Stefanie Linow

Mass, volume and velocity of the Antarctic Ice Sheet: present-day changes and error effects

This study examines present-day changes of the Antarctic ice sheet (AIS) by means of different data sets. We make use of monthly gravity field solutions acquired by the Gravity Recovery and Climate Experiment (GRACE) to study mass changes of the AIS for a 10-year period. In addition to ‘standard’ solutions of release 05, solutions based on radial base functions were used. Both solutions reveal an increased mass loss in recent years. For a 6-year period surface-height changes were inferred from laser altimetry data provided by the Ice, Cloud, and land Elevation Satellite (ICESat). The basin-scale volume trends were converted into mass changes and were compared with the GRACE estimates for the same period. Focussing on the Thwaites Glacier, Landsat optical imagery was utilised to determine ice-flow velocities for a period of more than two decades. This data set was extended by means of high-resolution synthetic aperture radar (SAR) data from the TerraSAR-X mission, revealing an accelerated ice flow of all parts of the glacier. ICESat data over the Thwaites Glacier were complemented by digital elevation models inferred from TanDEM-X data. This extended data set exhibits an increased surface lowering in recent times. Passive microwave remote sensing data prove the long-term stability of the accumulation rates in a low accumulation zone in East Antarctica over several decades. Finally, we discuss the main error sources of present-day mass-balance estimates: the glacial isostatic adjustment effect for GRACE as well as the biases between laser operational periods and the volume–mass conversion for ICESat.

Reliability Measures for Sea Ice Motion Retrieval From Synthetic Aperture Radar Images

Sea ice motion is triggered by wind and ocean currents. Its magnitude and direction can be automatically retrieved using pairs of satellite images acquired over the same area. However, external reference data for validation of drift retrievals, such as tracks from buoys, are sparse. Information about the reliability of the retrieved ice drift field is crucial for applications such as operational sea ice mapping or validation of computer models for simulations of sea ice dynamics. In this paper, we introduce an intrinsic measure based on the properties of radar image pairs to assess the reliability of the retrieved ice drift vectors. The proposed method combines different parameters, e.g., correlation coefficient and two textural quantities, to provide information about the suitability of subimage regions for pattern matching. In this way, we generate a quality parameter [called confidence factor (CFA)] for the calculated ice drift velocities. The CFA is compared to results obtained by “backmatching.” The latter requires that the drift field is computed twice using the image pair, first in sequential and then in reversed order. For stable ice conditions, the results show that areas regarded as unreliable by the CFA compare well with the areas revealing larger differences from backmatching.

Object-based detection of linear kinematic features in sea ice

Inhomogenities in the sea ice motion field cause deformation zones, such as leads, cracks and pressure ridges. Due to their long and often narrow shape, those structures are referred to as Linear Kinematic Features (LKFs). In this paper we specifically address the identification and characterization of variations and discontinuities in the spatial distribution of the total deformation, which appear as LKFs. The distribution of LKFs in the ice cover of the polar oceans is an important factor influencing the exchange of heat and matter at the ocean-atmosphere interface. Current analyses of the sea ice deformation field often ignore the spatial/geographical context of individual structures, e.g., their orientation relative to adjacent deformation zones. In this study, we adapt image processing techniques to develop a method for LKF detection which is able to resolve individual features. The data are vectorized to obtain results on an object-based level. We then apply a semantic postprocessing step to determine the angle of junctions and between crossing structures. The proposed object detection method is carefully validated. We found a localization uncertainty of 0.75 pixel and a length error of 12% in the identified LKFs. The detected features can be individually traced to their geographical position. Thus, a wide variety of new metrics for ice deformation can be easily derived, including spatial parameters as well as the temporal stability of individual features.

An assessment of the reliability of sea-ice motion and deformation retrieval using SAR images

Abstract The local mass balance of sea ice is dependent on the advection of ice into and out of an area and on the deformation processes in that area. Sea-ice motion can be observed from space by synthetic aperture radar (SAR) and quantified by drift-detection algorithms. Due to the scarcity of field observations, it remains a challenging task to validate the resulting motion fields. We analyse the quality of sea-ice motion fields derived from SAR data, using an example dataset from the Weddell Sea region. We apply a quality indicator for sea-ice motion fields which is independent of field data and evaluate it with reference data obtained from visual analysis of the SAR images. Together with the motion field, sea-ice deformation can also be retrieved from SAR data. Similarly to ice motion, it is very difficult to obtain field data to evaluate the quality of the results. Based on a manually derived reference dataset, we introduce a method to validate the retrieved deformation rates. This procedure requires no additional field data. Our analysis shows that deformation rates derived from SAR data are consistent with results obtained from buoy analysis by previous studies.

Continuous discontinuities: comparing observed and modelled sea ice deformation features

The Arctic is highly sensitive to changes in the global climate. One of the most prominent examples in this context is the shrinking sea ice cover over the past decades. The impact of the change in ice coverage on the global climate requires a thorough understanding of ice dynamics, as drifting sea ice transports salt and heat and in this way influences ocean dynamics. Sea ice motion is mainly driven by wind, ocean currents and internal ice stress. Convergent ice motion causes features such as ridges and rubble fields, which change the momentum exchange between atmosphere, ice and ocean. Openings in the ice due to divergent motion increase the exchange of heat and matter between ocean and atmosphere. On time scales of days to weeks, linear kinematic features, such as leads and ridges, evolve on spatial scales ranging from meters to tens and hundreds of kilometers. These features also emerge in numerical sea ice models when the resolution of the simulations is increased to a few kilometers. While plausible, their realism in the simulations is yet unclear and requires a detailed evaluation, with the help of (in our case satellite-based) observations. We use Arctic-wide MITgcm simulations for 2006 at a spatial resolution of approximately 4km for a regional comparison with microwave satellite observations, e.g. from Synthetic Aperture Radar (SAR). We derive sea ice displacement from a sequence of satellite images by measuring the offset between matching patterns in different images. Discontinuities in the resulting velocity field indicate regions of instantaneous deformation that occur at some point in the time interval between the acquisition of the two SAR images used for displacement retrieval. The obtained quantities of deformation by divergence, shear and vorticity are scale-dependent. As a consequence, they depend on the spatial resolution of the SAR images and differ from the quantities calculated by the model. Hence, the comparison between model simulations and results of retrievals from remote sensing data is not straightforward. Therefore, we pay special attention to the spatial and temporal scales of the observed / modelled processes and introduce appropriate statistical tools. By evaluating the kinematic features in the results of high-resolution sea ice models based on the microwave remote sensing data we expect to be able to assess and improve the ice rheology in these sea ice models.