Properties Of Sea Ice Research Articles

Impact of Microstructure on Solar Radiation Transfer Within Sea Ice During Summer in the Arctic: A Model Sensitivity Study

The recent rapid changes in Arctic sea ice have occurred not only in ice thickness and extent, but also in the microstructure of ice. To understand the role of microstructure on partitioning of incident solar shortwave radiation within the ice and upper ocean, this study investigated the sensitivity of the optical properties of summer sea ice on ice microstructures such as the volume fraction, size, and vertical distribution of gas bubbles, brine pockets, and particulate matter (PM). The results show that gas bubbles are the predominant scatterers within sea ice. Their effects on the scattering coefficient and ice albedo are 5 and 20 times stronger respectively than the effect of brine pockets. Albedo and transmittance of ice decrease with higher concentration and larger size of PM particles. A 4-cm top layer of ice with high PM concentration (50 g/m3) results in a 10% increase in radiation absorption. The role of ice microstructure in the partitioning of radiation transfer is more important for seasonal than for multiyear ice, and more important for ponded than for snow-covered ice. Varying ice microstructure can obviously alter solar radiation transfer in the ice-ocean system, even with a constant ice thickness. Our results suggest that numerical models should take the variable microstructure of sea ice into account to improve model accuracy and to understand the interaction between internal variations in Arctic sea ice and the ocean, especially in summer.

Theory of Radar Polarimetric Interferometry and Its Application to the Retrieval of Sea Ice Elevation in the Western Weddell Sea, Antarctic

AbstractSea ice elevation plays a crucial role in sea ice dynamic processes driven by winds and waves, for which satellite radar remote sensing becomes indispensable to monitor snow‐covered sea ice across the vast polar regions regardless of darkness and clouds. To measure sea ice elevation, a theory of polarimetric interferometry for both monostatic and bistatic radars is developed based on analytic solutions of Maxwell's equations, accounting for realistic and complicated properties of snow, sea ice, and seawater. This analytic method inherently preserves phase information that is imperative for radar polarimetry and interferometry. Among a multitude of complex radar coefficients in the general polarimetric interferometric covariance matrix, the symmetry group theory is utilized to identify and select appropriate terms pertaining to the retrieval of sea ice elevation while avoiding radar parameters that may inadvertently introduce non‐uniqueness and excessive uncertainty. Theoretical calculations compare well with field observations for rough and old sea ice encountered in the Operation‐IceBridge and TanDEM‐X Antarctic Science Campaign over the Western Weddell Sea. The results show that the magnitude of the coefficient of normalized correlation between co‐polarized horizontal and co‐polarized vertical radar returns is inversely related to sea ice elevation, while the associate phase term is nonlinear and noisy and should be excluded. From these analyses, a protocol is set up to measure Antarctic sea ice elevation to be presented in the next companion paper.

Landsat-8 Sea Ice Classification Using Deep Neural Networks

Knowing the location and type of sea ice is essential for safe navigation and route optimization in ice-covered areas. In this study, we developed a deep neural network (DNN) for pixel-based ice Stage of Development classification for the Baltic Sea using Landsat-8 optical satellite imagery to provide up-to-date ice information for Near-Real-Time maritime applications. In order to train the network, we labeled the ice regions shown in the Landsat-8 imagery with classes from the German Federal Maritime and Hydrographic Agency (BSH) ice charts. These charts are routinely produced and distributed by the BSH Sea Ice Department. The compiled data set for the Baltic Sea region consists of 164 ice charts from 2014 to 2021 and contains ice types classified by the Stage of Development. Landsat-8 level 1 (L1b) images that could be overlaid with the available BSH ice charts based on the time of acquisition were downloaded from the United States Geological Survey (USGS) global archive and indexed in a data cube for better handling. The input variables of the DNN are the individual spectral bands: aerosol coastal, blue, green, red and near-infrared (NIR) out of the Operational Land Imager (OLI) sensor. The bands were selected based on the reflectance and emission properties of sea ice. The output values are 4 ice classes of Stage of Development and Ice Free. The results obtained show significant improvements compared to the available BSH ice charts when moving from polygons to pixels, preserving the original classes. The classification model has an accuracy of 87.5% based on the test data set excluded from the training and validation process. Using optical imagery can therefore add value to maritime safety and navigation in ice- infested waters by high resolution and real-time availability. Furthermore, the obtained results can be extended to other optical satellite imagery such as Sentinel-2. Our approach is promising for automated Near-Real-Time (NRT) services, which can be deployed and integrated at a later stage at the German Aerospace Center (DLR) ground station in Neustrelitz.

Fatigue life prediction of pile-supported sea-crossing bridges subject to random ice forces

As a natural disaster in cold sea regions, ice forces can cause strong vibration of offshore structures, and even threaten the safety of structures. The elevated steel pile caps are the most commonly used substructure foundation type of sea-crossing bridges and the large-scale caps are generally used to resist ice forces. The fatigue damage of sea-crossing bridges with steel piles under ice forces is directly related to the fluctuating stresses. Therefore, this paper carried out the fatigue damage analysis and fatigue life prediction of sea-crossing bridges subjected to random ice forces in Bohai Sea. In order to considering long-range condition of sea ice, the statistical characters of sea ice properties such as, ice velocity, ice thickness and ice strength are carried out. The ice forces processes are simulated utilizing the random ice force spectrum model for vertical and conical structures. Fatigue damage of sea-crossing bridges with vertical and conical caps is analyzed using the rainflow cycle counting technique and Palmgren-Miner's rule. The fatigue life is estimated based on the cumulative fatigue damage and the real period of drift ice in Bohai Sea. Results show that the fatigue damage increases obviously as a result of increases in the stress caused by increased sea ice parameters. The sea-crossing bridges with conical caps can prolong the fatigue life by more than 45% in comparison with sea-crossing bridges with vertical caps. The cone ice-resistant cap has a good effect on mitigating fatigue damage of sea-crossing bridges in drift ice covered water regions.

Measurements of 540–1740 MHz Brightness Temperatures of Sea Ice During the Winter of the MOSAiC Campaign

A ground-based ultra-wideband radiometer operating at 540, 900, 1380, and 1740 MHz was used to measure microwave thermal emissions from an Arctic sea ice floe as part of the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) Expedition. The instrument was deployed on a drifting ice floe near 86°N, 120°E in leg 1 of the expedition (December 2019) and observed second-year ice (potentially with refrozen melt ponds) that experienced new ice growth at its base over a ten-day period. Measured circularly polarized brightness temperatures were compared with the predictions of a radiative transfer (RT) model for a layered medium consisting of ocean, growing new ice, desalinated remnant second-year ice/refrozen melt pond, and snow layers. Characteristics of the sea ice composition used in the model were determined from <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in-situ</i> measurements. Comparisons of the measured and modeled wideband brightness temperatures showed good agreement consistently over the observation period and for various off-nadir observation angles. The results demonstrate the capabilities of 0.5–2 GHz microwave radiometry for observing sea ice properties and also show the impact of a saline ice layer at the ice bottom on the measured brightness temperature.

A Random Pore Model of sea ice for predicting its mechanical properties

Sea ice contains gas and brine pockets arising from its special growth environments.The mechanical properties are strongly dependent on the microstructure of sea ice, the most prominent of which is described by its porosity. In this paper a Random Pore Model (RPM) is proposed, the basic assumption of which is that sea ice is made of pure ice sheet embedded with a lot of randomly distributed pores of random sizes. Monte Carlo method is then employed to generate random pore field. Simple numerical tests show that there exists upper limit value for porosity, which is close to 0.61. Simple numerical tests are then performed to find the dependence of tensile and compressive strengths of sea ice on porosity. The results turn out to be in good agreement with experimental results. This is followed by further studies on the effect of porosity on mechanical properties of sea ice, such as elastic modulus, Poisson's ratio, failure strains. The mechanical properties of sea ice at low porosity and scale effect are also discussed.

Microstructure evolution of young sea ice from a Svalbard fjord using micro-CT analysis

AbstractWe analysed the three-dimensional microstructure of sea ice by means of X-ray-micro computed tomography. Microscopic (brine- and air- pore sizes, numbers and connectivity) and macroscopic (salinity, density, porosity) properties of young Arctic sea ice were analysed. The analysis is based on ice cores obtained during spring 2016. Centrifuging of brine prior to CT imaging has allowed us to derive confident relationships between the open, vertically connected and total porosity of young sea ice at relatively high temperatures. We analysed the dependence of the microscopic properties on vertical position and total brine porosity. Most bulk properties (salinity, density) and pore space properties (pore sizes and their distribution) show a strong dependence on total brine porosity, but did not change significantly over the course of the field work. However, significant changes were observed for pore numbers (decreasing over time) and pore connectivity (increasing over time). CT-based salinity determinations are subject to larger than standard uncertainties (from conductivity), while the CT method yields important information about the salinity contributions from closed and open pores. We also performed a comparison of CT-based air porosity with calculations based on density from hydrostatic weighing. The consistency is encouraging and gives confidence to our CT-based results.

A New Model Ice for Wave-Ice Interaction

The interaction of waves and ice is of significant relevance for engineers, oceanographers and climate scientists. In-situ measurements are costly and bear uncertainties due to unknown boundary conditions. Therefore, physical laboratory experiments in ice tanks are an important alternative to validate theories or investigate particular effects of interest. Ice tanks use model ice which has down-scaled sea ice properties. This model ice in ice tanks holds disadvantages due to its low stiffness and non-linear behavior which is not in scale to sea ice, but is of particular relevance in wave-ice interactions. With decreasing stiffness steeper waves are required to reach critical stresses for ice breaking, while the non-linear, respectively non-elastic, deformation behavior is associated with high wave damping. Both are scale effects and do not allow the direct transfer of model scale test results to scenarios with sea ice. Therefore, the alternative modeling approach of Model Ice of Virtual Equivalent Thickness (MIVET) is introduced. Its performance is tested in physical experiments and compared to conventional model ice. The results show that the excessive damping of conventional model ice can be reduced successfully, while the scaling of the wave induced ice break-up still requires research and testing. In conclusion, the results obtained are considered a proof of concept of MIVET for wave-ice interaction problems.

Long time-series measurements of high-frequency acoustic scattering from sea ice

Understanding relatively recent rapid changes in Arctic sea ice calls for extensive and continuous observations of the ice cover. Utilizing datasets obtained with 38, 70, and 200 kHz upward-looking transducers in the northeast Chukchi Sea, seasonal and year-to year changes in acoustic scattering were measured. Two complete annual cycles of scattering returns from the winters of 2017–2018 and 2018–2019 were analyzed. The initial effort in this research is constrained to the stable growth phase of sea ice in winter through the early melt onset the following spring. We also restricted our analysis to individual, minimally deformed ice floes to limit the variability caused by large-scale roughness. Results from this two-year analysis will be discussed along with their implications for future measurements and acoustic remote sensing of sea ice properties.

Development of a diffuse reflectance probe for in situ measurement of inherent optical properties in sea ice

Abstract. Detailed characterization of the spatially and temporally varying inherent optical properties (IOPs) of sea ice is necessary to better predict energy and mass balances, as well as ice-associated primary production. Here we present the development of an active optical probe to measure IOPs of a small volume of sea ice (dm3) in situ and non-destructively. The probe is derived from the diffuse reflectance method used to measure the IOPs of human tissues. The instrument emits light into the ice by the use of an optical fibre. Backscattered light is measured at multiple distances away from the source using several receiving fibres. Comparison to a Monte Carlo simulated lookup table allows, in theory, retrieval of the absorption coefficient, the reduced scattering coefficient and a phase function similarity parameter γ, introduced by Bevilacqua and Depeursinge (1999). γ depends on the two first moments of the Legendre polynomials, allowing the analysis of the backscattered light not satisfying the diffusion regime. The depth reached into the medium by detected photons was estimated using Monte Carlo simulations: the maximum depth reached by 95 % of the detected photons was between 40±2 and 270±20 mm depending on the source–detector distance and on the ice scattering properties. The magnitude of the instrument validation error on the reduced scattering coefficient ranged from 0.07 % for the most scattering medium to 35 % for the less scattering medium over the 2 orders of magnitude we validated. Fixing the absorption coefficient and γ, which proved difficult to measure, vertical profiles of the reduced scattering coefficient were obtained with decimetre resolution on first-year Arctic interior sea ice on Baffin Island in early spring 2019. We measured values of up to 7.1 m−1 for the uppermost layer of interior ice and down to 0.15±0.05 m−1 for the bottommost layer. These values are in the range of polar interior sea ice measurements published by other authors. The inversion of the reduced scattering coefficient at this scale was strongly dependent on the value of γ, highlighting the need to define the higher moments of the phase function. This newly developed probe provides a fast and reliable means for measurement of scattering in sea ice.

Sensitivity of Arctic sea ice to melt pond processes and atmospheric forcing: A model study

Melt ponds are pools of meltwater forming principally on Arctic sea ice during the melt season. The albedo of melt ponds is a key component of the surface energy balance. For this reason, various melt pond schemes have been developed for climate models. These schemes require assumptions on the physical processes governing melt ponds as well as a knowledge of the atmospheric state, which are not perfectly known. In this study, we investigate the effects of the sources of uncertainty from the prescribed atmospheric surface state, the melt pond scheme definition and the refreezing formulation of melt ponds on the simulated Arctic sea ice and melt pond properties with the NEMO-LIM3 ocean–sea ice general circulation model. We find that the simulated melt pond state is largely controlled by the freezing point of melt ponds. The representation of melt ponds is in better agreement with observations when using the freezing point of −0.15°C compared to the value of −2.00°C, in our model set-up. All the simulations feature positive trends in melt pond area fraction over the past decades. However, only 3 out of 8 simulations have significant positive trends in melt pond volume per sea ice area. This suggests an influence of the sea ice state for melt ponds over the last 30 years. Overall, we find that the simulated sea ice state, and in particular sea ice volume, is more affected by changes in the prescribed atmospheric forcing than by changes in the prescribed melt pond scheme or refreezing formulation. Including explicit melt pond schemes in large-scale sea-ice models offer the possibility to improve the representation of the surface energy balance in climate general circulation models. Our results underline that, in parallel to these efforts in model developments, improved estimates of surface atmospheric conditions will be required to achieve more realistic sea ice states.

Impacts of strong wind events on sea ice and water mass properties in Antarctic coastal polynyas

Strong offshore wind events (SOWEs) occur frequently near the Antarctic coast during austral winter. These wind events are typically associated with passage of synoptic- or meso-scale cyclones, which interact with the katabatic wind field and affect sea ice and oceanic processes in coastal polynyas. Based on numerical simulations from the coupled Finite Element Sea-ice Ocean Model (FESOM) driven by the CORE-II forcing, two coastal polynyas along the East Antarctica coast––the Prydz Bay Polynya and the Shackleton Polynya are selected to examine the response of sea ice and oceanic properties to SOWEs. In these polynyas, the southern or western flanks of cyclones play a crucial role in increasing the offshore winds depending on the local topography. Case studies for both polynyas show that during SOWEs, when the wind speed is 2–3 times higher than normal values, the offshore component of sea ice velocity can increase by 3–4 times. Sea ice concentration can decrease by 20–40%, and sea ice production can increase up to two to four folds. SOWEs increase surface salinity variability and mixed layer depth, and such effects may persist for 5–10 days. Formation of high salinity shelf water (HSSW) is detected in the coastal regions from surface to 800 m after 10–15 days of the SOWEs, while the HSSW features in deep layers exhibit weak response on the synoptic time scale. HSSW formation averaged over winter is notably greater in years with longer duration of SOWEs.

Benefits of sea ice initialization for the interannual-to-decadal climate prediction skill in the Arctic in EC-Earth3

Abstract. A substantial part of Arctic climate predictability at interannual timescales stems from the knowledge of the initial sea ice conditions. Among all sea ice properties, its volume, which is a product of sea ice concentration (SIC) and thickness (SIT), is the most responsive parameter to climate change. However, the majority of climate prediction systems are only assimilating the observed SIC due to lack of long-term reliable global observation of SIT. In this study, the EC-Earth3 Climate Prediction System with anomaly initialization to ocean, SIC and SIT states is developed. In order to evaluate the regional benefits of specific initialized variables, three sets of retrospective ensemble prediction experiments are performed with different initialization strategies: ocean only; ocean plus SIC; and ocean plus SIC and SIT initialization. In the Atlantic Arctic, the Greenland–Iceland–Norway (GIN) and Barents seas are the two most skilful regions in SIC prediction for up to 5–6 lead years with ocean initialization; there are re-emerging skills for SIC in the Barents and Kara seas in lead years 7–9 coinciding with improved skills of sea surface temperature (SST), reflecting the impact of SIC initialization on ocean–atmosphere interactions for interannual-to-decadal timescales. For the year 2–9 average, the region with significant skill for SIT is confined to the central Arctic Ocean, covered by multi-year sea ice (CAO-MYI). Winter preconditioning with SIT initialization increases the skill for September SIC in the eastern Arctic (e.g. Kara, Laptev and East Siberian seas) and in turn improve the skill of air surface temperature locally and further expanded over land. SIT initialization outperforms the other initialization methods in improving SIT prediction in the Pacific Arctic (e.g. East Siberian and Beaufort seas) in the first few lead years. Our results suggest that as the climate warming continues and the central Arctic Ocean might become seasonal ice free in the future, the controlling mechanism for decadal predictability may thus shift from sea ice volume to ocean-driven processes.

Probabilistic estimation of level ice resistance on ships based on sea ice properties measured along summer Arctic cruise paths

The decline of Arctic sea ice makes trans-Arctic transport possible in the summer, which increases the requirement of ice-strengthened vessels. Accurate estimation of ice resistance is necessary for ice-strengthened ship design and this process requires data on various ice properties. The objective of this study was to investigate the probabilistic characteristics of Arctic sea ice properties associated with estimations of ice resistance on ships. To achieve this aim, measurements of ice properties were collected from Arctic voyages conducted in the summers of 2008−2016. Ice thickness and ice concentration were observed along ship routes, and ice flexural strength and effective modulus were determined based on the physical properties of ice. The ice–hull kinetic friction coefficient was estimated according to ship velocity. Statistical analyses were conducted on these ice properties. The results showed that most ice properties can be described using existing statistical models (Burr XII, Beta, Gamma, and generalized extreme value distributions), with the exceptions of ice concentration, ice salinity and temperature, as well as friction coefficient. Furthermore, a Monte Carlo simulation was employed to estimate the ice resistance of a ship moving forward in a level ice zone based on a well-established empirical equation. The results showed that the distribution of ice resistance conformed to a Gamma distribution. The correlation coefficients between ice density and flexural strength as well as effective modulus had negligible effects on the simulation results. This study provides a helpful guide for the design of ice-strengthened ships and related numerical simulations required for summer trans-Arctic navigation.

Review of research on ice loading of offshore structures in level ice fields

The physical and mechanical properties of sea ice and the failure modes of ice sheets significantly affect the ice loading of offshore structures. This paper first introduces the physical and mechanical characteristics of sea ice, then describes the physical processes of the interaction of ice sheets with offshore structures. Finally, the progress of research on the ice loading of offshore structures is briefly analyzed, including the three main approaches of formula estimation, experimental testing and numerical simulation. By discussing the applicability of formula estimation, the research focus and difficulties of experimental testing, and the development trends of numerical simulation, we find that the model testing of ice loading is an effective method. This paper covers the research trends and problems faced by ice loading on offshore structures, and it can provide certain guiding references the related research of scholars in the field.

A Comparison of Decimeter Scale Variations of Physical and Photobiological Parameters in a Late Winter First-Year Sea Ice in Southwest Greenland

Small-scale variation in the physical and biological properties of sea ice was examined by collecting nine sea ice cores within 1 m2 in a land-fast first-year ice in southwest Greenland in late winter. Cores were sectioned in four segments and sea ice physical, biological, and photobiological parameters were measured. The main purpose was to explore the decimeter-scale horizontal and vertical variations in common sea ice parameters. ANOVA analyses revealed significant within-core variations for bulk salinity, brine salinity, brine volume, gas volume, chlorophyll a (Chl a), and the maximum light-limited photosynthetic efficiency (α). Only temperature and bulk salinity variations were significant between cores, and no significant variations were found within or between cores for other photobiological parameters. Power analyses were applied to determine the number of replicates needed to achieve a significance at p &lt; 0.05 with sufficient power, and showed a minimum of four and preferably five replicate cores to detect the observed variability in this first-year ice. It is emphasized that these results only apply to this type of first-year ice in late winter/early spring, and that different variations may apply to other types of ice.

Sea Ice Properties in High-Resolution Sea Ice Models.

An Arctic sea ice-ocean model is run with three uniform horizontal resolutions (6, 4, and 2km) and identical sea ice and ocean model parameterizations, including an isotropic viscous-plastic sea ice rheology, a mechanical ice strength parameterization, and an ice ridging parameterization. Driven by the same atmospheric forcing, the three model versions all produce similar spatial patterns and temporal variations of ice thickness and motion fields, resulting in almost identical magnitude and seasonal evolution of total ice volume and mean ice concentration, ice speed, and fractions of ice of various thickness categories over the Arctic Ocean. Increasing model resolution from 6 to 2km does not significantly improve model performance when compared to NASA IceBridge ice thickness observations. This suggests that the large-scale sea ice properties of the model are insensitive to varying high resolutions within the multifloe scale (2-10km), and it may be unnecessary to adjust model parameters constantly with increasingly high resolutions. This is also true with models within the aggregate scale (10-75km), indicating that model parameters used at coarse resolution may be used at high or multiscale resolution. However, even though the three versions all yield similar mean state of sea ice, they differ in representing anisotropic properties of sea ice. While they produce a basic pattern of major sea ice leads similar to satellite observations, their leads are distributed differently in space and time. Without changing model parameters and sea ice spatiotemporal variability, the 2-km resolution model tends to capture more leads than the other two models.

Operational Service for Mapping the Baltic Sea Landfast Ice Properties

The Baltic Sea is partly covered by sea ice in every winter season. Landfast ice (LFI) on the Baltic Sea is a place for recreational activities such as skiing and ice fishing. Over thick LFI ice roads can be established between mainland and islands to speed up transportation compared to the use of ferries. LFI also allows transportation of material to or from islands without piers for large ships. For all these activities, information on LFI extent and sea ice thickness, snow thickness and degree of ice deformation on LFI is very important. We generated new operational products for these LFI parameters based on synthetic aperture radar (SAR) imagery and existing products and prediction models on the Baltic Sea ice properties. The products are generated daily and have a 500 m pixel size. They are visualized in a web-portal titled “Baltic Sea landfast ice extent and thickness (BALFI)” which has free access. The BALFI service was started in February 2019. Before the BALFI service, information on the LFI properties in fine scale (&lt;1 km) was not available from any single source or product. We studied the accuracy and quality of the BALFI products for the ice season 2019–2020 using ice charts and in-situ coastal ice station data. We suggest that the current products give usable information on the Baltic LFI properties for various end-users. We also identify some topics for the further development of the BALFI products.

Polarimetric Behavior for the Derivation of Sea Ice Topographic Height From TanDEM-X Interferometric SAR Data

Single-pass interferometric synthetic aperture radar (InSAR) is an effective technique for sea ice topographic retrieval despite the inherent dynamics of sea ice. However, the penetration of microwaves into snow-covered thick ice and the achievable height sensitivity for tens-of-centimeters thin ice are two major issues, which limit the accuracy of InSAR-derived sea ice topography. Polarimetry provides scattering information concerning the sea ice properties and has the potential, in combination with interferometry, to achieve an accurate reconstruction of a sea ice digital elevation model (DEM). This article studies the relation between polarimetric signatures and sea ice topography, and explores the possibility to compensate the penetration bias by merging copolar coherence into InSAR processing. The newly generated topographic map has a root-mean-square error under 0.3 m. For thin ice below 1 m, a positive relation between copolar phase φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coPol</sub> and surface height is observed, suggesting that φ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">coPol</sub> can effectively characterize thin sea ice topography. For thick ice with ridges, the maximum polarimetric phase difference Δφ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">maxPol</sub> reveals a particular shape of the coherence region, which can be interpreted as oriented volume scattering. It suggests that the model-based approach using polarimetric SAR interferometry assuming an oriented volume scattering model is promising in measuring the scattering centers in thick and deformed sea ice. The study of polarimetric behavior for the InSAR DEM is, therefore, a step forward toward accurate modeling of sea ice topography from polarimetric single-pass InSAR data.

Accurate Estimations of Sea‐Ice Thickness and Elastic Properties From Seismic Noise Recorded With a Minimal Number of Geophones: From Thin Landfast Ice to Thick Pack Ice

AbstractDespite their high potential for accurate sea ice properties estimation, seismic methods are still limited by the difficulty of access and the challenging logistics of polar environments. Conventional seismic methods generally require tens of geophones together with active seismic sources for monitoring applications. While this is not an issue for mainland environment, it is restrictive for sea ice and prevents long‐term monitoring. We introduce a method to estimate sea ice thickness and elastic properties from passive recordings of the ambient seismic field with a minimal number of geophones. In comparison with our previous work (Moreau et al., 2020; https://doi.org/10.1029/2019JC015709) where about 50 sensors were used, the number of geophones is reduced by 1 order of magnitude, thanks to a new strategy of inversion of the passive seismic data. The method combines noise interferometry for estimating the elastic properties, with a Bayesian inversion of the dispersion in the waveforms of icequakes for inferring ice thickness, based on passive recordings from only 3–5 geophones, depending on the signal to noise ratio. We demonstrate its potential both on data recorded on thin landfast ice in Svalbard, and on data recorded on thick pack ice in the Arctic ocean.