Pancake Ice Research Articles

Numerical simulation of ice loading characteristics of ships in a wave-current environment in a broken ice area

A numerical simulation technique with CFD-DEM coupling is used to simulate a full-scale KCS model sailing in the pancake ice region. Based on the actual polar information, the Stokes fifth-order wave model is selected. The pancake ice particle of two scales is established based on the DEM, and the corresponding broken-ice region is generated. The contact and collision problems of ice-ice and ice-wall (ship) were analyzed by solving the Hertz-Mindlin model, and the monitored data were processed and summarized. The results show that the ice load on the ship is a repetitive “loading-unloading” process, and the peak ice load curve is characterized by segmented and concentrated fluctuations. In terms of ice parameters, the pancake diameter has a great degree of influence on the crushed ice loads, and the size of the crushed ice loads increases with the diameter of the crushed ice particles.

Rafting of Growing Antarctic Sea Ice Enhances In‐Ice Biogeochemical Activity in Winter

AbstractThe study of Antarctic first‐year sea‐ice biogeochemistry has been limited by samples mostly being collected in pack ice during summer, with few winter data available. Measurements from the Antarctic marginal ice zone (AMIZ) have proven even more difficult to obtain. The AMIZ is a broad, circumpolar feature of the Southern Ocean found at different latitudes during the year where light and nutrients are high enough to sustain primary production and influence ecosystem functioning. We present the first biogeochemical data set from growing ice collected in the Atlantic AMIZ during winter 2019, including measurements from young pancake ice (YI) and consolidated first‐year ice (FYI). Measurements of sea‐ice temperature, salinity, crystal structure, δ18O, chlorophyll, and nutrient concentrations were used to investigate the winter sea‐ice habitat and decipher the conditions under which the ice formed and grew. Model simulations support the hypothesis that nutrient accumulation in advancing sea ice cannot be explained by passive seawater entrainment and thermodynamics alone. Our data confirm that winter sea ice is biogeochemically active and accumulates remineralized nutrients. We further propose that mechanical thickening enhances the reservoir of nutrients during the ice growth season. The biogeochemical transition from YI to FYI does not appear to be a linear progression of thickness with habitat space reduction as sea ice consolidates. Instead, FYI bulk biogeochemistry results from multiple cycles of rafting of YI, which conserves the biogeochemical properties of YI in the FYI, ultimately increasing the overall nutrient and chlorophyll content.

Estimation of sea ice drift and concentration during melt season using C-band dual-polarimetric Sentinel-1 data

Characterizing sea ice changes over the Arctic can give proper insight into its extent in the current context of climate change. Sea ice motion's effect on ice area is influenced by seasonal conditions and the prevailing climate. However, the motion of thinner ice during the melt season can result in polynyas and cause significant ice loss that lasts the entire year. Sea ice drift and concentration during melt season are rarely discussed. The present study attempts to estimate sea ice drift during the melt season (July–August) using Differential Synthetic Aperture Radar Interferometry (DInSAR) with the usability of C-band Sentinel-1 data. The displacements/drifts were estimated using the phase shifts between two complex images with a temporal extent of 12 days. The study also proposed estimating sea ice concentration using polarimetric descriptors (power, purity, and randomness). The results revealed drifts of about 14–15 cm in the northeastern and southern parts followed by 45–50 cm in the middle parts and 20–25 cm along the coasts and open areas. Based on three polarimetric descriptors, thin ice and other ice fragments over open areas have shown less sea ice concentration as compared to the more concentrated pancake and pack ice with more compactness over the ice crystals. The study can be useful for computing drift in the melt season and carrying out spatiotemporal time-series analysis of seasonal sea ice drift/deformation studies. The algorithm and results can be used as a base study for the upcoming L- and S-band carrying NASA-ISRO Synthetic Aperture Radar (NISAR) mission.

Physical and mechanical properties of winter first-year ice in the Antarctic marginal ice zone along the Good Hope Line

Abstract. As part of the 2019 Southern oCean seAsonal Experiment (SCALE) Winter Cruise of the South African icebreaker SA Agulhas II, first-year ice was sampled at the advancing outer edge of the Antarctic marginal ice zone along a 150 km Good Hope Line transect. Ice cores were extracted from four solitary pancake ice floes of 1.83–2.95 m diameter and 0.37–0.45 m thickness as well as a 12×4 m pancake ice floe of 0.31–0.76 m thickness that was part of a larger consolidated pack ice domain. The ice cores were subsequently analysed for temperature, salinity, texture, anisotropic elastic properties and compressive strength. All ice cores from both solitary pancake ice floes and consolidated pack ice exhibited predominantly granular textures. The vertical distributions of salinity, brine volume and mechanical properties were significantly different for the two ice types. High salinity values of 12.6±4.9 PSU were found at the topmost layer of the solitary pancake ice floes but not for the consolidated pack ice. The uniaxial compressive strengths for pancake ice and consolidated pack ice were determined as 2.3±0.5 and 4.1±0.9 MPa, respectively. Young's and shear moduli in the longitudinal core direction of solitary pancake ice were obtained as 3.7±2.0 and 1.3±0.7 GPa, respectively, and of consolidated pack ice as 6.4±1.6 and 2.3±0.6 GPa, respectively. Comparing Young's and shear moduli measured in longitudinal and transverse core directions, a clear directional dependency was found, in particular for the consolidated pack ice.

Atmospheric drivers of a winter-to-spring Lagrangian sea-ice drift in the Eastern Antarctic marginal ice zone

AbstractSea-ice drift in the Antarctic marginal ice zone (MIZ) is discussed using data from a 4-month-long drift of a buoy deployed on a pancake ice floe during the winter sea-ice expansion. We demonstrate increased meandering and drift speeds, and changes in the dynamical regimes of the absolute dispersion during cyclone activity, together with high correlations between drift velocities and wind from atmospheric reanalyses. This indicates a dominant physical control of wind forcing on ice drift and the persistence of free-drift conditions. These conditions occurred despite the buoy remaining largely in >80% ice concentrations and at distances >200 km from the estimated ice edge. The drift is additionally characterised by a strong inertial signature at 13.47 h, which appears initiated by passing cyclones. A wavelet analysis of the buoy's velocity confirms that the momentum transfer from winds at the multi-day frequencies is due to atmospheric forcing, while the initiation of inertial oscillations of sea ice has been identified as the secondary effect. Propagating storm-generated waves may initiate inertial oscillations by increasing the mobility of floes and enhance the drag of the inertial current. This analysis indicates that the Antarctic MIZ in the Indian Ocean sector remains much wider and mobile, during austral winter-to-spring, than defined by sea-ice concentration.

High-Resolution Radar Sensing Sea Surface States During AMK-82 Cruise

Every year situation when theArctic seas are free of ice is becoming more frequent. It allows scientists to study hard-to-reach areas using well-equipped research vessels instead of icebreakers. During the COVID-19 pandemic, the successful expedition of the research vessel “Academician Mstislav Keldysh” with more than 60 scientists from 15 countries across the four Arctic seas (Barents, Kara, Laptev, and East Siberian) on September–November 2020 seems like a real wonder. One of the expedition tasks was remote sensing of different hydrophysical processes by their manifestation on the sea surface using marine radar. This article proposes the method of generating high spatial resolution radar maps of the sea surface and algorithms of hydrophysical processes identification. This article also presents examples of registered processes, such as wind waves, ice fields with different types of ice (grease ice, pancake ice, nilas, and young ice), manifestations of internal waves observed in the Kara Gate and Vilkitsky Strait, as well as manifestations of intense methane seeps on the sea surface. This article contains quantitative estimations of the physical parameters of the observed processes underlying the effectiveness of Doppler marine radars in harsh conditions of the Arctic seas.

Inside Katabatic Winds Over the Terra Nova Bay Polynya: 2. Dynamic and Thermodynamic Analyses

AbstractSurface fluxes and atmospheric boundary layer budgets of enthalpy and momentum are quantified using bulk and integral methods based on measurements obtained during a research vessel transect across the Terra Nova Bay polynya during an intense late autumnal katabatic wind event. The surface sensible, latent, and net radiation heat fluxes had maximum upward values of 2,500 ± 600, 400 ± 100, and 100 ± 7 Wm−2, respectively, which occurred over open regions that had been cleared of sea ice by the wind stress. In these open areas, sea spray enhanced the sensible and latent heat fluxes (LHFs) by an estimated 106% and 18%, respectively. As sea ice formed on the surface and became thicker in the downwind direction, the sensible and LHFs decreased to 50% of their maximum values over pancake ice and to 5% at the downwind end of the transect, where snow‐covered young ice flows were present. Snow growth removed over 50% of the water vapor that came from the surface. The surface wind stress ranged from mean values of 2.9 Nm−2 in the most upwind regions to 0.9 Nm−2 at the end of the transect, with smaller scale variations of almost a factor of three due to different surface types (roughnesses) and gustiness. The downwind slowing and turning of the wind vector can be almost entirely explained by frictional and inertial forces, indicating the horizontal pressure gradient was weak. This case could serve as a case study for future modeling studies of coastal polynyas.

A Physical Model of Wave Attenuation in Pancake Ice

A physical model is discussed that mimics the interaction between ocean waves and a multitude of loose pancake ice floes, which form the outer edge of the Arctic and Antarctic marginal ice zones during winter sea ice formation. The pancakes were modeled by using ice cubes with different concentrations, while waves were generated mechanically. The ice cubes had a dimension of a few centimeters, which was two orders of magnitude smaller than the dominant wavelength. Experiments consisted of tracking the propagation of regular and irregular wave fields along the flume as they crossed the ice cover to measure the rate of ice-induced wave attenuation. Results indicate that wave attenuation increases with ice concentration with only 30% of energy allowed to pass through high-density covers. Wave energy is attenuated across the entire spectral domain and is strongest at high frequencies. This results in a downshift of the spectral peak.

Wave-influenced formation of new ice: Model building and a test case

A parameterization of new ice formation in a wave field has been developed. Detailed processes of new sea ice growth with multiple ice types (nilas, grease, and pancake ice) and associated wave attenuation by different sea ice types are presented and implemented into a coupled ROMS and SWAN model. Sea ice formation includes growth and transformation of frazil, grease, pancake, nilas and solid ice. Ice types are determined by thermal condition and the wave fields. Simultaneously, the attenuation of wave energy through the ice cover is dependent on ice types. The two processes are coupled by COAWST. The resulting coupled model is applied to the Bohai Sea. When compared with in-situ data from oil platforms, the model performed well in distinguishing solid ice, pancake and nilas. When compared with previous ice models without the consideration of new ice types, the appearance of solid ice is delayed, the total ice area and ice volume are increased, solid ice thickness is increased while the type-averaged thickness is reduced and heat flux from the ocean to air is increased during the freezing phase. By considering the new ice formation, wave attenuation in the test case is enhanced.

Supplementing Remote Sensing of Ice: Deep Learning-Based Image Segmentation System for Automatic Detection and Localization of Sea-ice Formations From Close-Range Optical Images

This paper presents a three-stage approach for the automated analysis of close-range optical images containing ice objects. The proposed system is based on an ensemble of deep learning models and conditional random field postprocessing. The following surface ice formations were considered: Icebergs, Deformed ice, Level ice, Broken ice, Ice floes, Floebergs, Floebits, Pancake ice, and Brash ice. Additionally, five non-surface ice categories were considered: Sky, Open water, Shore, Underwater ice, and Melt ponds. To find input parameters for the approach, the performance of 12 different neural network architectures was explored and evaluated using a 5-fold cross-validation scheme. The best performance was achieved using an ensemble of models having pyramid pooling layers (PSPNet, PSPDenseNet, DeepLabV3+, and UPerNet) and convolutional conditional random field postprocessing with a mean intersection over union score of 0.799, and this outperformed the best single-model approach. The results of this study show that when per-class performance was considered, the Sky was the easiest class to predict, followed by Deformed ice and Open water. Melt pond was the most challenging class to predict. Furthermore, we have extensively explored the strengths and weaknesses of our approach and, in the process, discovered the types of scenes that pose a more significant challenge to the underlying neural networks. When coupled with optical sensors and AIS, the proposed approach can serve as a supplementary source of large-scale `ground truth' data for validation of satellite-based sea-ice products. We have provided an implementation of the approach at https://github.com/panchinabil/sea_ice_segmentation.

SAR image wave spectra to retrieve the thickness of grease-pancake sea ice using viscous wave propagation models

Young sea ice composed of grease and pancake ice (GPI), as well as thin floes, considered to be the most common form of sea ice fringing Antarctica, is now becoming the “new normal” also in the Arctic. A study of the rheological properties of GPI is carried out by comparing the predictions of two viscous wave propagation models: the Keller model and the close-packing (CP) model, with the observed wave attenuation obtained by SAR image techniques. In order to fit observations, it is shown that describing GPI as a viscous medium requires the adoption of an ice viscosity which increases with the ice thickness. The consequences regarding the possibility of ice thickness retrieval from remote sensing data of wave attenuation are discussed. We provide examples of GPI thickness retrievals from a Sentinel-1 C band SAR image taken in the Beaufort Sea on 1 November 2015, and three CosmoSkyMed X band SAR images taken in the Weddell Sea on March 2019. The estimated GPI thicknesses are consistent with concurrent SMOS measurements and available local samplings.

In Silico Modeling of Coupled Physical‐Biogeochemical (P‐BGC) Processes in Antarctic Sea Ice

AbstractAntarctic sea ice formation in the Marginal Ice Zone (MIZ) and its mutual effects on ocean biology and chemistry is very sensitive to climate change. The formation of ’pancake’ ice floes and the coupled physical‐biogeochemical (P‐BGC) processes can be modeled by using the continuum mechanical multi‐phase description of the extended Theory of Porous Media (eTPM), and simulated with the Finite Element Method (FEM). The ice formation is described via a salinity‐temperature equilibrium of the enclosed ocean water. The growth of the thriving ice algae, enclosed and attached to the pancake ice floes, is modeled with a phenomenological approach given with an ordinary differential equation.

Measuring Deformed Sea Ice in Seasonal Ice Zones Using L-Band SAR Images

In order to improve the understanding of the dynamical deformation processes of sea ice in the seasonal ice zone (SIZ), measures to detect deformed ice were developed and validated using satellite L-band synthetic aperture radar (ScanSAR) images for the southern Sea of Okhotsk. To approach, sea ice was categorized into three ice types, typical of the sea ice in this region: nilas (thin level), pancake ice (thin rough), and deformed ice (thick rough), and then the measures to classify into these categories were developed using ALOS/Phased Array type L-band Synthetic Aperture Radar (PALSAR) as a function of backscatter coefficients at HH polarization ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text {HH}}^{0}$ </tex-math></inline-formula> ) and incidence angle ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta _{i}$ </tex-math></inline-formula> ), based on the field observations. Comparative analysis confirmed that PALSAR can detect deformed ice more efficiently than RADARSAT-2 (C-band SAR). The temporal evolution of the area, judged as deformed ice from these measures, shows significant variability with both time and space, and deformed ice regions appear in relatively linear alignments with a width of a few tens of kilometers in the inner ice pack region, consistent with ice drift convergence. To confirm the results, PALSAR-2 images at HH and HV polarizations were examined as a function of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\theta _{i}$ </tex-math></inline-formula> , based on the four-year field observations in the same area. The results revealed that <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text {HH}}^{0}$ </tex-math></inline-formula> and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text{HV}}^{0}$ </tex-math></inline-formula> are both subject to floe sizes as well as deformed ice, and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sigma _{\text{HV}}^{0}$ </tex-math></inline-formula> is more sensitive. This indicates that care should be taken when applying these measures to the ice areas where significantly small floes are dominant like the marginal ice zone.

Attenuation of Ocean Surface Waves in Pancake and Frazil Sea Ice Along the Coast of the Chukchi Sea

AbstractAlaskan Arctic coastlines are protected seasonally from ocean waves by the presence of coastal and shorefast sea ice. This study presents field observations collected during the autumn 2019 freeze up near Icy Cape, a coastal headland in the Chukchi Sea of the Western Arctic. The evolution of the coupled air‐ice‐ocean‐wave system during a 4‐day wave event was monitored using drifting wave buoys, a cross‐shore mooring array, and ship‐based measurements. The incident wavefield with peak period of 2.5 s was attenuated by coastal pancake and frazil sea ice, reducing significant wave height by 40% over less than 5 km of cross‐shelf distance spanning water depths from 13 to 30 m. Spectral attenuation coefficients are evaluated with respect to wave and ice conditions and the proximity to the ice edge. Attenuation rates are found to be three times higher within 500 m of the ice edge, relative to values farther in the ice cover. Attenuation coefficients are in the range of 〈2.3, 2.7〉x10‐3 m−1, and follow a power‐law dependence on frequency.

On the coagulated pancake ice formation: Observation in the refreezing Chukchi Sea and comparison to the Antarctic consolidated pancake ice

Emergence of pancake ice in the western Arctic Ocean is understood to be an effect of the increasing wave activity. During the 2018 R/V Mirai field campaign in the refreezing Chukchi Sea, we obtained observational evidence of sea ice formation that resembled the pancake cycle—frazil/pancake/consolidated pancake ice—first discovered and known to be ubiquitous in the Antarctic. Apparently, R/V Mirai encountered a sequence of environmental conditions that were conducive to producing coagulated pancake ice, of which the formation process was relatable to the Antarctic consolidated pancake ice. We investigate how pancake ice form in two different environments: the Antarctic marginal ice zone (MIZ) is adjacent to the vigorous Antarctic Circumpolar region whereas the western Arctic Ocean is surrounded by sea ice and land. We found that a key distinction between the two regimes is that rafting is less likely in the western Arctic Ocean due to its wave climate, meaning the coagulated pancake ice is thinner than the Antarctic consolidated pancake ice. As the sea ice extent declines, thinner coagulated pancake ice may dominate the refreezing ocean, which affects the melting and possibly the broader Arctic climate.

Observation of on-ice wind waves under grease ice in the western Arctic Ocean

The significant reduction of the sea ice extent in the western Arctic has been observed by the sustained satellite observations since 1979. The opening ocean is now allowing waves to evolve and propagate under the presence of the Arctic sea ice. A better understanding of the wave-ice interaction is necessary for the safe shipping over the sea ice covered Arctic Ocean and to improve the Arctic climate projection. During R/V Mirai Arctic Expedition in October 2019, two drifting wave-buoys were concurrently deployed in the open ocean and the Arctic marginal ice zone (MIZ) with grease ice and pancake ice. The wave and sea ice conditions at the time of the deployments were documented in detail. Based on the wave buoy data and the sea ice edge estimated by a Synthetic Aperture Radar (SAR) image, attenuation rates of waves under the grease ice are estimated. Good agreement in the frequency dependency of the wave attenuation rate is presented between the observational results and the theoretical prediction from the viscous model of Weber (1987). The agreement implies that the observed surface waves were attenuated by the viscous damping beneath grease ice. It is also found that the observed attenuation rates were generally lower than the previous studies. Several possible factors are discussed, such as the sea ice type, wind energy input, and uncertainties in the estimation of the attenuation rate.

Observations of exponential wave attenuation in Antarctic sea ice during the PIPERS campaign

AbstractQuantifying the rate of wave attenuation in sea ice is key to understanding trends in the Antarctic marginal ice zone extent. However, a paucity of observations of waves in sea ice limits progress on this front. We deployed 14 waves-in-ice observation systems (WIIOS) on Antarctic sea ice during the Polynyas, Ice Production, and seasonal Evolution in the Ross Sea expedition (PIPERS) in 2017. The WIIOS provide in situ measurement of surface wave characteristics. Two experiments were conducted, one while the ship was inbound and one outbound. The sea ice throughout the experiments generally consisted of pancake and young ice &lt;0.5 m thick. The WIIOS survived a minimum of 4 d and a maximum of 6 weeks. Several large-wave events were captured, with the largest recorded significant wave height over 9 m. We find that the total wave energy measured by the WIIOS generally decays exponentially in the ice and the rate of decay depends on ice concentration.

Spectral attenuation of ocean waves in pack ice and its application in calibrating viscoelastic wave-in-ice models

Abstract. We investigate a case of ocean waves through a pack ice cover captured by Sentinel-1A synthetic aperture radar (SAR) on 12 October 2015 in the Beaufort Sea. The study domain is 400 km by 300 km, adjacent to a marginal ice zone (MIZ). The wave spectra in this domain were reported in a previous study (Stopa et al., 2018b). In that study, the authors divided the domain into two regions delineated by the first appearance of leads (FAL) and reported a clear change of wave attenuation of the total energy between the two regions. In the present study, we use the same dataset to study the spectral attenuation in the domain. According to the quality of SAR-retrieved wave spectrum, we focus on a range of wave numbers corresponding to 9–15 s waves from the open-water dispersion relation. We first determine the apparent attenuation rates of each wave number by pairing the wave spectra from different locations. These attenuation rates slightly increase with increasing wave number before the FAL and become lower and more uniform against wave number in thicker ice after the FAL. The spectral attenuation due to the ice effect is then extracted from the measured apparent attenuation and used to calibrate two viscoelastic wave-in-ice models. For the Wang and Shen (2010b) model, the calibrated equivalent shear modulus and viscosity of the pack ice are roughly 1 order of magnitude greater than that in grease and pancake ice reported in Cheng et al. (2017). These parameters obtained for the extended Fox and Squire model are much greater, as found in Mosig et al. (2015) using data from the Antarctic MIZ. This study shows a promising way of using remote-sensing data with large spatial coverage to conduct model calibration for various types of ice cover.Highlights. Three key points: The spatial distribution of wave number and spectral attenuation in pack ice are analyzed from SAR-retrieved surface wave spectra. The spectral attenuation rate of 9–15 s waves varies around 10−5 m2 s−1, with lower values in thicker semicontinuous ice fields with leads. The calibrated viscoelastic parameters are greater than those found in pancake ice.

Satellite Observations for Detecting and Forecasting Sea-Ice Conditions: A Summary of Advances Made in the SPICES Project by the EU’s Horizon 2020 Programme

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.

Drift of Pancake Ice Floes in the Winter Antarctic Marginal Ice Zone During Polar Cyclones

AbstractHigh temporal resolution in situ measurements of pancake ice drift are presented, from a pair of buoys deployed on floes in the Antarctic marginal ice zone during the winter sea ice expansion, over 9 days in which the region was impacted by four polar cyclones. Concomitant measurements of wave‐in‐ice activity from the buoys are used to infer that the ice remained unconsolidated, and pancake ice conditions were maintained over at least the first 7 days. Analysis of the data shows (i) the fastest reported ice drift speeds in the Southern Ocean; (ii) high correlation of drift velocities with the surface wind velocities, indicating absence of internal ice stresses &gt;100 km from the ice edge where remotely sensed ice concentration is 100%; and (iii) presence of a strong inertial signature with a 13 hr period. A Lagrangian free drift model is developed, including a term for geostrophic currents that reproduce the 13 hr period signature in the ice motion. The calibrated model provides accurate predictions of the ice drift for up to 2 days, and the calibrated parameters provide estimates of wind and ocean drag for pancake floes under storm conditions.