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 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.

Sea Ice Thickness Forecast Performance in the Barents Sea

2020 ◽  
Author(s):  
Laura Hume-Wright ◽  
Emma Fiedler ◽  
Nicolas Fournier ◽  
Joana Mendes ◽  
Ed Blockley ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Forecast Model ◽  
The Arctic ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Forecast Performance ◽  
In Situ Data ◽  
The Barents Sea ◽  
Ice Conditions

Abstract The presence of sea ice has a major impact on the safety, operability and efficiency of Arctic operations and navigation. While satellite-based sea ice charting is routinely used for tactical ice management, the marine sector does not yet make use of existing operational sea ice thickness forecasting. However, data products are now freely available from the Copernicus Marine Environment Monitoring Service (CMEMS). Arctic asset managers and vessels’ crews are generally not aware of such products, or these have so far suffered from insufficient accuracy, verification, resolution and adequate format, in order to be well integrated within their existing decision-making processes and systems. The objective of the EU H2020 project “Safe maritime operations under extreme conditions: The Arctic case” (SEDNA) is to improve the safety and efficiency of Arctic navigation. This paper presents a component focusing on the validation of an adaption of the 7-day sea ice thickness forecast from the UK Met Office Forecast Ocean Assimilation Model (FOAM). The experimental forecast model assimilates the CryoSat-2 satellite’s ice freeboard daily data. Forecast skill is evaluated against unique in-situ data from five moorings deployed between 2015 and 2018 by the Barents Sea Metocean and Ice Network (BASMIN) Joint Industry Project. The study shows that the existing FOAM forecasts produce adequate results in the Barents Sea. However, while studies have shown the assimilation of CryoSat-2 data is effective for thick sea ice conditions, this did not improve forecasts for the thinner sea ice conditions of the Barents Sea.

Advances in Sea Ice Mechanics in the USA

1987 ◽  
Vol 40 (9) ◽  
pp. 1232-1242 ◽  
Author(s):  
Devinder S. Sodhi ◽  
Gordon F. N. Cox
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Failure Process ◽  
Field Measurements ◽  
Small Scale ◽  
Ice Thickness ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Ice Failure ◽  
Ice Forces

A brief review of significant advances in the field of sea ice mechanics in the United States is presented in this paper. Emphasis is on ice forces on structures, as the subject relates to development of oil and gas resources in the southern Beaufort Sea. The main topics discussed here are mechanical properties, ice–structure interaction, modeling of sea ice drift, and oil industry research activities. Significant advances in the determination of ice properties are the development of testing procedures to obtain consistent results. Using stiff testing machines, researchers have been able to identify the dependence of tensile and compressive strengths on different parameters, eg, strain rate, temperature, grain size, c-axis orientation, porosity, and state of stress (uniaxial or multiaxial). Now reliable data exist on the tensile and compressive strengths of first-year and multi-year sea ice. Compressive strengths obtained from field testing of large specimens (6 × 3 × 2 m thick) were found to be within 30% of the strengths obtained from small samples tested in laboratory at the same temperature and strain rate as found in the field. Recent advances in the development of constitutive relations and yield criteria have incorporated the concept of damage mechanics to include the effect of microfracturing during the ice failure process. Ice forces generated during an ice–structure interaction are related to ice thickness and properties by conducting analytical or small-scale experimental studies, or both. Field measurements of ice forces have been made to assess the validity of theoretical and small-scale experimental results. There is good agreement between theoretical and small-scale experimental results for ice forces on conical structures. Theoretical elastic buckling loads also agree with the results of small-scale experiments. Though considerable insight has been achieved for ice crushing failure, estimation of ice forces for this mode is based on empirical relations developed from small-scale experiments. A good understanding of the ice failure process has been achieved when ice fails in a single failure mode, but our understanding of multi-modal ice failure still remains poor. Field measurements of effective pressure indicate that it decreases with increasing contact area. Research in fracture mechanics and nonsimultaneous failure is underway to explain this observed trend. Ice ridge formation and pile-up have been modeled, and the forces associated with these processes are estimated to be low. The modeling of sea ice drift has progressed to a point where it is able to determine the extent, thickness distribution, and drift velocity field of sea ice over the entire arctic basin. Components of this model relate to momentum balance, thermodynamic processes, ice thickness distribution, ice strength, and ice rheology.

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.

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.

Design Optimization of Ship’s Bow Sailing in Kara Sea and Barents Sea

2019 ◽  
Author(s):  
Jianfei Liu ◽  
Guoqing Feng ◽  
Huilong Ren ◽  
Wenjia Hu ◽  
Yuwei Sun
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Kara Sea ◽  
Structural Safety ◽  
Ice Thickness ◽  
Polar Regions ◽  
Sea Ice Thickness ◽  
Collision Model ◽  
The Barents Sea ◽  
Probability Density Curve

Abstract Ships performing their missions in the polar regions will inevitably suffer from sea ice collision, which will lead to structural safety problems. Therefore, ships should be designed according to the characteristics of polar sea ice to enable them to navigate safely in the polar regions. Based on the probability density curve of sea ice thickness and the occurrence frequency of sea ices of different sizes of the Kara Sea and the Barents Sea, this paper preliminarily designs ship’s bow sailing in the Kara Sea and the Barents Sea, establishes the ship’s bow-ice collision model and carries out numerical simulation to obtain the stress distribution. Then it optimizes the structure of the parts of the ship’s bow. After the optimization, the bow structure meets the strength requirements and the weight of the ship’s bow is relatively light.

scholarly journals On the influence of model physics on simulations of Arctic and Antarctic sea ice

2011 ◽  
Vol 5 (3) ◽  
pp. 687-699 ◽  
Author(s):  
F. Massonnet ◽  
T. Fichefet ◽  
H. Goosse ◽  
M. Vancoppenolle ◽  
P. Mathiot ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Initial Conditions ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Global Ocean ◽  
Small Scale ◽  
Ice Thickness ◽  
Model Skill ◽  
Antarctic Sea Ice

Abstract. Two hindcast (1983–2007) simulations are performed with the global, ocean-sea ice models NEMO-LIM2 and NEMO-LIM3 driven by atmospheric reanalyses and climatologies. The two simulations differ only in their sea ice component, while all other elements of experimental design (resolution, initial conditions, atmospheric forcing) are kept identical. The main differences in the sea ice models lie in the formulation of the subgrid-scale ice thickness distribution, of the thermodynamic processes, of the sea ice salinity and of the sea ice rheology. To assess the differences in model skill over the period of investigation, we develop a set of metrics for both hemispheres, comparing the main sea ice variables (concentration, thickness and drift) to available observations and focusing on both mean state and seasonal to interannual variability. Based upon these metrics, we discuss the physical processes potentially responsible for the differences in model skill. In particular, we suggest that (i) a detailed representation of the ice thickness distribution increases the seasonal to interannual variability of ice extent, with spectacular improvement for the simulation of the recent observed summer Arctic sea ice retreats, (ii) the elastic-viscous-plastic rheology enhances the response of ice to wind stress, compared to the classical viscous-plastic approach, (iii) the grid formulation and the air-sea ice drag coefficient affect the simulated ice export through Fram Strait and the ice accumulation along the Canadian Archipelago, and (iv) both models show less skill in the Southern Ocean, probably due to the low quality of the reanalyses in this region and to the absence of important small-scale oceanic processes at the models' resolution (~1°).

scholarly journals On the influence of model physics on simulations of Arctic and Antarctic sea ice

2011 ◽  
Vol 5 (2) ◽  
pp. 1167-1200 ◽  
Author(s):  
F. Massonnet ◽  
T. Fichefet ◽  
H. Goosse ◽  
M. Vancoppenolle ◽  
P. Mathiot ◽  
...  
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Initial Conditions ◽  
Thickness Distribution ◽  
Arctic Sea Ice ◽  
Global Ocean ◽  
Small Scale ◽  
Ice Thickness ◽  
Model Skill ◽  
Antarctic Sea Ice

Abstract. Two hindcast (1983–2007) simulations are performed with the global, ocean-sea ice models NEMO-LIM2 and NEMO-LIM3 driven by atmospheric reanalyses and climatologies. The two simulations differ only in their sea ice component, while all other elements of experimental design (resolution, initial conditions, atmospheric forcing) are kept identical. The main differences in the sea ice models lie in the formulation of the subgrid-scale ice thickness distribution, of the thermodynamic processes, of the sea ice salinity and of the sea ice rheology. To assess the differences in model skill over the period of investigation, we develop a set of metrics for both hemispheres, comparing the main sea ice variables (concentration, thickness and drift) to available observations and focusing on both mean state and seasonal to interannual variability. Based upon these metrics, we discuss the physical processes potentially responsible for the differences in model skill. In particular, we suggest that (i) a detailed representation of the ice thickness distribution increases the seasonal to interannual variability of ice extent, with spectacular improvement for the simulation of the recent observed summer Arctic sea ice retreats, (ii) the elastic-viscous-plastic rheology enhances the response of ice to wind stress, compared to the classical viscous-plastic approach, (iii) the grid formulation and the air-sea ice drag coefficient affect the simulated ice export through Fram Strait and the ice accumulation along the Canadian Archipelago, and (iv) both models show less skill in the Southern Ocean, probably due to the low quality of the reanalyses in this region and to the absence of important small-scale oceanic processes at the models' resolution (~1°).

scholarly journals Evaluation of AMSR-E Thin Ice Thickness Algorithm from a Mooring-Based Observation: How Can the Satellite Observe a Sea Ice Field with Nonuniform Thickness Distribution?

2019 ◽  
Vol 36 (8) ◽  
pp. 1623-1641 ◽  
Author(s):  
Haruhiko Kashiwase ◽  
Kay I. Ohshima ◽  
Yasushi Fukamachi ◽  
Sohey Nihashi ◽  
Takeshi Tamura
Keyword(s):  
Sea Ice ◽  
Global Climate ◽  
Thickness Distribution ◽  
Global Ocean ◽  
Small Scale ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Coastal Polynya ◽  
Ice Floes ◽  
Ice Production

AbstractThe quantification of sea ice production in coastal polynyas is a key issue to understand the global climate system. In this study, we directly compared Advanced Microwave Scanning Radiometer-EOS (AMSR-E) data with the sea ice thickness distribution obtained from a mooring observation during the winter of 2003 off Sakhalin in the Sea of Okhotsk to evaluate the algorithm for estimation of sea ice thickness in coastal polynyas. By using thermal ice thickness as a target physical quantity, we found that the obtained relationship between the polarization ratio (PR) and ice thickness can provide an appropriate AMSR-E algorithm to estimate thin ice thickness, irrespective of the uniform or nonuniform ice thickness field. The relationship between the PR value and thermal ice thickness is likewise consistent with the local PR–thickness relationship that is observed at individual ice floes. This is because both the PR value and thermal ice thickness are more sensitive to thinner ice. Furthermore, we evaluated the method for detection of active frazil in a coastal polynya by comparing with the mooring data, and subsequently modified it to classify the coastal polynya into three thin ice types, namely, active frazil, thin solid ice, and mixed ice (mixture of active frazil and thin solid ice). The improved algorithm successfully represents the thermal ice thickness even for a relatively small-scale polynya off Sakhalin and is expected to be useful for better quantification of sea ice production in the global ocean owing to its high versatility.

Sea Ice Thickness Forecast Performance in the Barents Sea

2021 ◽  
Author(s):  
Laura Hume-Wright ◽  
Emma Fiedler ◽  
Joana Mendes ◽  
Nicolas Fournier ◽  
Ed Blockley ◽  
...  
Keyword(s):  
Sea Ice ◽  
Barents Sea ◽  
Ice Thickness ◽  
Sea Ice Thickness ◽  
Forecast Performance ◽  
The Barents Sea
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