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 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.

Abstract. The presence of melt ponds on the Arctic sea ice strongly affects the energy balance of the Arctic Ocean in summer. It affects albedo as well as transmittance through the sea ice, which has consequences for the heat balance and mass balance of sea ice. An algorithm to retrieve melt pond fraction and sea ice albedo from Medium Resolution Imaging Spectrometer (MERIS) data is validated against aerial, shipborne and in situ campaign data. The results show the best correlation for landfast and multiyear ice of high ice concentrations. For broadband albedo, R 2 is equal to 0.85, with the RMS (root mean square) being equal to 0.068; for the melt pond fraction, R 2 is equal to 0.36, with the RMS being equal to 0.065. The correlation for lower ice concentrations, subpixel ice floes, blue ice and wet ice is lower due to ice drift and challenging for the retrieval surface conditions. Combining all aerial observations gives a mean albedo RMS of 0.089 and a mean melt pond fraction RMS of 0.22. The in situ melt pond fraction correlation is R 2 = 0.52 with an RMS = 0.14. Ship cruise data might be affected by documentation of varying accuracy within the Antarctic Sea Ice Processes and Climate (ASPeCt) protocol, which may contribute to the discrepancy between the satellite value and the observed value: mean R 2 = 0.044, mean RMS = 0.16. An additional dynamic spatial cloud filter for MERIS over snow and ice has been developed to assist with the validation on swath data.

A portable surface-based polarimetric C-band scatterometer for field deployment over sea ice is presented. The scatterometer system, its calibration, signal processing, and near-field correction are described. The near-field correction is shown to be effective for both linear polarized and polarimetric backscatter. Field methods for the scatterometer are described. Sample linear polarized and polarimetric backscatter results are presented for snow-covered first-year sea ice (FYI), multiyear hummock ice, and rough melt pond water on FYI. The magnitude of backscatter signature variability due to system effects is presented, providing the necessary basis for quantitative analysis of field data.

Year-round observations of the physical snow and ice properties and processes that govern the ice pack evolution and its interaction with the atmosphere and the ocean were conducted during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition of the research vessel Polarstern in the Arctic Ocean from October 2019 to September 2020. This work was embedded into the interdisciplinary design of the 5 MOSAiC teams, studying the atmosphere, the sea ice, the ocean, the ecosystem, and biogeochemical processes. The overall aim of the snow and sea ice observations during MOSAiC was to characterize the physical properties of the snow and ice cover comprehensively in the central Arctic over an entire annual cycle. This objective was achieved by detailed observations of physical properties and of energy and mass balance of snow and ice. By studying snow and sea ice dynamics over nested spatial scales from centimeters to tens of kilometers, the variability across scales can be considered. On-ice observations of in situ and remote sensing properties of the different surface types over all seasons will help to improve numerical process and climate models and to establish and validate novel satellite remote sensing methods; the linkages to accompanying airborne measurements, satellite observations, and results of numerical models are discussed. We found large spatial variabilities of snow metamorphism and thermal regimes impacting sea ice growth. We conclude that the highly variable snow cover needs to be considered in more detail (in observations, remote sensing, and models) to better understand snow-related feedback processes. The ice pack revealed rapid transformations and motions along the drift in all seasons. The number of coupled ice–ocean interface processes observed in detail are expected to guide upcoming research with respect to the changing Arctic sea ice.

Cryosat-2 has provided measurements of pan-Arctic sea ice thickness since 2010 with unprecedented spatial coverage and frequency. However, it remains uncertain how the Ku-band radar interacts with the vast range of scatterers that can be present within the satellite footprint, including sea ice with varying physical properties and multi-scale roughness, snow cover, and leads. Here, we present a numerical model designed to simulate delay-Doppler SAR (Synthetic Aperture Radar) altimeter echoes from snow-covered sea ice, such as those detected by Cryosat-2. Backscattered echoes are simulated directly from triangular facetbased models of actual sea ice topography generated from Operation IceBridge Airborne Topographic Mapper (ATM) data, as well as virtual statistical models simulated artificially. We use these waveform simulations to investigate the sensitivity of SAR altimeter echoes to variations in satellite parameters (height, pitch, roll) and sea ice properties (physical properties, roughness, presence of water). We show that the conventional Gaussian assumption for sea ice surface roughness may be introducing significant error into the Cryosat-2 waveform retracking process. Compared to a more representative lognormal surface, an echo simulated from a Gaussian surface with rms roughness height of 0.2 m underestimates the ice freeboard by 5 cmpotentially underestimating sea ice thickness by around 50 cm. We present a set of 'ideal' waveform shape parameters simulated for sea ice and leads to inform existing waveform classification techniques. This model will ultimately be used to improve retrievals of key sea ice properties, including freeboard, surface roughness and snow depth, from SAR altimeter observations.