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We present new high-resolution snow depth data on Arctic sea ice derived from airborne microwave radar measurements from the IceBird campaigns of the Alfred Wegener Institute (AWI) together with a new retrieval method using signal peakiness based on an intercomparison exercise of colocated data at different altitudes. We aim to demonstrate the capabilities and potential improvements of radar data, which were acquired at a lower altitude (200 ft) and slower speed (110 kn) and had a smaller radar footprint size (2-m diameter) than previous airborne snow radar data. So far, AWI Snow Radar data have been derived using a 2–18-GHz ultrawideband frequency-modulated continuous-wave (FMCW) radar in 2017–2019. Our results show that our method in combination with thorough calibration through coherent noise removal and system response deconvolution significantly improves the quality of the radar-derived snow depth data. The validation against a 2-D grid of in situ snow depth measurements on level landfast first-year ice indicates a mean bias of only 0.86 cm between radar and ground truth. Comparison between the radar-derived snow depth estimates from different altitudes shows good consistency. We conclude that the AWI Snow Radar aboard the IceBird campaigns is able to measure the snow depth on Arctic sea ice accurately at higher spatial resolution than but consistent with the existing airborne snow radar data of NASA Operation IceBridge. Together with the simultaneous measurements of the total ice thickness and surface freeboard, the IceBird campaign data will be able to describe the whole sea-ice column on regional scales. [ABSTRACT FROM AUTHOR]

Local-scale variations in snow density and layering on Arctic sea ice were characterized using a combination of traditional snow pit and SnowMicroPen (SMP) measurements. In total, 14 sites were evaluated within the Canadian Arctic Archipelago and Arctic Ocean on both first-year (FYI) and multi-year (MYI) sea ice. Sites contained multiple snow pits with coincident SMP profiles as well as unidirectional SMP transects. An existing SMP density model was recalibrated using manual density cutter measurements (n=186) to identify best-fit parameters for the observed conditions. Cross-validation of the revised SMP model showed errors comparable to the expected baseline for manual density measurements (RMSE = 34 kg m -3 or 10.9 %) and strong retrieval skill (R2= 0.78). The density model was then applied to SMP transect measurements to characterize variations at spatial scales of up to 100 m. A supervised classification trained on snow pit stratigraphy allowed separation of the SMP density estimates by layer type. The resulting dataset contains 58 882 layer-classified estimates of snow density on sea ice representing 147 m of vertical variation and equivalent to more than 600 individual snow pits. An average bulk density of 310 kg m -3 was estimated with clear separation between FYI and MYI environments. Lower densities on MYI (277 kg m -3) corresponded with increased depth hoar composition (49.2 %), in strong contrast to composition of the thin FYI snowpack (19.8 %). Spatial auto-correlation analysis showed layered composition on FYI snowpack to persist over long distances while composition on MYI rapidly decorrelated at distances less than 16 m. Application of the SMP profiles to determine propagation bias in radar altimetry showed the potential errors of 0.5 cm when climatology is used over known snow density. [ABSTRACT FROM AUTHOR]

The timing and intensity of snowmelt processes on sea ice are key drivers determining the seasonal sea-ice energy and mass budgets. In the Arctic, satellite passive microwave and radar observations have revealed a trend towards an earlier snowmelt onset during the last decades, which is an important aspect of Arctic amplification and sea-ice decline. Around Antarctica, snowmelt on perennial ice is weak and very different than in the Arctic, with most snow surviving the summer. Here we compile time series of snowmelt onset dates on seasonal and perennial Antarctic sea ice from 1992 to 2014/15 using active microwave observations from the European Space Agency's (ESA) European Remote Sensing (ERS) 1 and 2 missions (ERS-1 and ERS-2), Quick Scatterometer (QSCAT), and Advanced Scatterometer (ASCAT) radar scatterometers. We define two snowmelt transition stages: a weak backscatter rise, indicating the initial warming and destructive metamorphism of the snowpack (pre-melt), followed by a rapid backscatter rise, indicating the onset of thaw–freeze cycles (snowmelt). Results show large interannual variability, with an average pre-melt onset date of 29 November and melt onset of 10 December, respectively, on perennial ice, without any significant trends over the study period, consistent with the small trends of Antarctic sea-ice extent. There was a latitudinal gradient from early snowmelt onsets in mid-November in the northern Weddell Sea to late (end of December) or even absent snowmelt conditions in the southern Weddell Sea. We show that QSCAT Ku-band-derived (13.4 GHz signal frequency) pre-melt and snowmelt onset dates are earlier by 20 and 18 d, respectively, than ERS and ASCAT C-band-derived (5.6 GHz) dates. This offset has been considered when constructing the time series. Snowmelt onset dates from passive microwave observations (37 GHz) are later by 14 and 6 d than those from the scatterometers, respectively. Based on these characteristic differences between melt onset dates observed by different microwave wavelengths, we developed a conceptual model which illustrates how the seasonal evolution of snow temperature profiles may affect different microwave bands with different penetration depths. These suggest that future multi-frequency active and passive microwave satellite missions could be used to resolve melt processes throughout the vertical snow column of thick snow on perennial Antarctic sea ice. [ABSTRACT FROM AUTHOR]

Snow thickness on sea ice is a largely undersampled parameter yet of importance for the sea ice mass balance and for satellite-based sea ice thickness estimates and thus our general understanding of global ice volume change. Traditional direct thickness measurements with meter sticks can provide accurate but only spot information, referred to as 'needles' due to their pinpoint focus and information, while airborne and satellite remote sensing snow products, referred to as 'the haystack,' have large uncertainties due to their scale. We demonstrate the remarkable accuracy and applicability of ground-penetrating radar (GPR) snow thickness measurements by comparing them with in situ meter stick data from two field campaigns to Antarctica in late winter/early spring. The efficiency and millimeter-to-centimeter accuracy of GPR enables practitioners to acquire extensive, semiregional data with the potential to upscale needles to the haystack and to potentially calibrate satellite remote sensing products that we confirm to derive roughly 30% of the in situ thickness. We find the radar wave propagation velocity in snow to be rather constant (± 6%), encouraging regional snow thickness surveys. Snow thinner than 10 cm is under the detection limit with the off-the-shelf GPR setup utilized in our study. [ABSTRACT FROM AUTHOR]

Satellite observations have revealed decreases in the duration of the seasonal snow and ice coverage of Great Slave Lake (GSL) and Great Bear Lake (GBL), large freshwater lakes in Northern Canada. However, limited information is available about ice thickness changes. Here, we present and validate a method to retrieve lake ice thickness using the CryoSat-2 (CS2) radar altimeter. These are the first satellite altimeter retrievals of lake ice thickness. Under optimal conditions, the CS2 signal is scattered from both the snow–ice and the ice–water interfaces, with returns from each interface being of sufficient power to be identified in the radar waveform. The distance between the scattering horizons is used to determine the ice thickness, similar to traditional ground penetrating radar measurements. The seasonal evolution of ice thickness of GBL and GSL is compared with in situ measurements, modeled ice thicknesses, and previous studies. The impact of ice and snow properties on signal penetration and the thickness retrieval are examined with synthetic aperture radar imagery. The CS2 ice thickness retrievals are able to observe the seasonal thickening of the lake ice and closely match the in situ measurements over both lakes ( $R > 0.65$ , \text {RMSE} < 0.33 m). Thickness retrievals of thin ice are limited by a minimum waveform peak separation of 2 range bins, approximately 0.26 m in ice. Although not designed for lake ice studies, CS2 and future SAR satellite altimeter missions offer new possibilities to monitor the ice and water levels of climatically sensitive and influential lakes. [ABSTRACT FROM AUTHOR]

Measurements of superimposed ice formation and snow properties as a function of the surface energy balance during melt-onset are presented. They were performed on fast ice on Kongsfjorden, Svalbard, between late May and early June 2002. On May 27, rapid snow melt commenced and within 5 days the snow cover initially 0.23 m thick transformed completely into 0.05–0.06 m of superimposed ice. The superimposed ice formed by both percolation of melt water to ice layers and by settling of snow between ice layers. Melt-onset was characterized by rapid changes in the total energy balance, which became positive throughout the whole day after May 27. The increased energy fluxes were mainly caused by higher incoming longwave radiation due to overcast conditions. The observations show that superimposed ice contributes significantly to sea ice mass balance. [Copyright &y& Elsevier]