scholarly journals Simulation of optimal arctic routes using a numerical sea ice model based on an ice-coupled ocean circulation method

2013 ◽  
Vol 5 (2) ◽  
pp. 210-226 ◽  
Author(s):  
Jong-Ho Nam ◽  
Inha Park ◽  
Ho Jin Lee ◽  
Mi Ok Kwon ◽  
Kyungsik Choi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Circulation Method ◽  
Model Based ◽  
Ice Model

scholarly journals Simulation of optimal arctic routes using a numerical sea ice model based on an ice-coupled ocean circulation method

2013 ◽  
Vol 5 (2) ◽  
pp. 210-226 ◽  
Author(s):  
Jong-Ho Nam ◽  
Inha Park ◽  
Ho Jin Lee ◽  
Mi Ok Kwon ◽  
Kyungsik Choi ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Circulation Method ◽  
Model Based ◽  
Ice Model

GPU acceleration of the FESOM-2 ocean and sea-ice model

2021 ◽  
Author(s):  
Gijs van den Oord ◽  
Alessio Sclocco ◽  
Georges-Emmanuel Moulard ◽  
David Guibert ◽  
Dmitry Sidorenko ◽  
...  
Keyword(s):  
Sea Ice ◽  
Ocean Circulation ◽  
Gpu Acceleration ◽  
Dominant Factor ◽  
Tracer Transport ◽  
Fortran Code ◽  
Mesh Resolution ◽  
Engineering Effort ◽  
Access Patterns ◽  
Ice Model

<p>FESOM-2 is a finite volume ocean circulation and sea ice model developed by the Alfred Wegener Institute (AWI). It solves the primitive equations using the hydrostatic and Bousinessq approximations on an unstructured grid, allowing seamless mesh resolution increase towards eddy-resolving scales in regions of high variability or along coast lines. FESOM-2 is a highly optimized MPI-parallel Fortran code that displays excellent scaling to tens of thousands of cores. In the context of ESiWACE-2 services, we have explored the benefits of GPU acceleration of FESOM-2 in a six-month engineering effort. We have determined the flux-corrected tracer transport, and in particular the advection of temperature and salinity, to be a dominant factor in the application profile and we have ported this routine to GPUs using both OpenACC and CUDA-C. We conclude that the memory access patterns in FESOM-2 are suitable to map onto GPU accelerators and that both strategies are viable options, giving significant speedups for tracer advection in high-resolution mesh configurations. We have benchmarked the ported application on Nvidia Kepler, Volta and Ampere architectures and observe that our tuned kernels can approach the peak memory bandwidth, and we also see that OpenACC offers a competitive performance with less development and maintenance effort. We conclude that an expansion of the OpenACC directives is the most promising road to utilize upcoming GPU-equipped exascale machines for FESOM-2.</p>

scholarly journals On The Interannual Variability Of A Diagnostic Ice-Ocean Model

1990 ◽  
Vol 14 ◽  
pp. 339-339
Author(s):  
W.D Hibler ◽  
Peter Ranelli
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Ocean Circulation ◽  
Model Simulation ◽  
Coupled Model ◽  
Ocean Model ◽  
The Arctic ◽  
Time Interval ◽  
Ice Drift ◽  
Ice Model

Sea-ice drift and dynamics can significantly affect the exchanges of heat between the atmosphere and ocean and salt fluxes into the ocean. The ice drift and dynamics, in turn, can be modified by the ocean circulation. This is especially true of the ice margin location whose seasonal characteristics are largely controlled by the substantial oceanic heat flux in the Greenland Sea due to convective overturning.A useful framework to analyze the interannual variability of ice–ocean interaction effects relevant to climatic change is the diagnostic ice–ocean model developed by Hibler and Bryan (1987). In this model, the oceanic temperature and salinity is weakly relaxed (except in the upper layer of the ocean which is essentially driven by the ice dynamic-thermodynamic sea-ice model) to climatological temperature and salinity data. This procedure allows seasonal and interannual variability to be simulated while still preventing the baroclinic characteristics of the ocean circulation from gradually drifting off into a total model defined state. However, in the work of Hibler and Bryan only the seasonal equilibrium characteristics of this model with the same forcing repeated year after year have been considered.In order to begin to examine the interannual behavior of this model, we have carried out a three-year simulation for the Arctic Greenland and Norwegian seas over the time period 1981–83. (The geographical region is essentially the same as used by Hibler and Bryan.) This three year simulation is carried out after an initial two year spin up using the 1981 atmospheric forcing data. For comparison purposes, an ice model simulation with only a fixed depth mixed layer was also carried out over this time interval.The results of these two simulations are analyzed with special attention to the ice margin characteristics in the Greenland and Norwegian seas to determine the role of ocean circulation on the variability there. The ice margin results are also compared to the variability in the northward transports of heat through the Faero-Shetland passage which in the fully-coupled model are calculated rather than specified.

scholarly journals On The Interannual Variability Of A Diagnostic Ice-Ocean Model

1990 ◽  
Vol 14 ◽  
pp. 339
Author(s):  
W.D Hibler ◽  
Peter Ranelli
Keyword(s):  
Sea Ice ◽  
Interannual Variability ◽  
Ocean Circulation ◽  
Model Simulation ◽  
Coupled Model ◽  
Ocean Model ◽  
The Arctic ◽  
Time Interval ◽  
Ice Drift ◽  
Ice Model

Sea-ice drift and dynamics can significantly affect the exchanges of heat between the atmosphere and ocean and salt fluxes into the ocean. The ice drift and dynamics, in turn, can be modified by the ocean circulation. This is especially true of the ice margin location whose seasonal characteristics are largely controlled by the substantial oceanic heat flux in the Greenland Sea due to convective overturning. A useful framework to analyze the interannual variability of ice–ocean interaction effects relevant to climatic change is the diagnostic ice–ocean model developed by Hibler and Bryan (1987). In this model, the oceanic temperature and salinity is weakly relaxed (except in the upper layer of the ocean which is essentially driven by the ice dynamic-thermodynamic sea-ice model) to climatological temperature and salinity data. This procedure allows seasonal and interannual variability to be simulated while still preventing the baroclinic characteristics of the ocean circulation from gradually drifting off into a total model defined state. However, in the work of Hibler and Bryan only the seasonal equilibrium characteristics of this model with the same forcing repeated year after year have been considered. In order to begin to examine the interannual behavior of this model, we have carried out a three-year simulation for the Arctic Greenland and Norwegian seas over the time period 1981–83. (The geographical region is essentially the same as used by Hibler and Bryan.) This three year simulation is carried out after an initial two year spin up using the 1981 atmospheric forcing data. For comparison purposes, an ice model simulation with only a fixed depth mixed layer was also carried out over this time interval. The results of these two simulations are analyzed with special attention to the ice margin characteristics in the Greenland and Norwegian seas to determine the role of ocean circulation on the variability there. The ice margin results are also compared to the variability in the northward transports of heat through the Faero-Shetland passage which in the fully-coupled model are calculated rather than specified.

A dynamic sea ice model based on the formation direction of leads

2009 ◽  
Vol 58 (1-2) ◽  
pp. 36-46 ◽  
Author(s):  
Christian Nohr ◽  
Göran Björk ◽  
Bo G. Gustafsson
Keyword(s):  
Sea Ice ◽  
Model Based ◽  
Ice Model

On the impact of sea ice in a global ocean circulation model

1997 ◽  
Vol 25 ◽  
pp. 111-115 ◽  
Author(s):  
Achim Stössel
Keyword(s):  
Southern Ocean ◽  
Sea Ice ◽  
Ocean Circulation ◽  
General Circulation ◽  
Thermohaline Circulation ◽  
Circulation Model ◽  
Deep Ocean ◽  
Global Ocean ◽  
Convective Activity ◽  
The Impact

This paper investigates the long-term impact of sea ice on global climate using a global sea-ice–ocean general circulation model (OGCM). The sea-ice component involves state-of-the-art dynamics; the ocean component consists of a 3.5° × 3.5° × 11 layer primitive-equation model. Depending on the physical description of sea ice, significant changes are detected in the convective activity, in the hydrographic properties and in the thermohaline circulation of the ocean model. Most of these changes originate in the Southern Ocean, emphasizing the crucial role of sea ice in this marginally stably stratified region of the world's oceans. Specifically, if the effect of brine release is neglected, the deep layers of the Southern Ocean warm up considerably; this is associated with a weakening of the Southern Hemisphere overturning cell. The removal of the commonly used “salinity enhancement” leads to a similar effect. The deep-ocean salinity is almost unaffected in both experiments. Introducing explicit new-ice thickness growth in partially ice-covered gridcells leads to a substantial increase in convective activity, especially in the Southern Ocean, with a concomitant significant cooling and salinification of the deep ocean. Possible mechanisms for the resulting interactions between sea-ice processes and deep-ocean characteristics are suggested.

scholarly journals Sea Ice Concentration Products over Polar Regions with Chinese FY3C/MWRI Data

2021 ◽  
Vol 13 (11) ◽  
pp. 2174
Author(s):  
Lijian Shi ◽  
Sen Liu ◽  
Yingni Shi ◽  
Xue Ao ◽  
Bin Zou ◽  
...  
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Ocean Circulation ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Atmospheric Effects ◽  
Observation Data ◽  
Long Time Series ◽  
Ice Concentration ◽  
Long Time

Polar sea ice affects atmospheric and ocean circulation and plays an important role in global climate change. Long time series sea ice concentrations (SIC) are an important parameter for climate research. This study presents an SIC retrieval algorithm based on brightness temperature (Tb) data from the FY3C Microwave Radiation Imager (MWRI) over the polar region. With the Tb data of Special Sensor Microwave Imager/Sounder (SSMIS) as a reference, monthly calibration models were established based on time–space matching and linear regression. After calibration, the correlation between the Tb of F17/SSMIS and FY3C/MWRI at different channels was improved. Then, SIC products over the Arctic and Antarctic in 2016–2019 were retrieved with the NASA team (NT) method. Atmospheric effects were reduced using two weather filters and a sea ice mask. A minimum ice concentration array used in the procedure reduced the land-to-ocean spillover effect. Compared with the SIC product of National Snow and Ice Data Center (NSIDC), the average relative difference of sea ice extent of the Arctic and Antarctic was found to be acceptable, with values of −0.27 ± 1.85 and 0.53 ± 1.50, respectively. To decrease the SIC error with fixed tie points (FTPs), the SIC was retrieved by the NT method with dynamic tie points (DTPs) based on the original Tb of FY3C/MWRI. The different SIC products were evaluated with ship observation data, synthetic aperture radar (SAR) sea ice cover products, and the Round Robin Data Package (RRDP). In comparison with the ship observation data, the SIC bias of FY3C with DTP is 4% and is much better than that of FY3C with FTP (9%). Evaluation results with SAR SIC data and closed ice data from RRDP show a similar trend between FY3C SIC with FTPs and FY3C SIC with DTPs. Using DTPs to present the Tb seasonal change of different types of sea ice improved the SIC accuracy, especially for the sea ice melting season. This study lays a foundation for the release of long time series operational SIC products with Chinese FY3 series satellites.

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