In the context of quantifying Arctic ice-volume decrease at global scale, the CryoSat-2 satellite was launched in 2010 and is equipped with the Ku band synthetic aperture radar altimeter SIRAL (Synthetic Aperture Interferometric Radar Altimeter), which we use to derive sea-ice freeboard defined as the height of the ice surface above the sea level. Accurate CryoSat-2 range measurements over open water and the ice surface of the order of centimetres are necessary to achieve the required accuracy of the freeboard-to-thickness conversion. Besides uncertainties of the actual sea-surface height and limited knowledge of ice and snow properties, the composition of radar backscatter and therefore the interpretation of radar echoes is crucial. This has consequences in the selection of retracker algorithms which are used to track the main scattering horizon and assign a range estimate to each CryoSat-2 measurement. In this study we apply a retracker algorithm with thresholds of 40, 50 and 80% of the first maximum of radar echo power, spanning the range of values used in the current literature. By using the selected retrackers and additionally results from airborne validation measurements, we evaluate the uncertainties of sea-ice freeboard and higher-level products that arise from the choice of the retracker threshold only, independent of the uncertainties related to snow and ice properties. Our study shows that the choice of retracker thresholds does have a significant impact on magnitudes of estimates of sea-ice freeboard and thickness, but that the spatial distributions of these parameters are less affected. Specifically we find mean radar freeboard values of 0.121 m (0.265 m) for the 40% threshold, 0.086 m (0.203 m) for the 50% threshold and 0.024 m (0.092 m) for the 80% threshold, considering first-year ice (multiyear ice) in March 2013. We show that the main source of freeboard and thickness uncertainty results from the choice of the retracker and the unknown penetration of the radar pulse into the snow layer in conjunction with surface roughness effects. These uncertainties can cause a freeboard bias of roughly 0.06–0.12 m. Furthermore we obtain a significant rise of 0.02–0.15 m of freeboard from March 2013 to November 2013 in the area for multiyear sea ice north of Greenland and Canada. Since this is unlikely, it gives rise to the assumption that applying different retracker thresholds depending on seasonal properties of the snow load is necessary in the future.

We assess different methods and input parameters, namely snow depth, snow density and ice density, used in freeboard-to-thickness conversion of Arctic sea ice. This conversion is an important part of sea ice thickness retrieval from spaceborne altimetry. A data base is created comprising sea ice freeboard derived from satellite radar altimetry between 1993 and 2012 and co-locate observations of total (sea ice + snow) and sea ice freeboard from the Operation Ice Bridge (OIB) and CryoSat Validation Experiment (CryoVEx) airborne campaigns, of sea ice draft from moored and submarine upward looking sonar (ULS), and of snow depth from OIB campaigns, Advanced Microwave Scanning Radiometer (AMSR-E) and the Warren climatology (Warren et al., 1999). We compare the different data sets in spatiotemporal scales where satellite radar altimetry yields meaningful results. An inter-comparison of the snow depth data sets emphasizes the limited usefulness of Warren climatology snow depth for freeboard-to-thickness conversion under current Arctic Ocean conditions reported in other studies. We test different freeboard-to-thickness and freeboard-to-draft conversion approaches. The mean observed ULS sea ice draft agrees with the mean sea ice draft derived from radar altimetry within the uncertainty bounds of the data sets involved. However, none of the approaches are able to reproduce the seasonal cycle in sea ice draft observed by moored ULS. A sensitivity analysis of the freeboard-to-thickness conversion suggests that sea ice density is as important as snow depth.

Spaceborne radar altimeters record echo waveforms over all Earth surfaces, but their interpretation and quantitative exploitation over the Arctic Ocean is particularly challenging. Radar returns may be from all ocean, all sea ice, or a mixture of the two, so the first task is the determination of which surface and then an interpretation of the signal to give range. Subsequently, corrections have to be applied for various surface and atmospheric effects before making a comparison with a reference level. This paper discusses the drivers for improved altimetry in the Arctic and then reviews the various approaches that have been used to achieve the initial classification and subsequent retracking over these diverse surfaces, showing examples from both LRM (low resolution mode) and SAR (synthetic aperture radar) altimeters. The review then discusses the issues concerning corrections, including the choices between using other remote-sensing measurements and using those from models or climatology. The paper finishes with some perspectives on future developments, incorporating secondary frequency, interferometric SAR and opportunities for fusion with measurements from laser altimetry or from the SMOS salinity sensor, and provides a full list of relevant abbreviations.

We present freeboard measurements from airborne laser scanner (ALS), the Airborne Synthetic Aperture and Interferometric Radar Altimeter System (ASIRAS), and CryoSat‐2 SIRAL radar altimeter; ice thickness measurements from both helicopter‐borne and ground‐based electromagnetic‐sounding; and point measurements of ice properties. This case study was carried out in April 2015 during the N‐ICE2015 expedition in the area of the Arctic Ocean north of Svalbard. The region is represented by deep snow up to 1.12 m and a widespread presence of negative freeboards. The main scattering surfaces from both CryoSat‐2 and ASIRAS are shown to be closer to the snow freeboard obtained by ALS than to the ice freeboard measured in situ. This case study documents the complexity of freeboard retrievals from radar altimetry. We show that even under cold (below −15°C) conditions the radar freeboard can be close to the snow freeboard on a regional scale of tens of kilometers. We derived a modal sea‐ice thickness for the study region from CryoSat‐2 of 3.9 m compared to measured total thickness 1.7 m, resulting in an overestimation of sea‐ice thickness on the order of a factor 2. Our results also highlight the importance of year‐to‐year regional scale information about the depth and density of the snowpack, as this influences the sea‐ice freeboard, the radar penetration, and is a key component of the hydrostatic balance equations used to convert radar freeboard to sea‐ice thickness.

The ESA Earth Explorer CryoSat-2 was launched on 8 April 2010 to monitor the precise changes in the thickness of terrestrial ice sheets and marine floating ice. To do that, CryoSat orbits the planet at an altitude of around 720 km with a retrograde orbit inclination of 92^∘ and a quasi repeat cycle of 369 d (30 d subcycle). To reach the mission goals, the CryoSat products have to meet the highest quality standards to date, achieved through continual improvements of the operational processing chains. The new CryoSat Ice Baseline-D, in operation since 27 May 2019, represents a major processor upgrade with respect to the previous Ice Baseline-C. Over land ice the new Baseline-D provides better results with respect to the previous baseline when comparing the data to a reference elevation model over the Austfonna ice cap region, improving the ascending and descending crossover statistics from 1.9 to 0.1 m. The improved processing of the star tracker measurements implemented in Baseline-D has led to a reduction in the standard deviation of the point-to-point comparison with the previous star tracker processing method implemented in Baseline-C from 3.8 to 3.7 m. Over sea ice, Baseline-D improves the quality of the retrieved heights inside and at the boundaries of the synthetic aperture radar interferometric (SARIn or SIN) acquisition mask, removing the negative freeboard pattern which is beneficial not only for freeboard retrieval but also for any application that exploits the phase information from SARIn Level 1B (L1B) products. In addition, scatter comparisons with the Beaufort Gyre Exploration Project (BGEP; https://www.whoi.edu/beaufortgyre, last access: October 2019) and Operation IceBridge (OIB; Kurtz et al., 2013) in situ measurements confirm the improvements in the Baseline-D freeboard product quality. Relative to OIB, the Baseline-D freeboard mean bias is reduced by about 8 cm, which roughly corresponds to a 60 % decrease with respect to Baseline-C. The BGEP data indicate a similar tendency with a mean draft bias lowered from 0.85 to −0.14 m. For the two in situ datasets, the root mean square deviation (RMSD) is also well reduced from 14 to 11 cm for OIB and by a factor of 2 for the BGEP. Observations over inland waters show a slight increase in the percentage of good observations in Baseline-D, generally around 5 %–10 % for most lakes. This paper provides an overview of the new Level 1 and Level 2 (L2) CryoSat Ice Baseline-D evolutions and related data quality assessment, based on results obtained from analyzing the 6-month Baseline-D test dataset released to CryoSat expert users prior to the final transfer to operations.

In the summer of 2020, ESA changed the orbit of CryoSat‐2 to align periodically with NASA's ICESat‐2 mission, a campaign known as CRYO2ICE, which allows for near‐coincident CryoSat‐2 and ICESat‐2 observations in space and time over the Arctic until summer 2022, where the CRYO2ICE Antarctic campaign was initiated. This study investigates the Arctic CRYO2ICE radar and laser freeboards acquired by CryoSat‐2 and ICESat‐2, respectively, during the winter seasons of 2020–2022, and derives snow depths from their differences along the orbits. Along‐track snow depth observations can provide high‐resolution snow depth distributions which are vital for air‐ice‐ocean heat and momentum transfer, understanding light transmission, and snow‐ice‐interactions. Generally, ICESat‐2 is backscattered at a surface above the elevation of the CryoSat‐2 signal. CRYO2ICE snow depths are thinner than the daily model‐ or passive‐microwave‐based snow depth composites used for comparison, with differences being most pronounced in the Atlantic and Pacific Arctic. Satellite‐derived and model‐based snow estimates show similar seasonal accumulation over first‐year ice, but CRYO2ICE has limited seasonal accumulation over multi‐year ice which is linked to a slow increase in ICESat‐2, and to some extent CryoSat‐2, freeboards. We present a first estimation of spaceborne along‐track snow depth estimates with average uncertainty of 10–11 ± 2–3 cm for 7‐km segments, with random and systematic contributions of 7 and 4 cm. These observations show the potential for along‐track dual‐frequency observations of snow depth from the future Copernicus mission CRISTAL; but they also highlight uncertainties in radar penetration and the correlation length scales of snow topography that still require further research.