Seal census raw data during POLARSTERN expedition ANT-XV/3 (PS48)

The density, species composition, and possible change in the status of pack ice seals within the Weddell Sea were investigated during the 1997/1998 summer cruise of the RV "Polarstern" (ANT-XV/3, PS48). Comparisons were made with previous surveys in the Weddell Sea where it was assumed that all seals were counted in a narrow strip on either side oft he ship or aircraft. A total of 15 aerial censuses were flown during the period 23 January - 7 March 1998 in the area bounded by 07°08' and 45°33' West longitude. The censused area in the eastern Weddell Sea was largely devoid of pack ice while a well circumscribed pack ice field remained in the western Weddell Sea. A total of 3,636 (95.4 %) crabeater seals, 21 (0.5 %) Ross seals, 45 (1.2 %) leopard seals and 111 (2.9 %) Weddell seals were observed on the pack ice during a total of 1,356.57 linear nautical miles (244.2 nm) of transect line censused. At a mean density of 21.16 1/nm**2 over an area of 244.2 nm, it is the highest densities on record for crabeater seals, density of up to 411.7 1/nm**2 being found in small areas. The overall high densities of seals (30.18 1/nm**2) recorded for the eastern Weddell Sea (27.46 1/nm**2, 0.27 1/nm**2, and 0.66 1/nm**2 for crabeater, leopard and Weddell seals respectively) is a consequence of the drastically reduced ice cover and the inverse relationship that exists between cover and seal densities. Ross seal densities (0.08 1/nm**2) were the lowest on record fort the area. It is suggested that seals largely remain within the confines of the pack ice despite seasonal and annual changes in its distribution. Indications are that in 1998 the El Niño has manifested itself in the Weddell Sea, markedly influencing the density and distribution of pack ice seals.

Probabilistic models for the prediction of a ship performance in dynamic ice

We introduce two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is probable to get stuck in ice based on the joint effect of ice features such as the thickness and concentration of level ice, ice ridges, rafted ice, moreover ice compression is considered. To develop the models to datasets were utilized. First, the data from the Automatic Identification System about the performance of a selected ship was used. Second, a numerical ice model HELMI, developed in the Finnish Meteorological Institute, provided information about the ice field. The relations between the ice conditions and ship movements were established using Bayesian learning algorithms. The case study presented in this paper considers a single and unassisted trip of an ice-strengthened bulk carrier between two Finnish ports in the presence of challenging ice conditions, which varied in time and space. The obtained results show good prediction power of the models. This means, on average 80% for predicting the ship's speed within specified bins, and above 90% for predicting cases where a ship may get stuck in ice. We expect this new approach to facilitate the safe and effective route selection problem for ice-covered waters where the ship performance is reflected in the objective function.