Narrow Results

This paper is intended to determine physical parameters describing volumetric heat capacity and thermal conductivity of sea ice in a quasi-linear thermodynamic system using field observations. The quasi-linear thermodynamic system of sea ice with unknown physical parameters is described, and the existence and uniqueness of its solution is proved. Then the physical parameters are taken as control variable, temperature deviations as objective function, and a parameter identification model is established. The existence of its optimal solution is discussed. To solve the identification model, a new algorithm containing genetic algorithm, Hooke–Jeeves algorithm and semi-implicit finite difference scheme is constructed. The physical parameters are calculated using the observations measured at Nella Fjord around Zhongshan Station, Antarctic in CHINARE 2006. For comparability and consistency with other works, a new international standard named TEOS-10 is used. To examine the validity of the identified results, another simulation for temperature profiles in different measurement period is operated. Numerical results show that better simulations of temperature distribution are possible with the identified parameters than EC1993. Therefore not only the identified parameters can be applied in sea ice modeling, but also this study can enrich and supplement observations of sea ice.

The advent of rapid sea ice melting in the Arctic, in the frame of climate change, gave rise to scenarios of fast-expanding commercial shipping along Arctic passages. However, about 20 years after the emergence of such scenarios, Arctic shipping did expand but not transit shipping. This chapter analyzes traffic based on Canadian, Russian, and Greenlandic statistics with a view to drawing a portrait of traffic in their respective Arctic waters. The pictures, along with the analyses of shipping constraints in Arctic waters, shed light on the opportunities and obstacles that orient the development of sea transportation in the Arctic. They underline that shorter distances are not paramount in the shipowners’ strategies but that destinational traffic, driven by the expansion of natural resource extraction, is the driving engine of shipping in the Arctic.

Every year there are as many as 20,000 scientific papers and reports published about the science of climate and climate change, and the resulting impacts and policy implications. The vast majority of these publications are rigorously done and are peer reviewed before publication, Since about 1990, on a time scale of roughly every 4–6 years, top experts are being asked to assess the state of the science and the implications of the changes occurring in the climate. Internationally, this occurs through the Intergovernmental Panel on Climate Change (IPCC), and for the United States, through the US National Climate Assessments (NCAs). These assessments provide important input to policy considerations, at international, national, and local levels…

Relatively few people have visited the Earth’s icy cap, the Arctic. Cold and inhospitable, and dark for several months of the year, the region has been home to Inuit, Saami and other indigenous peoples for thousands of years. Adapting to the harsh conditions required plenty of ingenuity and persistence. Yet, the Arctic region is changing, and changing rapidly. The sea ice covering most of the Arctic Ocean, the vast Greenland ice sheet, the snow cover on land and the large area of frozen ground called permafrost, are all melting away. Why? Scientists are unanimous in the verdict: climate change caused by man-made emissions of greenhouse gases (Pachauri et al., 2014). The Arctic has been warming twice faster that the global average as a result (Overland et al., 2015), causing the extensive melting documented by several decades of satellite records and measurements on the ground (Stroeve et al., 2012; Mouginot et al., 2019; Chadburn et al., 2017). It is no wonder that the Arctic is sometimes called the barometer of global risk from climate change…

Sea ice at the middle latitudes demonstrates high variability and inhomogeneity. By using image data from both active RADARSAT SAR and passive DMSP SSM/I measurements over the Bohai Sea ice in January 23, 1999, we present a data fusion analysis for sea ice detection and classification. Numerical modeling of scattering and thermal radiative transfer for the Bohai Sea shore ice is studied. The scattering index (SI), polarization index (PI), and the polarization ratio (PR) from passive observation of the brightness temperature (T_B) are defined to detect the existence of sea ice. Further, by using the co-horizontally polarized back-scattering coefficient (σ_hh) measured by the RADARSAT high resolution SAR, we can further classify the sea ice of the Bohai Sea. Temporal and spatial variations of these indexes are discussed and applied to monitor the Sea ice of the Bohai Sea.