Snow thickness retrieval over thick Arctic sea ice using SMOS satellite data

4, we finally compare the magnitude and pacing of past temperature changes reconstructed from deep ice cores to the changes simulated by coupled ocean-atmosphere-sea- ice models

The reconstructions are based on a multilinear regression of forecasted ice concentration, level ice thickness, ridge density, ice speed and an additional factor which is based,

The mean values and uncertainties of snow depth and ice and snow densities, determined for FY ice and MY ice, were used to calculate the total error in ice thickness retrieval

ther investigations of surface structure and snow cover on seasonal sea ice in parallel to measurements of driving pa- rameters like heat flux, radiation, ice temperature and

of vertical transport by measuring u ∗ directly, and under katabatic conditions, estimate boundary layer depth (Ne ff et al., 2007) as well as giving wind speed, direction

The averaged MODIS ice thickness in 12.5 km grid resolution (upper left), SMOS ice thicknesses retrieved from Algorithm I (upper right) and II (lower left), and the histogram of

Based on the challenge to determine an average grease-ice temperature for the salinity method and the comparatively large error of the volume method, we recommend to use the

To investigate the influence of snow depth, sea ice density, and area on sea ice thick- ness and volume estimates we use freeboard retrievals from ICESat, together with dif-