Arctic warming is a global issue. The sea ice in the Arctic plays a crucial role in the climate system. We thought that a recent abnormality in many countries in the northern hemisphere could be related to the effects of shrinking sea ice in the Arctic. Many research groups monitor sea ice in the Arctic for climate research. Satellite remote sensing is an integral part of Arctic sea ice research due to the Arctic’s large size, making it difficult to observe with general research equipment, and its extreme environment that is difficult for humans to access. Along with monitoring recent weather changes, Korea scientists are conducting polar remote sensing using a Korean satellite series to actively cope with environmental changes in the Arctic. The Korean satellite series is known as KOMPSAT (Korea Multi-Purpose Satellite, Korean name is Arirang) series, and it carries optical and imaging radar. Since the organization of the Satellite Remote Sensing and Cryosphere Information Center in Korea in 2016, Korean research on and monitoring of Arctic sea ice has accelerated rapidly. Moreover, a community of researchers studying Arctic sea ice by satellite remote sensing increased in Korea. In this article, we review advances in Korea's remote sensing research for the polar cryosphere over the last several years. In addition to satellite remote sensing, interdisciplinary studies are needed to resolve the current limitations on research on climate change.

전지구 해양 해빙 예측시스템인 NEMO-CICE/NEMOVAR의 해빙 초기조건의 특성을 2013년 6월부터 2014년 5월까지 북극영역에 대하여 분석하였다. 이를 위하여 관측 자료와 재분석 자료를 모델의 초기조건과 비교하였다. 모델 초기조건은 관측에서 나타나는 해빙 면적과 해빙 두께의 월 변동을 잘 보이는 반면, 분석 기간 동안 관측과 재분석 자료보다 북극의 해빙 면적을 좁게, 해빙 두께를 얇게 나타내었다. 모델 초기조건의 북극 해빙 면적이 좁은 것은 해빙의 경계 지역에서 해빙 농도 초기조건이 약 20% 정도 재분석자료보다 낮기 때문이다. 또한 북극 평균 해빙 두께가 얇게 나타나는 이유는 연중 두꺼운 해빙이 유지되는 그린란드 및 북극 군도와 인접한 북극해 영역에서 모델의 초기조건이 약 60 cm 정도 얇기 때문이다.

The measurement of ice properties such as thickness, strength are important to know the performance of the ice breaking vessel. The measuring equipment of ice properties and methods are summarized in this paper. The actual measured data are also described. The strength of ice at Svalbard area on April 2010 is much stronger than the Chukchi Sea on August 2010. The mean strength of Svalbard is about 500 kPa and one of Chukchi Sea is 250 kPa. The first sea trial in Arctic sea using Araon was carried out in the Chukchi Sea. The power and speed was also measured to check the ship performance in ice. The speed was measured from GPS(Global Positioning System) and engine power was recorded from DPS(Dynamic Positioning system) of Araon. The design target of Araon in level ice is 3 knots in 1m thickness and 630 kPa flexible strength but mean speed in Chuckchi sea is 3.98 knots when 6.6 MW engine power, 2.4m ice thickness and 250 kPa strength. This results comes from the difference of ice types and the weak flexible strength of ice but it will be a good information to know the performance of Araon in similar ice condition.

Arctic sea ice as an indicator of climate change plays an important role in controlling global climate system. Thus, accurate observation and prediction of Sea Ice Concentration (SIC) is essential for understanding global climate change. In this study, we aim to improve the prediction accuracy of SIC by using machine learning and Regional Climate Model (RCM) data for a more robust method and a higher spatial resolution. Using the CORDEX RCM and NASA SIC data between January 1981 and December 2015, we developed three statistical models using Multiple Linear Regression (MLR), Support Vector Machine (SVM), and Deep Neural Network (DNN) which can deal with the non-linearity problem, respectively. The DNN model showed the best performance among the three models with the significant correlation between the predictive and observed SIC (r=0.811, p-value < 0.01)and the Root Mean Square Error (RMSE) of 0.258. With deeper considerations of the polar fronts and the characteristics of ocean current and tide, the DNN model can be applied for near future prediction of Arctic sea ice changes.

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).

본 연구의 목적은 빙해역을 운항하는 선박의 쇄빙시 깨진 해빙에 의해 선수부 좌현 외판에 작용하는 국부 빙하중을 계측한 신호의 특성을 조사하는 것이다. 한국 최초의 쇄빙연구선인 아라온호를 이용하여 2010년 8월초 북극해에서 실선 시험을 수행하여 선수부에 설치된 변형률 게이지로부터 국부적인 빙하중에 의한 신호를 계측하였다. 국부적인 빙하중에 의한 구조 응답 특성을 반영한 계측 신호는 전형적인 유체 충격압에 대한 하중-시간 이력과 다소 다른 특성을 갖는 것도 있음이 파악되었다. 본 연구에서는 계측 신호에 대한 시간 이력을 분석하고, 무차원화된 특성값들로 나타내었다.