EPICA Dronning Maud Land EDML ice core drilling protocol

We report on the EPICA Dronning Maud Land (East Antarctica) deep drilling operation. Starting with the scientific questions that led to the outline of the EPICA project, we introduce the setting of sister drillings at NorthGRIP and EPICA Dome C within the European ice-coring community. The progress of the drilling operation is described within the context of three parallel, deep-drilling operations, the problems that occurred and the solutions we developed. Modified procedures are described, such as the monitoring of penetration rate via cable weight rather than motor torque, and modifications to the system (e.g. closing the openings at the lower end of the outer barrel to reduce the risk of immersing the drill in highly concentrated chip suspension). Parameters of the drilling (e.g. core-break force, cutter pitch, chips balance, liquid level, core production rate and piece number) are discussed. We also review the operational mode, particularly in the context of achieved core length and piece length, which have to be optimized for drilling efficiency and core quality respectively. We conclude with recommendations addressing the design of the chip-collection openings and strictly limiting the cable-load drop with respect to the load at the start of the run.

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.