• Journal of Coastal Disaster Prevention
• /
• v.4 no.3
• /
• pp.133-137
• /
• 2017

The Arctic region remains surrounded by sea ice during most of the period of the year. In the Arctic Ocean the Northern Sea Route (NSR) has been used as an important route for shipping. The arctic sea ice is decreasing since 1979; hence needs to be monitored. In this research work sea ice concentration in the recent years and sea ice concentration anomalies of few months with long term sea ice concentration are studied. The climatology of long term ice concentration data from various satellites, and the recent sea ice concentration data from Advanced Microwave Scanning Radiometer 2 (AMSR2) were used. The results show that sea ice concentration and sea ice extent in the Arctic region decreased by around 5% from 2015 to 2016, but in 2017 increased again in smaller amount in some areas like around Novaya Zemlya, and parts of the sea in between Greenland and Longyearbyen, and around Banks Island. The percentages of sea ice area in NSR for July 7 in 2015 to 2017 were 37%, 39% and 33%, respectively, indicating a large area (around ten thousand $km^2$) become ice free in 2017 compared to the previous year.

• Proceedings of the KSRS Conference
• /
• v.2
• /
• pp.892-895
• /
• 2006

The atmospheric effects on the retrieval of sea ice concentration from passive microwave sensors are examined using simulated data typical for the Arctic summer. The simulation includes atmospheric contributions of cloud liquid water and water vapor and surface wind on surface emissivity on the microwave signatures. A plane parallel radiative transfer model is used to compute brightness temperatures at SSM/I frequencies over surfaces that contain open water, first-year (FY) ice and multi-year (MY) ice and their combinations. Synthetic retrievals in this study use the NASA Team (NT) algorithm for the estimation of sea ice concentrations. This study shows that if the satellite sensor’s field of view is filled with only FY ice the retrieval is not much affected by the atmospheric conditions due to the high contrast between emission signals from FY ice surface and the signals from the atmosphere. Pure MY ice concentration is generally underestimated due to the low MY ice surface emissivity that results in the enhancement of emission signals from the atmospheric parameters. Simulation results in marginal ice areas also show that the atmospheric and surface effects tend to degrade the accuracy at low sea ice concentration. FY ice concentration is overestimated and MY ice concentration is underestimated in the presence of atmospheric water and surface wind at low ice concentration. In particular, our results suggest that strong surface wind is more important than atmospheric water in contributing to the retrieval errors of total ice concentrations over marginal ice zones.

• 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.

• Proceedings of the KSRS Conference
• /
• 1998.09a
• /
• pp.76-82
• /
• 1998

Increase of greenhouse gas due to $CO_2$ and CH$_4$ gases would cause the global warming in the atmosphere. According to the global circulation model, it is pointed out in the Okhotsk Sea that the large increase of atmospheric temperature might be occurredin this region by global warming due to the doubling of greenhouse effectgases. Therefore, it is very important to monitor the sea ice extents in the Okhotsk Sea. To improve the sea ice extents and concentration with more highly accuracy, the field experiments have begun to comparewith Airborne Microwave Radiometer (AMR) and video images installed on the aircraft (Beach-200). The sea ice concentration is generally proportional to the brightness temperature and accurate retrieval of sea ice concentration from the brightness temperature is important because of the sensitivity of multi-channel data with the amount of open water in the sea ice pack. During the field experiments of airborned AMR the multi-frequency data suggest that the sea ice concentration is slightly dependending on the sea ice types since the brightness temperature is different between the thin and small piece of sea ice floes, and a large ice flow with different surface signatures. On the basis of classification of two sea ice types, it is cleary distinguished between the thin ice and the large ice floe in the scatter plot of 36.5 and 89.0GHz, but it does not become to make clear of the scatter plot of 18.7 and 36.5GHz Two algorithms that have been used for deriving sea ice concentrations from airbomed multi-channel data are compared. One is the NASA Team Algorithm and the other is the Bootstrap Algorithm. Intrercomparison on both algorithms with the airborned data and sea ice concentration derived from video images bas shown that the Bootstrap Algorithm is more consistent with the binary maps of video images.

• International Journal of Naval Architecture and Ocean Engineering
• /
• v.12 no.1
• /
• pp.414-427
• /
• 2020

The finite element method is used to simulate the navigation of an ice-area bulk carrier in broken ice fields. The ice material is defined as elastic, and the simulations are accomplished at four model speeds and three ice concentrations. The movements of ice floes in the simulation are consistent with those in the model test, and the percentage deviation of the numerical ice resistance from the ice resistance in the model test can be controlled to be less than 15 %. The key characteristics of ice loads, including the average ice loads, extreme ice loads, and characteristic frequency, are analyzed thoroughly in a comprehensive manner. Moreover, the effects of sailing speed and ice concentration on the ice loads are analyzed. In particular, the stress distribution of ice floes is presented to help understand how model speed and concentration affect the ice loads. The "ice pressure" phenomenon is observed at 90 % ice concentration, and it is realistically reflected both in the time―and frequency―domain ice force curves.

• Journal of the Society of Naval Architects of Korea
• /
• v.54 no.1
• /
• pp.49-56
• /
• 2017

Recently, many research works on the icebreaking vessels have been published as the possibility of passing Arctic routes has been increasing. The model ship test on the pack ice model in the ice basin is actively carried out as a way to investigate the performance of icebreaking vessels. In this test, the concentration of pack ice is important since it directly affects the performance. However, it is difficult to measure the concentration because not only the pack ice has uneven shape but also it keeps floating around in the basin. In this paper, an algorithm to identify the concentration of pack ice is introduced. From a digital image of pack ice obtained in the ice basin, the goal is to measure the area of pack ice using an image processing technique. Instead of the general global binarization that yields numerical errors in this problem, a local binarization technique, coupled with image subdivision based on the quadtree structure, is developed. The concentration results obtained by the developed algorithm are compared with the manually measured data to prove its accuracy.

• Korean Journal of Remote Sensing
• /
• v.31 no.4
• /
• pp.347-351
• /
• 2015

Sea ice is a key parameter for understanding the climate change in cryosphere. In this study, we investigated the correlation with the factors that influenced change of the sea ice extent. We used the Sea Ice Concentration (SIC) from Ocean and Sea Ice Satellite Application Facility (OSI-SAF), and surface albedo provided by The Satellite Application Facility on Climate Monitoring (CM SAF). We converted the same temporal and spatial resolution of the data and detected the sea ice using SIC data. We performed the relationship analysis between SIC and sea ice albedo. As a result, we found they have a strong positive correlation. We performed the linear regression between SIC and sea ice albedo, and found they have high-level coefficient of determination. It shows using either SIC or sea ice albedo is possible to estimate the sea ice products.

• Journal of Astronomy and Space Sciences
• /
• v.33 no.4
• /
• pp.305-311
• /
• 2016

The spatial size and variation of Arctic sea ice play an important role in Earth's climate system. These are affected by conditions in the polar atmosphere and Arctic sea temperatures. The Arctic sea ice concentration is calculated from brightness temperature data derived from the Defense Meteorological Satellite program (DMSP) F13 Special Sensor Microwave/Imagers (SSMI) and the DMSP F17 Special Sensor Microwave Imager/Sounder (SSMIS) sensors. Many previous studies point to significant reductions in sea ice and their causes. We investigated the variability of Arctic sea ice using the daily sea ice concentration data from passive microwave observations to identify the sea ice melting regions near the Arctic polar ice cap. We discovered the abnormal melting of the Arctic sea ice near the North Pole during the summer and the winter. This phenomenon is hard to explain only surface air temperature or solar heating as suggested by recent studies. We propose a hypothesis explaining this phenomenon. The heat from the deep sea in Arctic Ocean ridges and/or the hydrothermal vents might be contributing to the melting of Arctic sea ice. This hypothesis could be verified by the observation of warm water column structure below the melting or thinning arctic sea ice through the project such as Coriolis dataset for reanalysis (CORA).

• Korean Journal of Remote Sensing
• /
• v.34 no.6_2
• /
• pp.1229-1238
• /
• 2018

We examined if a state of sea-ice in Bering Sea acts as a prelude of variation in that of Chukchi Sea by using satellites-based Arctic sea-ice concentration time series. Datasets consist of monthly values of sea-ice concentration during 36 years (1982-2017). Time series analysis armed with Transfer entropy is performed to describe how sea-ice data in Chukchi Sea is affected by that in Bering Sea, and to explain the relationship. The transfer entropy is a measure which identifies a nonlinear coupling between two random variables or signals and estimates causality using modification of time delay. We verified this measure checked a nonlinear coupling for simulated signals. With sea-ice concentration datasets, we found that sea-ice in Bering Sea is influenced by that in Chukchi Sea 3, 5, 6 months ago through the transfer entropy measure suitable for nonlinear system. Particularly, when a sea-ice concentration of Bering Sea has a local minimum, sea ice concentration around Chukchi Sea tends to decline 5 months later with about 70% chance. This finding is considered to be a process that inflow of Pacific water through Bering strait reduces sea-ice in Chukchi Sea after lowering the concentration of sea-ice in Bering Sea. This approach based on information theory will continue to investigate a timing and time scale of interesting patterns, and thus, a coupling inherent in sea-ice concentration of two remote areas will be verified by studying ocean-atmosphere patterns or events in the period.

• Economic and Environmental Geology
• /
• v.36 no.2
• /
• pp.141-148
• /
• 2003

We investigated the distribution of sea ice using various microwave remote sensing techniques including radar altimeter, radiometer, and scatterometer data in the part of Drake passage, Antarctica, between the area 45$^{\circ}$-75$^{\circ}$W and 55$^{\circ}$-66$^{\circ}$S. Topex/poseidon radar altimeter data were used to analyze the monthly distribution of sea ice surface area between 1992 and 1999 by using Geo_bad_1 flag or MGDR. From satellite radiometer measurements of DMSP's SSM/I, sea ice concentration was extracted during the period from 1993 to 1996. To select a value of ice concentration, normally ranging from 0 to 100%, that can be used as a critical value of judging the existence for ice, sea ice areas estimated from various ice concentrations of radiometer measurements were correlated with the area estimated from the radar altimeter measurements. As a result, 20% of ice concentration was selected, and, then this value was used to integrate radiometer data with radar altimeter and ERS-1/2 scatterometer data. To indirectly verify the result, the last 20 year's sea ice concentration was correlated with surface temperature data near Esper-anza Observation Station. The two data showed a high correlation coefficient of 0.86. The amount of sea ice and temperature variation were found to be closely related in the study area, and this indirectly verifies the result of this study. We provided a method to judge the existence of sea ice from ice concentration of satellite radiometer data and suggested a method to monitor more detailed temporal and spatial variation of sea ice distribution by integra-tion of various microwave remote sensing techniques.