Sea Ice Roughness Research Articles

Arctic Sea Ice Topography Information From RADARSAT Constellation Mission (RCM) Synthetic Aperture Radar (SAR) Backscatter

AbstractSea ice topography information can be obtained from altimetry but these data are spatially and temporally limited compared to recent synthetic aperture radar (SAR) missions such as the RADARSAT Constellation Mission (RCM). We analyze the relationship between sea ice roughness and height obtained from three Ice, Cloud, and Land Elevation Satellite (ICESat)‐2 tracks on two dates in March 2022, with RCM backscatter from 17 images in the McClintock Channel, Canadian Arctic. We analyze how this relationship varies with ice type, polarization, and incidence angle. We find particularly notable relationships between sea ice roughness and horizontal‐transmit/vertical‐receive backscatter for first‐year ice, and sea ice height and backscatter for multi‐year ice. We develop a preliminary model for winter sea ice roughness retrieval using RCM. In comparison with independent ICESat‐2 data in our study region, we find the model performs effectively at estimating a roughness distribution and key roughness statistics, and characterizes spatial variations in roughness at a sub‐kilometer scale.

Investigating Future Arctic Sea Ice Loss and Near‐Surface Wind Speed Changes Related to Surface Roughness Using the Community Earth System Model

AbstractThe Arctic is undergoing a pronounced and rapid transformation in response to changing greenhouse gasses, including reduction in sea ice extent and thickness. There are also projected increases in near‐surface Arctic wind. This study addresses how the winds trends may be driven by changing surface roughness and/or stability in different Arctic regions and seasons, something that has not yet been thoroughly investigated. We analyze 50 experiments from the Community Earth System Model Version 2 (CESM2) Large Ensemble and five experiments using CESM2 with an artificially decreased sea ice roughness to match that of the open ocean. We find that with a smoother surface there are higher mean wind speeds and slower mean ice speeds in the autumn, winter, and spring. The artificially reduced surface roughness also strongly impacts the wind speed trends in autumn and winter, and we find that atmospheric stability changes are also important contributors to driving wind trends in both experiments. In contrast to the clear impacts on winds, the sea ice mean state and trends are statistically indistinguishable, suggesting that near‐surface winds are not major drivers of Arctic sea ice loss. Two major results of this work are: (a) the near‐surface wind trends are driven by changes in both surface roughness and near‐surface atmospheric stability that are themselves changing from sea ice loss, and (b) the sea ice mean state and trends are driven by the overall warming trend due to increasing greenhouse gas emissions and not significantly impacted by coupled feedbacks with the surface winds.

Effects of sea ice form drag on the polar oceans in the NEMO-LIM3 global ocean–sea ice model

The surface roughness of sea ice is highly variable because of the diversity of discrete obstacles to the flow present on the sea ice surface. These obstacles result in form drag, an effect poorly accounted for in the calculation of surface fluxes over sea ice in climate models. In this study, we implement in the ocean–sea ice model NEMO-LIM3 a variable formulation of the atmospheric and oceanic neutral drag coefficients over sea ice that includes the form drag effect. We examine the impact of this formulation on the ice cover and ocean surface properties over the past decades in the Arctic and Antarctic. Including form drag in the simulations leads to 10–50cm thinner sea ice in the peripheral Arctic seas and ∼10cm thicker sea ice in the Antarctic ice pack on annual means. Form drag significantly impacts summer sea surface temperatures in the marginal ice zone (+0.5 to +1.5°C in the Arctic and -0.5°C in the Antarctic) and sea surface salinities under the ice cover (+0.52 to +0.89g/kg in both polar regions). The seasonality of the ocean surface stress under sea ice is reversed, with a maximum (minimum) ocean surface stress under sea ice in summer (winter) when including form drag. However, this seasonal reversal is likely too intense in our simulations due to the underestimation of the prescribed mean floe lengths in summer. In the Arctic, the long-term declines in sea ice volume and multi-year ice are associated with decreasing neutral drag coefficients and negative trends in ocean surface stress. In the Antarctic, the trends in the neutral drag coefficients control the increase (decrease) in ocean surface stress in the Weddell and Ross (Bellingshausen and Amundsen) Seas. Form drag results in a 22–38% deeper summer mixed layer under polar sea ice and a year-long 10–25m deeper mixed layer in the Central Arctic. All these impacts on the ocean surface properties demonstrate the relevance of form drag for polar ocean modelling.

Mapping Arctic Sea-Ice Surface Roughness with Multi-Angle Imaging SpectroRadiometer

Sea-ice surface roughness (SIR) is a crucial parameter in climate and oceanographic studies, constraining momentum transfer between the atmosphere and ocean, providing preconditioning for summer-melt pond extent, and being related to ice age and thickness. High-resolution roughness estimates from airborne laser measurements are limited in spatial and temporal coverage while pan-Arctic satellite roughness does not extend over multi-decadal timescales. Launched on the Terra satellite in 1999, the NASA Multi-angle Imaging SpectroRadiometer (MISR) instrument acquires optical imagery from nine near-simultaneous camera view zenith angles. Extending on previous work to model surface roughness from specular anisotropy, a training dataset of cloud-free angular reflectance signatures and surface roughness, defined as the standard deviation of the within-pixel lidar elevations, from near-coincident operation IceBridge (OIB) airborne laser data is generated and is modelled using support vector regression (SVR) with a radial basis function (RBF) kernel selected. Blocked k-fold cross-validation is implemented to tune hyperparameters using grid optimisation and to assess model performance, with an R2 (coefficient of determination) of 0.43 and MAE (mean absolute error) of 0.041 m. Product performance is assessed through independent validation by comparison with unseen similarly generated surface-roughness characterisations from pre-IceBridge missions (Pearson’s r averaged over six scenes, r = 0.58, p < 0.005), and with AWI CS2-SMOS sea-ice thickness (Spearman’s rank, rs = 0.66, p < 0.001), a known roughness proxy. We present a derived sea-ice roughness product at 1.1 km resolution (2000–2020) over the seasonal period of OIB operation and a corresponding time-series analysis. Both our instantaneous swaths and pan-Arctic monthly mosaics show considerable potential in detecting surface-ice characteristics such as deformed rough ice, thin refrozen leads, and polynyas.

A New Data Processing System for Generating Sea Ice Surface Roughness Products from the Multi-Angle Imaging SpectroRadiometer (MISR) Imagery

Sea ice roughness can serve as a proxy for other sea ice characteristics such as ice thickness and ice age. Arctic-wide maps that represent spatial patterns of sea ice roughness can be used to better characterize spatial patterns of ice convergence and divergence processes. Sea ice surface roughness can also control and quantify turbulent exchange between sea ice surface and atmosphere and therefore influence surface energy balance at the basin scale. We have developed a data processing system that produces georeferenced sea ice roughness rasters that can be mosaicked to produce Arctic-wide maps of sea ice roughness. This approach starts with Top-of-Atmosphere radiance data from the Multi-angle Imaging SpectroRadiometer (MISR). We used red-band angular data from three MISR cameras (Ca, Cf, An). We created a training data set in which MISR pixels were matched with co-located and concurrent lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). We used a K-nearest neighbor algorithm with the training data to calibrate the multi-angle data to values of surface roughness and then applied the algorithm to Arctic-wide MISR data for two 16-day periods in April (spring) and July (summer). After georeferencing the roughness rasters, we then mosaicked each 16-day roughness dataset to produce Arctic-wide maps of sea ice roughness for spring and summer. Assessment of the results shows good agreement with independent ATM roughness data, not used in model development. A preliminary exploration of spatial and seasonal changes in sea ice roughness for two locations shows the ability to characterize the roughness of different ice types and the results align with previous studies. This processing system and its data products can help the sea ice research community to gain insights into the seasonal and interannual changes in sea ice roughness over the Arctic.

Coupled modeling of the seasonal transmission loss in the Beaufort Sea

An eight-element drifting vertical line array was deployed overwinter 2019–20 in the Beaufort Sea to record transmissions from two moored 35 Hz sources deployed as part of the Coordinated Arctic Acoustic Thermometry Experiment (CAATEX), and seven 925 Hz sources deployed by the Arctic Mobile Observing Systems (AMOS) experiment. Transmissions were received every day on the array at ranges of 10 to 850 km. A probabilistic rough-ice canopy was integrated to a Parabolic Equation code to predict the propagation loss. Sound speed profiles and percent ice cover along the propagation paths were obtained at daily resolution from data-assimilative Global Ice Ocean Prediction System (GIOPS, 20 km spatial resolution). Sea ice roughness and keel properties are empirically determined from the GIOPS ice thickness. The ice roughness is generated using a random pulse-train model with a characteristic wavenumber spectrum slope determined by ice age. Empirical probability distributions of ice keel depth, slope, and spatial densities are used to randomly add keels. Spatial averaging is used to account for signal bandwidth and uncertainty in receiver positions. The environment files and modeling are performed in piece-wise steps to manage memory footprints over long ranges. Modeled propagation losses compare well with the data and reproduce the seasonal variability observed in the measured results.

Sensitivity to changes in the surface‐layer turbulence parameterization for stable conditions in winter: A case study with a regional climate model over the Arctic

AbstractThe modeling of the atmospheric boundary layer over sea ice is still challenging because of the complex interaction between clouds, radiation and turbulence over the often inhomogeneous sea ice cover. There is still much uncertainty concerning sea ice roughness, near‐surface thermal stability and related processes, and their accurate parameterization. Here, a regional Arctic climate model forced by ERA‐Interim data was used to test the sensitivity of climate simulations to a modified surface flux parameterization for wintertime conditions over the Arctic. The reference parameterization as well as the modified one is based on Monin–Obukhov similarity theory, but different roughness lengths were prescribed and the stability dependence of the transfer coefficients for momentum, heat and moisture differed from each other. The modified parameterization accounts for the most comprehensive observations that are presently available over sea ice in the inner Arctic. Independent of the parameterization used, the model was able to reproduce the two observed dominant winter states with respect to cloud cover and longwave radiation. A stepwise use of the different parameterization assumptions showed that modifications of both surface roughness and stability dependence had a considerable impact on quantities such as air pressure, wind and near‐surface turbulent fluxes. However, the reduction of surface roughness to values agreeing with those observed during the Surface Heat Budget of the Arctic Ocean campaign led to an improvement in the western Arctic, while the modified stability parameterization had only a minor impact. The latter could be traced back to the model's underestimation of the strength of stability over sea ice. Future work should concentrate on possible reasons for this underestimation and on the question of generality of the results for other climate models.

Causes and Consequences of Sea Ice Initialization Shock in Coupled NWP Hindcasts with the GC2 Climate Model

AbstractWe present a coupled retrospective forecast (hindcast) study using the Met Office Global Coupled Model, version 2 (GC2), in which we identify and mitigate causes of initialization shock that lead to rapid error growth in sea ice forecasts. Sea ice state variables and volume budget terms as a function of forecast lead time are evaluated relative to analyses from an uncoupled Met Office ocean–sea ice analysis system [Forecast Ocean Assimilation Model, version 13 (FOAMv13)]. Two sources of initialization shock are highlighted and addressed, both of which are related to effective differences in physics between the analysis system and coupled forecast model. The primary shock to sea ice state variables arises from the use of a salinity-independent freezing temperature for seawater in GC2 as opposed to a salinity-dependent formulation in FOAMv13. A secondary effect arises from differences in the sea ice roughness and hence air–ice drag in the GC2 forecast model compared to the FOAMv13 analysis system. Generalizing from the findings of this study, we suggest that using nonnative analyses as initial conditions for coupled numerical weather prediction (NWP) studies will likely make them prone to initialization shock in some model components, to the detriment of forecast skill. To reduce the undesirable impacts of initialization shock on short-range forecast skill noted in this study we would therefore recommend the use of initial conditions (analyses) physically consistent with the native model components of the coupled forecast model, a native coupled analysis likely being the optimal initialization method.

Rough Surface Scattering Model Comparisons for Radar Altimetry of Sea Ice

Currently, sea-ice thickness is often estimated using microwave radar altimetry. It is well known that radar measurements are highly dependent on the roughness characteristics of the observed scene and can have a profound effect on altimetry-based thickness estimates. This simulation-based study highlights the importance of sea-ice roughness parameterization in the context of radar altimetry. This work provides a new sea-ice roughness parameterization in the form of an elevation spectrum developed from field campaign data. The formulation of this elevation spectrum is partly based on the generalized power law. Simulations have been developed to predict electromagnetic scattering from surfaces described by the new spectrum, with a focus on the X-band (10 GHz) scattering with nadir incidence. In this work, both the analytical small slope approximation and numerically exact Method of Moments are applied to various sea-ice surface spectra, representing a wide range of surface roughness conditions. The results show that the normalized radar cross section of the sea-ice interface varies significantly depending on the surface elevation spectrum and the statistics of the rough interface. Comparisons with numerically exact methods show that analytical approximations to rough surface scattering work reasonably well for the cases considered here, except at large scattering angles.

Characterizing winter landfast sea-ice surface roughness in the Canadian Arctic Archipelago using Sentinel-1 synthetic aperture radar and the Multi-angle Imaging SpectroRadiometer

AbstractTwo satellite datasets are used to characterize winter landfast first-year sea-ice (FYI), deformed FYI (DFYI) and multiyear sea-ice (MYI) roughness in the Canadian Arctic Archipelago (CAA): (1) optical Multi-angle Imaging SpectroRadiometer (MISR) and (2) synthetic aperture radar Sentinel-1. The Normalized Difference Angular Index (NDAI) roughness proxy derived from MISR, and backscatter from Sentinel-1 are intercompared. NDAI and backscatter are also compared to surface roughness derived from an airborne LiDAR track covering a subset of FYI and MYI (no DFYI). Overall, NDAI and backscatter are significantly positively correlated when all ice type samples are considered. When individual ice types are evaluated, NDAI and backscatter are only significantly correlated for DFYI. Both NDAI and backscatter are correlated with LiDAR-derived roughness (r= 0.71 andr= 0.74, respectively). The relationship between NDAI and roughness is greater for MYI than FYI, whereas for backscatter and ice roughness, the relationship is greater for FYI than MYI. Linear regression models are created for the estimation of FYI and MYI roughness from NDAI, and FYI roughness from backscatter. Results suggest that using a combination of Sentinel-1 backscatter for FYI and MISR NDAI for MYI may be optimal for mapping winter sea-ice roughness in the CAA.

Surface roughness signatures of summer arctic snow-covered sea ice in X-band dual-polarimetric SAR

ABSTRACT Surface roughness of sea ice is primary information for understanding sea ice dynamics and air–ice–ocean interactions. Synthetic aperture radar (SAR) is a powerful tool for investigating sea ice surface roughness owing to the high sensitivity of its signal to surface structures. In this study, we explored the surface roughness signatures of the summer Arctic snow-covered first-year sea ice in X-band dual-polarimetric SAR in terms of the root mean square (RMS) height. Two ice campaigns were conducted for the first-year sea ice with dry snow cover in the marginal ice zone of the Chukchi Sea in August 2017 and August 2018, from which high-resolution (4 cm) digital surface models (DSMs) of the sea ice were derived with the help of a terrestrial laser scanner to obtain the in situ RMS height. X-band dual-polarimetric (HH and VV) SAR data (3 m spatial resolution) were obtained for the 2017 campaign, at a high incidence angle (49.5°) of TerraSAR-X, and for the 2018 campaign, at a mid-incidence angle (36.1°) of TanDEM-X 1–2 days after the acquisition of the DSMs. The sea ice drifted during the time between the SAR and DSM acquisitions. As it is difficult to directly co-register the DSM to SAR owing to the difference in spatial resolution, the two datasets were geometrically matched using unmanned aerial vehicle (4 cm resolution) and helicopter-borne (30 cm resolution) photographs acquired as part of the ice campaigns. A total of five dual-polarimetric SAR features―backscattering coefficients at HH and VV polarizations, co-polarization ratio, co-polarization phase difference, and co-polarization correlation coefficient ―were computed from the dual-polarimetric SAR data and compared to the RMS height of the sea ice, which showed macroscale surface roughness. All the SAR features obtained at the high incidence angle were statistically weakly correlated with the RMS height of the sea ice, possibly influenced by the low backscattering close to the noise level that is attributed to the high incidence angle. The SAR features at the mid-incidence angle showed a statistically significant correlation with the RMS height of the sea ice, with Spearman’s correlation coefficient being higher than 0.7, except for the co-polarization ratio. Among the intensity-based and polarimetry-based SAR features, HH-polarized backscattering and co-polarization phase difference were analyzed to be the most sensitive to the macroscale RMS height of the sea ice. Our results show that the X-band dual-polarimetric SAR at mid-incidence angle exhibits potential for estimation of the macroscale surface roughness of the first-year sea ice with dry snow cover in summer.

Sea Ice Roughness Overlooked as a Key Source of Uncertainty in CryoSat‐2 Ice Freeboard Retrievals

AbstractESA's CryoSat‐2 has transformed the way we monitor Arctic sea ice, providing routine measurements of the ice thickness with near basin‐wide coverage. Past studies have shown that uncertainties in the sea ice thickness retrievals can be introduced at several steps of the processing chain, for instance, in the estimation of snow depth, and snow and sea ice densities. Here, we apply a new physical model to CryoSat‐2, which further reveals sea ice surface roughness as a key overlooked feature of the conventional retrieval process. High‐resolution airborne observations demonstrate that snow and sea ice surface topography can be better characterized by a lognormal distribution, which varies based on the ice age and surface roughness within a CryoSat‐2 footprint, than a Gaussian distribution. Based on these observations, we perform a set of simulations for the CryoSat‐2 echo waveform over “virtual” sea ice surfaces with a range of roughness and radar backscattering configurations. By accounting for the variable roughness, our new lognormal retracker produces sea ice freeboards that compare well with those derived from NASA's Operation IceBridge airborne data and extends the capability of CryoSat‐2 to profile the thinnest/smoothest sea ice and thickest/roughest ice. Our results indicate that the variable ice surface roughness contributes a systematic uncertainty in sea ice thickness of up to 20% over first‐year ice and 30% over multiyear ice, representing one of the principal sources of pan‐Arctic sea ice thickness uncertainty.

Effects of decimetre-scale surface roughness on L-band brightness temperature of sea ice

Abstract. Sea ice thickness is an essential climate variable. Current L-Band sea ice thickness retrieval methods do not account for sea ice surface roughness that is hypothesised to be not relevant to the process. This study attempts to validate this hypothesis that has not been tested yet. To test this hypothesis, we created a physical model of sea ice roughness based on geometrical optics and merged it into the L-band emissivity model of sea ice that is similar to the one used in the operational sea ice thickness retrieval algorithm. The facet description of sea ice surface used in geometrical optics is derived from 2-D surface elevation measurements. Subsequently the new model was tested with TB measurements performed during the SMOSice 2014 field campaign. Our simulation results corroborate the hypothesis that sea ice surface roughness has a marginal impact on near-nadir TB (used in the current operational retrieval). We demonstrate that the probability distribution function of surface slopes can be approximated with a parametric function whose single parameter can be used to characterise the degree of roughness. Facet azimuth orientation is isotropic at scales greater than 4.3 km. The simulation results indicate that surface roughness is a minor factor in modelling the sea ice brightness temperature. The change in TB is most pronounced at incidence angles greater than 40∘ and can reach up to 8 K for vertical polarisation at 60∘. Therefore current and future L-band missions (SMOS, SMAP, CIMR, SMOS-HR) measuring at such angles can be affected. Comparison of the brightness temperature simulations with the SMOSice 2014 radiometer data does not yield definite results.

Circumpolar Thin Arctic Sea Ice Thickness and Small-Scale Roughness Retrieval Using Soil Moisture and Ocean Salinity and Soil Moisture Active Passive Observations

The variations in the Arctic sea ice thickness (SIT) due to climate change have both positive and negative effects on commercial human activities, the ecosystem, and the Earth’s environment. Satellite microwave remote sensing based on microwave reflection signals reflected by the sea ice surface has been playing an essential role in monitoring and analyzing the Arctic SIT and sea ice concentration (SIC) during the past decades. Recently, passive microwave satellites incorporating an L-band radiometer, such as soil moisture and ocean salinity (SMOS) and soil moisture active passive (SMAP), have been used for analyzing sea ice characteristics, in addition to land and ocean research. In this study, we present a novel method to estimate thin SIT and sea ice roughness (SIR) using a conversion relationship between them, from the SMAP and SMOS data. Methodologically, the SMAP SIR is retrieved. The SMAP thin SIT and SMOS SIR are estimated using a conversion relationship between thin SIT data from SMOS data and SMAP-derived SIR, which is obtained from the spatial and temporal collocation of the SMOS thin SIT and the SIR retrieved from SMAP. Our results for the Arctic sea ice during December for four consecutive years from 2015 to 2018, show high accuracy (bias = −2.268 cm, root mean square error (RMSE) = 15.919 cm, and correlation coefficient (CC) = 0.414) between the SMOS-provided thin SIT and SMAP-derived SIT, and good agreement (bias = 0.03 cm, RMSE = 0.228 cm, and CC = 0.496) between the SMOS-estimated SIR and SMAP-retrieved SIR. Consequently, our study could be effectively used for monitoring and analyzing the variation in the Arctic sea ice.

Estimation of Level and Deformed First-Year Sea Ice Surface Roughness in the Canadian Arctic Archipelago from C- and L-Band Synthetic Aperture Radar

Utilizing synthetic aperture radar (SAR) data to understand and map sea ice roughness is an active area of research. An object-based image analysis is here used to quantitatively link the winter first-year sea ice surface roughness to C-band RADARSAT-2 and L-band ALOS-2 PALSAR-2 SAR backscatter measured in the Canadian Arctic. Results indicate: (1) C-band HH-polarization backscatter is correlated with roughness (r = 0.86) at a shallow incidence angle; and (2) L-band HH- and VV-polarization backscatter is correlated with roughness (r = 0.82) at a shallow incidence angle. Retrieval models for surface roughness are developed and applied to the imagery to demonstrate the potential of SAR for mapping deformation with a best case RMSE of 5 mm. Additionally, strong correlations between C- and L-band SAR backscatter acquired during the advanced melt period, and the winter measured surface roughness, are observed. These results are discussed in the context of SAR potentially providing significant sea ice structural information despite the occurrence of melt and presence of surface melt ponds.

Arctic Sea Ice Surface Roughness Estimated from Multi-Angular Reflectance Satellite Imagery

Sea ice surface roughness affects ice-atmosphere interactions, serves as an indicator of ice age, shows patterns of ice convergence and divergence, affects the spatial extent of summer meltponds, and affects ice albedo. We have developed a method for mapping sea ice surface roughness using angular reflectance data from the Multi-angle Imaging SpectroRadiometer (MISR) and lidar-derived roughness measurements from the Airborne Topographic Mapper (ATM). Using an empirical data modeling approach, we derived estimates of Arctic sea ice roughness ranging from centimeters to decimeters within the MISR 275-m pixel size. Using independent ATM data for validation, we find that histograms of lidar and multi-angular roughness values were nearly identical for areas with a roughness < 20 cm, but for rougher regions, the MISR-estimated roughness had a narrower range of values than the ATM data. The algorithm was able to accurately identify areas that transition between smooth and rough ice. Because of its coarser spatial scale, MISR-estimated roughness data have a variance about half that of ATM roughness data.

Low–frequency acoustic transmissions under sea ice as measured in the Beaufort Sea

A tomography array was deployed in the Beaufort Sea for a year beginning in late summer 2016 during the Office of Naval Research sponsored project called the Canada Basin Acoustic Propagation Experiment (CANAPE.) This talk will look in detail at the propagation characteristics of broadband sound transmitted at 250 Hz over 108 km to 285 km ranges as measured on a 60 element vertical line array. Over the year, the heat content and vertical stratification of the ocean change as heat is gained and lost to the atmosphere and as the momentum transfer from the wind mixes the upper layers. The changes in heat content and stratification over the year affects the travel-time of the sound as well as its fluctuations. Examples will be shown of each. Sea-ice roughness adds scattering loss as the ice thickness increases. Thus, the seasonal ice-cover modulates the transmission loss of the acoustic paths. The losses on deep turning paths are different from the sound trapped in the surface duct. The ambient noise level also changes as the seasons progress.

Effect of salinity on sea ice motion

We combined large eddy simulation (LES) with a thermodynamic slab ice model to simulate and study the sea ice motion and frazil ice dynamics in the ocean mixed layer in the Arctic winter. To show the accurate representation of leads in models, fluxes distributed laterally beneath leads and sea ice need to be parameterized. The 3-D LES model, which is developed from a 2-D turbulence model, is used to model the convection of beneath leads and sea ice. The experiments were then achived by combining the LES model with the ice model. The concentration of frazil ice was modeled using the Omstedt and Svensson model. The ice crystal radius and growth rates were assumed to be constant and the temperature and salinity changes with depth were taken into account. Salinity distribution and frazil ice concentration were influenced by ice motion, and variations in ocean salinity during freezing and thawing were also investigated. Entrained flow caused by the movement of sea ice has a significant influence on the eddy. Sea ice roughness is also important in the formation of the eddy current, and the values of the ice crystal rise velocity and the ice concentration source term coefficient influence frazil ice dynamics. The effects of sea ice thermodynamic dissipation on the sea is more remarkable, affecting the heat transfer to the atmosphere. The brine rejected during ice crystal formation and dilution of seawater are other important mechanisms of marine cyclical shocks.

Linking Regional Winter Sea Ice Thickness and Surface Roughness to Spring Melt Pond Fraction on Landfast Arctic Sea Ice

The Arctic sea ice cover has decreased strongly in extent, thickness, volume and age in recent decades. The melt season presents a significant challenge for sea ice forecasting due to uncertainty associated with the role of surface melt ponds in ice decay at regional scales. This study quantifies the relationships of spring melt pond fraction (fp) with both winter sea ice roughness and thickness, for landfast first-year sea ice (FYI) and multiyear sea ice (MYI). In 2015, airborne measurements of winter sea ice thickness and roughness, as well as high-resolution optical data of melt pond covered sea ice, were collected along two ~5.2 km long profiles over FYI- and MYI-dominated regions in the Canadian Arctic. Statistics of winter sea ice thickness and roughness were compared to spring fp using three data aggregation approaches, termed object and hybrid-object (based on image segments), and regularly spaced grid-cells. The hybrid-based aggregation approach showed strongest associations because it considers the morphology of the ice as well as footprints of the sensors used to measure winter sea ice thickness and roughness. Using the hybrid-based data aggregation approach it was found that winter sea ice thickness and roughness are related to spring fp. A stronger negative correlation was observed between FYI thickness and fp (Spearman rs = −0.85) compared to FYI roughness and fp (rs = −0.52). The association between MYI thickness and fp was also negative (rs = −0.56), whereas there was no association between MYI roughness and fp. 47% of spring fp variation for FYI and MYI can be explained by mean thickness. Thin sea ice is characterized by low surface roughness allowing for widespread ponding in the spring (high fp) whereas thick sea ice has undergone dynamic thickening and roughening with topographic features constraining melt water into deeper channels (low fp). This work provides an important contribution towards the parameterizations of fp in seasonal and long-term prediction models by quantifying linkages between winter sea ice thickness and roughness, and spring fp.

Measured and modeled acoustic propagation underneath the rough Arctic sea-ice.

A characteristic surface duct beneath the sea-ice in the Marginal Ice Zone causes acoustic waves to be trapped and continuously interact with the sea-ice. The reflectivity of the sea-ice depends on the thickness, the elastic properties, and its roughness. This work focuses on the influence of sea-ice roughness on long-range acoustic propagation, and on how well the arrival structure can be predicted by the full wave integration model OASES. In 2013, acoustic signals centered at 900 Hz were transmitted every hour for three days between ice-tethered buoys in a drifting network in the Fram Strait. The experiment was set up to study the signal stability in the surface channel below the sea-ice. Oceanographic profiles were collected during the experiment, while a statistical description of the rough sea-ice was established based on historical ice-draft measurements. This environmental description is used as input to the range independent version of OASES. The model simulations correspond fairly well with the observations, despite that a flat bathymetry is used and the sea-ice roughness cannot be fully approximated by the statistical representation used in OASES. Long-range transmissions around 900 Hz are found to be more sensitive to the sea-ice roughness than the elastic parameters.