The increase in Arctic marine activity due to rapid warming and significant sea ice loss necessitates highly reliable, short-term sea ice forecasts to ensure maritime safety and operational efficiency. In this work, we present a novel data-driven approach for sea ice condition forecasting in the Gulf of Ob, leveraging sequences of radar images from Sentinel-1, weather observations, and GLORYS forecasts. Our approach integrates advanced video prediction models, originally developed for vision tasks, with domain-specific data preprocessing and augmentation techniques tailored to the unique challenges of Arctic sea ice dynamics. Central to our methodology is the use of uncertainty quantification to assess the reliability of predictions, ensuring robust decision-making in safety-critical applications. Furthermore, we propose a confidence-based model mixture mechanism that enhances forecast accuracy and model robustness, crucial for reliable operations in volatile Arctic environments. Our results demonstrate substantial improvements over baseline approaches, underscoring the importance of uncertainty quantification and specialized data handling for effective and safe operations and reliable forecasting.

The modeling and forecasting of sea ice conditions in the Arctic region are important tasks for ship routing, offshore oil production, and environmental monitoring. We propose the adaptive surrogate modeling approach named LANE-SI (Lightweight Automated Neural Ensembling for Sea Ice) that uses ensemble of relatively simple deep learning models with different loss functions for forecasting of spatial distribution for sea ice concentration in the specified water area. Experimental studies confirm the quality of a long-term forecast based on a deep learning model fitted to the specific water area is comparable to resource-intensive physical modeling, and for some periods of the year, it is superior. We achieved a 20% improvement against the state-of-the-art physics-based forecast system SEAS5 for the Kara Sea.

Forecasting sea ice concentration (SIC) and sea ice drift (SID) in the Arctic Ocean is of great significance as the Arctic environment has been changed by the recent warming climate. Given that physical sea ice models require high computational costs with complex parameterization, deep learning techniques can effectively replace the physical model and improve the performance of sea ice prediction. This study proposes a novel multi-task fully conventional network architecture named hierarchical information-sharing U-net (HIS-Unet) to predict daily SIC and SID. Instead of learning SIC and SID separately at each branch, we allow the SIC and SID layers to share their information and assist each other's prediction through the weighting attention modules (WAMs). Consequently, our HIS-Unet outperforms other statistical approaches, sea ice physical models, and neural networks without such information-sharing units. The improvement of HIS-Unet is obvious both for SIC and SID prediction when and where sea ice conditions change seasonally, which implies that the information sharing through WAMs allows the model to learn the sudden changes of SIC and SID. The weight values of the WAMs imply that SIC information plays a more critical role in SID prediction, compared to that of SID information in SIC prediction, and information sharing is more active in sea ice edges (seasonal sea ice) than in the central Arctic (multi-year sea ice).

A deep sea blanketed in a thick shell of ice. Yet during a daunting October 2021 mission called the HACON project, a group of over two dozen scientists and engineers used an underwater robot to successfully explore a cryptic ocean world some 13,000 feet beneath the surface of the ice-covered Arctic Ocean. It was the first time researchers surveyed rare volcanic vents -- and the life there -- in the remote Arctic. "It opens a new frontier of exploration in the Arctic," Eva Ramirez-Llodra, a deep sea ecologist for the Norwegian government who co-led the mission, told Mashable. "It's a challenge, but it can be done."

In the next week or so, the sea ice floating atop the Arctic Ocean will shrink to its smallest size this year, as summer-warmed waters eat away at the ice's submerged edges. Record lows for sea ice levels will probably not be broken this year, scientists say. In 2020, the ice covered 3.74 million square kilometers of the Arctic at its lowest point, coming nail-bitingly close to an all-time record low. Currently, sea ice is present in just under 5 million square kilometers of Arctic waters, putting it on track to become the 10th-lowest extent of sea ice in the area since satellite record keeping began in 1979. It's an unexpected finish considering that in early summer, sea ice hit a record low for that time of year. The surprise comes in part because the best current statistical- and physics-based forecasting tools can closely predict sea ice extent only a few weeks in advance, but the accuracy of long-range forecasts falters.

Extreme waves in the Arctic typically occur every 20 years, but as climate change continues to plague the region these events could happen every two to five years, a new study reveals. Much of this area is frozen for a majority of the year, but rising temperatures have increased periods of open water that could result in catastrophic waves. Using computer models, researchers found the area hit the hardest was in the Greenland Sea, which could experience maximum annual wave heights of more than 19 feet. The team also warns coastal flooding might increase by a factor of four to 10 by the end of this century. Extreme waves in the Arctic typically occur every 20 years, but as climate change continues to plague the region these events could happen every two to five years, a new study reveals.

For vessels that journey into the polar seas, keeping control of the spread of sea ice is critical, which means that large resources are spent to collect data and determine future developments to provide reliable sea ice warnings. "As of now, large resources are needed to create these ice warnings, and most of them are made by The Norwegian Meteorological Institute and similar centres", Sindre Markus Fritzner tells us. He is a Doctoral Research Fellow at UiT The Arctic University of Norway. Fritzner is employed at the Department of Physics and Technology and has recently submitted a doctoral thesis where he has looked at the option of using artificial intelligence to make ice warnings faster, better, and more accessible than they are today. The ice warnings used today are traditionally based on dynamic computer models that are fed with satellite observations of the ice cover, and whatever updated data can be gathered about ice thickness and snow depth.

The assignment of individual fish to its stock of origin is important for reliable stock assessment and fisheries management. Otolith shape is commonly used as the marker of distinct stocks in discrimination studies. Our literature review showed that the application and comparison of alternative statistical classifiers to discriminate fish stocks based on otolith shape is limited. Therefore, we compared the performance of two traditional and four machine learning classifiers based on Fourier analysis of otolith shape using selected stocks of Atlantic cod (Gadus morhua) in the southern Baltic and Atlantic herring (Clupea harengus) in the western Norwegian Sea, Skagerrak and the southern Baltic Sea. Our results showed that the stocks can be successfully discriminated based on their otolith shapes.

Reliable maps of sea-ice conditions and forecasts are of vital importance for maritime safety, safe navigation and planning. The continued retreating and thinning of Arctic sea ice calls for a more effective way of producing detailed and timely ice information--which is where artificial intelligence comes in. Manual ice-charting from multi-sensor satellite data has been used for years, but it is time-consuming because of the vast area of the Arctic Ocean. In order to provide relevant ice data, there is a need for automated ice observations from satellite data, to integrate into ice forecast models. In response to this, the Danish Meteorological Institute (DMI) and Technical University of Denmark have initiated the project Automated Sea Ice Products (ASIP) – funded by the Innovation Fund Denmark.

Scientists have long struggled with how to measure the effects of climate change on wildlife. This is especially true for birds flying in and out of coastal areas bordering the Arctic Ocean. In the past, researchers depended mainly on information gathered by satellite to follow the movement of birds and animals. But this method can be costly and result in huge amounts of information, which can be difficult to process. Now scientists are turning to another kind of technology to help them follow birds and other wildlife.