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Snow depth observations from airborne snow radars, such as the NASA's Operation IceBridge (OIB) mission, have recently been used in altimeter-derived sea ice thickness estimates, as well as for model parameterization. A number of validation studies comparing airborne and in situ snow depth measurements have been conducted in the western Arctic Ocean, demonstrating the utility of the airborne data. However, there have been no validation studies in the Atlantic sector of the Arctic. Recent observations in this region suggest a significant and predominant shift towards a snow-ice regime caused by deep snow on thin sea ice. During the Norwegian young sea Ice, Climate and Ecosystems (ICE) expedition (N-ICE2015) in the area north of Svalbard, a validation study was conducted on 19 March 2015. This study collected ground truth data during an OIB overflight. Snow and ice thickness measurements were obtained across a two-dimensional (2-D) 400 m × 60 m grid. Additional snow and ice thickness measurements collected in situ from adjacent ice floes helped to place the measurements obtained at the gridded survey field site into a more regional context. Widespread negative freeboards and flooding of the snowpack were observed during the N-ICE2015 expedition due to the general situation of thick snow on relatively thin sea ice. These conditions caused brine wicking into and saturation of the basal snow layers. This causes the airborne radar signal to undergo more diffuse scattering, resulting in the location of the radar main scattering horizon being detected well above the snow–ice interface. This leads to a subsequent underestimation of snow depth; if only radar-based information is used, the average airborne snow depth was 0.16 m thinner than that measured in situ at the 2-D survey field. Regional data within 10 km of the 2-D survey field suggested however a smaller deviation between average airborne and in situ snow depth, a 0.06 m underestimate in snow depth by the airborne radar, which is close to the resolution limit of the OIB snow radar system. Our results also show a broad snow depth distribution, indicating a large spatial variability in snow across the region. Differences between the airborne snow radar and in situ measurements fell within the standard deviation of the in situ data (0.15–0.18 m). Our results suggest that seawater flooding of the snow–ice interface leads to underestimations of snow depth or overestimations of sea ice freeboard measured from radar altimetry, in turn impacting the accuracy of sea ice thickness estimates.

Owing to differing and complex snow geophysical properties, radar waves of different wavelengths undergo variable penetration through snow-covered sea ice. However, the mechanisms influencing radar altimeter backscatter from snow-covered sea ice, especially at Ka- and Ku-band frequencies, and the impact on the Ka- and Ku-band radar scattering horizon or the “track point” (i.e. the scattering layer depth detected by the radar re-tracker) are not well understood. In this study, we evaluate the Ka- and Ku-band radar scattering horizon with respect to radar penetration and ice floe buoyancy using a first-order scattering model and the Archimedes principle. The scattering model is forced with snow depth data from the European Space Agency (ESA) climate change initiative (CCI) round-robin data package, in which NASA's Operation IceBridge (OIB) data and climatology are included, and detailed snow geophysical property profiles from the Canadian Arctic. Our simulations demonstrate that the Ka- and Ku-band track point difference is a function of snow depth; however, the simulated track point difference is much smaller than what is reported in the literature from the Ku-band CryoSat-2 and Ka-band SARAL/AltiKa satellite radar altimeter observations. We argue that this discrepancy in the Ka- and Ku-band track point differences is sensitive to ice type and snow depth and its associated geophysical properties. Snow salinity is first increasing the Ka- and Ku-band track point difference when the snow is thin and then decreasing the difference when the snow is thick (>0.1 m). A relationship between the Ku-band radar scattering horizon and snow depth is found. This relationship has implications for (1) the use of snow climatology in the conversion of radar freeboard into sea ice thickness and (2) the impact of variability in measured snow depth on the derived ice thickness. For both (1) and (2), the impact of using a snow climatology versus the actual snow depth is relatively small on the radar freeboard, only raising the radar freeboard by 0.03 times the climatological snow depth plus 0.03 times the real snow depth. The radar freeboard is a function of both radar scattering and floe buoyancy. This study serves to enhance our understanding of microwave interactions towards improved accuracy of snow depth and sea ice thickness retrievals via the combination of the currently operational and ESA's forthcoming Ka- and Ku-band dual-frequency CRISTAL radar altimeter missions.

Uncertainty in snow properties impacts the accuracy of Arctic sea ice thickness estimates from radar altimetry. On first-year sea ice (FYI), spatiotemporal variations in snow properties can cause the Ku-band main radar scattering horizon to appear above the snow/sea ice interface. This can increase the estimated sea ice freeboard by several centimeters, leading to FYI thickness overestimations. This article examines the expected changes in Ku-band main scattering horizon and its impact on FYI thickness estimates, with variations in snow temperature, salinity, and density derived from ten naturally occurring Arctic FYI Cases encompassing saline/nonsaline, warm/cold, simple/complexly layered snow (4-45 cm) overlying FYI (48-170 cm). Using a semi-empirical modeling approach, snow properties from these Cases are used to derive layer-wise brine volume and dielectric constant estimates, to simulate the Ku-band main scattering horizon and delays in radar propagation speed. Differences between modeled and observed FYI thickness are calculated to assess sources of error. Under both cold and warm conditions, saline snow covers are shown to shift the main scattering horizon above from the snow/sea ice interface, causing thickness retrieval errors. Overestimates in FYI thicknesses of up to 65% are found for warm, saline snow overlaying thin sea ice. Our simulations exhibited a distinct shift in the main scattering horizon when the snow layer densities became greater than 440 kg/m3, especially under warmer snow conditions. Our simulations suggest a mean Ku-band propagation delay for snow of 39%, which is higher than 25%, suggested in previous studies.

Mean sea ice thickness is a sensitive indicator of Arctic climate change and is in long-term decline despite significant interannual variability. Current thickness estimations from satellite radar altimeters employ a snow climatology for converting range measurements to sea ice thickness, but this introduces unrealistically low interannual variability and trends. When the sea ice thickness in the period 2002–2018 is calculated using new snow data with more realistic variability and trends, we find mean sea ice thickness in four of the seven marginal seas to be declining between 60 %–100 % faster than when calculated with the conventional climatology. When analysed as an aggregate area, the mean sea ice thickness in the marginal seas is in statistically significant decline for 6 of 7 winter months. This is observed despite a 76 % increase in interannual variability between the methods in the same time period. On a seasonal timescale we find that snow data exert an increasingly strong control on thickness variability over the growth season, contributing 46 % in October but 70 % by April. Higher variability and faster decline in the sea ice thickness of the marginal seas has wide implications for our understanding of the polar climate system and our predictions for its change.

Studying carbon dioxide in the ocean helps to understand how the ocean will be impacted by climate change and respond to increasing fossil fuel emissions. The marine carbonate system is not well characterized in the Arctic, where challenging logistics and extreme conditions limit observations of atmospheric CO2 flux and ocean acidification. Here, we present a high-resolution marine carbon system data set covering the complete cycle of sea-ice growth and melt in an Arctic estuary (Nunavut, Canada). This data set was collected through three consecutive yearlong deployments of sensors for pH and partial pressure of CO2 in seawater (pCO2sw) on a cabled underwater observatory. The sensors were remarkably stable compared to discrete samples: While corrections for offsets were required in some instances, we did not observe significant drift over the deployment periods. Our observations revealed a strong seasonality in this marine carbon system. Prior to sea-ice formation, air–sea gas exchange and respiration were the dominant processes, leading to increasing pCO2sw and reduced aragonite saturation state (ΩAr). During sea-ice growth, water column respiration and brine rejection (possibly enriched in dissolved inorganic carbon, relative to alkalinity, due to ikaite precipitation in sea ice) drove pCO2sw to supersaturation and lowered ΩAr to < 1. Shortly after polar sunrise, the ecosystem became net autotrophic, returning pCO2sw to undersaturation. The biological community responsible for this early switch to autotrophy (well before ice algae or phytoplankton blooms) requires further investigation. After sea-ice melt initiated, an under-ice phytoplankton bloom strongly reduced aqueous carbon (chlorophyll-a max of 2.4 µg L–1), returning ΩAr to > 1 after 4.5 months of undersaturation. Based on simple extrapolations of anthropogenic carbon inventories, we suspect that this seasonal undersaturation would not have occurred naturally. At ice breakup, the sensor platform recorded low pCO2sw (230 µatm), suggesting a strong CO2 sink during the open water season.

The snow cover on sea ice is an important parameter controlling heat and momentum fluxes in our polar regions. Our understanding of snow thickness distributions on sea ice is severely limited by its vastness and numerous logistical difficulties. As such, we rarely collect enough in situ data from similar geographic locations to determine if and how the snow thickness distribution changes spatiotemporally. Geophysical changes in snow cover manifest as differences in dielectric properties, which are detectable in microwave emission and backscatter. Active microwave remote sensing offers improved spatial resolution when compared to passive microwave approaches. We apply our recently developed method that exploits the indirect thermodynamic control of the snow cover on near ice surface geophysical properties. The variance of C-band (5.3 GHz HH-polarization) microwave backscatter in winter (prior to melt) is assessed and is then used to estimate relative snow cover thickness and distribution. We assess the capability of our approach over landfast, first-year sea ice in the Canadian Arctic Archipelago and evaluate and compare our method against the Canadian Regional Ice Ocean Prediction system and AMSR2 passive microwave snow thickness estimates. Results demonstrate that this method can separate thick snow from thin snow on thick FYI within a thickness range of 5 to 45 cm at a spatial resolution of less than 5 km.

In this study, we compare eight recently developed snow depth products that use satellite observations, modeling or a combination of satellite and modeling approaches. These products are further compared against various ground-truth observations, including those from ice mass balance buoys (IMBs), snow buoys, snow depth derived from NASA's Operation IceBridge (OIB) flights, as well as snow depth climatology from historical observations. Large snow depth discrepancies between the different snow depth data sets are observed over the Atlantic and Canadian Arctic sectors. Among the products evaluated, the University of Washington snow depth product (UW) produces the overall deepest spring (March-April) snow packs, while the snow product from the Danish Meteorological Institute (DMI) provide the shallowest spring snow depths. There is no significant trend in the mean snow depth among all snow products since the 2000s, despite the great differences in regional snow depth. Two products, SnowModel-LG and the NASA Eulerian Snow on Sea Ice Model (NESOSIM), also provide estimates of snow density. Arctic-wide, these density products show the expected seasonal evolution with varying inter-annual variability, and no significant trend since the 2000s. The snow density in SnowModel-LG is generally higher than climatology, whereas NESOSIM density is generally lower. Both SnowModel-LG and NESOSIM densities have a larger seasonal change than climatology. Inconsistencies in the reconstructed snow parameters among the products, as well as differences between in-situ and airborne observations can in part be attributed to differences in effective footprint and spatial/temporal coverage, as well as insufficient observations for validation/bias adjustments. Our results highlight the need for more targeted Arctic surveys over different spatial and temporal scales to allow for a more systematic comparison and fusion of airborne, in-situ and remote sensing observations.

Synthetic aperture radar (SAR) incidence angle has a significant effect on the microwave backscatter from sea ice. This paper investigates the incidence angle dependence of C- and L-band HH-polarized microwave backscatter coefficient over Arctic first-year sea ice (FYI) and multiyear sea ice (MYI) in winter. Advanced Land Observation Satellite Phased Array type L-band SAR (L-band) and RADARSAT-2 (C-band) images are used to derive ice type-specific incidence angle dependencies calculated using linear regression models. For L-band, mean ice type-specific incidence angle dependencies for FYI and MYI are −0.21 and −0.30 dB/1°, respectively; and for C-band, they are −0.22 and −0.16 dB/1°, respectively. To validate our results, we calculated root-mean-square deviation (RMSD) by comparing the ice type-specific dependence from 2010 with individual dependencies from 2009 based on ice types and frequencies. The RMSD is found to be smaller than the standard deviation of ice type-specific dependencies for both frequencies. The RMSD values for the L-band incidence angle dependencies are 0.03 and 0.04 dB/1° for FYI and MYI, respectively. For C-band, the RMSD values for the FYI and MYI dependencies are 0.03 and 0.01 dB/1°, respectively. Subsequently, we demonstrate that after applying incidence angle normalization, the variability of C- and L-band SAR backscatter reduces and separability of ice types increase substantially.

Estimating sea ice motion from synthetic aperture radar (SAR) imagery at C-band is the most reliable approach because of its high spatial resolution and ever increasing temporal resolution given the multiple current and upcoming SAR platforms. However, there is still uncertainty in SAR derived sea ice motion depending on the ice type and its thermodynamic state. There have been suggestions (mostly theoretical) that use of L-band SAR and its inherent longer wavelength (15–30 cm) and subsequent increased penetration capability could be beneficial for estimating sea ice motion, especially during the melt season. Here, we estimate and analyze sea ice motion for 9 pairs of C- and L-band SAR imagery from RADARSAT-2, PALSAR-1 and PALSAR-2 located in the Canadian Arctic over a variety of sea ice types at different thermodynamic states. Results show that the increased signal penetration of L-band SAR into multi-year ice (MYI) during the melt season facilitates the detection of more motion vectors with stronger cross-correlation coefficients compared to C-band SAR. Over newly formed ice and dry first-year ice, the reduced sensitivity to surface scattering and richer texture from L-band SAR imagery facilitates the detection of more motion vectors with stronger cross-correlation coefficients compared to C-band SAR. Over dry MYI, L-band provided stronger cross-correlation coefficients but C-band detected more motion vectors with a more representative spatial distribution. With Arctic sea ice continuing shift from a multi-year to first-year dominated icescape, coupled with an increasing melt season length, L-band SAR's ability to provide improved sea ice motion estimates during both the melt and freeze-up time periods could prove even more useful in the coming decades.

Melt ponds play a significant role in the summer decay of sea ice due to the fact that their albedo is significantly lower than surrounding snow and sea ice surface. Despite its requirement for thermodynamic sea ice modeling, measurement of melt pond areal coverage using satellite remote sensing has proven difficult due to significant spatiotemporal variability in the timing and evolution of melt ponds. Less than optimal results from prior studies employing a spectral mixture analysis (SMA) towards the determination of melt pond areal coverage from satellite remote sensing data provided the incentive for a multiple endmember spectral mixture analysis (MESMA) approach. The MESMA was performed on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery using endmember spectra obtained from atmospherically corrected coincident high resolution imagery, surface observations and modeling. Results were validated against a high resolution Quickbird image acquired coincident to the MODIS image. The validation indicates that the best MESMA results provide consistent estimates of melt pond coverage for regions with high pond coverage (within 5% melt pond coverage) but overestimate pond fraction for regions with low pond coverage (by 10% or more). This may be due to deficiencies in the representation of sea ice surfaces within the endmember library used, oversimplified modeling of the ice surface and shortcomings in the validation process. However, it is assumed that with further refinement, the MESMA technique could allow for reliable estimates of the areal coverage of sea ice melt ponds using low resolution (large spatial coverage) optical satellite imagery under a wide variety of spatiotemporal pond evolution and fraction conditions.

This study investigates changes in Ku-, X- and C-band microwave backscatter, co-polarized ratio and dual-frequency ratios, to changes in in - situ measured snow thermophysical properties, driven by surface radiation measurements, during a 7-day transition from late winter to pre-early melt on a highly saline snow cover on smooth first-year ice. A surface-based Ku-, X- and C-band microwave scatterometer system is used near coincident with in-situ thermophysical snow measurements. A frequency-dependent penetration depth model is utilized to simulate diverse variations in Ku-, X- and C-band penetration during fluctuating snow thermophysical and atmospheric conditions. These results helped improve the interpretation of observed changes in Ku-, X- and C-band backscatter. Overall, dielectric loss associated with presence of brine throughout the snow volume, is observed to be the governing factor affecting microwave penetration. Our results indicate significant differences in observed microwave backscatter and derived co-polarized ratio and dual-frequency ratios, for all three frequencies during the transition from cold late winter phase to warm pre-early melt onset phase. C-band demonstrates the greatest increase in backscatter and co-pol ratios, followed by X- and Ku-bands. The dual-frequency ratio combinations clearly demonstrate their ability to separate sensitivities in frequencies by characterizing changes in backscatter during the 7-day transition. Our results indicate strong direct influence of downwelling shortwave and longwave radiation on snow brine volume and dielectric properties, which drives changes to Ku-, X- and C-band microwave backscatter and its associated co-polarized ratio and dual-frequency ratios. Our results demonstrate the ability of an observational multi-frequency active microwave approach to illustrate frequency-, polarization- and incidence angle-dependent changes in microwave backscatter from snow covers on first-year ice, at varying atmospheric and snow thermophysical conditions.

Diurnal observations of coincident in situ physical-, electrical-, and surface-based C-band microwave scattering properties of a 16-cm saline snow cover on smooth, moderately saline, first-year sea ice are presented for the transition period between late winter and early melt. Statistical regression analysis and backscatter modeling are employed to explore the scattering mechanisms within the snowpack and to assess associations between backscatter and snow properties for both periods. Our results demonstrate substantial variation in both measured snow properties and C-band microwave backscatter over the diurnal cycle during the late winter period when the difference between maximum and minimum air and snow surface temperature was approximately 5 °C. No such variation in snow properties and backscatter occurred for the early melt period when our case study exhibited a small diurnal variation (~1°C) in air and snow surface temperature. Statistical and modeled results show significant association between the microwave backscatter and snow properties for the top 8 cm of the snowpack. Basal snow properties do not contribute toward total backscatter in either case. As a result, we are certain that the sea ice surface was a negligible scattering interface during both cases. Correlations between backscatter and snow properties are incidence angle dependent, demonstrating the highest association at 50°. Diurnal backscatter from RADARSAT-2 synthetic aperture radar acquisitions support the influence of varying diurnal snow properties on C-band backscatter, showing a difference of ~4 dB for ${ \sigma }_{\vphantom {R_{j}}{\text {hh}}}^{o}$ and ${ \sigma }_{{\text {vv}}}^{o}$ during the late winter period. This difference is reduced to

A theoretical understanding of a multifrequency microwave approach to understand complex microwave interactions from a highly saline snow cover on a relatively smooth first-year sea ice is presented. We examine the sensitivity of Ku-, X-, and C-band ${\sigma }_{{\text {VV}}}^{0}$ and ${\sigma }_{{\text {HH}}}^{0}$ to variability in snow geophysical properties such as salinity, density, temperature, and snow grain radius, sampled from a highly saline snow cover on first-year sea ice. A first-order multilayer snow and ice backscatter model is used to calculate ${\sigma }_{{\text {VV}}}^{0}$ and ${\sigma }_{{\text {HH}}}^{0}$ by taking into account the surface and volume scattering contributions within each snow layer of the snow pack. Penetration depth models are used to calculate the potential penetration of all three frequencies, at initial and perturbed snow property conditions. Sensitivity analyses suggest that variability in salinity and snow grain radius have the greatest effect, followed by density and temperature. This phenomenon is observed for all three frequencies, influencing microwave penetration and backscatter. Dielectric loss associated with highly saline snow covers and substantial changes in scattering contributions from snow grain radius perturbations were found to be the dominant factors affecting microwave penetration and backscatter. Results from this paper demonstrate the potential of using a multifrequency theoretical approach to correlate with active microwave observations to determine the geophysical and electrical state of snow/sea ice system. The paper also represents an evolution in a theoretical understanding on how an active microwave approach using multiple frequencies can be further utilized toward the development of snow thickness and/or snow water equivalent algorithm on smooth FYI.

Ku- and C-band spaceborne scatterometer sigma nought (&sigma, &deg, ) backscatter data of snow covered landfast first-year sea ice from the Canadian Arctic Archipelago are acquired during the winter season with coincident in situ snow-thickness observations. Our objective is to describe a methodological framework for estimating relative snow thickness on first-year sea ice based on the variance in &sigma, from daily time series ASCAT and QuikSCAT scatterometer measurements during the late winter season prior to melt onset. We first describe our theoretical basis for this approach, including assumptions and conditions under which the method is ideally suited and then present observational evidence from four independent case studies to support our hypothesis. Results suggest that the approach can provide a relative measure of snow thickness prior to &sigma, detected melt onset at both Ku- and C-band frequencies. We observe that, during the late winter season, a thinner snow cover displays a larger variance in daily &sigma, compared to a thicker snow cover on first-year sea ice. This is because for a given increase in air temperature, a thinner snow cover manifests a larger increase in basal snow layer brine volume owing to its higher thermal conductivity, a larger increase in the dielectric constant and a larger increase in &sigma, at both Ku- and C bands. The approach does not apply when snow thickness distributions on first-year sea ice being compared are statistically similar, indicating that similar late winter &sigma, variances likely indicate regions of similar snow thickness.

In this study, we inter-compare observed and modeled Ku-, X- and C-band microwave backscatter for two snow temperature conditions for a highly saline snow cover on smooth first-year sea ice. A new surface-based multi-frequency (Ku-, X- and C-bands) microwave scatterometer system is used quasi-coincident with in situ geophysical snow measurements. A multilayer snow and ice backscatter model is used to calculate the total co-polarized backscatter coefficient for two snow temperature conditions. The model provides the surface and volume scattering contributions for each snow layer, as well as the frequency-dependent penetration depth. These results aid interpretation of observed backscatter. Joint use of Ku-, X- and C-band microwaves provide an enhanced understanding of diverse variations in geophysical, thermodynamic and electrical state of snow/sea ice system. Our results indicate that the effect of dielectric loss associated with highly saline snow covers is the dominant factor affecting microwave penetration and backscatter from all three frequencies. The observed and modeled C-band backscatter shows good agreement, followed by X- and Ku-bands, at both snow temperature conditions. Microwave backscatter shows greater sensitivity to variations in plot-scale surface roughness, for all three frequencies. Additionally, Ku-band wavelength exhibits greater sensitivity to snow grain radius, over X-, and C-bands. Our results demonstrate the future potential of a multi-frequency approach towards the development of snow thickness and snow water equivalent algorithms on first-year sea ice.

We construct and present the first baseline, time series evolution of L-band SAR microwave backscatter (σ0HH) for landfast Arctic first-year (FYI) and multi-year (MYI) sea ice from ALOS PALSAR. We compare the winter to freeze-up seasonal L-band σ0HH signatures with C-band RADARSAT-2 SAR imagery and Ku-band QuikSCAT scatterometer data. We also explored the utility of L-band σ0HH to detect FYI and MYI melt onset dates. For both ice types, L-band exhibited a similar σ0HH evolution to C- and Ku-bands. A rise in L-band σ0HH was observed from winter to melt onset, however, with a lower magnitude attributed to the larger penetration of L-band microwaves through the snow-covered sea ice medium. Over FYI, L-band σ0HH exhibited the most robust response and greater diversity in melt onset signature, compared to C- and Ku-bands. Over MYI, L-band σ0HH declined during the winter to melt onset transition contrasting the C- and Ku-band evolution of σ0HH during melt onset. Although, a distinct L-band σ0HH response was not found during pond onset, a very strong σ0HH response was found during sea ice break-up, which could operationally be utilized to identify sea ice break-up timing. During freeze-up, L-band σ0HH showed greater diversity compared to C- and Ku-bands. Our study also found that, L-band detected melt onset dates were up to 6 days later than C- and Ku-band detected dates, for both ice types. The melt response was the strongest at L-band for FYI, where C-band detected a robust melt onset date for MYI. Therefore, for operational sea ice monitoring, L-band suits better for FYI, whereas C-band is still a better choice for MYI. In the warming Arctic, where FYI has become the dominant ice type, L-band demonstrates promise to be the optimal choice for sea ice operational monitoring with high spatiotemporal data acquisition from existing, upcoming and proposed L-band SAR missions.

Spring melt pond fraction (fp) has been shown to influence September sea ice extent and, with a growing need to improve melt pond physics in climate and forecast models, observations at large spatial scales are needed. We present a novel technique for estimating fp on sea ice at high spatial resolution from the Sentinel-1 satellite during the winter period leading up to spring melt. A strong correlation (r = -0.85) is found between winter radar backscatter and fp from first-year and multiyear sea ice data collected in the Canadian Arctic Archipelago (CAA) in 2015. Observations made in the CAA in 2016 are used to validate a fp retrieval algorithm, and a fp prediction for the CAA in 2017 is made. The method is effective using the horizontal transmit and receive polarization channel only and shows promise for providing seasonal, pan-Arctic, fp maps for improved understanding of melt pond distributions and forecast model skill.

In this study, we inter-compared observed Ku-, X- and C-band microwave backscatter from saline 14 cm, 8 cm, and 4 cm snow covers on smooth first-year sea ice. A Ku-, X- and C-band surface-borne polarimetric microwave scatterometer system was used to measure fully-polarimetric backscatter from the three snow covers, near-coincident with corresponding in situ snow thermophysical measurements. The study investigated differences in co-polarized backscatter observations from the scatterometer system for all three frequencies, modeled penetration depths, utilized co-pol ratios, and introduced dual-frequency ratios to discriminate dominant polarization-dependent frequencies from these snow covers. Results demonstrate that the measured co-polarized backscatter magnitude increased with decreasing snow thickness for all three frequencies, owing to stronger gradients in snow salinity within thinner snow covers. The innovative dual-frequency ratios suggest greater sensitivity of Ku-band microwaves to snow grain size as snow thickness increases and X-band microwaves to snow salinity changes as snow thickness decreases. C-band demonstrated minimal sensitivity to changes in snow salinities. Our results demonstrate the influence of salinity associated dielectric loss, throughout all layers of the three snow covers, as the governing factor affecting microwave backscatter and penetration from all three frequencies. Our “plot-scale” observations using co-polarized backscatter, co-pol ratios and dual-frequency ratios suggest the future potential to up-scale our multi-frequency approach to a “satellite-scale” approach, towards effective development of snow geophysical and thermodynamic retrieval algorithms on smooth first-year sea ice.

This study inter-compares observed Ku-, X- and C-band microwave backscatter from saline 14 cm, 8 cm, and 4 cm snow covers on smooth first-year sea ice. A surface-borne multi-frequency (Ku-, X- and C-bands) polarimetric microwave scatterometer system is used near-coincident with in situ snow geophysical measurements. The study investigated differences in scatterometer observations for all three frequencies, co-pol ratios, and introduced new dual-frequency ratios to discriminate dominant polarization-dependent frequencies from these snow covers. Preliminary results suggest that, thinnest 4 cm snow cover demonstrate greatest increase in microwave backscatter from all three frequencies, followed by backscatter from thicker 8 cm and 14 cm snow covers. Dual-frequency indices derived for all frequency and polarization combinations suggest greater sensitivity of Ku-band microwaves to snow grain microstructure with increasing snow thicknesses, X-band microwaves to changes in snow salinities with decreasing snow thicknesses. Our results indicate the effect of dielectric loss associated with high salinities throughout all layers of the three snow covers, as the dominant factor affecting microwave penetration and backscatter from all three frequencies.

The European Space Agency's CryoSat-2 satellite mission provides radar altimeter data that are used to derive estimates of sea ice thickness and volume. These data are crucial to understanding recent variability and changes in Arctic sea ice. Sea ice thickness retrievals at the CryoSat-2 frequency require accurate measurements of sea ice freeboard, assumed to be attainable when the main radar scattering horizon is at the snow/sea ice interface. Using an extensive snow thermophysical property dataset from late winter conditions in the Canadian Arctic, we examine the role of saline snow on first-year sea ice (FYI), with respect to its effect on the location of the main radar scattering horizon, its ability to decrease radar penetration depth, and its impact on FYI thickness estimates. Based on the dielectric properties of saline snow commonly found on FYI, we quantify the vertical shift in the main scattering horizon. This is found to be approximately 0.07 m. We propose a thickness-dependent snow salinity correction factor for FYI freeboard estimates. This significantly reduces CryoSat-2 FYI retrieval error. Relative error reductions of ~ 11% are found for an an ice thickness of 0.95 m and ~ 25% for 0.7 m. Our method also helps to close the uncertainty gap between SMOS and CryoSat-2 thin ice thickness retrievals. Our results indicate that snow salinity should be considered for FYI freeboard estimates.