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.

Analysis of tidal sea-ice movement using a drifting ice beacon array in the Barents Sea

2020 ◽  
Author(s):  
Amey Vasulkar ◽  
Lars Kaleschke ◽  
Martin Verlaan ◽  
Cornelis Slobbe
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Global Ocean ◽  
Series Data ◽  
Small Scale ◽  
Ice Thickness ◽  
Drag Coefficients ◽  
The Barents Sea ◽  
Ice Drift ◽  
Drifting Ice

<p>In an experiment to validate an ice forecast and route optimization system, an array of 15 ice drift beacons/buoys were deployed between Edgeøya and Kong Karls Land in the east of Svalbard to measure the sea ice movement. These beacons recorded data at a sampling frequency of 15 minutes in the duration from March 2014 to May 2014 with different start and end dates based on their life. The particularly short time step captures the small scale effect of tides on the drifting ice. In this region of the Barents Sea, the frequency of the inertial motion is very close to the M2 tidal frequency. Hence, it is not possible to extract the tidal motion from the time series data of the buoys by using a Fourier analysis. It is also likely that these effects will interact. Instead, we develop a physics-based <em>free drift</em> ice model that can simulate the drift at all tidal and other frequencies.</p><p>The model is forced by winds obtained from the ERA5 Reanalysis dataset of ECMWF and ocean currents obtained from the Global Ocean Analysis product of CMEMS. Due to the effect of tides, the model is also forced by the tides obtained from the Global Tide and Surge Model (GTSM v3.0) which is built upon Delft3D-FM unstructured mesh code. This free drift model is validated against 8 of the 15 beacon trajectories. The model along with the observed data can be then be used to obtain insights on the relationship between the sea ice velocities and the tides. This will be particularly useful to obtain the effect of ice drift on tides in tidal models.</p><p>The model uncertainty is mainly due to oceanic and atmospheric drag coefficients, C<sub>dw</sub> and C<sub>da</sub>, respectively, and the sea ice thickness, h<sub>i</sub>. This study also focuses on optimizing the ratio of drag coefficients (C<sub>dw</sub>/C<sub>da</sub>) for the different beacon trajectories while varying the ice thickness between 0.1 m - 1.5 m and the ice-air drag coefficient between (0.5-2.5)x10<sup>-3</sup>. This ratio facilitates the evaluation of the frictional drag between the ice-water interface and thus, helps in determining the effect of ice on tides in tidal models.</p>

scholarly journals Observation of sea-ice thickness fluctuation in the seasonal ice-covered area during 1992−99 winters

2001 ◽  
Vol 33 ◽  
pp. 449-456 ◽  
Author(s):  
Kazutaka Tateyama ◽  
Hiroyuki Enomoto
Keyword(s):  
Sea Ice ◽  
Bering Sea ◽  
Sea Of Okhotsk ◽  
Retrieval Algorithm ◽  
Ice Thickness ◽  
The Sea Of Okhotsk ◽  
Ice Volume ◽  
Fast Ice ◽  
Ice Floes ◽  
The Bering Sea

AbstractSea-ice fluctuations in the Sea of Okhotsk and the Bering Sea during the winters of 1992−99 were investigated by using the Special Sensor Microwave/ Imager dataset and a new ice-property retrieval algorithm This algorithm can distinguish between ice types such as fast ice floes, young ice and new ice, in an area covered by concentrations of >80% ice, and also has improved display resolution because it uses one of the 85 GHz channels. The ice thicknesses derived from the ice-thickness parameter of the new algorithm were compared with ship-based ice-thickness measurements, and were assumed to be 1−10, 11−34, 35−85 and 86−120 cm for new ice, young ice, floes (first-year ice) and fast ice, respectively. The results showed that ice volume can be small even if the ice area is large, due to thinness of the ice (e.g. in 1999 in the Sea of Okhotsk). A significant out-of-phase response, i.e. ice volume is larger in the Sea of Okhotsk when ice volume is smaller in the Bering Sea, was observed. The period of this see-saw showed two different time-scales, which were short (1 week) and long (2−4 weeks).

Parameterization of an Iceberg Drift Model in the Barents Sea

2009 ◽  
Vol 26 (10) ◽  
pp. 2216-2227 ◽  
Author(s):  
Intissar Keghouche ◽  
Laurent Bertino ◽  
Knut Arild Lisæter
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Reanalysis Data ◽  
Ocean Model ◽  
Form Drag ◽  
Drag Coefficients ◽  
Drag Forces ◽  
Drift Model ◽  
The Barents Sea ◽  
Iceberg Drift

Abstract The problem of parameter estimation is examined for an iceberg drift model of the Barents Sea. The model is forced by atmospheric reanalysis data from ECMWF and ocean and sea ice variables from the Hybrid Coordinate Ocean Model (HYCOM). The model is compared with four observed iceberg trajectories from April to July 1990. The first part of the study focuses on the forces that have the strongest impact on the iceberg trajectories, namely, the oceanic, atmospheric, and Coriolis forces. The oceanic and atmospheric form drag coefficients are optimized for three different iceberg geometries. As the iceberg mass increases, the optimal form drag coefficients increase linearly. A simple balance between the drag forces and the Coriolis force explains this behavior. The ratio between the oceanic and atmospheric form drag coefficients is similar in all experiments, although there are large uncertainties on the iceberg geometries. Two iceberg trajectory simulations have precisions better than 20 km during two months of drift. The trajectory error for the two other simulations is less than 25 km during the first month of drift but increases rapidly to over 70 km afterward. The second part of the study focuses on the sea ice parameterization. The sea ice conditions east of Svalbard in winter 1990 were too mild to exhibit any sensitivity to the sea ice parameters.

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 THIN ICE AREA EXTRACTION IN THE SEASONAL SEA ICE ZONES OF THE NORTHERN HEMISPHERE USING ASMR2 DATA

2019 ◽  
Vol XLII-3/W7 ◽  
pp. 5-9
Author(s):  
K. Cho ◽  
K. Miyao ◽  
K. Naoki
Keyword(s):  
Sea Ice ◽  
Brightness Temperature ◽  
Bering Sea ◽  
Microwave Radiometer ◽  
Ice Thickness ◽  
Horizontal Polarization ◽  
Brightness Temperatures ◽  
Extraction Algorithm ◽  
Scatter Plots ◽  
The Bering Sea

<p><strong>Abstract.</strong> Sea ice has an important role of reflecting the solar radiation back into space. In addition, the heat flux of ice in thin ice areas is strongly affected by the ice thickness difference. Therefore, ice thickness is one of the most important parameters of sea ice. In our previous study, the authors have developed a thin ice area extraction algorithm using passive microwave radiometer AMSR2 for the Sea of Okhotsk. The basic idea of the algorithm is to use the brightness temperature scatter plots of AMSR2 19&amp;thinsp;GHz polarization on difference (V-H) vs 19&amp;thinsp;GHz&amp;thinsp;V polarization. The algorithm was also applicable to the Bering Sea, and could extract most of the thin n ice areas. However, two problems have become clear. One was that some of the thin ice areas were not well extracted, and the other was that some of the consolidated ice were mis-extracted as thin ice areas. In this study, the authors have improved the thin ice area extraction algorithm to solve these problems. By adjusting the parameters of the algorithm applied to the brightness temperature scatter plots of AMSR2 19&amp;thinsp;GHz polarization difference (V-H) vs 19&amp;thinsp;GHz&amp;thinsp;V polarization, most of the thin ice areas were also well extracted in the Bering Sea. The authors also introduced an equation using the brightness temperatures difference of 89GHz vertical and horizontal polarization to reject the thin ice area misextracted over consolidated ice. By applying the above two methods to AMSR2 data, most of the thin ice areas in the Bering Sea were well extracted. The algorithm was also applied to the Gulf of St. Lawrence with good result. The thin ice area extracted data are planed to be approved by JAXA as a AMSR2 research product.</p>

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.

Sign in / Sign up

Export Citation Format

Share Document