Observation of manganese nodules and crusts in the Blake Plateau area

Visual observations of manganese deposits on the Blake plateau from a manned submersible indicate that the occurrence of manganese as nodules, slabs, or pavement may be related to localized environmental conditions. Manganese is concentrated at the crests of sand waves and, in areas of gentle slope, grades locally from nodules to solid pavement.

Geochemical composition of sediments from ODP Site 164-997, Blake Ridge

Twenty-two trace elements in 355 sediment samples from Site 997 on the Blake Ridge were examined by inductively coupled plasma-optical emission spectrometry and inductively coupled plasma-mass spectrometry, for respective fractions of acid-soluble and insoluble compositions. Downhole profiles of these elements exhibit complicated fluctuations throughout late Miocene to Pleistocene, principally due to the variations in the acid-soluble fraction. Noncarbonate composition is given from the acid-insoluble residues, which permits us to recognize secular feature of selected element variance for four intervals. These intervals (I: 0-183 mbsf; II: 183- 440 mbsf; III: 440-618 mbsf; and IV: 618-750 mbsf) are interpreted to have originated from changes in the suite of sediments of particular sources and chemical composition, sedimentation rate, dilution of biogenic carbonate abundance, and possibly the current system that controlled deposition and reworking of the terrigenous materials.

(Table 3) Details of field-based ice and snow profile measurements from the SIMBA 2007 experiment

Although sea-ice extent in the Bellingshausen-Amundsen (BA) seas sector of the Antarctic has shown significant decline over several decades, there is not enough data to draw any conclusion on sea-ice thickness and its change for the BA sector, or for the entire Southern Ocean. This paper presents our results of snow and ice thickness distributions from the SIMBA 2007 experiment in the Bellingshausen Sea, using four different methods (ASPeCt ship observations, downward-looking camera imaging, ship-based electromagnetic induction (EM) sounding, and in situ measurements using ice drills). A snow freeboard and ice thickness model generated from in situ measurements was then applied to contemporaneous ICESat (satellite laser altimetry) measured freeboard to derive ice thickness at the ICESat footprint scale. Errors from in situ measurements and from ICESat freeboard estimations were incorporated into the model, so a thorough evaluation of the model and uncertainty of the ice thickness estimation from ICESat are possible. Our results indicate that ICESat derived snow freeboard and ice thickness distributions (asymmetrical unimodal tailing to right) for first-year ice (0.29 ± 0.14 m for mean snow freeboard and 1.06 ± 0.40 m for mean ice thickness), multi-year ice (0.48 ± 0.26 and 1.59 ± 0.75 m, respectively), and all ice together (0.42 ± 0.24 and 1.38 ± 0.70 m, respectively) for the study area seem reasonable compared with those values from the in situ measurements, ASPeCt observations, and EM measurements. The EM measurements can act as an appropriate supplement for ASPeCt observations taken hourly from the ship's bridge and provide reasonable ice and snow distributions under homogeneous ice conditions. Our proposed approaches: (1) of using empirical equations relating snow freeboard to ice thickness based on in situ measurements and (2) of using isostatic equations that replace snow depth with snow freeboard (or empirical equations that convert freeboard to snow depth), are efficient and important ways to derive ice thickness from ICESat altimetry at the footprint scale for Antarctic sea ice. Spatial and temporal snow and ice thickness from satellite altimetry for the BA sector and for the entire Southern Ocean is therefore possible.

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.