Abstract. In order to derive long-term changes in sea-ice volume, a multi-decadal sea-ice thickness record is required. CryoSat-2 has showcased the potential of radar altimetry for sea-ice mass-balance estimation over the recent years. However, precursor altimetry missions such as Environmental Satellite (Envisat) have not been exploited to the same extent so far. Combining both missions to acquire a decadal sea-ice volume data set requires a method to overcome the discrepancies due to different footprint sizes from either pulse-limited or beam-sharpened radar echoes. In this study, we implemented an inter-mission-consistent surface-type classification scheme for both hemispheres, based on the waveform pulse peakiness, leading-edge width, and surface backscatter. In order to achieve a consistent retracking procedure, we adapted the threshold first-maximum retracker algorithm, previously used only for CryoSat-2, to develop an adaptive retracker threshold that depends on waveform characteristics. With our method, we produce a global and consistent freeboard data set for CryoSat-2 and Envisat. This novel data set features a maximum monthly difference in the mission-overlap period of 2.2 cm (2.7 cm) for the Arctic (Antarctic) based on all gridded values with spatial resolution of 25 km × 25 km and 50 km × 50 km for the Arctic and Antarctic, respectively.

The Arctic is undergoing significant environmental changes due to climate warming. The most evident signal of this warming is the shrinking and thinning of the ice cover of the Arctic Ocean. If the warming continues, as global climate models predict, the Arctic Ocean will change from a perennially ice-covered to a seasonally ice-free ocean. Estimates as to when this will occur vary from the 2030s to the end of this century. One reason for this huge uncertainty is the lack of systematic observations describing the state, variability, and changes in the Arctic Ocean

Abstract. 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.

[1] We have used Radar Altimeter 2 (RA-2) onboard ESA's EnviSAT and Geosciences Laser Altimeter System (GLAS) onboard NASA's ICESat to map the elevation change of the Flade Isblink Ice Cap (FIIC) in northern Greenland. Based on RA-2 data we show that the mean surface elevation change of the FIIC has been near zero (0.03 ± 0.03 m/a) between fall 2002 and fall 2009. We present the elevation change rate maps and assess the elevation change rates of areas above the late summer snow line (0.09 ± 0.04 m/a) and below it (−0.16 ± 0.05 m/a). The GLAS elevation change rate maps show that some outlet glaciers, previously reported to have been in a surge state, are thickening rapidly. Using the RA-2 measured average elevation change rates for different parts of the ice cap we present a mass change rate estimate of 0.0 ± 0.5 Gt/a for the FIIC. We compare the annual elevation changes with surface mass balance (SMB) estimates from a regional atmospheric climate model RACMO2. We find a strong correlation between the two (R = 0.94 and P < 0.002), suggesting that the surface elevation changes of the FIIC are mainly driven by net SMB. The correlation of modeled net SMB and measured elevation change is strong in the southern areas of the FIIC (R = 0.97 and P < 0.0005), but insignificant in the northern areas (R = 0.38 and P = 0.40). This is likely due to higher variability of glacier flow in the north relative to the south.

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.