VK: T20404For safe and efficient exploitation of ice-covered waters, knowledge about ship performance in ice is crucial. The literature describes numerical and semi-empirical models that characterize ship speed in ice. These however often fail to account for the joint effect of the ice conditions on ship's speed. Moreover, they omit the effect of ice compression. The latter, when combined with the presence of ridges, can significantly limit the capabilities of an ice-strengthened ship, and potentially bring her to a halt, even if the actual ice conditions are within the design range for the given ship. This paper introduces two probabilistic, data-driven models that predict a ship's speed and the situations where a ship is likely 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, two full-scale datasets were utilized. First, the dataset about the performance of a selected ship in ice is acquired from the automatic identification system. Second, the dataset containing numerical description of the ice field is obtained from a numerical ice model HELMI, developed in the Finnish Meteorological Institute. The collected datasets describe 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 relations between ship performance and the ice conditions were established using Bayesian networks and selected learning algorithms. 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.Peer reviewe

We studied small‐scale (50 m to 5 km) sea ice deformation from ship radar images recorded during the N‐ICE2015 campaign. The campaign consisted of four consecutive drifting ice stations (Floes 1–4) north of Svalbard, with a total duration of nearly 5 months. Deformation was calculated using 5 different time intervals from 10 min to 24 h, and the deformation rate was found to depend strongly on the time scale. Floes 1–3 had a mean deformation rate within the range of 0.06–0.07 h⁻¹ with the interval of 10 min, and 0.03–0.04 h⁻¹ with the interval of 1 h. Floe 4 represented marginal ice zone (MIZ) with very high deformation rate, 0.14/0.08 h⁻¹ with the interval of 10 min/1 h. Deep in the ice pack, high deformation rates occurred only with high wind and drift speed, while in MIZ they were found also during calm conditions. The deformation rates were found to follow power law scaling with respect to length and time scale even on this small scale and in small domain (15 km × 15 km). The length scale dependence of deformation rate depends on the time scale: the power law scaling exponent β of the whole study period decreases from 0.82 to 0.52 with the time interval increasing from 10 min to 24 h. Ship radar images reveal the importance of the deformation history of the ice pack, since the deformation events were initialized along the lines of previous damages.

A set of hindcast simulations with the new version 3.6 of the Nucleus for European Modelling of the Ocean (NEMO) ocean–ice model in the ORCA1 configuration and forced by the DRAKKAR Forcing Set version 5.2 (DFS5.2) atmospheric data was performed from 1958 to 2012. Simulations differed in their sea-ice component: the old standard version Louvain-la-Neuve Sea Ice Model (LIM2) and its successor LIM3. Main differences between these sea-ice models are the parameterisations of sub-grid-scale sea-ice thickness distribution, ice deformation, thermodynamic processes, and sea-ice salinity. Our main objective was to analyse the response of the ocean–ice system sensitivity to the change in sea-ice physics. Additional sensitivity simulations were carried out for the attribution of observed differences between the two main simulations.In the Arctic, NEMO-LIM3 compares better with observations by realistically reproducing the sea-ice extent decline during the last few decades due to its multi-category sea-ice thickness. In the Antarctic, NEMO-LIM3 more realistically simulates the seasonal evolution of sea-ice extent than NEMO-LIM2. In terms of oceanic properties, improvements are not as evident, although NEMO-LIM3 reproduces a more realistic hydrography in the Labrador Sea and in the Arctic Ocean, including a reduced cold temperature bias of the Arctic Intermediate Water at 250 m. In the extra-polar regions, the oceanographic conditions of the two NEMO-LIM versions remain relatively similar, although they slowly drift apart over decades. This drift is probably due to a stronger deep water formation around Antarctica in LIM3.

For ship navigation in the Baltic Sea ice, parameters such as ice edge, ice concentration, ice thickness and degree of ridging are usually reported daily in manually prepared ice charts. These charts provide icebreakers with essential information for route optimization and fuel calculations. However, manual ice charting requires long analysis times, and detailed analysis of large areas (e.g. Arctic Ocean) is not feasible. Here, we propose a method for automatic estimation of the degree of ice ridging in the Baltic Sea region, based on RADARSAT-2 C-band dual-polarized (HH/HV channels) SAR texture features and sea ice concentration information extracted from Finnish ice charts. The SAR images were first segmented and then several texture features were extracted for each segment. Using the random forest method, we classified them into four classes of ridging intensity and compared them to the reference data extracted from the digitized ice charts. The overall agreement between the ice-chart-based degree of ice ridging and the automated results varied monthly, being 83, 63 and 81 % in January, February and March 2013, respectively. The correspondence between the degree of ice ridging reported in the ice charts and the actual ridge density was validated with data collected during a field campaign in March 2011. In principle the method can be applied to the seasonal sea ice regime in the Arctic Ocean.

Maritime safety relies on navigation, detection, and radio communication technologies that function through electromagnetic radiation. Propagation of electromagnetic radiation can be impacted by a disruptive phenomenon known as ducting. Our four-week study using a X-band coastal radar and various meteorological and marine observations, including vertical profiles of temperature, humidity and wind, in early spring 2022 concluded that the combination of measurements at the Utö observatory provides a reliable means of detecting ducting in the Archipelago Sea. The modified refractivity calculated from the vertical profiles for the 22–59 m and 32–59 m altitude layers and coastal radar over-the-horizon observations agree 77% and 85% of the time, respectively. As such, the modified refractivity gradient can be considered a good indicator for over-the-horizon detection with the Utö coastal radar over the open sea. The horizontal wind profiles also revealed a low-level jet at the radar height that often coincided with the ducting observations. To quantify the results, we created an empirical ducting index which showed that ducting is spatially variable, showing the capabilities of the Utö observatory for research oriented towards monitoring and improving maritime safety and security.

The detection, monitoring, and forecasting of sea-ice conditions, including their extremes, is very important for ship navigation and offshore activities, and for monitoring of sea-ice processes and trends. We summarize here recent advances in the monitoring of sea-ice conditions and their extremes from satellite data as well as the development of sea-ice seasonal forecasting capabilities. Our results are the outcome of the three-year (2015–2018) SPICES (Space-borne Observations for Detecting and Forecasting Sea-Ice Cover Extremes) project funded by the EU’s Horizon 2020 programme. New SPICES sea-ice products include pancake ice thickness and degree of ice ridging based on synthetic aperture radar imagery, Arctic sea-ice volume and export derived from multisensor satellite data, and melt pond fraction and sea-ice concentration using Soil Moisture and Ocean Salinity (SMOS) radiometer data. Forecasts of July sea-ice conditions from initial conditions in May showed substantial improvement in some Arctic regions after adding sea-ice thickness (SIT) data to the model initialization. The SIT initialization also improved seasonal forecasts for years with extremely low summer sea-ice extent. New SPICES sea-ice products have a demonstrable level of maturity, and with a reasonable amount of further work they can be integrated into various operational sea-ice services.