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Decadal Arctic Sea Ice Variability and Its Implication in Climate Change
Published in Neloy Khare, Climate Change in the Arctic, 2022
The NASA team SSMI sea ice algorithm (Cavalieri et al. 1984) and the Bootstrap algorithms (Comiso 1995) were developed to derive sea ice concentration from Tb using empirically derived algorithms. The algorithms use different combinations of frequency and polarization channels but generally share the common approach of determining the coefficients for pure surface types (so-called ‘tie points’) (100% ice, 100% water) and interpolating between these points to find the ice concentration that corresponds to a set of Tb values. The NASA team uses polarization ratios at 19 GHz and gradient ratios of vertically polarized channels at 19 and 37 GHz to obtain the tie points for sea ice and open water. The advantage of the NASA team algorithm is that it takes into account changes in surface temperatures through the use of ratios of differences of Tb. The polarization ratios are, however, affected by the physical characteristics of some types of sea ice cover (Mätzler et al. 1984).
Remote Sensing of Sea Ice Hazards
Published in George P. Petropoulos, Tanvir Islam, Remote Sensing of Hydrometeorological Hazards, 2017
In recent years, ice edge prediction has drawn utmost attention of researchers and stakeholders (e.g., oil companies) who require accurate location of sea ice edge for hazard-free ship navigation, conducting scientific experiments, and planning of infrastructure development for installation of new offshore oil platforms. U.S. National Ice Center (NIC) generates daily ice edge products using multiple sources of NRT satellite data (visible, infrared, passive microwave, scatterometer, and SAR images), buoy data, satellite-derived products, and meteorological data (Posey et al. 2015). These products define ice edge as areas of less than 10% sea ice concentration (Figure 7.14) and are used for navigational purposes to avoid ice hazards. Ice edge detection techniques combining different kinds of satellite sensors are also in use, for example, QuikSCAT, AMSR-E, and SSM/I (Meier and Stroeve 2008). In recent decades, the volume of data from various kinds of satellite sensors, airborne sensors, and in situ observations have grown substantially. This useful data is assimilated in thermodynamic/numerical models of ice edge detection to get the most accurate sea ice–water edge discrimination, and the products are continually updated with new observations at high temporal and spatial resolution (Posey et al. 2015).
Future changes in extreme storm surges based on mega-ensemble projection using 60-km resolution atmospheric global circulation model
Published in Coastal Engineering Journal, 2019
Nobuhito Mori, Tomoya Shimura, Kohei Yoshida, Ryo Mizuta, Yasuko Okada, Mikiko Fujita, Temur Khujanazarov, Eiichi Nakakita
A series of ensemble projections were conducted using two different configurations (Mizuta et al. 2017). The forcing for the AGCM was sea surface temperature (SST), sea-ice concentration (SIC), sea-ice thickness (SIT), global mean concentration of greenhouse gases (GHG), and 3D distributions of ozone and aerosols. The ensemble covers a 60-year time frame and investigates two ensemble set scenarios: a historical climate run and a + 4K future climate run. The historical climate runs from 1951 to 2010 used historical SST, SIC, and SIT with perturbations related to the observed errors from SST analyses (δSST) (see Ishii et al. 2005). A total of 100 ensemble runs were performed for the historical climate condition runs. The future climate condition was assumed to be +4K warmer than the preindustrial climate, a constantly applied temperature increase that corresponds to the end of the 21st century under the Representative Concentration Pathways RCP8.5 scenario in CMIP5, approximately. The constant +4K experiment run is quite different from the general GHG emission scenario run, but it is important when assessing extreme phenomena to avoid the effects of trends for extreme values. The future climate runs excluded 60-year trends, and included 90 ensemble runs perturbed by two different uncertainty factors. The perturbations for the future climate experiments were δSST and climatological SST warming patterns (ΔSST). The ΔSST values were given by the differences between the 1991–2010 and 2080–2099 values in the historical and RCP8.5 experiments as determined by CMIP5. Six different ΔSSTs and 15 δSST were considered for the future ensemble run.
A voyage planning tool for ships sailing between Europe and Asia via the Arctic
Published in Ships and Offshore Structures, 2020
Zhiyuan Li, Jonas W. Ringsberg, Francisco Rita
Safe navigation in ice-infested waters depends on the severity of ice conditions, the ship’s ice class, and the ship speed. Along with the Polar Code, IMO adopted the Polar Operational Limit Assessment Risk Indexing System (POLARIS), as a guidance on methodologies for assessing operational capabilities and limitations in ice (TraFi 2010). In contrast to the Polar Code, POLARIS is not mandatory, but could be included in some decision-support tools. In this study, POLARIS is implemented in the voyage planning tool. POLARIS calculates the risk index based on the following factors: the ship’s ice class, the sea ice thickness (SIT), and the sea ice concentration (SIC)