Description: In the Arctic sea ice grows thermodynamically until an equilibrium thickness of 2-3m is reached. This formation of level ice is disturbed by deformation processes, which evolve under convergent and shear conditions of sea ice motion. Rafting and ridging of the ice cover occur. This builds thicker ice, accounting for about two-thirds of the Arctic sea ice volume. It is important to describe these dynamical deformation processes in numerical models, which are used to investigate climate change in Arctic and Subarctic regions.Efforts have been made during the last thirty years to implement these deformation processes to numerical models based on very different theories. In this study three different approaches to model a deformed sea ice cover are compared. These approaches include (1) an additional prognostic equation for ice roughness from which ridge parameters are diagnostically derived, applicable to single ice class models, (2) a redistribution model with two ice categories, level and ridged ice, including a statistical derivation of ridge parameters, and (3) a set of prognostic equations for ridge parameters, i.e. ridge density and sail height, from which a ridged ice class is finally derived.Basically all three models were able to reproduce the governing spatial distribution of Arctic sea ice thickness. However, there are differences in structure and absolute values. For example, the algorithm listed first tends to give a less discrete distribution of deformed ice while the third shows very distinct areas of heavy ridging. Results of a model experiment with simplified grid and forcing are presented and differences between the ridging schemes are discussed. The results of a first application to realistic Arctic conditions are compared to data from a control run of the sea ice model without ridging in order to express the effect of the ridging on the model behaviour.

Description: Between December 1996 and February 1997, weaned pups and postmoult female elephant seals (Mirounga leonina) were fitted with satellite transmitters at King George Island (South Shetlands). Of the nine adult females tracked for more than two months, three stayed in a localized area between the South Shetlands and the South Orkneys. The other six females travelled southwest along the coast of the Antarctic Peninsula up to the Bellingshausen Sea. Two of them then moved far northeast and hauled out on South Georgia in October. One female was last located north of the South Shetlands in March 1998. In total, eight females were again sighted on King George Island and six of the transmitters removed. The tracks of the weaners contrasted with those of the adults. In January, five juveniles left King George Island for the Pacific sector ranging about four weeks in the open sea west of the De Gerlache Seamounts. Three of them returned to the tip of the Antarctic Peninsula in June, of which one was last located on the Patagonian Shelf in November 1997. The juveniles avoided sea ice while the adults did not. The latter displayed behavioural differences in using the pack ice habitat during winter. Some females adjusted their movement patterns to the pulsating sea ice fringe in far-distant foraging areas while others ranged in closed pack ice of up to 100 %. The feeding grounds of adult female elephant seals are more closely associated with the pack ice zone than previously assumed. The significance of midwaterfish Pleuragramma antarcticum as a potential food resource is discussed.

Description: Sea ice grows thermodynamically until heat flux from, and conductive heat loss to, the ocean are balanced. In the Arctic this equilibrium thickness reaches 2-3 m. Thicker ice and especially morphological features are the product of dynamic deformation. About 70% of the Arctic sea ice volume evolves from deformation processes. These need to be represented in a dynamical sea ice model in order to cover dynamical growth and lead opening. Due to convergent and shear drift ice floes collide and raft on top of each other or pile up in broken blocks, so-called ridges. Here, three different ways to incorporate ridge build up and development of a deformed ice class into a numerical sea ice model for the Arctic are compared. Ridge density, number of ridges per kilometer along a random profile, is used as variable of comparison.

Description: Sea ice drift is measured by deploying buoys and is derivable from satellite data. These observations can be used to improve the dynamics of numerical sea ice models. We apply the Single Evolutive Interpolated Kalman filter (SEIK) to assimilate Arctic ice drift into a dynamic-thermodynamic Sea Ice Model (SIM), which includes viscous-plastic rheology. Observations are used to evaluate the sea ice models performance. How significant are the differences between modelled and observed ice drift? How long can sea ice drift be forecasted in practice?We aim at the assimilation of daily and three-daily drift fields derived from satellite scatterometry and passive microwave sensors imagery. Additionally, drift data from buoys of the International Arctic Buoy Program (IABP) are included in our study. We attempt to reach a more realistic representation of sea ice dynamics and to reduce the model error statistics using these observational data sets for assimilation. The implementation of the SEIK into the SIM delivers a new feature to assimilate data of several parameters in space and time simultaneously. Besides sea ice drift, it is planned to assimilate ice thickness data from CryoSat.

Description: Sea ice deforms under convergent and shear motion, causing rafting and ridging. This results in thicker ice than could be formed by thermodynamic growth only. About two-thirds of the Arctic sea ice volume consists of deformed sea ice. Three different approaches to simulating the formation of pressure ridges in a dynamic-thermodynamic continuum model are considered. They are compared with and evaluated by airborne laser profiles of the sea ice surface roughness. The different ridging schemes are (1) an additional prognostic equation for the deformation energy from which ridge parameters are derived, (2) a redistribution model with two ice categories, level and ridged ice, combined with a Monte Carlo simulation of ridging and (3) additional prognostic equations for ridge density and height resulting in the formation of ridged ice volume. The model results show that the ridge density is typically related to the state of ice motion whereas the mean sail height is related to the thickness of the ice involved in the ridging. In general, all of the three models reproduce realistic spatial distributions of ridges. Finally, the second ridging scheme is regarded to be best appropriate for climate modelling while the third scheme has advantages in sea ice forecasting.

Description: Drift is a prominent parameter characterizing the Arctic sea ice cover that has a deep impact on the climate system. Hence, it is a key issue to both, the remote sensing as well as the modeling community, to provide reliable sea ice drift fields. This study focuses on the comparison of sea ice drift results from different sea ice-ocean coupled models and the validation with observational data in the period 19792001. The models all take part in the Arctic Ocean Model Intercomparison Project (AOMIP) and the observations are mainly based on satellite imagery. According to speed distributions one class of models has a mode at drift speeds around 3 cm/s and a short tail towards higher speeds. Another class shows a more even frequency distribution with large probability of drift speeds of 10 to 20 cm/s. Observations clearly agree better with the first class of model results. Reasons for these differences are manifold and lie in discrepancies of wind stress forcing as well as sea ice model characteristics and sea ice-ocean coupling. Moreover, we investigated the drift patterns of anticyclonic and cyclonic wind-driven regimes. The models are capable of producing realistic drift pattern variability. The winter of 1994/95 stands out because of its maximum in Fram Strait ice export. Although export estimates of some models agree with observations, the corresponding inner Arctic drift pattern is not reproduced. The reason for this is found in the wind forcing as well as in differences in ocean velocities.