Mean sortable silt values from Holocene and glacial Scotia Sea sediments

The Antarctic Circumpolar Current is key to the mixing and ventilation of the world's oceans. This current flows from west to east between about 45° and 70° S connecting the Atlantic, Pacific and Indian oceans, and is driven by westerly winds and buoyancy forcing. High levels of productivity in the current regulate atmospheric CO2 concentrations. Reconstructions of the current during the last glacial period suggest that flow speeds were faster or similar to present, and it is uncertain whether the strength and position of the westerly winds changed. Here we reconstruct Antarctic Circumpolar Current bottom speeds through the constricting Drake Passage and Scotia Sea during the Last Glacial Maximum and Holocene based on the mean grain size of sortable silt from a suite of sediment cores. We find essentially no change in bottom flow speeds through the region, and, given that the momentum imparted by winds, and modulated by sea-ice cover, is balanced by the interaction of these flows with the seabed, this argues against substantial changes in wind stress. However, glacial flow speeds in the sea-ice zone south of 56° S were significantly slower than present, whereas flow in the north was faster, but not significantly so. We suggest that slower flow over the rough topography south of 56° S may have reduced diapycnal mixing in this region during the last glacial period, possibly reducing the diapycnal contribution to the Southern Ocean overturning circulation.

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.