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Using the psychometric paradigm of risk in conjunction with surveys of the Michigan public (n = 638) and a regional planning organization (n = 65), we examine the perceived risk and concerns associated with underwater oil pipelines, the Enbridge Line 5 pipeline in particular, and oil spills under ice. The fate of Line 5 is heavily debated in Michigan, specifically the portion that traverses the Straits of Mackinac, which can be ice-covered for months. Scant literature examines how individuals perceive the risk associated with Line 5, its alternatives, or potential spills in open water or under ice. Here we identify considerable concern regarding both the pipeline and the potential for spills under ice on behalf of the public, and increased concern about spills under ice on behalf of the planning organization. Organization members' concerns are significantly predicted by beliefs about the difficulty in remediating spills, however not by beliefs about spills' likelihood, difficulty in detection, noticeability, or consequences. Our results identify the need to better examine and communicate the risks associated with underwater pipelines and spills, both in open water and under ice, as well as options for remediating oil captured under ice. Furthermore, we recommend the adoption of decision-making and risk governance processes that explicitly expand analysis of the social, economic and environmental tradeoffs of underwater pipelines such as Line 5. [ABSTRACT FROM AUTHOR]

High-resolution lake ice/water observations retrieved from satellite imagery through efficient, automated methods can provide critical information to lake ice forecasting systems. Synthetic aperture radar (SAR) data is well-suited to this purpose due to its high spatial resolution (approximately 50 m). With recent increases in the volume of SAR data available, the development of automated retrieval methods for these data is a priority for operational centres. However, automated retrieval of ice/water data from SAR imagery is difficult, due to ambiguity in ice and open water signatures, both in terms of image tone and in terms of parameterized texture features extracted from these images. Convolutional neural networks (CNNs) can learn features from imagery in an automated manner, and have been found effective in previous studies on sea ice concentration estimation from SAR. In this study the use of CNNs to retrieve ice/water observations from dual-polarized SAR imagery of two of the Laurentian Great Lakes, Lake Erie and Lake Ontario, is investigated. For data assimilation, it is crucial that the retrieved observations are of high quality. To this end, quality control measures based on the uncertainty of the CNN output to eliminate incorrect retrievals are discussed and demonstrated. The quality control measures are found to be effective in both dual-polarized and single-polarized retrievals. The ability of the CNN to downscale the coarse resolution training labels is demonstrated qualitatively. [ABSTRACT FROM AUTHOR]

Lake Ontario ice conditions are statistically linked to regional temperatures recorded in Toronto, during the most recent climate normal (1980/81–2009/10). A metric was developed to capture the net melting effect of average winter temperatures to characterize lake ice conditions, referred to as Net Melting-Degree Days (NMDD). This metric was able to account for 78% of lake ice interannual variability (R2 = 0.783, P < 0.001). Based on NMDD parameters, current lake ice conditions were characterized in four ways: heavy, moderate, light and very light. Lake Ontario ice conditions were reconstructed to create a hindcast for the span of the instrumental temperature record (1840/41–1979/80). Based on a decadal analysis, heavy ice seasons decreased significantly (R2 = 0.658, P < 0.001) from the 1840s to the 2000s, declining from an average of 6 heavy ice seasons per decade during the most distant climate normal (1840s to 1960s) to an average of only 1 heavy ice season per decade during the most recent climate normal (1980s to 2000s). Finally, lake ice conditions are projected to the end of the 21st century, using an optimal ensemble of Global Climate Model outputs for two different climate change scenarios (RCP4.5, RCP8.5). Heavy ice seasons no longer occur as early as the 2050s under both RCP4.5 and RCP8.5. Whereas, very light ice seasons go from being an extreme in the baseline period (10%), to the dominant characterization of Lake Ontario ice conditions by the 2080s, for both RCP4.5 (73%) and RCP8.5 (100%). [ABSTRACT FROM AUTHOR]

Observations of lake ice at the shore, complete ice cover, ice duration, ice thickness and other measures for 18 Polish lakes were collected for the 50 year period (1961–2010). Average ice dates in early winter became later: first appearance of ice along shore 2.3 days decade −1 and complete ice cover 1.2 days decade −1 while complete ice cover disappeared earlier (5.6 days decade −1 ) as did last ice at the shore (4.3 days decade −1 ). The duration of ice cover decreased by 5.6 days decade −1 and average ice thickness declined by 6.1 cm decade −1 . The magnitude of these values for individual lakes decreased from eastern to western Poland. This geographic gradient is likely related to regional atmospheric circulation because in winter this part of Europe is strongly affected by continental air, an influence that is greater in the east. A multivariate redundancy analysis (RDA), used in order to examine the dependence of ice measures on lake physical properties and location, indicated longitude and altitude as key factors explaining lake ice dynamics such as the disappearance of ice and ice cover, ice cover duration and thickness. Lake volume and average depth influenced mostly the appearance of ice and ice cover. [ABSTRACT FROM AUTHOR]

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Abstract: A hydrodynamic model that includes ice processes and is optimized for parallel processing was configured for Lake Erie in order to study the ice–water coupling processes in the lake. A hindcast from April 2003 to December 2004 with hourly atmospheric forcing was conducted. The model reproduced the seasonal variation of ice cover, but the development of ice extent in January and its decay in March somewhat preceded the observations. Modeled lake circulation in ice-free seasons is consistent with previous studies for Lake Erie. Thermal structure of the lake was reasonably comparable to both satellite-derived observations and in-situ measurements, with mean differences ranging from −2°C to 4°C, depending on the season. The impacts of ice–water stress coupling and basal melting of ice were examined based on numerical experiments. The results show that: 1) ice–water stress coupling significantly dampens the subjacent lake circulation in winter due to packed ice cover that slows down the surface water, and 2) basal melting of ice contributes to widespread ice cover in the lake. The demonstrated model validity could lead to further studies of ice–water processes in the lake, including interannual variation and impacts on ecosystems. [Copyright &y& Elsevier]

The results from a winter field study in 2009-2010 in Lake Vanajanselkä are presented. This lake is shallow, eutrophic, and annually ice-covered on average for 5 months. The ice sheet was 41-55 cm thick at the annual maximum, consisting of snow-ice and congelation ice. The e-folding depth of light intensity was 50-100 cm for congelation ice and 5-10 cm for snow. The water body had a 4-m thick upper mixed layer and lower continuously stratified layer. Fall cooling process was crucial to determine the temperature of the lower layer at freeze-up, anything within 0-4 °C. Oxygen concentration decreased in winter, especially close to the bottom sediments, and carbon dioxide concentration increased due to respiration activity. Phytoplankton production and biomass level were low or very low and, therefore, heterotrophic and mixotrophic species were abundant. Oxygen depletion in the hypolimnium had several chemical and ecological consequences, such as release of phosphorus from the bottom sediments. In spring, just before the ice-out, photosynthesis was at a high level beneath the ice due to improved light conditions and started to elevate the oxygen concentration in the topmost water layer. [ABSTRACT FROM AUTHOR]

Ice cover in the Hudson Bay region (HBR) goes through a complete cryogenic cycle each year. Freeze-up typically occurs in October and November, ice cover reaches its peak thickness from late March to May, and water bodies in the HBR are usually ice-free beginning in early August. In this study, the timing and magnitude of the annual peak in ice thickness were identified for each year from weekly ice observations compiled by the Canadian Ice Service. The Mann*Kendall test was used to determine the statistical significance of the temporal trends, and their magnitude was estimated using the Theil-Sen approach. The results indicate an asymmetry in temporal trends of land-fast ice thickness; statistically significant thickening of the ice cover over time was detected on the western side of Hudson Bay, while a slight thinning lacking statistical significance was observed on the eastern side. This asymmetry is related to the variability of air temperature, snow depth, and the dates of ice freeze-up and break-up. Increasing maximum ice thickness at a number of stations is correlated to earlier freeze-up due to negative temperature trends in autumn. Nevertheless, changes in maximum ice thickness were reciprocal to the variability in the amount of snow covering the ground. These results are in contrast to the projections from general circulation models (GCMs), and to the reduction in sea-ice extent and thickness observed in other regions of the Arctic. This contradiction must be addressed in regional climate change impact assessments. [ABSTRACT FROM AUTHOR]