Different mechanisms of Arctic first-year sea-ice ridge consolidation observed during the MOSAiC expedition

Sea-ice ridges constitute a large fraction of the ice volume in the Arctic Ocean, yet we know little about the evolution of these ice masses. Here we examine the thermal and morphological evolution of an Arctic firstyear sea-ice ridge, from its formation to advanced melt. Initially the mean keel depth was 5.6 m and mean sail height was 0.7 m. The initial rubble macroporosity (fraction of seawater filled voids) was estimated at 29% from ice drilling and 43%–46% from buoy temperature. From January until mid-April, the ridge consolidated slowly by heat loss to the atmosphere and the total consolidated layer growth during this phase was 0.7 m. From mid-April to mid-June, there was a sudden increase of ridge consolidation rate despite no increase in conductive heat flux. We surmise this change was related to decreased macroporosity due to transport of snow-slush to the ridge keel rubble via adjacent open leads. In this period, the mean thickness of the consolidated layer increased by 2.1 m. At the peak of melt in June–July we suggest that the consolidation was related to the refreezing of surface snow and ice meltwater and of ridge keel meltwater (the latter only about 15% of total consolidation). We used the morphology parameters of the ridge to calculate its hydrostatic equilibrium and obtained a more accurate estimate of the actual consolidation of the keel, correcting from 2.2 m to 2.8 m for average keel consolidation. This approach also allowed us to estimate that the average keel melt of 0.3 m, in June–July, was accompanied by a decrease in ridge draft of 0.9 m. An ice mass balance buoy in the ridge indicated total consolidation of 2.8 m, of which 2.1 m was related to the rapid mode of consolidation from April to June. By mid-June, consolidation resulted in a drastic decrease of the macroporosity of the interior of keel while the flanks had little or no change in macroporosity. These results are important to understanding the role of ridge keels as meltwater sources and sinks and as sanctuary for ice-associated organisms in Arctic pack ice

Snowmelt contribution to Arctic first-year ice ridge mass balance and rapid consolidation during summer melt

An assessment of potential groundwater areas in the Ifni basin, located in the western AntiAtlas range of Morocco, was conducted based on a multicriteria analytical approach that integrated a set of geomorphological and hydroclimatic factors influencing the availability of this resource. This approach involved the use of geographic information systems (GIS) and hierarchical analytical process (AHP) models. Different factors were classified and weighted according to their contribution to and impact on groundwater reserves. Their normalized weights were evaluated using a pairwise comparison matrix. Four classes of potentiality emerged: very high, high, moderate, and low, occupying 15.22%, 20.17%, 30.96%, and 33.65%, respectively, of the basin’s area. A groundwater potential map (GWPA) was validated by comparison with data from 134 existing water points using a receiver operating characteristic (ROC) curve. The AUC was calculated at 80%, indicating the good predictive accuracy of the AHP method. These results will enable water operators to select favorable sites with a high groundwater potential

Measurements and Simulations of Consolidation in First-Year Sea Ice Ridges, and some Aspects of Mechanical Behaviour

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Consolidation of fresh ice ridges for different scales

This study characterizes the refreezing process of deformed ice. Twenty laboratory experiments in ice ridge consolidation were conducted to study the influence of ridge blocks size, initial temperature, and top surface roughness on the consolidation rate. Experiments covered a ridge block thickness range of 2–6 cm, initial block temperatures from −1 °C to −23 °C, ridge sail height up to 3 cm, and consolidated layer thickness up to 14 cm. Experiments were conducted with the average value of the convectional heat transfer coefficient of 20 W/m2K. The presented analytical model for ridge solidification was able to predict the observed ice growth rates and differences between level ice and consolidated layer thicknesses at different stages of the experiments. For the provided experiments, the consolidated layer was as much as 2.2–2.8 times thicker than the surrounding ice level. The consolidation rate was lower than in the analytical solution at the start of the experiment and approached the analytical solution only when the thickness of the surrounding level ice was larger than the ridge void width. The developed numerical model confirmed the observed experimental effects from the block size, initial temperature and surface roughness. Both numerical and analytical models can predict solidification rates for previous studies at the large range of scales for both fresh and saline ice. The advantages of the simplified experimental ridge geometry include high accuracy of the main parameters governing the process, including the ridge macroporosity

Medium-scale experiment in consolidation of an artificial sea ice ridge in Van Mijenfjorden, Svalbard

This study characterizes a consolidation of undeformed level ice and ice ridges. Field investigations were performed in the Van Mijenfjorden, Svalbard for 66 days between February and May of 2017. The thickness and properties of the level ice that was used to make the ridge were measured, and thermistor-strings were installed in the ridge and the neighboring level ice. The ridge was visited four times for drilling and sampling. During our field experiment, the level ice (LI) grew from 50 to 99 cm, the consolidated layer (CL) grew up to 120 cm, and the ridge initial macroporosity was about 0.36. The experimental results provided enough information for accurate growth prediction and validation of ridge consolidation models. Two analytical resistive models and two-dimensional discretized numerical models are presented. All models need general met-ocean conditions and general ice physical properties. The ridge model includes the effect of the inhomogeneous top and bottom surfaces of the consolidated layer. The models were validated against the field measurements, and the further details of the analytical models were validated against the numerical model. The analytical resistive ridge model with convective atmospheric flux captures the relevant phenomena well and could be used for prediction of the consolidated layer thickness in probabilistic analysis of ice actions on structures. The model including the radiative terms predicted heat fluxes in level ice and ridge better than the convective model but required more input data. Vertical temperature profiles through the consolidated layer and further into respectively a void and an ice block may result in significantly different estimations of the consolidated layer thickness. The difference between fresh and saline ice growth is becoming significant only during the warming phase due to significant change of sea ice microporosit

Thermodynamics and Consolidation of Ice Ridges for Laboratory Scale

First-year ice ridge interaction with structures often gives highest loads and can be modelled in controlled environment in ice basins. Five laboratory experiments were performed to study model-scale first-year ice ridge development. Effect of initial rubble temperature on consolidated level growth was observed. For both ridges with low and high initial rubble temperatures, consolidated layer was 2–4 times thicker than surrounding level ice at the initial phase of experiment. At the main phase of consolidation this ratio approaches lower equilibrium value of 1.2–1.7 of level ice thickness that is also depends on initial rubble temperature. Non-linear sea ice specific heat capacity can change consolidation development that results in sufficient difference from ice thickness prediction using Leppäranta (1993) and Ashton (1989) approaches. Observed ratios of air, ice top and bottom surface temperatures can be used for consolidated layer thickness predictions in laboratory conditions using obtained heat transfer coefficient Hia. During the main phase vertical conductive heat flux at the top of consolidated layer was about two times higher than heat flux at the bottom part due to sea ice cooling. Latent heat flux was slightly lower than vertical conductive heat flux at the bottom of consolidated layer due to natural water convection. Consolidated layer bulk salinity was always lower than salinity of surrounding level ice for provided experiments. This difference was becoming larger after melting phase. This study can be approach for better understanding of the main differences between thermodynamics of model-scale and full-scale ice ridges

The Determination of Heat Transfer Coefficient on Water-ice Surface in a Free Convection

In ice ridges consolidation, the convective heat flux term comes critical due to the larger contact areas and surface temperature differences compare with those from level ice. In this paper, a submerging experiment was designed to determine the heat transfer coefficient (h) between fresh ice and fresh water in a free convection. A thermistor string was used to measure temperature changes while ice growth was recorded by photograph. To study the factor, the tests were carried on different ice thickness (4.9cm to 20.5cm) and initial temperatures (-20oC and -32oC). The result shows that the h exponential increased with temperature difference from 0.3 W/m2K to 175 W/m2K. On the other hand, the variation of initial thickness and temperature was not a direct influence for h. For convective heat transfer, the boundary layer condition is central for understanding the convection between ice surface and water flowing past it. From the governing equation, the water flow in a free convection is caused by density difference, which is driven by the thermal expansion. A large temperature difference between surface and environmental water creates a thicker boundary layer, which leads to a higher h

Eliminating the Uncertainties in Hydraulic and Ice Loads on Berm Breakwaters

A model for the computation of failure probabilities for partly reshaping mass-armored berm breakwaters in the Arctic is presented. The model consists of a reliable tool for the design of port structures in the rapidly changing Arctic environment and considers the simultaneous effects of wave and ice forces. The applied probabilistic approach was based on Bayesian inference. Hydrodynamic and ice historical data from Prudhoe Bay, Alaska were collected and analyzed to supply the Bayesian network with a large pool of information for the analysis. The model performed real-time predictions based on historical data and the user’s prior knowledge and assigned relevant values to load and resistance parameters. The predictive skill of the Bayesian network was validated with log-likelihood tests. Furthermore, the main outputs were applied for a Level III (fully probabilistic) reliability assessment of the structure. The study shows that a well-formulated Bayesian network can be a powerful tool in the design process and for the purpose of reliability analysis of coastal structures in highly unpredictable environments, such as the Arctic. The model can represent the dependencies between wave and ice loads in relation to the characteristics of the breakwater, as well as, its response. The average deviation of computed probabilities of failure relative to the prior estimates was 58.7%

Extreme keel drafts in the Fram Strait 2006-2011

Knowledge about extreme keel drafts is needed for appropriate design of offshore installations in ice ridge infested waters. Ice drafts were measured with upward looking sonar by the Norwegian Polar Institute in the western part of the Fram Strait along 79°N in the period 2006 to 2011. This is where the Transpolar Drift exits the Arctic Ocean, and the ice consists of a mixture of first-year and old ice originating from most parts of the Arctic Ocean. In total, 8 year-long deployments at 4 locations were analyzed. A generalized Pareto distribution was fitted to all ridges deeper than 17 m. This only amounts to a small fraction of all the ridges, but follows the methodology common for the calculation of extremes. All ridges deeper than 25 m were investigated prior to the analysis to ensure that no icebergs or other misidentified features were included. In total, 5 identified ridges were removed. The deepest ridge observed in the period was 35 m deep and 5 more ridges were deeper than 30 m in these 8 deployment seasons. Since the shape parameter in the generalized Pareto Distribution was close to zero the distribution could be simplified into an exponential distribution. Assuming an exponential distribution gave an estimated 100-year return value in the range 37 to 41 m