Mikko Lensu

Rafting and ridging of thin ice sheets

Rafting and pressure ridging are important processes in the deformation of sea ice that occur when two ice sheets are pushed together. In this study a two-dimensional computer model of the rafting and ridging process is used to simulate a situation in which two identical ice sheets are pushed together at constant speed. Each model ice sheet is composed of two thicknesses of ice. The ratio of the thicknesses is varied to obtain degrees of inhomogeneity. The accuracy of the simulations is assessed by comparison with a series of similar physical experiments performed in a refrigerated basin. Following this comparison, the computer model is used to perform an extensive series of simulations to explore the effect of the thickness and the thickness inhomogeneity of the model ice sheets on the likelihood of occurrence of ridging and rafting. During the simulations the energy consumption and forces are explicitly calculated. The energy consumed during the simulations is used to demonstrate the smooth transition between ridging and rafting that occurs when the homogeneity of the sheets is varied.

Small-scale horizontal variability of snow, sea-ice thickness and freeboard in the first-year ice region north of Svalbard

Abstract Variability of sea-ice and snow conditions on the scale of a few hundred meters is examined using in situ measurements collected in first-year pack ice in the European Arctic north of Svalbard. Snow thickness and surface elevation measurements were performed in the standard manner using a snow stick and a rotating laser. Altogether, 4109 m of measurement lines were surveyed. The snow loading was large, and in many locations the ice freeboard was negative (38.8% of snowline measurements), although the modal ice and snow thickness was 1.8 m. The mean of all the snow thickness measurements was 36 cm, with a standard deviation of 26 cm. The mean freeboard was only 3 cm, with a standard deviation of 23 cm. There were noticeable differences in snow thickness among the measurement sites. Over the undeformed ice areas, the mean snow thickness and freeboard were 23 and 2.4 cm, respectively. Over the ridged ice areas, the mean freeboard was only –0.3 cm due to snow accumulation on the sails of ridges (average thickness 54 cm). These findings imply that retrieval algorithms for converting freeboard to ice thickness should take account of spatial variability of snow cover.

Small‐scale sea ice deformation during N‐ICE2015: From compact pack ice to marginal ice zone

We studied small scale (50 m to 5 km) sea ice deformation from ship radar images recorded during the N-ICE2015 campaign. The campaign consisted of 4 consecutive drifting ice stations (Floes 1 to 4) north of Svalbard, with a total duration of nearly 5 months. Deformation was calculated using 5 different time intervals from 10 min to 24 h, and the deformation rate was found to depend strongly on the time scale. Floes 1 to 3 had a mean deformation rate within the range of 0.06 to 0.07 h– 1 with the interval of 10 min, and 0.03 to 0.04 h– 1 with the interval of 1 h. Floe 4 represented marginal ice zone (MIZ) with very high deformation rate, 0.14/0.08 h– 1 with the interval of 10 min/1 h. Deep in the ice pack, high deformation rates occurred only with high wind and drift speed, while in MIZ they were found also during calm conditions. The deformation rates were found to follow power law scaling with respect to length and time scale even on this small scales and in small domain (15 km × 15 km). The length scale dependence of deformation rate depends on the time scale: the power law scaling exponent β of the whole study period decreases from 0.82 to 0.52 with the time interval increasing from 10 min to 24 h. Ship radar images reveal the importance of the deformation history of the ice pack, since the deformation events were initialized along the lines of previous damages.

Range compensation in pack ice imagery retrieved by coastal radars

Finnish Meteorological Institute has instrumented coastal radars with radar servers for coastal ice field monitoring. The servers capture the radar data and processes one image per revolution with given parameters. The image time series provide real time high temporal and spatial resolution data on ice characteristics and are accompanied by kinematic products. The data are used in research and provided to end users, mainly icebreakers. A basic problem in the use and analysis of radar data is that the intensity of the ice signatures decreases with range. When uncompensated this effect decreases the usefulness of the images as navigational aid. More importantly, the compensation is required when the images are classified or quantitative ice parameters are retrieved, especially for ridging. The intensity decrease is mainly due to the physical properties of the radar, but also the ice area that is shadowed by ice ridge sails increases with distance. The range compensation problem is approached by three different methods. The observed decrease of intensity can be used the make a range dependent scaling for any image. Secondly, order statistical methods not dependent on absolute intensity values can be applied for more universal approach. Third, homomorphic filtering provides a method independent on assumptions concerning on the characteristics of the ice field. The three methods are compared for selected cases.

Comparison of ice stress records in terms of extreme value analysis

Abstract Dynamic ice models use stress tensor to describe the forces arising from internal ice friction. The model stress values are typically one to two magnitudes smaller than values measured by stressmeters deployed on ice floes. The synthesis of the pack-ice stress state from the measurements has been complicated by the peaky character of stress records, and the means to connect them with spatial stress distribution of the floe system have been lacking. Here a reanalysis of Arctic Sea Ice Mechanics Initiative (SIMI) data is made in terms of extreme value statistics. The basic quantity is the maximum stress observed during a time period. The records exhibit self-affine scaling. The statistics are then determined by two parameters, the Hurst exponent H and a reference stress level. Similar analysis is possible for the kinematic data. This establishes the comparability of stress records with each other and with kinematic records. The results suggest that the exponent is related to the stress state of the regional floe system, while the stress level is determined by local floe characteristics. Based on this a characterization of spatial distribution of pack-ice stresses is given.