scholarly journals A coupled pelagic-benthic-sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model

2019 ◽  
Author(s):  
Kelly Kearney ◽  
Albert Hermann ◽  
Wei Cheng ◽  
Ivonne Ortiz ◽  
Kerim Aydin
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Trophic Level ◽  
Bering Sea ◽  
Ocean Model ◽  
Biogeochemical Model ◽  
Interannual Variations ◽  
Lower Trophic Level ◽  
Regional Ocean Model ◽  
The Bering Sea

Abstract. The Bering Sea is a highly productive ecosystem, supporting a variety of fish, seabird, and marine mammal populations as well as large commercial fisheries. Due to its unique shelf geometry and the presence of seasonal sea ice, the processes controlling productivity in the Bering Sea ecosystem span the pelagic water column, the benthic sea floor, and the sympagic sea ice environments. The BESTNPZ model has been developed to simulate the lower trophic level processes throughout this region. Here, we present a version of this lower trophic level model coupled to a three-dimensional regional ocean model for the Bering Sea. We quantify the model's ability to reproduce key physical features of biological importance as well as its skill in capturing the seasonal and interannual variations in primary and secondary productivity. We find that the ocean model demonstrates considerable skill in replicating observed horizontal and vertical patterns of water movement, mixing, and stratification, as well as the temperature and salinity signatures of various water masses throughout the Bering Sea. It is also able to capture the mean seasonal cycle of primary production observed on the data-rich eastern middle shelf. However, its ability to replicate domain-wide patterns in nutrient cycling, primary production, and zooplankton community composition, particularly with respect to the interannual variations that are important in a fisheries management context, remains limited.

scholarly journals A coupled pelagic–benthic–sympagic biogeochemical model for the Bering Sea: documentation and validation of the BESTNPZ model (v2019.08.23) within a high-resolution regional ocean model

2020 ◽  
Vol 13 (2) ◽  
pp. 597-650 ◽  
Author(s):  
Kelly Kearney ◽  
Albert Hermann ◽  
Wei Cheng ◽  
Ivonne Ortiz ◽  
Kerim Aydin
Keyword(s):  
Nutrient Cycling ◽  
Sea Ice ◽  
Primary Production ◽  
Trophic Level ◽  
Bering Sea ◽  
Ocean Model ◽  
Interannual Variations ◽  
Lower Trophic Level ◽  
Regional Ocean Model ◽  
The Bering Sea

Abstract. The Bering Sea is a highly productive ecosystem, supporting a variety of fish, seabird, and marine mammal populations, as well as large commercial fisheries. Due to its unique shelf geometry and the presence of seasonal sea ice, the processes controlling productivity in the Bering Sea ecosystem span the pelagic water column, the benthic sea floor, and the sympagic sea ice environments. The Bering Ecosystem Study Nutrient-Phytoplankton-Zooplankton (BESTNPZ) model has been developed to simulate the lower-trophic-level processes throughout this region. Here, we present a version of this lower-trophic-level model coupled to a three-dimensional regional ocean model for the Bering Sea. We quantify the model's ability to reproduce key physical features of biological importance as well as its skill in capturing the seasonal and interannual variations in primary and secondary productivity over the past several decades. We find that the ocean model demonstrates considerable skill in replicating observed horizontal and vertical patterns of water movement, mixing, and stratification, as well as the temperature and salinity signatures of various water masses throughout the Bering Sea. Along the data-rich central portions of the southeastern Bering Sea shelf, it is also able to capture the mean seasonal cycle of primary production. However, its ability to replicate domain-wide patterns in nutrient cycling, primary production, and zooplankton community composition, particularly with respect to the interannual variations that are important when linking variation in productivity to changes in longer-lived upper-trophic-level species, remains limited. We therefore suggest that near-term application of this model should focus on the physical model outputs, while model development continues to elucidate potential mechanisms controlling nutrient cycling, bloom processes, and trophic dynamics.

scholarly journals Contrasting trends in sea ice and primary production in the Bering Sea and Arctic Ocean

2012 ◽  
Vol 69 (7) ◽  
pp. 1180-1193 ◽  
Author(s):  
Zachary W. Brown ◽  
Kevin R. Arrigo
Keyword(s):  
Sea Ice ◽  
Primary Production ◽  
Arctic Ocean ◽  
Bering Sea ◽  
Net Primary Productivity ◽  
Remote Sensing Data ◽  
Ice Cover ◽  
The Arctic ◽  
Light Limitation ◽  
The Bering Sea

Abstract Brown, Z. W., and Arrigo, K. R. 2012. Contrasting trends in sea ice and primary production in the Bering Sea and Arctic Ocean. – ICES Journal of Marine Science, 69: . Satellite remote sensing data were used to examine recent trends in sea-ice cover and net primary productivity (NPP) in the Bering Sea and Arctic Ocean. In nearly all regions, diminished sea-ice cover significantly enhanced annual NPP, indicating that light-limitation predominates across the seasonally ice-covered waters of the northern hemisphere. However, long-term trends have not been uniform spatially. The seasonal ice pack of the Bering Sea has remained consistent over time, partially because of winter winds that have continued to carry frigid Arctic air southwards over the past six decades. Hence, apart from the “Arctic-like” Chirikov Basin (where sea-ice loss has driven a 30% increase in NPP), no secular trends are evident in Bering Sea NPP, which averaged 288 ± 26 Tg C year−1 over the satellite ocean colour record (1998–2009). Conversely, sea-ice cover in the Arctic Ocean has plummeted, extending the open-water growing season by 45 d in just 12 years, and promoting a 20% increase in NPP (range 441–585 Tg C year−1). Future sea-ice loss will likely stimulate additional NPP over the productive Bering Sea shelves, potentially reducing nutrient flux to the downstream western Arctic Ocean.

scholarly journals An Atmospherically Driven Sea-Ice Drift Model for the Bering Sea

1984 ◽  
Vol 5 ◽  
pp. 111-114 ◽  
Author(s):  
C. H. Pease ◽  
J. E. Overland
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Drift Rate ◽  
Sensitivity Study ◽  
Ocean Model ◽  
Ice Thickness ◽  
Drag Coefficients ◽  
Drift Model ◽  
Ice Drift ◽  
The Bering Sea

A free-drift sea-ice model for advection is described which includes an interactive wind-driven ocean for closure. A reduced system of equations is solved economically by a simple iteration on the water stress. The performance of the model is examined through a sensitivity study considering ice thickness, Ekman-layer scaling, wind speed, and drag coefficients. A case study is also presented where the model is driven by measured winds and the resulting drift rate compared to measured ice-drift rate for a three-day period during March 1981 at about 80 km inside the boundary of the open pack ice in the Bering Sea. The advective model is shown to be sensitive to certain assumptions. Increasing the scaling parameter A for the Ekman depth in the ocean model from 0.3 to 0.4 causes a 10 to 15% reduction in ice speed but only a slight decrease in rotation angle (α) with respect to the wind. Modeled α is strongly a function of ice thickness, while speed is not very sensitive to thickness. Ice speed is sensitive to assumptions about drag coefficients for the upper (CA) and lower (CW) surfaces of the ice. Specifying CA and the ratio of CA to CW are important to the calculations.

scholarly journals An Atmospherically Driven Sea-Ice Drift Model for the Bering Sea

1984 ◽  
Vol 5 ◽  
pp. 111-114 ◽  
Author(s):  
C. H. Pease ◽  
J. E. Overland
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Drift Rate ◽  
Sensitivity Study ◽  
Ocean Model ◽  
Ice Thickness ◽  
Drag Coefficients ◽  
Drift Model ◽  
Ice Drift ◽  
The Bering Sea

A free-drift sea-ice model for advection is described which includes an interactive wind-driven ocean for closure. A reduced system of equations is solved economically by a simple iteration on the water stress. The performance of the model is examined through a sensitivity study considering ice thickness, Ekman-layer scaling, wind speed, and drag coefficients. A case study is also presented where the model is driven by measured winds and the resulting drift rate compared to measured ice-drift rate for a three-day period during March 1981 at about 80 km inside the boundary of the open pack ice in the Bering Sea.The advective model is shown to be sensitive to certain assumptions. Increasing the scaling parameter A for the Ekman depth in the ocean model from 0.3 to 0.4 causes a 10 to 15% reduction in ice speed but only a slight decrease in rotation angle (α) with respect to the wind. Modeled α is strongly a function of ice thickness, while speed is not very sensitive to thickness. Ice speed is sensitive to assumptions about drag coefficients for the upper (CA) and lower (CW) surfaces of the ice. Specifying CA and the ratio of CA to CW are important to the calculations.

On the Bering Sea ice edge front

1985 ◽  
Vol 90 (C2) ◽  
pp. 3185 ◽  
Author(s):  
Robin D. Muench ◽  
James D. Schumacher
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Ice Edge ◽  
Sea Ice Edge ◽  
The Bering Sea

scholarly journals A Study of the Fluctuations in Sea-Ice Extent of the Bering and Okhotsk Seas with Passive Microwave Satellite Observations (Abstract)

1987 ◽  
Vol 9 ◽  
pp. 236-236
Author(s):  
D.J. Cavalieri ◽  
C.L. Parkinson
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Sea Of Okhotsk ◽  
Large Scale ◽  
Phase Relationship ◽  
Kamchatka Peninsula ◽  
Sea Ice Extent ◽  
Atmospheric Circulation Patterns ◽  
Circulation Patterns ◽  
The Bering Sea

The seasonal sea-ice cover of the combined Bering and Okhotsk Seas at the time of maximum ice extent is almost 2 × 106 km2 and exceeds that of any other seasonal sea-ice zone in the Northern Hemisphere. Although both seas are relatively shallow bodies of water overlying continental shelf regions, there are important geographical differences. The Sea of Okhotsk is almost totally enclosed, being bounded to the north and west by Siberia and Sakhalin Island, and to the east by Kamchatka Peninsula. In contrast, the Bering Sea is the third-largest semi-enclosed sea in the world, with a surface area of 2.3 × 106 km2, and is bounded to the west by Kamchatka Peninsula, to the east by the Alaskan coast, and to the south by the Aleutian Islands arc.While the relationship between the regional oceanography and meteorology and the sea-ice covers of both the Bering Sea and Sea of Okhotsk have been studied individually, relatively little attention has been given to the occasional out-of-phase relationship between the fluctuations in the sea-ice extent of these two large seas. In this study, we present 3 day averaged sea-ice extent data obtained from the Nimbus-5 Electrically Scanning Microwave Radiometer (ESMR-5) for the four winters for which ESMR-5 data were available, 1973 through 1976, and document those periods for which there is an out-of-phase relationship in the fluctuations of the ice cover between the Bering Sea and the Sea of Okhotsk. Further, mean sea-level pressure data are also analyzed and compared with the time series of sea-ice extent data to provide a basis for determining possible associations between the episodes of out-of-phase fluctuations and atmospheric circulation patterns.Previous work by Campbell and others (1981) using sea-ice concentrations also derived from ESMR-5 data noted this out-of-phase relationship between the two ice packs in 1973 and 1976. The authors commented that the out-of-phase relationship is “... surprising as these are adjacent seas, and one would assume that they had similar meteorologic environments”. We argue here that the out-of-phase relationship is consistent with large-scale atmospheric circulation patterns, since the two seas span a range of longitude of about 60°, corresponding to a half wavelength of a zonal wave-number 3, and hence are quite susceptible to changes in the amplitude and phase of large-scale atmospheric waves.

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