• Atmosphere
• /
• v.24 no.4
• /
• pp.555-563
• /
• 2014

The impacts of dynamic and thermodynamic schemes used in the Community Ice CodE (CICE), the Los Alamos sea ice model, on sea ice concentration, extent and thickness over the Arctic and Antarctic regions are evaluated. Using the six dynamic and thermodynamic schemes such as sea ice strength scheme, conductivity scheme, albedo type, advection scheme, shortwave radiation method, and sea ice thickness distribution approximation, the sensitivity experiments are conducted. It is compared with a control experiment, which is based on the fixed atmospheric and oceanic forcing. For sea ice concentration and extent, it is found that there are remarkable differences between each sensitivity experiment and the control run over the Arctic and Antarctic especially in summer. In contrast, there are little seasonal variations between the experiments for sea ice thickness. In summer, the change of the albedo type has the biggest influence on the Arctic sea ice concentration, and the Antarctic sea ice concentration has a greater sensitivity to not only the albedo type but also advection scheme. The Arctic sea ice thickness is significantly affected by the albedo type and shortwave radiation method, while the Antarctic sea ice thickness is more sensitive to sea ice strength scheme and advection scheme.

• Journal of Ship and Ocean Technology
• /
• v.8 no.2
• /
• pp.20-28
• /
• 2004

A dynamic-thermodynamic sea ice model with 50-km spatial and 24-hour temporal resolution is used to investigate the spatial and long-term temporal variability of the sea ice cover the Arctic Basin. The model satisfactorily reproduces the averaged main characteristics of the sea ice and the sea ice extent in the Arctic Basin and its decrease in early 1990th. At times model allows to suppose partial recovery of sea ice cover in the last years of twenty century. The employment of explicit form for description of ridging gives opportunity to assume that the observed thinning is the result of reduction the intensity of ridging processes and to estimate long-term variability of probability the ridge free navigation in the different parts of the Arctic Ocean including the North Sea Route area.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.6 no.4
• /
• pp.225-233
• /
• 2001

In order to find out the role of sea-ice in the climate system, a thermodynamic sea-ice model has been developed and included in the ocean general circulation model, MOM2, for the construction of OGCM/sea-ice coupled model in this study. By using the model developed, seasonal mean sea-ice distribution has been simulated, first of all. The role of sea-ice in the sense of large scale ocean circulation has been studied by comparing the results of OGCM/sea-ice coupled model experiment with OGCM-standalone experiment. At the same time, the coupled model has been verified by comparing and analysing the results of the other models and observation. The coupled model has reasonably simulated the overall seasonal distribution of sea-ice in the high latitudes of both hemispheres. In the comparative analysis between the OGCM/sea-ice coupled and OGCM-standalone experiments, the sea-ice is playing important roles on maintaining not only the distributions of temperature and salinity in high latitudes of both hemispheres, but also the meridional ocean circulation associated with south ocean cell, southern hemisphere cell and zonal ocean circulation such as a circum-polar current.

• Journal of the Society of Naval Architects of Korea
• /
• v.52 no.2
• /
• pp.104-109
• /
• 2015

The Korea Research Institute of Ships & Ocean Engineering (KRISO) has an ice tank to make a test environment similar to the real ice in the polar sea in order to carry out model tests. One of the most important task of the ice tank is to generate the model ice to have similar material properties as sea ice. The primary properties of sea ice which influence the ice performance of ice breakers and ice-strengthened vessels traveling in the polar sea are ice thickness, flexural strength, density, modulus of elasticity and crystal structure etc. Among them, since the density of model ice influences the buoyance resistance of ice for the ship model, the accurate measurement of ice density should be used to obtain the accurate analysis results from the model test. In this paper, some existing methods to measure the density of model ice are reviewed and a new one is proposed to measure it accurately and easily as possible. In this study, the measuring system including an UTM and several measuring devices was established to obtain the model ice density. Polyethylene and ice specimens are used for a series of repeatable measurement tests. From the results, it was recognized that both of the displacement method and the weight/weight methods gave the stable and favorable tendency.

• Journal of the Society of Naval Architects of Korea
• /
• v.48 no.5
• /
• pp.390-395
• /
• 2011

The main purpose of ice model basin is to assess and evaluate the performance of the Arctic ships and offshore structures because the full-scale tests in ice covered sea are usually very expensive and difficult. There are various ice conditions, such as level ice, brash ice, pack ice and ice ridge, in the real sea. To estimate their capacities in ice tank accurately, an appropriate model ice sheet and prepared ice conditions copied from actual sea ice conditions are needed. Pack ice is a floating ice that has been driven together into a single mass and a mixture of ice fragments of varying size and age that are squeezed together and cover the sea surface with little or no open water. So Ice-class vessels and Icebreaker are usually operated in pack ice conditions for the long time of her voyage. The most ice model tests include the pack ice test with the change of pack ice concentration. In this paper, the effect of pack ice size and channel breadth in pack ice model test is conducted and analyzed. Also we presented some techniques for the calculation of pack ice concentration in the model test. Finally, we developed a new model test methodology of pack ice condition in square type ice tank.

• Journal of the Korean earth science society
• /
• v.36 no.1
• /
• pp.82-89
• /
• 2015

In this study, the characteristics of sea ice initial conditions generated from a global ocean and sea ice prediction system, the Nucleus for European Modeling of the Ocean (NEMO) - Los Alamos Sea Ice Model (CICE)/NEMOVAR were analyzed for the period June 2013 to May 2014 over the Arctic region. For the purpose, the observed and reanalyzed data were used to compare with the sea ice initial conditions. Results indicated that the variability of the monthly sea ice extent and thickness in model initial conditions were well represented as compared to the observation, while it was found that the extent and thickness of Arctic sea ice in initial data were narrower and thinner than those in reanalysis and observation for the period. The reason for the narrower sea ice extent in model initial conditions seems to be due to the fact that the initial sea ice concentration at the boundary area of sea ice was about 20 percent less than the reanalysis data. Also, the reason for the thinner sea-ice thickness in the Arctic region is due to the underestimation of Arctic sea ice thickness (about 60 cm) of the model initial conditions in the Arctic Ocean area adjacent to Greenland and Arctic archipelago where thick sea ice appears all the year round.

• Korean Journal of Remote Sensing
• /
• v.38 no.6_1
• /
• pp.1047-1056
• /
• 2022

Sea ice, frozen sea water, in the Artic is a primary indicator of global warming. Due to its importance to the climate system, shipping-route navigation, and fisheries, Arctic sea ice prediction has gained increased attention in various disciplines. Recent advances in artificial intelligence (AI), motivated by a desire to develop more autonomous and efficient future predictions, have led to the development of new sea ice prediction models as alternatives to conventional numerical and statistical prediction models. This study aims to evaluate the performance of the two-stream convolutional long-and short-term memory (TS-ConvLSTM) AI model, which is designed for learning both global and local characteristics of the Arctic sea ice changes, for the minimum September Arctic sea ice from 2001 to 2021, and to show the possibility for an operational prediction system. Although the TS-ConvLSTM model generally increased the prediction performance as training data increased, predictability for the marginal ice zone, 5-50% concentration, showed a negative trend due to increasing first-year sea ice and warming. Additionally, a comparison of sea ice extent predicted by the TS-ConvLSTM with the median Sea Ice Outlooks (SIOs) submitted to the Sea Ice Prediction Network has been carried out. Unlike the TS-ConvLSTM, the median SIOs did not show notable improvements as time passed (i.e., the amount of training data increased). Although the TS-ConvLSTM model has shown the potential for the operational sea ice prediction system, learning more spatio-temporal patterns in the difficult-to-predict natural environment for the robust prediction system should be considered in future work.

• Atmosphere
• /
• v.28 no.1
• /
• pp.15-24
• /
• 2018

Sea ice plays an important role in modulating surface conditions at high and mid-latitudes. It reacts rapidly to climate change, therefore, it is a good indicator for capturing these changes from the Arctic climate. While many models have been used to study the predictability of climate variables, their performance in predicting sea ice was not well assessed. This study examines the predictability of the Arctic sea ice extent from ensemble prediction systems. The analysis is focused on verification of predictability in each model compared to the observation and prediction in particular, on lead time in Sub-seasonal to Seasonal (S2S) scales. The S2S database now provides quasi-real time ensemble forecasts and hindcasts up to about 60 days from 11 centers: BoM, CMA, ECCC, ECMWF, HMCR, ISAC-CNR, JMA, KMA, Meteo France, NCEP and UKMO. For multi model comparison, only models coupled with sea ice model were selected. Predictability is quantified by the climatology, bias, trends and correlation skill score computed from hindcasts over the period 1999 to 2009. Most of models are able to reproduce characteristics of the sea ice, but they have bias with seasonal dependence and lead time. All models show decreasing sea ice extent trends with a maximum magnitude in warm season. The Arctic sea ice extent can be skillfully predicted up 6 weeks ahead in S2S scales. But trend-independent skill is small and statistically significant for lead time over 6 weeks only in summer.

• Korean Journal of Remote Sensing
• /
• v.4 no.1
• /
• pp.1-16
• /
• 1988

Multiyear ice is quite thick in general, and it needs to be distinguished from thinner types of ice because it represents a severe navigational hazard. Here, models are described for the radar backscatter from multiyear sea ice, based on simple scattering layers. Under cold conditions, the radiative transfer volume-scatter model can describe the backscattering from multiyear ice for frequencies higher than about X-band, while the surface scattering contribution has to be included for lower frequencies. A simple semi-empirical model is shown to be a good approximation to the radiative transfer model in describing the volume scattering from multiyear ice.

• Journal of the Society of Naval Architects of Korea
• /
• v.48 no.5
• /
• pp.457-464
• /
• 2011

This paper deal with the validation of correction method of ice strength and thickness to the sea trial condition based on the ice model test results. It is very difficult to conduct the model test corresponding to the sea trial condition exactly. In addition, the available sea trial data is not sufficient for the validation of correction method. In the present study, the model test results of Terry-Fox ice breker have been used to compare the corrected results of sea trial test by varying its thickness and strength of model ice. The HSVA and ITTC methods have been applied to the present comparisions and the required power has been also validated by using the HSVA method. There are rather good agreement between the sea trial result and model test corrected by the HSVA and ITTC method. The more comparisons are expected to be carried out in near future.