) were calculated separately and entered into the model at 0000 hrs of simulated time. The 10 layer biological submodel was run from July 08 (Julian Day 190) through September 26 (Julian Day 270) under the 1985 and 1988 boundary conditions. The calculations took an average of 48 hours for 81 day simulation on a DEC Microvax III computer and used approximately 8 megabytes of memory.

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105 3.3 Validation Data 3.3.1 Fluorometry Chlorophyll a time series were obtained from 7 fluorometers moored at 4 locations during the 1985 experiment, (see Figure 5). Mooring 3 in Shpanberg strait, contained one fluorometer at a depth of 21 meters, which recorded for 84 days. Mooring 7 in Anadyr Strait contained two fluorometers, one at 19 meters and the second at 40 meters which recorded for 86 days. The fluorometers on moorings 3 and 7 were used, in conjunction with ship samples at these Straits, to construct a time series of 1985 boundary chlorophyll data for the model. Chlorophyll g time series were also obtained from fluorometers moored at two locations within the interior of the model domain (Figure 5) during the 1985 experiment. These sites were sampled with 4 fluorometers, of which one failed and the data were lost. The first mooring, !08, contained two surviving fluorometers (at depths of 22 and 36 m, designated as I08t and I08b) and was situated on the International Date Line approximately 58 Km northeast of Anadyr Strait. The second mooring, !10, yielded one valid fluorometer record (at 20 m, designated I10t) at approximately 110 Km northeast of mooring !08.

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106 The in situ fluorometers used in this experiment are similar to the fluorescence sensor developed by Aiken (1981). light at 470 nm is produced by a xenon flash and detection of fluorescence at 570 nm is achieved with a photodiode sensor. The energy of each flash is also recorded for reference (Whitledge and Wirick, 1986). The sensors were burst sampled, 6 readings in 1 minute at 34 minute intervals. Obvious errors and outliers within each burst were removed during data processing. The time series were then filtered with a 2-hour low pass filter. Fluorometers were bench tested against a laboratory fluorometer before and after deployment, using the in vivo fluorescence of dilutions of a diatom culture. Calibration constants for each fluorometer were estimated from a linear regression of paired calibration measurements. When calibrated in this way, fluorometers are accurate to within 05 p.g Chl 1"1 Chlorophyll a estimates from fluorometer data were calculated by multiplying the in situ fluorescence with a conversion factor. This factor was determined from 240 water samples, collected at hydrographic stations during September and October of 1985. The in vivo fluorescence, chlorophyll g and phaeophorbide concentrations of each sample were fluorometrically determined (Lorenzen, 1966). The extracted chlorophyll a concentrations were regressed against in vivo fluorescence. Calibration of in situ

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fluorometers with this method is considered best because ambient phytoplankton have the same light and nutritional history as those measured at the mooring. 107 For comparison with the model results, daily averages of the chlorophyll time series fromfluorometers were compared against daily averages of the model results at the approximate positions and depths of the moored fluorometers. This process of averaging filters out some of the short term variability in the chlorophyll records, and allows a comparison with the model results. 3.3.2 Shipboard Observations During 1985 there were five cruises of the ELY Alpha Helix in the northern Bering and Chukchi Sea. In addition to mooring deployment and retrievals, a total of 337 hydrographic and productivity stations were occupied. Nutrients and chlorophyll samples were analyzed at each of these stations yielding a total of 3093 individual sampling points during this year. As a point of reference from a modelling perspective, the model can calculate the nutrient and chlorophyll concentrations at 3062 points in this area in approximately 20 seconds. The ISHTAR sampling protocol designated 11 stations in Shpanberg Strait, and 5 stations in Anadyr Strait for hydrographic sampling. These Anadyr Strait stations covered

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108 only the eastern half of the strait during the 1985 field experiments, because we were unable to sample the western half of this strait within Soviet waters. The entire width of Shpanberg Strait was sampled in 1985. During 1988, a joint US-USSR expedition aboard the ELY Akademik Korolov provided the only complete cross-strait coverage of Anadyr Strait. These data were also used to extrapolate the nutrient and chlorophyll conditions to the western side of Anadyr Strait during 1985. In most cases, all of the cross-strait stations were occupied consecutively, thereby providing a reasonably synoptic picture of the chlorophyll and nutrient distribution. Four cross-strait transects of chlorophyll and nutrients were used to construct the 1985 boundary data and one for 1988 in the simulation model. The remaining shipboard data were used to validate the results of the model. In order to minimize spatial and temporal aliasing when comparing the ship data to the model results, the measured depth-integrated stocks of nutrients and chlorophyll were compared to depth-integrated model results at similar times. Nutrient and chlorophyll samples were collected from rosette samples in conjunction with CTD profiles. Chlorophyll was estimated using an acetone extracted fluorescence procedure and a fluorometer calibrated against a spectrophotometer (Strickland and Parsons, 1977).

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109 were analyzed on an autoanalyzer at the time of sampling using standard methods (Whitledge, et Al., 1981). 3 .3.3 Satellite Observations A total of 4 synoptic Coastal Zone Color Scanner (CZCS) images of the Bering and Chukchi Sea were utilized to provide a comparison of the spatial pattern of nutrient supply and utilization described in the model. To obtain these images, the available CZCS data set over the first 2 years of operation (1979-1980) were examined. Of the actual satellite data examined for the Bering Sea (>100 orbits), only 6 scenes in 1979 and ten in 1980 were applicable to the ISHTAR study site; of these 4 were used for comparison to model results. Processing of the czcs data incorporated the "clear water" radiance technique and bio-optical algorithms developed by Gordon, et al., (1983a), and Gordon, et al., (1983b), using, as a baseline, the waters of both the North Pacific and deep Bering Sea to the west of the shelf break. The water-leaving radiances detected by the czcs are partially a function of the combined chlorophyll A, phaeopigments and dissolved organic matter in the upper optical depth of the water column. However the optical characteristics of nearshore waters are also influenced by

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110 suspended inorganic material from Alaskan rivers (Maynard and Clark, 1987) and coastal lagoons (Barsdate, Nebert, and McRoy, 1974). The spurious high chlorophyll concentration of Norton Sound (Figure 29) reflect such contamination. The observed horizontal surface structure of temperature over the model area was obtained from an August 3, 1985 scene constructed from data from the Advanced Very High Resolution Radiometer (AVHRR) aboard the TIROS satellite (Walsh, et Al., 1989).

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111 4.0 RESULTS OF THE COUPLED PHYSICAL/BIOLOGICAL MODEL Twelve cases of the coupled physical-biological models were run to test the sensitivity of several parameters of the model. The different values of these parameters for each case are shown in Table 3. The results of each simulation are presented below in terms of (1) horizontal fluxes, (2) productivity, (3) temporal comparisons, (4) spatial comparisons, and (5) penthic fluxes. Comparisons with moored fluorometers, satellite and ship data are made where possible. 4.1 Horizontal Fluxes Downstream fluxes of nitrate through the Bering and Chukchi Straits vary somewhat, indicating the relative differences in nitrate utilization between cases (Table 4). The greatest nitrate depletion occurred in case (3) where the maximum growth rate of equation (43) was 1.03 doublings day1 In this case, nearly 55% of the influx of nitrate was depleted before reaching Bering Strait and -10% more was utilized crossing the Chukchi Sea. The lower algal growth rates of 0.52 and 0.25 doublings day1 of cases (2) and (1) resulted in less nitrate utilization south of

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112 TABLE 3: Parameters used in different cases of the coupled physical-biological models. CASE # 1 2 3 4 5 6 7 8 9 10 11 12 MAX GROWTH RATE (doublings day-1 ) 0.25 0.52 1.02 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 0.52 FRACTION OF SURFACE RADIATION 0.75 0.75 0.75 1.00 0.50 0.75 0.75 0.75 0.75 0.75 0.75 0.75 SINKING RATE (m day-1 ) 1.0 1.0 1.0 1.0 1.0 2.5 5.0 1.0 1.0 1.0 1.0 1.0 ZOOPLANKTON BENTHIC EXCRETION REGEN. RATE RATE MIXING (percent (fraction of ingest) of flux) 30.0 0.80 ENABLED 30.0 0.80 ENABLED 30.0 0.80 ENABLED 30.0 0.80 ENABLED 30.0 0.80 ENABLED 30.0 0.80 ENABLED 30.0 0.80 ENABLED 30.0 0 .50 ENABLED 30.0 0.00 ENABLED 0.0 0 .80 ENABLED 0.0 0.00 ENABLED 30.0 0.80 DISABLED

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TABLE 4: Total fluxes of nitrate for the 81 day simulation of the coupled physical-biological model. TOTAL is the amount of nitrate entering the model domain through Anadyr and Shpanberg Straits combined. BERING is the flux across Bering Strait. CHUKCHI is the flux across "Chukchi Stfait" described in the physical sub-model. Fluxes are # X 10 IJ.g N03 m-2 81 days-1 Percent of the total influx is in parentheses. CASE # TOTAL BERING CHUKCHI 1 110.0 75.3 {68.5) 75.2 (68.4) 2 110.0 68.6 (62.4) 63.9 (58.1) 3 110.0 50.7 ( 46 .1) 40.7 (37.0) 4 110.0 64.3 (58. 5) 57.6 (52.4) 5 110.0 74.5 (67.7) 74.0 (67.3) 6 110.0 73.4 {66.7) 73.3 (66.6) 7 110.0 76.7 (69.7) 76.4 (69.5) 8 110.0 68.6 (62.4) 63.9 (58.1) 9 110.0 64.7 (58.8) 59.6 (54.2) 10 110.0 68.4 (62.2) 63.6 (57.8) 11 110.0 64.7 (58.8) 59.6 (54.2) 12 110.0 73.4 (66.7) 72.3 (65.7) 113

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114 .Bering Strait, -37% and -31%, respectively. Both of these cases indicate that further nitrate utilization by phytoplankton at lower growth rates between Bering and Chukchi Straits was small, -st for case (2) and <1% for case (1). The uptake of nitrate in the Chukchi Sea was expected to be lower, since ammonium concentrations are greater and the model favors the uptake of ammonium over nitrate. The specification of maximum growth rates had the greatest effect on nitrate utilization of all the parameters tested. Increased light availability results in greater utilization of nitrate, however the horizontal fluxes of nitrate for 100% (Case (4)) and SO% (Case (5)) of incident surface radiation, at Bering and Chukchi Straits fell between these fluxes obtained with the highest and lowest maximum growth rate cases. A reduction in the sinking rates of phytoplankton from s.o, to 2.5, to 1.0 m day"1 (cases (7), (6), and (2)) resulted in correspondingly smaller nitrate fluxes out Bering Strait and hence greater rates of utilization within the Chirikov Basin. The increased nitrate utilization is the result of a longer residence time of phytoplankton within the euphotic zone, caused by a smaller sinking rate. The corresponding mean primary production increased from 1.41 g C m"2 day1 of case (7) to 1.80 g c m-2 day1 in case (6) and 3.27 g C m"2 day"1 in case (2) (Table 7). No vertical mixing (case (12)) in the Chirikov Basin is

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115 equivalent to increasing the algal sinking rate from 1.0 to 2. 5 m day1 (Table 4) Varying the regeneration rate of ammonium by the benthos and eliminating excretion by zooplankton (comparing case (2) with cases (8), and (10)) had little effect on nitrate utilization. Cases (9) and (11), in which the benthic regeneration of ammonium was zero and the zooplankton excretion changed, results in considerably less nitrate flux through both straits, i.e. more nitrate utilization than cases (2), (8), or (10). Benthic processes are responsible for more than 90% of the ammonium regenerated in the model, the remainder comes from zooplankton. These results emphasize the importance of the benthic regeneration processes as a source of nitrogen substrate to phytoplankton, suggesting that zooplankton excretion as formulated in the model is inconsequential. The state equations of the model allow preferential utilization of ammonium, such that in its absence, more nitrate is utilized. Preferential uptake of ammonium is forced in two ways; one through the half-saturation constants in the Michaelis-Menton uptake part of equations (32) and (33), and the other in the numerical scheme, whereby phytoplankton are allowed to uptake ammonium for growth first, then uptake nitrate to achieve a growth rate of Total nitrogen utilization was never allowed to exceed 100% of

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116 The downstream fluxes of ammonium through Bering and Chukchi Straits are compared to the total boundary influx of this nutrient from Anadyr and Shpanberg Straits (Total) in Table 5. The outfluxes show an increase in each simulation except cases (9) and (11), in which respectively benthic and all 1n situ sources for ammonium to the system were eliminated. The zooplankton contribution to regenerated nitrogen sources is about 10% of that of the benthos. Decreasing the maximum growth rate of phytoplankton, decreasing availability of light, decreasing the mixing rate, increasing the algal sinking rate and increasing the benthic regeneration rate all had the effect of increasing the fluxes of unutilized ammonium through Bering and Chukchi straits. The highest ammonium fluxes occurred within the model in case (7), with a sinking rate of 5 m day1 and in case (12), where mixing was eliminated. In the latter case (12), most of the regenerated ammonium remains unutilized and in the near bottom layer, after its regeneration by the benthos. The zooplankton excretory products are dispersed over the water column. With no mixing, only horizontal transport occurs, such that the bulk of the regenerated ammonium remains below the euphotic zone sequestered in the bottom layer of the model, and unavailable to the phytoplankton for growth. Consequently,

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117 TABLE 5: Total fluxes of ammonium for the 81 day simulation of the coupled physical-biological model. TOTAL is the amount of ammonium entering the model domain through Anadyr and Shpanberg Straits combined. BERING is the flux across Bering Strait. CHUKCHI is the flux across "Chukchi Stfait" as described in the physical sub-model. Fluxes are t X 10 J.'g NH4 m -2 81 days-1 Percent of total influx is in parentheses. CASE # TOTAL BERING CHUKCHI 1 12.5 28.3 (226.4) 29.0 (232.0) 2 12.5 21.1 (168.8) 20.9 (167.2) 3 12.5 13.8 (110.4) 13.8 (110.4) 4 12.5 18.0 (144.0) 17.5 (140.0) 5 12.5 27. 3 (218.4) 27.9 (223.2) 6 12.5 34.7 (277.6) 35.4 (283.2) 7 12.5 41.4 (331.2) 41.1 (328.8) 8 12.5 21. 1 (168.8) 20. 9 (167.2) 9 12.5 4 3 (34. 4) 3.3 (26.4) 10 12.5 20.1 (160.8) 19.9 (159.2) 11 12.5 3.8 (30.4) 2.8 (22.4) 12 12.5 36.7 (293.6) 37.9 (303.2)

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118 little of the ammonium is utilized over the model domain in this case. The dominance of the fastest sinking rate of 5 m day"1 over the wind driven mixing rate acts to remove phytoplankton in the former case (7), leading to less chlorophyll in surface layers (Table 6) to take up ammonium. This high sinking rate also deposits more chlorophyll in the sediments for consumption by the benthos. This increased delivery of organic matter to the benthos, its subsequent regeneration, and the resulting depletion of phytoplankton in the upper layers all generate the largest accumulation of ammonium within the model (Table 5). Most of these model cases suggest that the Chirikov Basin, south of Bering Strait is dominated by benthic regeneration of ammonium, with utilization of this source in the southern Chukchi Sea. Consistently higher fluxes of this nutrient pass through Bering strait in Table 5, compared with the boundary input through Anadyr and Shpanberg Straits (Total). In contrast, the relatively small changes in ammonium fluxes between Bering and Chukchi Straits indicate that there may be a steady state between the uptake and regeneration of ammonium in the southern Chukchi Sea. The areas of the two regions are respectively -1.1 x 105 Km"2 and -0.5 x 105 Km2 for the northern Bering and southern Chukchi Seas. The transit times, assuming an average transport at Bering Strait of 0.95 Sv, are 5.4 days

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119 TABLE 6: Total fluxes of chlorophyll for the 81 day simulation of the coupled physical-biological model. TOTAL is the amount of chlorophyll entering the model domain through Anadyr and Shpanberg Straits combined. BERING is the flux across Bering Strait. CHUKCHI is the flux across "Chukchi Strait" as in the physical sub-model. Fluxes are t X 106 mg Chl m 81 days-1 Percent of the total flux is in parentheses. CASE # TOTAL BERING CHUKCHI 1 7.1 9.0 (126.8) 8.5 (119.7) 2 7.1 15.9 (223.9) 18.1 (254.9) 3 7.1 28.1 (395.8) 32.8 (462.0) 4 7.1 19.6 (276.1) 23. 0 (323.9) 5 7.1 9.9 (139.4) 9.6 (135.2) 6 7.1 6.5 (91.5) 6.2 (87.3) 7 7.1 1.7 (23.9) 1.4 (19.7) 8 7.1 15.9 (223.9) 18.1 (254.9) 9 7.1 11.9 (167.6) 12.7 (178.9) 10 7.1 15.7 (221.1) 18.1 (254.9) 11 7.1 11.6 (163.4) 12.3 (173.2) 12 7.1 5.7 (80.3) 5 5 (77.5)

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120 and 5.5 days, allowing an average f ratio (new vs. total production) of 0.65 in the northern Bering Sea and 0.45 in the southern Chukchi Sea. f ratios of 0.35-0.80 (0.55 average) have been measured for the ISHTAR area (Sambrotto, et al., 1984). The horizontal fluxes of chlorophyll through Anadyr + Shpanberg (Total), Bering, and Chukchi Straits for the 12 cases of the model are presented in Table 6. Increasing the maximum growth rate (cases (1) -(3)) leads to an increase of the chlorophyll fluxes between Anadyr and Shpanberg (Total) and Bering Straits and between Bering and Chukchi Straits, but the increase in flux is not directly proportional to the increase in growth rate. Based on the specification of growth in the model, other factors such as light and nutrient limitation must be contributing to the non-linearity of growth and thus fluxes. Decreasing the percent of incident surface radiation reduces the chlorophyll fluxes through both Bering and Chukchi Straits, expressing the light dependency of growth. The lowest chlorophyll fluxes are obtained in cases (6), (7), and (12), when the sinking rate is higher (cases (6) and (7)), and when vertical mixing is eliminated (Case (12)). In a number of cases (7 out of 12), the chlorophyll fluxes increase between Bering Strait and Chukchi Strait by another 10-20% indicating a small additional net growth

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121 occurred within this northern region of slower currents. However, case (1), with the lowest growth rate, as well as case (5) with the lowest light availability actually show a decrease of the chlorophyll flux through Chukchi Strait. Since the chlorophyll flux through Bering strait was actually increased by approximately a third over that of the total flux through Anadyr and Shpanberg Straits in these two cases (Table 6), the growth processes south of Bering Strait still outstrip the loss processes, while north of Bering Strait, loss processes must dominate these situations. Furthermore, since there is little increase of ammonium between the two regions (Table 5), the phytoplankton organic matter must be accumulating in sediments of the Chukchi Sea rather than being remineralized, as in the northern Bering Sea. Recall the higher organic carbon content of sediments in the southern Chukchi Sea (Figure 31). Cases (6), (7), and (12) actually show a decline in chlorophyll flux between Anadyr/Shpanberg Straits (Total) and Bering Strait, as well as between Bering and Chukchi Strait. These results indicate that the loss processes dominate growth of phytoplankton in both regions when sinking fluxes of phytoplankton are increased. 4.2 Productivity of Phytoplankton Table 7 presents the average daily productivity for each of the 12 cases over the whole model domain.

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122 Productivity was calculated by summing the calculated increment of growth at each grid point and dividing by the number of points. The productivities were summed over 24 hours of simulated time. The total productivity for each of the 81 day simulations is also shown. Productivity for 150 days were extrapolated from the 81 day sum to compare with previous measurements. The average daily productivity of case (2) for example, extrapolates to -350 g C m"2 yr"1 over 150 days of ice-free growing season. This is equivalent to a summer production of -2. 4 g c m2 day1 The 14c measurements at 91 stations during the 1985-1987 field years yield the same mean of 2 4 g c m 2 day1 (Springer, et iti,., 1989a) of case (2). This yearly production (Table 7) is comparable to an estimate of 324 g C m2 yr"1 made previously (Sambrotto, et ll, 1984). The lowest productivities were calculated for the lowest growth rate case, (case (1)), the lowest available light case (case (5)), and cases (6) and (7) when the sinking rate was greater than 1.0 m day1 (Table 7). Productivity trends were consistent with chlorophyll fluxes, i .e. low where fluxes were low and vice versa. Zooplankton excretion had little impact on total primary production ("new"+ regenerated), while the effect of a 50% benthic regeneration rate of recycled nitrogen on carbon fixation was similar to the 80% release rate (Table 7).

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123 TABLE 7: Productivity within the model domain for the 12 cases of the coupled physical-biological model. The case numbers refer to TABLE 3. Maximum and average productivities are in g c m 2 day1 Total productivity l s the respective sum of the 81 and 150 day productivities of the model. JULIAN DAY OF TOTAL PRODUCTIVITY MAX MAX AVE. 81 150 CASE i PROD. PROD. PROD. DAYS DAYS 1 4.50 201 1.05 61.81 115.89 2 9.39 203 3.27 190.97 .353.65 3 18.99 195 7.48 368.94 683.22 4 11.79 203 4 .45 262.26 485.67 5 5.20 203 1.32 76.02 140.78 6 8.88 201 1.80 98.88 183.11 7 8.27 201 1.41 28.30 52.41 8 9.38 203 3.27 190.96 353.65 9 9.00 201 2.13 114.27 211.61 10 9.39 203 3.24 188.23 348.57 11 8.99 201 2.06 109.93 203.57 12 5.20 217 1.19 42.50 78.70

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124 Figure (33) exhibits the average spatial productivities over time for each case. The daily productivity of most cases follows a general time course of several initial days of increased productivity, after which there is a steady decline of carbon fixation in response to decreasing light availability (Figure 32). The intensity of the decline varies with the case. The exceptions to this scenario are the 5 m day1 sinking case (case (7)) and the no mixing case (case (12)), both of which decline in productivity from the onset of the simulation. Cases (1), (5), (7), and (12) indicate very little primary production; <1. 5 g C m-2 day1 from the onset of the simulation. Since a mean of 3. 37 g c m-2 day1 was observed in 1988, the parameters of these cases are unrealistic (Table 3). Cases (3) and (4) with the highest growth rate and surface radiation, lead to spatial-temporal means of > 4 g c m-2 day1 and severe nutrient depletion, which were not observed. Cases (2), (8), and. (10) are synonymous, showing a mean of -3.3 g c m2 day1 and little impact of zooplankton grazing or excretion. Finally, cases (9), and (11) generate lower primary productivities of ...:2 g c m-2 day1 in the absence of bottom regeneration, leads to unrealistic ammonium patterns as discussed earlier. The results of case (2) appear to best represent the observed primary production

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u 7 CASE 1 I ---------------CASE 2 I CASE 3 e 5 4 3 2 1 0 ,----,--1 .... __ ---CASE7 I e 5 4 3 2 1 0 7 I CASE9 I CASe 10 I CASE 11 I e 5 4 3 2 1 0 --;----T--.---r 180 210 230 250 180 210 230 250 180 210 230 280 180 210 JULIAN DAY Figure 33 Average daily productivity (g c m 2 day-1 ) calculated over themodel doma i n for cases (1) -(12). -CASE4 CASES CASE 230 280 1-' N V1

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126 over a number of years. Case (2) is thus used as a baseline for analysis of the moored fluorometer data in time and of the satellite and shipboard data in space. 4.3 Temporal Comparisons Figure (34) compares the fluorometer data with model results for cases (2), (11) and (12) of Table 3. All of the cases underestimated the mean chlorophyll concentrations at both of the instruments on mooring I08 (Figure 5) over most of the 81 day simulation period although the errors in chlorophyll concentrations estimated from fluorometer results range from half to double the value. In spite of slightly underestimating chlorophyll in case (2), the model results from this location exhibit similar increases and decreases in phytoplankton biomass as the fluorometer for the first 50 60 days of the simulation. A small peak in chlorophyll concentration from Julian Day 245 through Julian Day 254 in each of the model results appears about 7 days later in the fluorometer data (Figure 34). After Julian Day 254, all model results at I08 indicate very low concentrations of chlorophyll until the end of the simulation. The fluorometer data suggest that chlorophyll concentrations begin increasing again at about Julian Day 251, with several peaks in chlorophyll evident until the end of the record. Recall from earlier discussion and Figures

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3.0 ...... cu .r; 1.5 0 C) ::::J 0.0 3.0 :::::: cu 5 1.5 C) ::::J 0.0 5.0 4.0 ...... cu 3.0 .r; 0 2.0 C) ::::J 1.0 0.0 CASE 11 I CASE12 CASE 2 108t-22.0m CASE 11 FLUOROMETER 110t-20.0m 190 200 210 220 230 240 250 260 JULIAN DAY Figure 34. Comparison of moored fluorometers with model output for. the same location over the simulation period for cases (2) 1 (11) 1 and (12). 270 1-' tiJ -....1

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128 {9) {11) that a major reversal in the flow through the straits occurred at this time. Large peaks in chlorophyll seen in fluorometer data may be the results of bottom resuspension during these reversals. The two fluorometer records at mooring IOS do indeed suggest the benthos as a source of chlorophyll. The increase in chlorophyll at this location is seen first at the deeper instrument and in higher concentrations. Case {12) of no vertical mixing {Figure 34) at mooring I10 also illustrates how the absence of resuspension can suppress phytoplankton growth in the model: a mechanism for resuspension of benthic chlorophyll is not included. At the site of mooring I10, -110 Km downstream in a different water mass, a smooth increase and decrease of simulated chlorophyll concentrations occurs over time, unlike the observations of fluorometer IOS The fluorometer data show higher frequency fluctuations than the model results, but their mean tends to show the same trends in chlorophyll concentration after Julian day 205. The h igher frequency fluctuations in the fluorometer data at this mooring occur because of both bottom resuspension and an east-west movement of the frontal boundary between Anadyr and Bering Shelf water masses {Wirick, personal communication). Our barotrophic physical model does not resolve the interleaving of water mass boundaries that would be needed

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129 to reproduce the kind of fluctuations seen in the fluorometer records at this mooring. We shall see that the grid mesh is too coarse as well. A more complex baroclinic model with shorter gridscales, such as that of Nihoul, Al., (1989) is more appropriate for simulating these processes. The effect of flow reversals on the nutrient and chlorophyll patterns in the model was less obvious than originally expected. Recall Figures (14) -(18) of the flow field for Julian days 226 -230. Comparing the depthintegrated chlorophyll distributions over this period (Figure 35), we see almost no detectable change in the positions of isopleths. This situation is also evident for the integrated nitrate and ammonium distributions that are not shown here. An examination of the flow field within the Chukchi Sea off Ft. Hope during this period indicates that simulated speeds change from -50 em sec1 to the north on Julian day 226 to -10 em sec-1 to the south on Julian day 229. These velocities suggest a net southward movement of -8.6 Km on Julian day 229. This small north-south movement is difficult to detect over a 10 Km grid (i.e. a sub-grid scale change), let alone east-west movements of the front between Anadyr and Bering Shelf Water masses. Reversals of longer duration, which occur in late fall and winter, would be

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130 52-R Figure 35. The depth integrated, horizontal distribution of chlorophyll (mg Chl m"2 ) for case (2) at (A) Julian day 225, (B) Julian day 227, (C) Julian day 229, and (D) Julian day 231.

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131 expected to move the isopleths of nutrients and chlorophyll more towards the southeast than observed here. Reversals in flow are probably most important in forcing the resuspension of chlorophyll off the bottom (Wirick, personal communication). For example, evidence of resuspension of bottom sediments was observed in the concomitant transmissometer records during 1985 (Wirick, 1985 ISHTAR Progress Report). The present model does not contain a mechanism.to resuspend particles from the benthos, preventing a direct comparison of the fluorometer measurements with the model's results at these time scales. Figures 36 and 37 compare sections of ship data and model results of chlorophyll and ammonium. These sections are approximately 20 km upstream of mooring 110 on September 5, 1985. The measured chlorophyll and ammonium both show a large amount of horizontal variation across the entire section. This vertical structure is the result of interleaving of Anadyr Water and Bering Shelf. Water and is the area of the ergocline. East-west oscillations of this vertical structureare likely to be responsible for the fluctuations in fluorometer data at mooring IlO (Figure 34). Vertical variation of this magnitude is not well represented in the model results (Figures 35 and 36) because the interaction of the water masses is not simulated in this physical model.

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E i !_ 0 CHLOROPHYLL (ugFm3) OBSERVED PREDICTED I I I.V 1.5 I I I 2 : o 1 ...J I 1 z w >I 0.5 0 Figure 36. Vertical profile of chlorophyll (mg Chl m 3 ) south of mooring IlO from the convention line east approximately 70 km. A) observed, and B) predicted. I \ 3.0 I l \ 3.5 ....... w N

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0 -----AMMONIUM (U(rat/m') 1 1 1 I I Oi I 10 20 i z i30 > 50 1 w e2o z 01 I 1 3 /2.5\ i= z w > 30 z 0 0 Figure 37. Vertical profile of ammonium NH4 "3 ) south of mooring IlO from the convention l ine east approximately 70 km. A) observed, and B) predicted. 2.0 ....... w w

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134 In spite of poor vertical resolution, the horizontal gradient is well represented for both variables. Elevated concentrations of ammonium in near bottom layers on the eastern side of both ship and model profiles has its source as regenerated nitrogen from the benthos upstream of this location. Since vertical variation in the model results is small, integrated water column values of both ship data and model results were used to examine the horizontal variation in these variables. 4.4 Spatial Comparisons The general course of events and spatial consequences in the northern Bering and southern Chukchi Seas over longer time scales are illustrated by examining just one case of the model (Case (2)). With the initial conditions on July 8, 1985 (Julian day 190), the first day of simulation, high concentrations of nitrate (>25.0 N03 1-1 ) which summed to a depth integral of >450 mg-at N03 m-2 are being advected through Anadyr Strait along Siberia. The nitrate content of Anadyr Strait decreases eastward to 250 mg-at N03 m -2 off St. Lawrence Island (Figure 38). At this time, low concentrations of ammonium (<2.0 at NH4 liter-1), amounting to 30-50 mg-at NH4 m -2 enter Anadyr Strait (Figure 39), except for a local maximum of recycled

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I 171 I 1'{2 I 17' t 1f8 I 1" I 1f4 I 6662............ .._.._. aTflllAST Figure 38. Horizontal distribution of integrated nitrate (mg-at N03 m"2 ) used as initial conditions for the coupled model 135

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68-62....,.,,..,..,..,_. .T"AST Figure 39 Horizontal distribution of integrated ammonium (mg-at NH4 m 2 ) used as initial conditions for the coupled model 136

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137 I 171 I 172 I 171 I U12 1 1 -1,.-I 11j18 1f4 r 66-62-Figure 40. Horizontal distribution of integrated chlorophyll (mg Chl m2 ) used as initial conditions for the coupled model

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138 nitrogen along the Siberian Coast. This plume of elevated ammonium concentrations is most likely the result of prior biological activity in the Gulf of Anadyr. During 1985, high chlorophyll stocks are apparently restricted to the eastern side of Anadyr Strait within subsurface layers (>20m) of 3.0 10.0 Chl liter-1 yielding a total of 100 mg Chl m"2 (Figure 40). When shipboard sampling extended into Soviet water in August 1988, a different pattern emerged (Figures 26, 27, and 41). High chlorophyll concentrations were advected through the western side of Anadyr Strait in concentrations of 4 -8 Chl 1-1 equivelent to 50-100 mg Chl m"2 During July, 1985 within Shpanberg Strait, only small amounts of nitrate ( <4. o N03 1"1 ) were being advected along the western side of this strait within 50 Km of st. Lawrence Island, with <50 mg-at N03 m -2 found off Alaska (Figure 38). Chlorophyll concentrations, on the other hand, are high on the western side of this strait, > 5.0 Chl 1-1 yielding a depth integral of >100 mg Chl m-2 (Figure 40). Ammonium is generally low across the entire strait (Figure 39). The 1985 boundary values at Anadyr and Shpanberg straits change throughout the simulation period, while the 1988 boundaries are held constant; the 1985 results are discussed first. During 1985, the case (2) nitrate field for Julian day 200 (Figure 42a) appears only slightly

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Figure 41. The depth-integrated (A) nitrate (mg-at m"2 ) and (B) chlorophyll (mg m "2 ) stocks in the Bering/Chukchi Seas during August 1988 (from Al., 1989). 1--' w \0

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A 17' 1ft 1- IJ' If' If' If' 1ft -a-NIT.AT. aae J O 8aa INTeaaTaO O .aa 7 C A (aJ l .... .... o. o .... c l7' l7l l7' If' If' If' lfl -.... T.._ATe aae J Q -... INTaO&Teo O .aa Ya. -C .... ... . .... 8 M-a-HIT.aTe two-e/t aaae J O aoo INT.eA&T&D 0 .00 -C D II-a-HITA& T a lvo -e/-- ... J O -... INTOeaATOO o .OO 70a C . . . ... .... . ... .... . . . .... 140 Figure 42. The depth integrated, horizontal distribution of nitrate (mg-at N03 m-2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260.

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141 depleted from initial conditions just 10 days before (Figure 38). Most of the nitrate utilization appears along the eastern side of Anadyr strait, extending into the center of the Chirikov Basin. Very little nitrate depletion is observed along the Siberian Coast and in the Chukchi Sea at this time. By Julian day 220 (Figure 42b), further nitrate depletion is observed in the Chirikov Basin, but utilization of nitrate is more evident now in the Chukchi Sea. Declining productivity by Julian days 240 and 260 result in increased stocks of nitrate south of Bering Strait (Figures 42c and 42d). North of this Strait, nitrate continues through Julian day 240. By Julian day 260 north of the Bering Strait, nitrate stocks increase as a result of 1) decreased productivity in response to lower light conditions and 2) continued northward advection of unutilized nitrate through Bering Strait. In spite of the temporal changes in nitrate stocks over the model domain described above, nitrate remains high along the Siberian Coast, from its source through Anadyr Strait to the northwest end of the model domain. Low concentrations of <0.5 mg-at N03 m -3 are persistently found east of a line extending from the eastern side of st. Lawrence Island through Bering Strait and to pt Hope. This line has been described previously as the ergocline, where high concentrations of chlorophyll are observed (Figure 29).

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A ITt I" 1- 11' If' If' If' lfl -IZ-aee oo 0 . ,. c IZ-, ... J O a40 JNTCO.ATaO 0 . ,. C o. o " aoo. o .... t.o.o aoo.o B ITt 171 IJ' If' If' If' lfl c .... e-o/-J ... INTaeAAT.O 0 . ,. C IZ-a.-o-zUM aeoe J .D. ... o.ee 7ea C . 0 ... ,. . ... ....... 142 Figure 43. The depth integrated, horizontal distribution of ammonium (mg -at NH4 m-2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, {C) Julian day 240, and (D) Julian day 260.

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143 Ammonium concentrations increase dramatically in the model domain (Figures 43a -d) over the initial condition (Figure 39). Initially, ammonium is distributed along the western side of the Chirikov Basin, extending eastward to the ergocline, with only <75 mg-at NH4 m "2 ever found in this area. By Julian day 200 (Figure 43a), depth integrated stocks of ammonium exceeding 150 mg-at NH4 m"2 are found centered across the ergocline and through Bering Strait. A large pool of >150 mg-at NH4 m"2 is also located in the central Chukchi Sea. The high concentrations of ammonium on Julian day 200 are the result of the regeneration of detrital nitrogen by the benthos, with deposition of the initial stocks of phytoplankton depicted in Figure 40. At this time, ammonium is slightly depleted from the initial conditions along the Siberian Coast (Figure 40a), reflecting its utilization as the preferred nitrogen source of phytoplankton production. Twenty days later on Julian day 220 (Figure 43b), an area of >75 mg-at NH4 m"2 is still aligned along the ergocline, and along a line extending west of Pt. Hope to the edge of the model domain. Ammonium is depleted elsewhere reflecting both its utilization by phytoplankton and its advection through the model domain. Further depletion of ammonium stocks is evident on Julian days 240 and 260 (Figures 43c and d) within the Chirikov Basin. In the Chukchi Sea during this same period, the spatial

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144 distribution of ammonium stocks remains relatively constant, suggesting a balance between uptake and regeneration of this nutrient. The spatial patterns of ammonium after Julian day 220 (Figure 43b -d) are relatively constant throughout the simulation period. Elevated ammonium concentrations can always be found in a pool centered around the Chirikov ergocline, and in a large pool in the Chukchi Sea, extending from near Ft. Hope west and north to the end of the model domain. The Bering Sea pool contains concentrations of 50-100 mg-at NH4 m-2 at Julian day 220, decreasing to -50 mg-at NH4 m-2 by Julian day 260. The Chukchi Sea pool consistently contains >150 mg-at NH4 m-2 with the spatial extent of these high concentrations greater by Julian day 260. Initial stocks of 1985 chlorophyll (Figure 40) were relatively low (-50 mg Chl m-2 ) and concentrated in 3 areas of the model domain: along the ergocline in Chirikov Basin (as described for nitrate and ammonium), along the Siberian Coast near the northwest edge of the model domain, and in a pool west of Ft. Hope extending westward to the end of the model domain. After just 10 days of simulation (Figure 44a) the spatial distribution of chlorophyll is not drastically changed, but the stocks have increased to >100 mg Chl m-2 in these pools. After 30 days (Figure 44b), less chlorophyll is present just north of Anadyr Strait, and the areal extent of >100 mg

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B 114 o I)'Z 17' lfl lfl If' A 1;'4 liZ !;t Ill !II . ... 1212c D liZ -Figure 44. The depth integrated, horizontal distribution of chlorophyll (mg Chl m"2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260. 145

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146 1 2 Ch m concentrat1ons at the ergocline has extended laterally westward, nearly filling Chirikov Basin. The most noteworthy increase of chlorophyll concentrations is in the Chukchi Sea, where the initial pool of >100 mg Chl m2 now encompasses -75% of the model domain here. After 50 days of simulation (Figure 44c), a decrease in the flux of chlorophyll through Anadyr Strait leads to lower concentrations of chlorophyll just north of the strait,however, the pool of chlorophyll remains high, >100 mg Chl m2 along the ergocline. At Julian day 240 in the Chukchi Sea (Figure 44c), chlorophyll concentrations appear unchanged from those of Julian day 220. By Julian day 260 (Figure 44d), the pool of chlorophyll along the ergocline is still present south of Bering Strait, but the stocks are_greatly reduced in concentrations (-40 mg Chl m 2 ) as a result of a decrease in the boundary flux of chlorophyll and lower productivitiesdue to decreasing light availability. In the Sea, the pool size of high (>100 mg Chl m2 ) chlorophyll has decreased as well, reflecting a reduction in both local photosynthesis and in the flux of chlorophyll through Bering_Strait (i.e. a decrease in the upstream productivity). The 1985 productivity (Figure 45) was always highest near the ergocline in the Chirikov Basin, and in an arc extending from southeast of Ft. Hope west and north to the end of the model domain. carbon fixation exceeded 5 g C m 2

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A IT' .... o- IJW I!' ' I f' lfl a-CoO/--/ ) 10 ... 0 ,00 ?00 C c toC/-.; 0 ) ..,.,.,. ..... ., .. .., 1 0 0 .00 ?00 0 ....... B l7' 111 IJW I!' If' lfl a-"' OAI'-'Y .-.oOUO'TIVIT"' C .... TM.Hit toe;-.,; .OOO ........... 10 7 -o t m 0 '1' '11 '1' ' I f' '" ------a-0&1'-Y lOO; .-; O&Y 10 000 0 .00 ? 0 147 Figure 45. Horizontal distribution of average daily productivity (g c m-2 day-1 ) over the model domain for case (2), for (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260.

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148 day"1 near the ergocline and in the Chukchi Sea at Julian day 200 through 220 (Figure 45a-b). Declining light levels by Julian day 240 (Figure 45c) result in a decline of carbon fixation to <5 g c m"2 day"1 in this same area. Decreases in chlorophyll stock south of Bering strait by Julian day 260 along with the declining light availability contributed to further decreases in carbon fixation to less than 3 g C m"2 day"1 along the ergocline. Carbon fixation is depressed at this same time in the Chukchi Sea (Figure 45d), but high stocks of chlorophyll (Figure 44d) maintain -3 g C m"2 day"1 productivity in spite of the lower light availability. In 1985, a region of elevated productivity along the Siberian Coast is present during the first -50 days of the simulation (Figures 45a-c), resembling the downstream result of an upwelling center. This upwelling feature is also inferred from AVHRR imagery (Figure 28). Although this mechanism is not explicitly included in the model, the boundary conditions of elevated nitrate within Anadyr strait infer another upstream upwelling center south of the model domain. One simulation used the August 1988 distribution of chlorophyll and nutrients across Anadyr and Shpanberg Straits to evaluate the.effects of boundary conditions on the resulting spatial patterns within the interior of the model domain.

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149 The 1988 boundary conditions of nitrate and ammonium showed a similar cross-strait distribution as those of 1985. However, the measured cross-strait distribution of chlorophyll was very different in 1988 (Figure 41b). Recall that in 1985, the greatest fluxes of chlorophyll into the model domain were through the western side of Shpanberg Strait, and in near bottom water on the eastern side of Anadyr Strait. Instead, the 1988 simulation specifies the flux of high chlorophyll (concentrations of 6-8 mg Chl m"3 ) to be across the western side of Anadyr Strait, adjacent to the Siberian Coast. Only low algal biomass (<2 mg Chl m "3 ) was advected through either the eastern side of Anadyr Strait, or through Shpanberg strait during the 1988 scenario. In this last version of the model, only the nutrient and chlorophyll specifications were different from case (2). The u and v velocities, wind mixing, light regime, and the biological rate constants were all those of the 1985 case (2) simulation (Table 3). Boundary values of nutrients and chlorophyll were held constant for the entire 81 day simulation. The major difference in nitrate (Figure 46) and ammonium (Figure 47) distributions under the 1988 boundary conditions is that both of these nutrients are more depleted than on the corresponding days (Figures 42 and 43) of the case (2) under the 1985 boundary conditions. This is the

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B 0 0 F 0 '1' 0 f' 0 'f' 0 'f4 0 'fl 0 -H 1212c 0 0 '?2 11' 0 1f' 0 lfl 0 1ft 0 l fZ 0 12Figure 46. The depth integrated, horizontal distribution of nitrate (mg-at N03 m-2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260, for 1988 constant boundary conditions.

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151 A '7' ... '1' .,. .,. '" lfl a----... c '7' .,. ,, l7' t t If' 1f2 Figure 47. The depth integrated, hori-zontal distribution of ammonium (mg -at NH4 m-2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260, for 1988 constant boundary conditions.

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/ A I !'t :'2 ; I ;'I If' If' tt !f2 111 m :1' 'fl ,,._ 1tt 1F I I I -I ' i : 120 0-QW?N_ 1 I ooucvzvzTv t a C / / 1 """''-z ... ,.. ...... z e aao i 0 ,.AX O .. O .. TM "-l:O"""T lOOO c !? 1 1;'2 !jl = ,.. !"' .. r r l o 0 J 7 i 17l '1' . If' 1fi 1F 12-oouc.,.zvtTv CON ... ov .c:. -,.. .. .,, 1 0 A V J e o O .a ,._ tOaO Figure 48 Horizontal distribution of average daily productivity (g c m 2 day1 ) over the model domai n for the 1988 boundary condi t ion, for (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260. 152

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153 result of a slightly higher average daily productivity, 3.34 g C m"2 day1 for 1988 compared to 3. 27 g c m2 day1 for 1985, caused by the increase in chlorophyll flux in this model case. More important than the slightly higher average productivity, the 1988 boundary condition led to a broader area of elevated productivity than case (2) (Figure 48). Figure 48 shows the productivity profiles for Julian days 200, 220, 240, and 260 of the 1988 boundary condition. Areas of high productivity are centered around the ergocline in the Chirikov Basin and in the Chukchi Sea, at similar rates as those of the 1985 case (2) (Figure 45). The greatest difference between these two cases is that there is an extended area of high productivity, >5 g c m "2 day1 along the Siberian Coast in both the Bering and Chukchi Sea by Julian day 220, that is not as large in area in the 1985 results. Also noted was the persistence of higher production in this area through Julian day 260 (Figure 48d) in contrast with a decline in productivity in this area in 1985 (Figure 45d). Elevated chlorophyll concentrations (Figure 49) were still found centered along the ergocline in the Chirikov Basin. The eastward extent of this concentrated pool of chlorophyll is not as wide, reflecting the decrease in flux of chlorophyll through Shpanberg Strait. An additional pool of chlorophyll is now found along the Siberian Coast, beginning about 50 Km north of Anadyr Strait and extending

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A . . . c -c -c Figure 49 The depth integrated, horizontal distribution of chlorophyll (mg Chl m"2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260, for 1988 constant boundary conditions. 154

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155 into the Chukchi Sea. This feature is present in integrated chlorophyll profiles of the 1988 ship data (Figure 41). The chlorophyll features shown in Figure 49 compare well with the spatial distribution of integrated chlorophyll measured in August 1988 (Figure 41b). on Julian day 220, i.e. August 7, the highest algal stocks of >150 mg Chl m2 are obtained at both the and off the Siberian Coast (Figure 49b), mimicking the CZCS observations (Figure 29) as well. However, as with comparisons of other data sets with model results discussed in previous sections, the magnitude of the values of the model results is somewhat lower than those of the ship samples in the Chukchi Sea but coincident with those off the Siberian side of the Chirikov Basin. It appears, then that the 1988.boundary condition led to a more representative picture of the nutrients and chlorophyll distributions in both the Bering and Chukchi Seas. Had ship coverage extended to the western side of Anadyr Strait in 1985, a more appropriate boundary condition would have been possible, resulting in a better match of the model results with measured nutrients and chlorophyll. one might question the choice of the vertical boundary condition, as well as the lateral one. Case (2) with 75% of the incident surface radiation, was chosen as the standard case, as opposed to one of the other light parameterizations. In spite of a rigorous calculation of

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156 the irradiance at the surface, the effects of clouds and fog on light availability is, first of all, not well known, and second, not easily modeled. Attempts have been made to parameterize the decrease in radiation caused by clouds, but most of these formulations are based on factors which are difficult to determine; their results may not yield any greater accuracy than the present method. The current way of estimating light was used because, first it is entirely empirical and based on easily measurable and calculable factors, and second, it. was desired to use terms in the simulation model that were as accurate as possible. Of greater importance, is the seasonal decline in light for limiting the temporal response of phytoplankton growth. In nearly all cases of the model, including the 1988 constant boundary conditions case, there was a general decline in productivity with time. Concurrent with the decline in productivity throughout the simulation period, nutrient delivery to the system also declined somewhat under the 1985 boundary conditions, but >15 N03 1"1 were still being advected through the western side of Anadyr strait on Julian day 260. The decline of productivity over the last 30 days of each simulation is instead caused by the decrease in light availability with the approach of winter, as well as by the decrease in phytoplankton "seed" being delivered to the system.

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157 The 1985 conditions for chlorophyll at Anadyr and Shpanberg Straits involved a 10 fold decrease during the last 30 days of the simulation. Even when the 1988 boundary conditions are instead held constant, (i.e. a constant flux of chlorophyll into the model domain) the model still predicts a decline in chlorophyll, most evident in the eastern side of Chirikov Basin. The 1988 chlorophyll case leads to a 48% decline in productivity over the last 30 days, compared with a 36% decline in the 1985 case. Thus the declining light levels are more important to decreasing stocks of chlorophyll than the fluxes of algal "seed" across Anadyr and Shpanberg Straits. From the model results it appears that both the 1985 and 1988 spatial patterns of algal biomass for the Bering Sea portion of the model domain are entirely determined by the boundary conditions specified at Anadyr and Shpanberg Straits. The resulting productivity centers and nutrient distributions of the Chirikov Basin are forced by the physics, primarily by the advection through the boundary and to a lesser degree by the initial spatial gradients of nutrients and chlorophyll within the flow field. The Chukchi Sea domain is sufficiently downstream of the model's boundary conditions to allow for biological modification of the chemical parameters. The initial ammonium stocks crossing Anadyr and Shpanberg Straits are utilized within a few 10's of Km of both straits (Figure

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158 43). Although this situation of preferential uptake is forced by the model's equations, field data in August 1988 suggest that a large amount of ammonium is also utilized within a short distance of Anadyr Strait (Figure 27). In the model, ammonium is not an important nitrogen source again until near Bering Strait, when sufficient turnover of phytoplankton by the benthos and the zooplankton has occurred. Fluxes of nitrate (Table 4) suggest that this nutrient is most important as a nitrogen source south of Bering Strait. In case (2), 37.6% of the total input of nitrate has been depleted by Bering Strait. Only 4.2% more nitrate is depleted by Chukchi Strait in spite of an increase in chlorophyll flux of 31.0%. Model results for case (2) indicate that -60% of the total primary production is new production (i.e. derived from nitrate as a substrate) within the Chirikov Basin, in the southern Chukchi Sea section, only -45% of the total production is new production. The benthic food web of these shallow seas is the major source of recycled nitrogen. 4.5 Benthic Fluxes The daily fluxes of phytoplankton, fecal pellets, and ammonium, expressed as carbon over the model domain, to and from the sediments were summed for each case of the model

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""' -II e :o:c e 0 [J ...., X ::l ...1 1.1. 0 H I z w m w z 33 30 27 24 21 18 15 12 9 6 3 0 -3 -6 -9 -12 -15 -18 -21 -24 -27 -30 -33 I'TTT 190 AVERAGE NET BENTHIC FLUX OVER THE MODEL DOMAIN COMPARISON OF SINKING RATE SPECIFICATION + 1 0 METERS/DAY M2 5 METERS/DAY 0 5 0 METERS/DAY 230 ..JULIAN DAY Figure 50. Average net benthic flux over the model domain. Negative values indicate a net flux out of the benthos, positive values are a net flux into the benthos. This figure is a comparison of the sinking rate specification (cases (2),(6), and (7)). 270 ...... Vl \0

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A 17' 172 11' lfl I f' I f' I I f' -M c M-IllB D 172 '1' .,. f' ., --o.oe 160 Figure 51. Horizontal distribution on carbon on the benthos (g C m "2 ) for case (2) at (A) Julian day 200, (B) Julian day 220, (C) Julian day 240, and (D) Julian day 260.

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161 TABLE 8: Average daily flux of carbon to and from the sediments north (CHUKCHI) and south (BERING) of Bering Strait and the total fluxes over the entire domain (TOTAL), (g c m"2 day-1). These numbers represent averages over the model domain and simulation period. INFLUX OUT FLUX CASE BERING CHUKCHI TOTAL BERING CHUKCHI TOTAL 1 1.26 0.66 0.88 1.05 0.54 0.73 2 1.66 1.29 1.43 1.38 1.08 1.19 3 2.33 2.15 2.18 1.94 1.79 1.85 4 1.81 1.59 1.67 1.51 1.33 1.39 5 1.33 0.73 0.95 1.10 0.61 0.79 6 1.88 1.02 1.34 1.56 0.85 1.12 7 1. 71 0.52 0.97 1.42 0.43 0.81 8 1.66 1.29 1.43 1.33 1.08 1.19 9 1. 37 1.18 1.25 o.oo 0.00 o.oo 10 1.59 1.27 1. 39 1.33 1.06 1.16 11 1.35 1.14 1.22 o.oo o.oo o.oo 12 1.39 0.65 0.92 1.16 0.54 0.77

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162 (Table 8). The net results of cases (2), (6), and (7), with the three different sinking rates, are shown in Figure so. Generally, these spatial averages can be described as a net influx into the sediments for the first half of the simulation, followed by a net efflux for the last half. The highest carbon fluxes to and from the sediments occurred in those cases of the greatest algal growth (Cases (3) and (4)). The impact of an increased sinking rate was to deposit the bulk of the boundary flux of chlorophyll within approximately 100 Km of Anadyr and Shpanberg Straits, leaving very little chlorophyll for further production and deposition downstream. Examination of the spatial distribution of carbon deposition over the model domain for case (2), under the 1985 boundary condition shows some general trends (Figure 51). First, carbon accumulates within the sediments in discrete areas of the model domain. In every case, there is an accumulation of benthic carbon within Shpanberg and Anadyr Straits, reflecting the boundary input of phytoplankton, grown within the Gulf of Anadyr. Carbon also accumulates on the benthos early during each simulation within a narrow band (100 kilometers), centered along a line from the eastern tip of st. Lawrence Island to approximately Cape Prince of Wales, i.e. beneath the ergocline. Two other areas show high inputs of carbon to the benthos. The first is along the Siberian Coast; the

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.second zone of detrital input occurs along the Alaskan Coast, from Cape Prince of Wales to pt. Hope. 163 In 1988, one in situ measurement, using a sediment trap, was made of the vertical flux of material to the benthos, as part of the ISHTAR field program. The sediment trap was located at the northern extent of the high productivity zone (ergocline) in the Chirikov Basin. The vertical flux of carbon to the benthos was estimated over 96 days at -o. 5 g c m"2 day"1 (Fukuchi, personal communication). The case (2) results predict a mean influx of 1.43 g c m"2 day"1 over 81 days and over the entire model domain, considerably higher than the measured flux. The difference in these values of vertical flux may reside in the duration of the measurements. Recall that productivity declined throughout the last 30 days of the simulation due to decreased light availability. The sediment trap represents flux measurements over a longer duration of presumed declining productivity. A release rate of 1. 2 g C m "2 day"1 over 81 days for case (2), and a C/N ratio of 6/1 implies a nitrogen input of o. 2 g N m -z day"1 averaged over the model domain. Summing the maximum observed inputs of nitrate (6. 3 mg N03 m"2 day"1), ammonium (12.0 mg NH4 m"2 day"1), and urea (56.0 mg urea m"2 day"1 ) gives a total release of nitrogen of 0.074 g N m"2 day"1 (Walsh, et al., 1989). Thus the model overestimates the measured release of nitrogen in case (2). However, the

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.measured release rates do not account for the release of dissolved organic nitrogen (DON) from the sediments which was not measured as part of the ISHTAR sampling protocol. The release of amino-nitrogen from Buzzard Bay sediments was estimated as 0. 098 g DON m2 day1 164 (Christensen, gt al., 1983) and from Narragansett Bay, 0.136 g DON m2 day1 was released from in summer (Nixon, et Al., 1976). Therefore, the higher nitrogen release from the sediments predicted by case (2) may be accounted for by the inclusion of DON release. Since the model did not consider bacterioplankton, their recycling processes are included in the remineralization term of equation (26) as well. Maximum carbon mineralization rates measured for the Bering Sea ranged from 0.36-0.48 g c m2 day1 (Walsh, tl al., 1989). This is approximately one third the value predicted by the model. Again, as with nitrogen release, the release of dissolved organic carbon was not measured from either sediments or plankton, and so the differences between measured and predicted values may not be that great. The temporal progression of the accumulation of carbon in the benthos complicates this simple picture. Figures 51a-d show the sequence of detrital carbon stocks (g c m 2 ) for case (2). At Julian day 200, detrital carbon is accumulating near the southern boundaries, and underneath the ergocline in the Chirikov Basin. A moderate amount of

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165 .deposited carbon is also seen in the Chukchi Sea, just north of Bering Strait, both to the east of the strait along the Alaskan Coast, and to the west of the strait along the Siberian Coast. By Julian day 220 (Figure 51b), carbon accumulation within the Chirikov Basin has extended to Bering Strait, and the amount of detrital carbon in this region has increased over that of 20 days earlier. Carbon deposits within the Chukchi Sea has also increased in both area and amount. By Julian day 240 (Figure Slc), the carbon buiidup has continued within both the Bering and Chukchi depocenters; however, most noteworthy at this time, is the decrease of carbon near Anadyr and Shpanberg Straits. This reflects the decrease in flux of chlorophyll across these straits by the end of August in the model. By Julian day 260, the bulk of the sediment carbon within the Chirikov Basin has been remineralized, since the primary production and flux of carbon to the benthos have been reduced. Detrital carbon is left within the Chukchi Sea, however, similar to observations (Figure 31), and indicating that the tempo-spatial mean of carbon fluxes in Table 8 are somewhat deceptive. For case (2), the net accumulation rate (influx -outflux) is o. 28 g C m-2 day-1 for the Bering Sea portion and o. 22 g C m-2 day-1 for the Chukchi Sea region, implying a greater accumulation of carbon to the south over just 81 days.

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166 Analysis of the spatial distribution of the flux of carbon into and out of the benthos at 5 day intervals for each of the cases indicates, in fact, that a net flux out of the sediments. moves progressively northward starting at about Julian Day 235 and proceeding through the remainder of the simulation period. If the observed spatial gradient in size composition and metabolic rates of the benthos (decreasing from Bering to Chukchi Seas) had been invoked as justification for a greater regeneration rate, r, in Chirikov Basin, more detrital carbon would have been stored in the Chukchi Sea, i.e. case (9) of Table (8).

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167 5.0. CONCLUSIONS (1). The distribution of chlorophyll and productivity in the Bering Sea section of the model domain is primarily determined by the trajectories of the advection and secondarily by the nutrient and chlorophyll distributions at the boundaries of Anadyr and Shpanberg Straits. For example, the elevated phytoplankton concentrations and thus productivity are found along a line from the southeast side of St. Lawrence Island to Cape Prince of Wales, despite changes in boundary of the model (Figures 44 and 49). These same regions of enhanced algal biomass are seen in satellite imagery (Figure 29) and shipboard observations (Springer, et al. 19 .89a) T/S diagrams (ISHTAR CRUISE REPORTS, 1985) of stations taken east to west across the Chirikov basin mark the transition of an ecotone between Anadyr and Bering Shelf water masses. These two water masses are separated by a zone of horizontal mixing, created by the interleaving of the water mass types; this can be reproduced in more complex physical models (Nihoul, et al., 1989). When this occurs, the water column becomes stratified, an ergocline (Legendre, et al.,, 1986) is created, and growth is enhanced. The magnitude of the transport of the barotropic currents through Anadyr Strait relative to Shpanberg Strait

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168 as well as the bathymetry determines the east-west position of the horizontal nutricline, while the chlorophyll flux through Shpanberg Strait in 1985 provides "seed" to initiate a bloom along this nutricline (Figure 44). When the 1988 boundary condition was used, (Figure 49), in which the "seed" chlorophyll was delivered primarily through the western side of Anadyr Strait, the ergocline bloom develops in the same relative position as with the 1985 boundary data. The algal bloom off the Siberian Coast is more intense under the 1988 simulation conditions. When the currents were reversed in these simulations, the east-west structure of the horizontal nutricline did not show a detectable change in position. It was determined that the duration and magnitude of the reversals during this simulation period were not large enough to be detected at the physical model's grid scale of 10 Km. Part of the variance in fluorometer records for the 110 mooring (Figure 34) are attributable to the east-west displacements, again undetected by the biological model. The nutrient distributions within the Chukchi Sea are also determined by the structure of the current field, the distribution of the nutrients as they pass through Bering strait, and the bottom boundary conditions south of Bering strait. Nitrate remains high on the southwest side of the model domain, reflecting the Anadyr source of this water and its nutrient signature. Low concentrations of nitrate

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169 appear everywhere else in the simulated Chukchi Sea (Figure 42). Nitrate may be underestimated in the model, since no in source is provided in the model formulation, and higher stocks were measured on the Korolev cruise (Figure 41). Field data for 1988 indicate that large pools of nitrate may be present farther away from the Siberian Coast than the model predicts. Estimates for sedimentary release of nitrate suggest only an additional 5-10% might be added to the total stock (Blackburn, personal communication). Part of this nitrate deficit can be overcome by an in situ source, but the physical oceanography of the Chukchi Sea may be a major factor. The presence of a Siberian Coastal Current, running south and east along the Siberian coast, has been documented (Coachman, et al., 1975). The exact magnitude, duration and behavior of this current and its potential to deliver nutrients to the area must be examined, so that its effect on the nutrient regime of the Chukchi Sea can be determined. The nitrate source for this current may be relict winter concentrations carried north from the Gulf of Anadyr through Anadyr Strait (Figure 52). In contrast, elevated ammonium concentrations are aligned along the Alaskan side of the model in both the Bering and the Chukchi Sea sections of the domain (Figures 43 and 49). This structure is partly a reflection of a boundary condition, but also of the benthic source within

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170 the productive regions of the model. The high flux of phytoplankton to the benthos in these productivity zones provides the particulate substrate to generate ammonium. Thus, areas of high ammonium concentration are spatially determined by both benthic regeneration processes and by the velocity field. (2) Based on case (2), 60% of the nitrate entering the model domain through Anadyr and Shpanberg Straits remains unutilized. This amounts over 81 days of simulation to a -5 x 1010 mg-at N03 m 2 export across the model's downstream boundary. Most of the nitrate exits through the northwestern boundary within approximately 200 Km of the Siberian Coast. High concentrations of nitrate (10-15 N03 1 1 ) are found (Codispoti, 1965; and Codispoti and Richards, 1968) between the end of the model domain in the Chukchi Sea and the area west of Wrangel Island in the East Siberian Sea; their source is presumably unutilized nitrate from the Bering sea. In fact, nitrate sections within the East Siberian Sea (Figure 52) suggest that this Bering Sea derived nitrate is detectable as far west as the Kolyma River at about 155 E longitude, where nitrate concentrations finally fall to about 1. 0 N03 1 1 (C?dispoti and Richards, 1968).

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Figure 52. Horizontal distribution of nitrate N03 1"1 ) in bottom waters of the Chukchi, East Siberian and Laptev Seas, (from Codispoti, 1965). 10" .. 1-' -...J 1-'

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172 Only a very small amount of the total input of nitrate leaves unutilized through the northeastern boundary of the model. From here, this nitrate export is presumably carried within the Alaskan Coastal Water northeast past Point Barrow, and thence into the Beaufort Sea. Two previous investigations, one in August of 1968 and 1969 (Kinney, gt gl., 1970), and another in October of 1986 (Aagaard, et gl., 1988) have shown low concentrations of nitrate within these reqions of the northern Chukchi and Beaufort Seas. Thus the low fluxes predicted by the model appear to be confirmed by data from previous field investigations. Ammonium fluxes, exiting the downstream boundary of the model, are approximately 60% enriched over their boundary inputs at Anadyr and Shpanberg Straits. Total export of ammonium from the model domain amounted to approximately 2 x 1010 mg-at NH4 m -2 export over the 81 days of the simulation. Since 2-4 J,.'g-at NH4 1 -1 have been observed in the Beaufort sea during October and none in April (Aagaard, et al., 1988), the exported ammonium is presumed to help fuel the continuing summer productivity in both the East Siberian and Beaufort Seas. Chlorophyll concentrations, also exiting the model domain predominately near the Siberian Coast, are approximately 150% enriched over their boundary input at Anadyr and Shpanberg straits. Total export of chlorophyll amounted to approximately 2 x 1010 mg Chl m -2 over the 81

PAGE 189

173 days of the simulation. This chlorophyll provides "seed" for the -200 g c m-2 yr-1 of estimated productivity west of the model domain in the East Siberian Sea (Walsh, submitted). Recall the depletion of nitrate to the west of the model domain in the East Siberian Sea (Fiqure 52). Using a C/Chl ratio of 45/1 and case (2) of table 6, the net algal carbon export from the model domain is 6.1 x 10-3 g c m-2 day1 which represents the balance of lateral fluxes across the upstream and downstream boundaries of the model. To place this loss in the context of other grazing and burial losses of Table 9, I have multiplied.it by the area of the downstream interface of the model (2.6 x 107m2 ; assuming a 750 Km interface of an average depth of 35 m) to obtain 1. 6 x 1011 g C day-1 We next consider the carbon fixation as well as the grazing, sinking, and burial losses of the ecosystem. (3) In all cases of the model under the 1985 boundary condition, there was a decline in productivity over at least the last 40 days of the simulation period (Fiqure 33). Nutrient and chlorophyll inputs through Anadyr and Shpanberg strait declined over this same period, when 1985 boundary data were used. When the 1988 boundary data were used, in which the flux of nutrients and chlorophyll across Anadyr and Shpanberg Straits was held constant, a similar decline in productivity is still seen (Figure 53). Thus, it is evident that the declining light availability (Fiqure 32},

PAGE 190

Table 9 Carbon budget (x 1011 g c day"1 ) for case (2) of the Bering/Chukchi Sea model after 81 days of simulation. INPUT: OUTPUT: Photosynthesi s Net Export Grazing Burial Respiration Secondary Production (Zooplankton + Benthos) 5 .30 1.60 0.05 0.40 1.90 0.01 174

PAGE 191

> t= () ::)oo a:> a.< >0 -l < 0 (")---, I ,..._ I ;
PAGE 192

176 manifested in shorter day length and lower sun angles, leads to a decline in productivity. over 81 days of case (2), the mean carbon fixation of 3. 3 g c m -2 day-1 (table 7) amounts to 5.3 x 1011 g C day-1 over the total area (1.6 x 101 1 m2 ) of the model domain (See Table 9). (4). Within the context of the model, zooplankton are not an important influence on chlorophyll, productivity and nutrient distributions. The inclusion of zooplankton in the model showed very little effect on the nutrient fluxes and the resulting productivity of the ecosystem, when cases (10)and (2) are compared. For example, case (10), in which zooplankton grazing was deleted shows that there was only <1.5% decrease in productivity (Table 7), 0.4% more nitrate utilization, -8% less ammonium flux, and virtually no change in the flux of chlorophyll through Chukchi strait, compared to case (2). over all 12 cases of the model, zooplankton consumed about 1% of the daily productivity, or a loss of -0.05 x 1011 g c day-1 in Table 9 The zooplankton species composition of Anadyr Water contains herbivores from the southeast Bering Shelf (e.g. Neocalanus). Many of the representatives of this assemblage are ontogenic migrators that reside primarily at the shelf break or in deeper water. These individuals may be accidental residents of the study area, as none of these groups is ever found in the Arctic Ocean. Thus as expatriots, they may have little importance to this

PAGE 193

177 ecosystem, except as a small sink of carbon, or as food for fish and planktivorous birds. (5) Within both the Bering and Chukchi sections of the model domain, the physics (advection) and the chemical nature of the water masses combine to establish persistent zones of high primary production. The ungrazed algal carbon in these productivity zones contribute large local sinking fluxes to the benthos, which result in distinct patches of detrital carbon within the sediments. Changes of the sinking rate [cases (6) and (7)] and the deletion of vertical mixing [case (12)] in the model equations had a significant impact on productivity and nutrient distributions. In each case of the higher sinking rates, phytoplankton advected through Anadyr and Shpanberg Straits was removed from the euphotic zone, with no return of recycled nitrogen. The sinking rates of 2.5 and 5.0 m day-1 led to a downward flux, which was greater than the upward flux driven by mixing. Eventually, no phytoplankton were left in the euphotic zone, and thus little productivity was possible. In case (12), with no mechanism to provide an upward flux of phytoplankton cells and nutrients into the water column, two phenomena combine to limit production. First, there is a net removal of phytoplankton cells from the water column despite the modest sinking rate of 1.0 m day-1 secondly, a sequestering of ammonium occurs in the bottom

PAGE 194

178 layer, after regeneration of detrital nitrogen by the benthos. Thus, the wind induced mixing rate, and a sinking rate for phytoplankton of 1. o m day1 appear to fit the measured primary production of this ecosystem. A more complex physical model with spatially varying mixing coefficients and vertical advection (Nihoul, et Al, 1989), would generate local upwellings and downwellings, not simulated here. In situ measurements of the magnitude of the vertical flux of carbon and nitrogen to the benthos were made for the first time as part of the ISHTAR field program in 1988. The organic carbon flux at -64 58 and 169 10, roughly in the center of the northern extent of the zone of high benthic flux in the Chirikov Basin, yielded only -o. 5 g c m"2 day1 over 96 days (Fukuchi, personal communication). Throughout the whole area, the model predicts a mean influx of 1.43 g c m2 day1 (Table 8) yielding a sinking loss of 2. 3 x 1011 g c day1 to be oxidized or buried. (6) The benthic respiration of carbon, and concomitant regeneration of nitrogen, contribute important sources of dissolved carbon and. nitrogen to the model domain. Without benthic nutrient regeneration, productivity is severely curtailed. As a result of the detrital fluxes to the bottom, the effect of the benthos on nutrient fluxes and productivity was much more pronounced (comparing cases (9) and (2)) then that of the pelagic herbivores. The

PAGE 195

179 elimination of regeneration by the benthos generally resulted in more nitrate being utilized for algal growth, a depletion in the stocks of ammonium, and -30% less chlorophyll over the model domain. A mean regeneration rate of 0. 2 g N m-2 day-1 is similar to the observations of Walsh, et ll, (1989). It would thus seem that benthic processes are important to the continued productivity of the ecosystem. The high benthic biomass of invertebrates (Stoker, 1981; Grebmeier, et gl., 1989) must be a significant source of recycled nitrogen to the system, and spatial gradients of their abundance should be included in new models. Similarly, the simple specification of one type of the benthos should be improved to include both macrobenthic and other biological processes separately, with individual rates of activity. Additionally, some means of delineating the different degradation products of the benthos must be made, i.e. amino acid, urea, ammonium, and nitrate. (7) Secondary production of the model domain (GZ) for the case (2) parameters is 4. 3 mg c m -2 day-1 or approximately 0. 65 g c m -2 yr-1 calculated over a 150 day growth period. To compare this with the average primary production, it amounts to -0.2% of the estimated 354 g c m -2 yr-1 (Table 7). This estimate is low, however because the model does not consider the benthic secondary production. If we consider the benthic secondary production to be 10% of

PAGE 196

180 their ingestion, then this one component of production can be significantly higher. (8) Based on estimates of the amount of net carbon accumulation in the sediments of o. 24 g c m"2 day1 for case ( 2) of Table 8, perhaps 3. 6 g c m2 yr1 (calculated over 150 days) is buried in the Bering and Chukchi Sea sediments, i.e. -1% of the annual production. This is similar to carbon accumulation rates in the mid-Atlantic slope region (Walsh, et al., 1988). On a daily basis, the burial loss is 0.4 x 1011 g C day"1 during the first 81 days of the growing season (Table 9). (9) Finally, the results of Table 9 suggest that not all of the photosynthetic fixation of carbon is consumed within the first 81 days of the growing season. Approximately 25% of the carbon is still present in the model domain, and thus available for export to the Arctic or available to the other components of the ecosystem (Table 9), over the remaining months.

PAGE 197

181 6 0 REFERENCES Aagaard, K., C.H. Pease, and S .A. Salo. 1988. Beaufort Sea mesoscale circulation study -preliminary results. NOAA Technical PMEL-82. pp 1-171. Aagaard, K., A.T. Roach and J.D. Schumacher. 1985. On the wind-driven variability of the flow through Bering Strait. Journal of Geophysical Research, 90: 7213-7221. Aiken, J. 1981. A chlorophyll sensor for automatic, remote operation in the marine environment. Marine Ecology Progress Series, 255-239. Alton, M.S. 1974. Bering Sea benthos as a food resource for demersal fish populations. In: Oceanography of the D.W. Hood and E.J. Kelley, eds. Occasional paper No. 2, Institute of Marine Science, University of Alaska, Fairbanks, pp. 257-277. Arsen'ev, v.s. 1965. Circulation in the Bering Sea. Okeanologischeskie Issledovaniya (Section X of I .G.Y. Program), Akad. Nauka. SSSR. Moscow: 61-65. Arsen'ev, v.s. 1967. The current and water masses of the Bering Sea. (Translated from the Russian by s. Pearson, 1968). Biological Laboratory Bureau of Commercial Fisheries, Seattle. 146 pp. Bakkala, R.G., K. King, and W. Hirschberger. 1981. Commercial use and management of demersal fish. In: The Eastern Bering Sea Shelf: Oceanography and Resources. Volume 2, D.W. Hood and J.A. Calder, eds. University of Washington Press, Seattle, pp. 1015-1036. Barsdate, R.J., M. Nebert, and C .P. McRoy. 1974. Lagoon contributions to sediments and water of the Bering Sea. In: Oceanography of the Bering Sea with emphasis on renewable resources. o.w. Hood and E.J. Kelley, editors, University of Alaska, Fairbanks, pp. 553-576. Bedard, J. 1969. Feeding of the least, crested, and parakeet auklets around St. Lawrence Island, Alaska. Canadian Journal Q1 Zoology, 47: 1025-1050. Christensen, J.P., G.T. Rowe, and C.H. Clifford. 1983. The possible importance of primary amino nitrogen in nitrogen regeneration by coastal marine sediments in Buzzards Bay, Massachusetts. Int. Rev. Ges. Hydrobiol ... 68: 501-512.

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182 Coachman, L.K., K. Aagaard and R.B. Tripp. 1975. Bering Strait: Regional Physical Oceanography. University of Washington Press, Seattle. 172 pp. Coachman, L.K. and K. Aagaard. 1981. Reevaluation of water transports in the vicinity of Bering Strait. Ini The Eastern Bering Sea Shelf: Oceanography and Resources, Vol. I (D.W. Hood and J. A. Calder, eds.). University of Washington Press. Coachman, L.K. 1986. Circulation, water masses, and fluxes on the southeastern Bering Sea shelf. continental Shelf Research, 5: 23-108. Coachman, L.K. and K. Aagaard. 1988. Transports through Bering Strait: Annual and interannual variability. Journal Qf Geophysical Research. 15535-15539. Codispoti, L.A. 1965. Physical and chemical features of the waters in the East Siberian and Laptev Seas in the summer. Masters Thesis, University of Washington. 41pp. Codispoti,L.A., and F.A. Richards. 1968. Micronutrient distributions in the East Siberian and Laptev Seas during the summer of 1963. Arctic. 67-83. Cooney, R.T. 1981. Bering Sea zooplankton and micronekton communities with empahsis on annual production. In: The Eastern Bering Sea Shelf: Oceanography and Resources. Volume 2, o .w. Hood and J.A. Calder, eds. University of Washington Press, Seattle, pp 947-974. Cooney, R.T. and K.O. Coyle. 1982. Trophic implications of cross-shelf copepod distribution in the southeastern Bering Sea. Marine Biology, 70: 187-196. csanady, G.T. 1976. Mean-circulation in shallow seas. Journal of Geophysical Research, 5389-5399. oagg, M.J., J. Vidal, T.E. Whitledge, R.L. Iverson and J.J. Goering. 1982. The feeding, respiration and excretion of zooplankton in the Bering Sea during a spring bloom. Deep 45-63. Oodimead, A.J., F. Favorite and T. Hirano. 1963. Salmon of the North Pacific Ocean. II. Review of oceanography of the subarctic Pacific Region. Bulletin International North Pacific Commission, 13: 195. Eppley, R.W. 1972. Temperature and phytoplankton growth in the sea. Fisheries Bulletin, 70: 1063-1085.

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183 Eppley, R.W., J.N. Rogers, and J.J. McCarthy. 1969. Half saturation constants for the uptake of nitrate and ammonium by marine phytoplankton. Limnology and Oceanography, 14: 912-920. Favorite, F. 1966. Bering Sea. The Encyclopedia of Oceanography. (R.W. Fairbridge, ed.). Reinhold Publishing Corp., New York. 1021 pp. Fay, F.H., H.M. Feder, and s .w. Stoker. 1977. An estimation of the impact of the Pacific walrus population on its food resources in the Bering Sea. Final Report. Marine Mammal Commission. Washington, D.C. 38pp. Feder, H.M., R.H. Day, S.C. Jewett, K. McCumby, s. McGee, and s.v. Schonberg. 1985. Infauna of the northeastern Bering and southeastern Chukchi Sea. In: outer Continental Shelf Assessment Program, Final Reports of Principal Investigators 32. US department of Commerce NOAA, Washington DC, p 1-120. Fedorova, A.P. and A.S. Yakina. 1964. The passage of the Pacific Ocean water through the Bering Strait into the Chukchi Sea. Deep Sea Research, 11: 427-434. Frost, K.J. and L .F. Lowry. 1981. Foods and trophic relationships of cetaceans in the Bering Sea. In: The Eastern Bering Sea Shelf: Oceanography and Resources. Volume 2, D.W. Hood and J.A. Calder, eds. University of Washington Press, Seattle, pp. 825-836. Gordon, H.R. D.K. Clark, J.W. Brown, O.B. Brown, R.H. Evans and w.w Broenkow. 1983a. Phytoplankton pigment concentrations in the Middle Atlantic Bight: Comparison of ship determinations and czcs estimates. Applied Optics, 22: 20-36. Gordon, H.R., J.W. Brown, O.B. Brown, R.H. Evans and O.K. Clark. 1983b. Nimbus 7 CZCS: reduction of its radiometric sensitivity with time. Applied Optics. 22: 3929-3931. Grebmeier, J.M 1987. The ecology of benthic carbon cycling in the northern Bering and Chukchi Seas. PhD dissertation. Institute of Marine Science, University of Alaska, Fairbanks. Grebmeier, J.M., C.P. McRoy, and H.M. Feder. 1988. Pelagicbenthic coupling on the shelf of the northern Bering and Chukchi Seas I. Food supply source and benthic biomass. Marine Ecology Progress Series, 48: 57-67.

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184 Grebmeier, J.M., H.M. Feder, and C.P. McRoy. 1989. Pelagicbenthic coupling on the shelf of the northern Bering and Chukchi Seas. II. Benthic community structure. Marine Ecology Progress Series. (in press). Gregg, W.G. and K. Carder. 1989. A simple, very high spectral resolution solar irradiance model for cloudless maritime atmospheres. Limnology and Oceanography (submitted). Hansell, D.A., J.J. Goering, J.J. Walsh, C.P. McRoy, L .K. Coachman, and T.E. Whitledge. 1989. Summer phytoplankton production and transport along the shelf-break in the Bering Sea. Continental Shelf Research. (in press). Hopkins, T.L. Arctic Basin. 1969a. Zooplankton standing crops in the Limnology and Oceanography, 80-85. Hopkins, T.L., 1969b. Zooplankton biomass related to hydrography along the drift tract of Arlis II in the Acrtic Basin and the East Greenland Current. Journal Fisheries Research Board of Canada, 26: 305-310. Hufford, G.L. and D.M. Husby. 1970. Oceanographic survey of the Gulf of Anadyr, 2-16 August 1970. u.s. Coast Guard Oceanographic Report No. CG 373-52, Washington, D.C Ikeda, T. and s. Motoda. 1978. Zooplankton production in the Bering Sea calculated from 1956 1970 Oshoro Maru data. Marine Science Communications. 4: 329-346. Israeli, M. and S.A. Orszag. 1981. Approximation of radiative boundary conditions. Journal of Computational Physics, 41: 115-135. Johnson, M.W. 1958. Observations on inshore plankton collected during summer 1957 at Point Barrow, Alaska. Journal of Marine Research. 17: 272-281. Johnson, M.W. 1963. Zooplankton collections from the high Polar Basin with special reference to the Copepoda. Limnology and Oceanography, 8: 89-102. Kinder, T.H., L.K. coachman and J.A. Galt. 1975. The Bering Slope current system. Journal of Physical oceanography, 5: 231-244. Kinder, T.H. and J.D. Schumacher. 1981. Hydrographic structure over the continental shelf of the southeastern Bering Sea. In: The Eastern Bering Sea Shelf: oceanography and Resources. Volume 1, D.W. Hood and J.A. Calder eds. University of Washington Press, Seattle, 31-. 52.

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186 Nasu, K. 1974. Movement of baleen whales in relation to hydrographic conditions in the northern part of the North Pacific Ocean and the Bering Sea. In: Eastern Bering Shelf: Resources. Volume 2, D.W. Hood and J.A. Calder, eds. University of Washington Press, Seattle, pp. 345-361. Nerini, M. 1984. A review of gray whale feeding ecoloqy. Whale Eschrichtius robustus. Jones, M.L., S.L. swartz, and s. Leatherwood, eds Academic Press, New York, pp 423-450. Nihoul, J.C.J., E. Deleersnijer, s. Djenidi, and P. Brasseur. 1989. Mathematical visualization of general circulation fields and frontal structures in the Northern Bering Sea. (Submitted). Nihoul, J.J., F. Waleffe and s. Djenidi. 1986. A 3-dimensional numerical model of the northern Bering Sea. Environmental Software, 1: 76-81. Nixon, s.w., C.A. Oviatt, and s.s. Hale. 1976. Nitrogen regeneration and metabolism of coastal marine bottom communities. In: The Role of Terrestrial and Aquatic Organisms in Decomposition Processes. (J.M. Anderson and A MacFadyed, eds.), Blackwell, Oxford, pp. 269-283. Ohtani, K. 1970. Relative transport in the Alaskan stream in winter. Journal of the oceanographic society Q! Japan, 2....i. 271-282. Ohtani, K. 1973. Oceanic structure of the Bering sea. Memoirs of the Faculty of Fisheries, Hokkaido University, 21: 65-106. otto, R.S. 1981. Eastern Bering Sea crab fisheries. In: The Eastern Bering Sea Shelf: Oceanography and Resources. Volume 2, D.W. Hood and J.A. Calder, eds. University of Washington Press, Seattle, pp. 1037-1066. overland, J.E. and A.T. Roach 1987. Northward flow in the Bering and Chukchi Seas. Journal of Geophysical Research, lll. 7097-7105. Parker, P.L., and D. Scanlan, 1987. Stable carbon and nitrogen isot9pe studies. In: Component c. Organic matter production and degradation on the shelf of the north Bering/Chukchi shelves. ISHTAR 1986 Progress Report. Volume I. r"nstitute of Marine Science, University of Alaska, pp. 299-316.

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187 Reid, J.L. 1961. on the temperature, salinity and density differences between Atlantic and Pacific Oceans in the upper kilometer. Sea Research, ZL 265-275. Roach, P.J. 1982. Computational Fluid Dynamics. Hermosa Publishers, Albuquerque, New Mexico. 446 pp. Sambrotto, R.N., J.J. Goering, and C.P. McRoy. 1984. Large yearly production of phytoplankton in the western Bering Sea. Science, 225: 1147-1150. Smith, G.B. 1981. The biology of walleye pollock. In: The Eastern Bering Sea Shelf: Oceanography and Resources. Volume 1, D.W. Hood and J.A. Calder, eds. University of Washington Press, Seattle, pp. 527-551. Smith, R.C. and K.S. Baker. 1982. Oceanic chlorophyll concentrations as determined using Coastal Zone Color Scanner imagery. Marine Biology, 66: 269-279. smith, S.L. and J. Vidal. 1984. Spatial and temporal effects of salinity, temperature-and chlorophyll on the communities of zooplankton in the southeastern Bering Sea. Journal of Marine Research, 221-257. Smith, S.L. and J. Vidal. 1986. Variations in the distribution, abundance and development of copepods in the southeastern Bering Sea in 1980 and 1981. Continental Shelf Research, 5: 215-240. Spaulding, M. T. Isaji, D. Mendelsohn and A.C. Turner. 1987. Numerical simulation of the wind driven flow through the Bering strait. Journal Qf Physical Oceanography, 17: 1799-1816. Springer, A.M. and D.G. Roseneau. 1985. Copepod-based food webs: auklets and oceanography in the Bering Sea. Marine Ecology Progress Series, 2.!..i. 229-23.7. Springer, A.M., C.P. McRoy, and paradox of pelagic food webs in Patterns of primary production. (submitted). T.E . Whitledge, 1989a. The the northern Bering Sea. III Continental Shelf Research Springer, A.M., C.P. McRoy, and K.R. Turco. 1989b. The paradox of pelagic food webs in the northern Bering Sea: II. Zooplankton communities. Continental Shelf Research. (in press) steele, J.H. 1962. Environmental control of photosynthesis in the sea. Limnology and Oceanography, 7:137-150.

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188 Stigebrandt, A. 1984. The North Pacific: A global scale estuary. Journal Q1 Physical Oceanography. 14: 464-470. Stoker, s.w. 1978. Benthic invertebrate macrofauna of the eastern continental shelf of the Bering/Chukchi Seas. Ph.D. dissertation, Institute of Marine Science, University of Alaska, Fairbanks. Stoker, s.w. 1981. Benthic invertebrate macrofauna of the eastern Bering/chukchi continental shelf. In: Eastern Bering Shelf: Oceanography and Resources. Volume 2, D.W. Hood and J.A. Calder, eds. University of Washington Press, Seattle, p. 1069-1090. Strickland, J.D.H., and T.R. Parsons. 1968. A practical handbook of sea water analysis. Fisheries Research Board of Canada. 167: 1-311. Toulany, B, and c. Garrett. 1984. Geostrophic control of fluctuating barotropic flow through straits. Journal of Physical Oceanography, l!l 649-655. Vidal, J. and S .L. Smith. 1986. Biomass, growth and development of populations of herbivorous zooplankton in the southeastern Bering Sea.during summer. Deep Sea Research, 33: 523-556. Walsh, J.J. 1975. A spatial simulation model of the Peru upwelling ecosystem. Deep Sea Research. 22i 201-236. Walsh, J.J. 1988. On The Nature of Continental Shelves. Academic Press, New York. 520 pp. Walsh, J.J. and D.A. Dieterle, 1986. Simulation analysis of plankton dynamics in the northern Bering Sea. In: Marine Interfaces, Ecohydrodynamics, J.J. Nihoul, ed., Elsevier, Amsterdam. pp 401-428. Walsh, J.J., and C.P. McRoy, 1986. Ecosystem analysis in the southeastern Bering Sea. Continental Shelf Research, 5: 259-288. Walsh, J.J. C.P. McRoy, T.H. Blackburn, L.K. Coachman, J.J. Goering, K Henriksen, J.J. Nihoul, P.L. Parker, A.M. Springer, R.B. Tripp, T.E. Whitledge and C.D. Wirick. 1988. The role of the Bering Strait in the carbon/nitrogen fluxes of Polar Marine Ecosystems. In: Marine Living Systems of the Far North (L. Rey and V. Alexander, eds.), E.A. Brill, Leiden.

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189 Walsh, J.J., C.P. McRoy, L.K Coachman, J.J. Goering, J.J. Nihoul, T.E. Whitledge, T.H. Blackburn, P.L> Parker, C.D. Wirick, P.G. Shuert, J.M. Grebmeier, A.M. Springer, R .D. Tripp, D. Hansell, s. Djenidi, E. Deleersnijder, K. Henriksen, B.A. Lund, P. Anderson, F.E. Muller-Karger, and K. Dean. 1989. Carbon and nitrogen cycling within the Bering/Chukchi Seas: source regions for organic matter effecting AOU demands of the Arctic Ocean. Progress in Oceanography, 22: 279-361. Walsh, J.J. 1989. Arctic carbon sinks: present and future. Global Biogeochemical Cycles, (submitted) Whitledge, T.E., s. Malloy, c. Patton, and c. Wirick. 1981. Automated nutrient analysis in seawater. Brookhaven National Laboratory Technical Report BNL 51398. Whitledge, T.E. and C.D. Wirick. 1986. Development of a moored in situ fluorometer for phytoplankton studies. In: Tidal Mixing and Plankton Dynamics, Bowman, Yentsch, and Peterson (eds), Springer-Verlag. pp. 449-462. Whitledge, T.E., W.S. Reeburg, and J.J. Walsh. 1986. Seasonal inorganic nitrogen distributions and dynamics in the southeastern Bering Sea. Continental Shelf Research, 5: 109-132. Whitledge, T.E., .R.E. Bidigare, s. Zeeman, R.N. Sambrotto, P.F. Rescigno, J.R. Montgomery, T. McDonald, P.R. Jensen, D.M. Veight, and R.A. Gibson. 1988. Biological measurements and related chemical features in Soviet and u.s. regions of the Bering Sea. Continental Shelf Research, 8: 1299-1319. Worthington, L.V. 1970. The Norwegian Sea as a Mediterranean basin. Deep Sea Research, 17: 77-84.


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