CryoSat uses a synthetic aperture radar over areas of sea ice to measure a much smaller footprint than previous satellites. This provides the resolution to identify sea ice floe regions, and open ocean lead regions between floes, and to measure their surface elevations. Over sea ice, CryoSat echoes are assumed to scatter from the interface between the ice surface and the layer of overlying snow [Beaven et al., 1995; Laxon et al., 2013], and we can therefore measure the sea ice freeboard (the height of sea ice above water).
http://www.cpom.ucl.ac.uk/csopr/science.htmlvs
The 3D ocean model HYCOM and the sea-ice model CICE is developed at the University of Miami and Los Alamos National Laboratory. The models are fully coupled at each time step. Output are the surface variables sea level and ice conditions (concentration, thickness, velocity, convergense, strength, etc.) and 3-dimensional maps of current, temperature and salinity at sigma levels.
Model set-up
The
DMI HYCOM-CICE set-up covers the Atlantic, north of about 20°S and the Arctic Ocean, with a horizontal resolution of about 10 km. Model forcing is ECMWF weather forecasts. A 144 hour forecast is produces twice daily, at 00 and 12 UTC.
http://ocean.dmi.dk/models/hycom.uk.phpvs
PIOMAS Ice Volume Validation and Uncertainty
PIOMAS ice thickness and volume results have undergone substantial validation via comparison with ice thickness data from submarines, moorings, airborne electromagnetic induction (EM) measurements and ice thickness retrievals from
ICESat. In addition, model sensitivity studies were conducted to assess the impact of model parameters on ice volume anomaly trends.
The problem of validating sea ice volume
It is difficult to validate total Arctic sea ice volume directly. There are no true measurements of total ice volume that can be compared to model-derived estimates. Validation is best achieved through a comparison with in situ and satellite-derived ice thickness observations. The observations used here are collected at the Unified Sea Ice Thickness Climate Data Record. We can also try to estimate how much the model-derived volume and trends change when we change model parameters that are not well know. The combination of this allows us to make estimates of the uncertainty in the ice volume and trends from PIOMAS.
http://psc.apl.uw.edu/research/projects/arctic-sea-ice-volume-anomaly/validation/http://psc.apl.washington.edu/sea_ice_cdr/