Arctic sea ice altimetry - advances and current uncertainties

Abstract

One of the most prominent features of global climate change is the reduction in Arctic sea ice thickness. The main tool to derive sea ice thickness on an Arctic wide scale is altimetry from satellites, yet current estimates are associated with high uncertainties. In this thesis we present a new quantification of uncertainties in Arctic sea ice thickness and volume and identify the main sources of uncertainty. Furthermore, we explore the possibility for sea ice classification based on data from radar altimeters, which can be used to improve current estimates of sea ice thickness. We quantify uncertainties in Arctic sea ice thickness and volume using freeboard retrievals from ICESat and investigate different assumptions on snow depth, sea ice density and area. These geophysical parameters are needed when converting freeboard measurements from altimeters in estimates of sea ice thickness and volume. We show that these parameters have an influence on the overall mean, the year-to-year variability, and the longterm trends. The overall uncertainties appear larger than previous studies suggest, and the recent dramatic ice loss appears smaller. We find the total uncertainty in sea ice volume to be around 13% during the cold season. Uncertainties in ice area are of minor importance for the estimates of sea ice volume and thickness. The uncertainty in snow depth contributes up to 70% of the total uncertainty, and the ice density up to 30–35%. We analyze radar altimeter data over different Arctic sea ice regimes to develop a method for sea ice classification for CryoSat-2. Information about sea ice type is needed to be able to use ice type dependent values for snow and ice properties while converting freeboard into thickness. CryoSat’s payload instrument is the SAR/Interferometric Radar Altimeter (SIRAL), which uses the synthetic aperture radar (SAR) technique to enhance the resolution along track. First we present a case study based on data from the airborne synthetic aperture radar ASIRAS, which is a replica of SIRAL on-board CryoSat-2. We analyze different parameters that characterize the radar signal waveforms and identify parameters that are most sensitive to sea ice type. With a bayesian based method we are able to classify more than 80% of the signal waveforms correctly as First- or Multi- Year-Ice. In the final step we analyze signal waveforms from CryoSat-2 on an Arctic wide scale. We find several of the waveform parameters to be significantly different over First- and Multi-Year-Ice. Analyzing the spatial distribution, some discrepancies occur compared to other retrievals of sea ice type. CryoSat-2 waveform parameters have values typical for Multi-Year-Ice over large areas of First-Year-Ice. These areas of First-Year-Ice contain strong gradients in drift speed, indicating that the radar signal is mainly sensitive to surface roughness. The information about surface roughness can potentially be used to remove biases in current freeboard retrievals from CryoSat-2.