Airborne and spaceborne altimeters provide measurements of sea-ice elevation, from which sea-ice freeboard and thickness may be derived. Observations of the Arctic ice pack by satellite altimeters indicate a significant decline in ice thickness, and volume, over the last decade. NASA’s Ice, Cloud and land Elevation Satellite-2 (ICESat-2) is a next-generation laser altimeter designed to continue key sea-ice observations through the end of this decade. An airborne simulator for ICESat-2, the Multiple Altimeter Beam Experimental Lidar (MABEL), has been deployed to gather pre-launch data for mission development. We present an analysis of MABEL data gathered over sea ice in the Greenland Sea and assess the capabilities of photon-counting techniques for sea-ice freeboard retrieval. We compare freeboard estimates in the marginal ice zone derived from MABEL photon-counting data with coincident data collected by a conventional airborne laser altimeter. We find that freeboard estimates agree to within 0.03 m in the areas where sea-ice floes were interspersed with wide leads, and to within 0.07 m elsewhere. MABEL data may also be used to infer sea-ice thickness, and when compared with coincident but independent ice thickness estimates, MABEL ice thicknesses agreed to within 0.65 m or better.

Basal melt of ice shelves may lead to an accumulation of disc-shaped ice platelets underneath nearby sea ice, to form a sub-ice platelet layer. Here we present the seasonal cycle of sea ice attached to the Ekström Ice Shelf, Antarctica, and the underlying platelet layer in 2012. Ice platelets emerged from the cavity and interacted with the fast-ice cover of Atka Bay as early as June. Episodic accumulations throughout winter and spring led to an average platelet-layer thickness of 4 m by December 2012, with local maxima of up to 10 m. The additional buoyancy partly prevented surface flooding and snow-ice formation, despite a thick snow cover. Subsequent thinning of the platelet layer from December onwards was associated with an inflow of warm surface water. The combination of model studies with observed fast-ice thickness revealed an average ice-volume fraction in the platelet layer of 0.25 ± 0.1. We found that nearly half of the combined solid sea-ice and ice-platelet volume in this area is generated by heat transfer to the ocean rather than to the atmosphere. The total ice-platelet volume underlying Atka Bay fast ice was equivalent to more than one-fifth of the annual basal melt volume under the Ekström Ice Shelf.

We examine the melting of sea ice versus freshwater ice in laboratory experiments and with one-dimensional model simulations. Our primary aim is to investigate the vertical partitioning of heat between thinning and internal phase changes. In agreement with our general understanding of the two ice types, we find that freshwater ice quickly starts thinning and then keeps a constant melt rate for constant external heat input. In contrast, sea ice starts thinning later but then thins faster than freshwater ice. This temporal evolution is caused by the substantial amount of heat that is used for internal phase changes in sea ice. Those internal phase changes give rise to a nonlinear temperature profile in the sea ice during the entire melting period, whereas freshwater ice quickly reaches its melting temperature throughout its entire thickness. Infrared imagery provides additional insights into the surface temperature of both ice types during melting. We find that, during melting, sea ice can have a mean surface temperature several tenths of a degree above 0°C because of meltwater-filled millimetre-scale dimples at the ice surface.

We examine the role of ocean heat flux (OHF) in Antarctic sea-ice growth and melt using data from autonomous ice mass-balance buoys deployed on pack ice in the Bellingshausen Sea and on fast ice in the Amundsen Sea during the spring/summer (October-December 2007) and summer/ autumn (February-March 2009) transitions, respectively. OHFs are derived using two methods that examine changes in (1) sub-ice ocean water properties (OHF1) and (2) ice thickness (OHF2), the latter only applying to thick snow-covered ice (i.e. a near-zero temperature gradient near the ice bottom). Good agreement is found between the time-averaged estimates of OHF1 and OHF2. Average OHF measured was 8 ± 2 W m-2 under the pack ice and 17 ± 2 W m-2 under the landfast ice. Some short-term OHF values (OHF1) in both seas exceeded 55 W m-2. The spring OHF variations in the Bellingshausen Sea were periodic and controlled by semi-diurnal ice velocity fluctuations. Larger temperature fluctuations in the summer Amundsen Sea, originating from incursions of warm deep water masses, contributed to the OHF being twice as high as in the Bellingshausen Sea and also accounted for the irregular OHF variability there.

This work evaluates the fidelity of the polar marine Ekman layer in the Regional Arctic System Model (RASM) and Community Earth System Model (CESM) using sea-ice inertial oscillations as a proxy for ice-ocean Ekman transport. A case study is presented that demonstrates that RASM replicates inertial oscillations in close agreement with motion derived using the GPS. This result is obtained from a year-long case study pre-dating the recent decline in perennial Arctic sea ice, using RASM with sub-hourly coupling between the atmosphere, sea-ice and ocean components. To place this work in context, the RASM coupling method is applied to CESM, increasing the frequency of oceanic flux exchange from once per day in the standard CESM configuration, to every 30 min. For a single year simulation, this change causes a considerable increase in the median inertial ice speed across large areas of the Southern Ocean and parts of the Arctic sea-ice zone. The result suggests that processes associated with the passage of storms over sea ice (e.g. oceanic mixing, sea-ice deformation and surface energy exchange) are underestimated in Earth System Models that do not resolve inertial frequencies in their marine coupling cycle.

The local mass balance of sea ice is dependent on the advection of ice into and out of an area and on the deformation processes in that area. Sea-ice motion can be observed from space by synthetic aperture radar (SAR) and quantified by drift-detection algorithms. Due to the scarcity of field observations, it remains a challenging task to validate the resulting motion fields. We analyse the quality of sea-ice motion fields derived from SAR data, using an example dataset from the Weddell Sea region. We apply a quality indicator for sea-ice motion fields which is independent of field data and evaluate it with reference data obtained from visual analysis of the SAR images. Together with the motion field, sea-ice deformation can also be retrieved from SAR data. Similarly to ice motion, it is very difficult to obtain field data to evaluate the quality of the results. Based on a manually derived reference dataset, we introduce a method to validate the retrieved deformation rates. This procedure requires no additional field data. Our analysis shows that deformation rates derived from SAR data are consistent with results obtained from buoy analysis by previous studies.

We present sea-ice surface roughness estimates, i.e. the standard deviation of relative surface elevation, in the Arctic regions of Fram Strait and the Nansen Basin north of Svalbard acquired by an airborne laser scanner and a single-beam laser altimeter in 2010. We compare the scanner to the altimeter and compare the differences between the two survey regions. We estimate and correct sensor roll from the scanner data using the hyperbolic response of the scanner over a flat surface. Measurement surveys had to be longer than 5 km north of Svalbard and longer than 15 km in Fram Strait before the statistical distribution in surface roughness from the scanner and altimeter became similar. The shape of the surface roughness probability distributions agrees with those of airborne electromagnetic induction measurements of ice thickness. The ice in Fram Strait had a greater mean surface roughness, 0.16 m vs 0.09 m, and a wider distribution in roughness values than the ice in the Nansen Basin. An increase in surface roughness with increasing ice thickness was observed over fast ice found in Fram Strait near the coast of Greenland but not for the drift ice.

In March and April 2010, we investigated the development of young landfast sea ice in Kongsfjorden, Spitsbergen, Svalbard. We sampled the vertical column, including sea ice, brine, frost flowers and sea water, to determine the CO2 system, nutrients, salinity and bacterial and ice algae production during a 13 day interval of ice growth. Apart from the changes due to salinity and brine rejection, the sea-ice concentrations of total inorganic carbon (CT), total alkalinity (AT), CO2 and carbonate ions (CO32–) in melted ice were influenced by dissolution of calcium carbonate (CaCO3) precipitates (25–55 μmol kg-1) and played the largest role in the changes to the CO2 system. The CT values were also influenced by CO2 gas flux, bacterial carbon production and primary production, which had a small impact on the CT. The only exception was the uppermost ice layer. In the top 0.05 m of the ice, there was a CO2 loss of ∼20 μmol kg-1 melted ice (1 mmol m-2) from the ice to the atmosphere. Frost flowers on newly formed sea ice were important in promoting ice-air CO2 gas flux, causing a CO2 loss to the atmosphere of 140-800 μmol kg--1 d-1 melted frost flowers (7-40 mmol m-2 d–1).

A new numerical implementation is proposed for a wave-ice interaction model. It is applied to an idealized transect geometry. Wave attenuation due to ice floes and wave-induced ice fracture are both included in the model. The new method alleviates the need for subgrid spatial or temporal discretizations, thereby facilitating future integration of wave-ice interactions into large-scale coupled models.

Up to now, snow cover on Antarctic sea ice and its impact on radar backscatter, particularly after the onset of freeze/thaw processes, are not well understood. Here we present a combined analysis of in situ observations of snow properties from the landfast sea ice in Atka Bay, Antarctica, and high-resolution TerraSAR-X backscatter data, for the transition from austral spring (November 2012) to summer (January 2013). The physical changes in the seasonal snow cover during that time are reflected in the evolution of TerraSAR-X backscatter. We are able to explain 76-93% of the spatio-temporal variability of the TerraSAR-X backscatter signal with up to four snowpack parameters with a root-mean-squared error of 0.87-1.62 dB, using a simple multiple linear model. Over the complete study, and especially after the onset of early-melt processes and freeze/thaw cycles, the majority of variability in the backscatter is influenced by changes in snow/ice interface temperature, snow depth and top-layer grain size. This suggests it may be possible to retrieve snow physical properties over Antarctic sea ice from X-band SAR backscatter.

The seasonal variability of hydrography, heat content, freshwater budget and sea ice is examined for the past century in Tvärminne, Gulf of Finland. The aim was to evaluate the seasonal sea-ice impact on the physical properties of the coastal waters, which can also be subject to riverine influences. The Gulf of Finland is an optimal location for such a study, given the high interannual variability of the ice conditions. This is the first time annual cycles of physical properties have been analyzed based on such a long-term dataset in the Archipelago Sea. The maximum water temperature occurred in August at the sea surface, and at the bottom in September, with the 1 month lag due to vertical heat diffusion. The annual salinity cycle varied greatly in the surface layer. The variations were largely connected to sea-ice formation and break-up processes, and the ice cover, which prevented wind mixing of the surface waters. The largest heat loss occurred in November-December (∼150 W m-2) and the highest heat gain in June (∼180 W m-2) . Especially in April, latent heat released/absorbed in sea-ice formation/break-up contributed to a seasonal variation in heat budget of ∼40 W m -2.

This paper investigates automatic segmentation and classification of C-band, polarimetric synthetic aperture radar (SAR) satellite images of Arctic sea ice under freezing conditions prior to melt. The objective is to investigate the robustness of the results obtained under slightly varying environmental conditions and different viewing geometries. Initially, three geographically overlapping SAR images from consecutive days are incidence-angle corrected and segmented into unknown classes. The segmentation is performed by an unsupervised mixture-of-Gaussian segmentation algorithm utilizing six features extracted from the polarimetric data. After segmentation, the segments are contextually smoothed. One segmented image is manually labelled based on in situ data and expert knowledge. Using this scene as reference, we consider two strategies for class labelling of the other scenes. The first manually labels the classes based on visual inspection of the reference; the second utilizes various statistical distance measures to automatically assign each unknown class to the statistically nearest reference class. These two scenes are also classified pixel-wise by a supervised classification algorithm based on the reference data. Poor classification results are obtained when the incidence angle is very different from the reference scene. Similar viewing geometries reveal good classification and labelling results, the latter regardless of the distance measure used.

The first stage of sea-ice formation is often grease ice, a mixture of sea water and frazil ice crystals. Over time, grease ice typically congeals first to pancake ice floes and then to a solid sea-ice cover. Grease ice is commonly not explicitly simulated in basin-scale sea-ice ocean models, though it affects oceanic heat loss and ice growth and is expected to play a greater role in a more seasonally ice-covered Arctic Ocean. We present an approach to simulate the grease-ice layer with, as basic properties, the surface being at the freezing point, a frazil ice volume fraction of 25%, and a negligible change in the surface heat flux compared to open water. The latter governs grease-ice production, and a gradual transition to solid sea ice follows, with ∼50% of the grease ice solidifying within 24 hours. The new parameterization delays lead closing by solid ice formation, enhances oceanic heat loss in fall and winter, and produces a grease-ice layer that is variable in space and time. Results indicate a 10-30% increase in mean winter Arctic Ocean heat loss compared to a standard simulation, with instant lead closing leading to significantly enhanced ice growth.

Formation of supercooled water and frazil ice was studied in the Chukchi Sea coastal polynya off Barrow, Alaska, USA, in winter 2009/10, using moored salinity/temperature sensors and Ice Profiling Sonar (IPS) data along with satellite data. Oceanographic data from two moorings revealed episodic events of potential supercooling at 30–40m depth, including the possibility of in situ supercooling, while the polynya was open. We identified frazil ice-like signals in the IPS data down to 5–15 m depth, associated with large heat loss and windy, turbulent conditions in an active polynya. This likely represents the first IPS observation of frazil ice in the marine environment. On the day of the maximum signal of frazil ice, spaceborne synthetic aperture radar shows streaks of high backscatter within the polynya, indicating active frazil ice formation just downwind of the mooring sites. In addition, the longer-term potential supercooling that persisted for 1–3 weeks occurred twice despite the absence of polynya activity at the mooring sites. These events occurred during periods dominated by the northeastward current. A series of coastal polynyas had formed southwest of the mooring sites prior to these events. Thus, the water masses with potential supercooling were likely advected from these polynyas.

A new ocean wave/sea-ice interaction model is proposed that simulates how a directional wave spectrum evolves as it travels through an arbitrary finite array of circular ice floes, where wave/ ice dynamics are entirely governed by wave-scattering effects. The model is applied to characterize the wave reflection and transmission properties of a strip of ice floes, such as an ice edge band. A method is devised to extract the reflected and transmitted directional wave spectra produced by the array. The method builds upon an integral mapping from polar to Cartesian coordinates of the scattered wave components. Sensitivity tests are conducted for a row of floes randomly perturbed from a regular arrangement. Results for random arrays are generated using ensemble averaging. A realistic ice edge band is then reconstructed from field experiment data. Simulations show good qualitative agreement with the data in terms of transmitted wave energy and directional spreading. In particular, it is observed that short waves become isotropic quickly after penetrating the ice field.

Central to an understanding of evolution in sea-ice characteristics in response to climate change is an understanding of sea-ice dynamics. In this study, we investigate regional differences in ice dynamics in the Beaufort Sea and High Arctic using high-frequency ice buoy (beacon) data deployed during the SEDNA and IPY-CFL field campaigns from spring 2007 to winter 2008. Examined in particular are scaling laws determined from absolute dispersion statistics. We create temporal scaling maps to determine whether distinct dynamical regimes can be identified with differing scaling properties. The results from this analysis provide an alternative characterization to changes in sea ice based on dynamics rather than concentration and thickness, and thus insight into, and improved understanding of, the connections between sea-ice drift and morphology.

Passive microwave sensors have produced a 35 year record of sea-ice concentration variability and change. Operational analyses combine a variety of remote-sensing inputs and other sources via manual integration to create high-resolution, accurate charts of ice conditions in support of navigation and operational forecast models. One such product is the daily Multisensor Analyzed Sea Ice Extent (MASIE). The higher spatial resolution along with multiple input data and manual analysis potentially provide more precise mapping of the ice edge than passive microwave estimates. However, since MASIE is based on an operational product, estimates may be inconsistent over time due to variations in input data quality and availability. Comparisons indicate that MASIE shows higher Arctic-wide extent values throughout most of the year, largely because of the limitations of passive microwave sensors in some conditions (e.g. surface melt). However, during some parts of the year, MASIE tends to indicate less ice than estimated by passive microwave sensors. These comparisons yield a better understanding of operational and research sea-ice data products; this in turn has important implications for their use in climate and weather models.

During late winter 2007, coincident measurements of sea ice were collected using various sensors at an ice camp in the Beaufort Sea, Canadian Arctic. Analysis of the archived data provides new insight into sea-ice isostasy and its related R-factor through case studies at three scales using different combinations of snow and ice thickness components. At the smallest scale (<1 m; point scale), isostasy is not expected, so we calculate a residual and define this as Ж (‘zjey’) to describe vertical displacement due to deformation. From 1 to 10 m length scales, we explore traditional isostasy and identify a specific sequence of thickness calculations which minimize freeboard and elevation uncertainty. An effective solution exists when the R-factor is allowed to vary: ranging from 2 to 12, with mean of 5.17, mode of 5.88 and skewed distribution. At regional scales, underwater, airborne and spaceborne platforms are always missing thickness variables from either above or below sea level. For such situations, realistic agreement is found by applying small-scale skewed ranges for the R-factor. These findings encourage a broader isostasy solution as a function of potential energy and length scale. Overall, results add insight to data collection strategies and metadata characteristics of different thickness products.

We explore spatial aliasing of non-Gaussian distributions of sea-ice thickness. Using a heuristic model and >1000 measurements, we show how different instrument footprint sizes and shapes can cluster thickness distributions into artificial modes, thereby distorting frequency distribution, making it difficult to compare and communicate information across spatial scales. This problem has not been dealt with systematically in sea ice until now, largely because it appears to incur no significant change in integrated thickness which often serves as a volume proxy. Concomitantly, demands are increasing for thickness distribution as a resource for modeling, monitoring and forecasting air–sea fluxes and growing human infrastructure needs in a changing polar environment. New demands include the characterization of uncertainties both regionally and seasonally for spaceborne, airborne, in situ and underwater measurements. To serve these growing needs, we quantify the impact of spatial aliasing by computing resolution error (Er) over a range of horizontal scales (x) from 5 to 500 m. Results are summarized through a power law (Er= bxm) with distinct exponents (m) from 0.3 to 0.5 using example mathematical functions including Gaussian, inverse linear and running mean filters. Recommendations and visualizations are provided to encourage discussion, new data acquisitions, analysis methods and metadata formats.

Data from the Seasonal Ice Zone Observing Network (SIZONet) acquired near Barrow, Alaska, during the 2009/10 ice season allow novel comparisons between measurements of ice thickness and velocity. An airborne electromagnetic survey that passed over a moored Ice Profiling Sonar (IPS) provided coincident independent measurements of total ice and snow thickness and ice draft at a scale of 10 km. Once differences in sampling footprint size are accounted for, we reconcile the respective probability distributions and estimate the thickness of level sea ice at 1.48 ± 0.1 m, with a snow depth of 0.12 ± 0.07 m. We also complete what we believe is the first independent validation of radar-derived ice velocities by comparing measurements from a coastal radar with those from an under-ice acoustic Doppler current profiler (ADCP). After applying a median filter to reduce high-frequency scatter in the radar-derived data, we find good agreement with the ADCP bottom-tracked ice velocities. With increasing regulatory and operational needs for sea-ice data, including the number and thickness of pressure ridges, coordinated observing networks such as SIZONet can provide the means of reducing uncertainties inherent in individual datasets.