Related papers: Probabilistic Forecasting of the Arctic Sea Ice Ed…
Climate change is a reality of today. Paleoclimatic proxies and climate predictions based on coupled atmosphere-ocean general circulation models provide us with temperature data. Using Detrended Fluctuation Analysis, we are investigating…
1) The annual cycle of atmospheric methane in southern high latitudes is extremely highly correlated with Antarctic sea ice extent. 2) The annual cycle of atmospheric methane in the Arctic is highly correlated with Antarctic or Arctic plus…
Significant salinity anomalies have been observed in the Arctic Ocean surface layer during the last decade. Using gridded data of winter salinity in the upper 50 m layer of the Arctic Ocean for the period 1950-1993 and 2007-2012, we…
Rapid changes in Earth's cryosphere caused by human activity can lead to significant environmental impacts. Computer models provide a useful tool for understanding the behavior and projecting the future of Arctic and Antarctic ice sheets.…
A controlling factor in the seasonal and climatological evolution of the sea ice cover is its albedo $\alpha$. Here we analyze Arctic data from the Advanced Very High Resolution Radiometer (AVHRR) Polar Pathfinder and assess the seasonality…
Sea ice plays an important role in stabilising the Earth system. Yet, representing its dynamics remains a major challenge for models, as the underlying processes are scale-invariant and highly anisotropic. This poses a dilemma:…
Understanding local currents in the North Atlantic region of the ocean is a key part of modelling heat transfer and global climate patterns. Satellites provide a surface signature of the temperature of the ocean with a high horizontal…
The downward trend in Arctic sea ice extent is one of the most dramatic signals of climate change during recent decades. Comprehensive climate models have struggled to reproduce this, typically simulating a slower rate of sea ice retreat…
This paper combines fisheries dependent data and environmental data to be used in a machine learning pipeline to predict the spatio-temporal abundance of two species (plaice and sole) commonly caught by the Belgian fishery in the North Sea.…
Increased research interest and economic activity in the Arctic raise the need for new observations of sea ice dynamics. Remote sensing as well as mathematical and numerical models of wave propagation in sea ice would benefit from more in…
Arctic sea ice plays a critical role in regulating Earth's climate system, significantly influencing polar ecological stability and human activities in coastal regions. Recent advances in artificial intelligence have facilitated the…
Accurate and timely prediction of sea fog is very important for effectively managing maritime and coastal economic activities. Given the intricate nature and inherent variability of sea fog, traditional numerical and statistical forecasting…
Sea ice concentration is an important metric used to characterize polar sea ice behavior. Understanding this behavior and accurately representing it is of critical importance for climate science research, and also has important uses in the…
Various studies identified possible drivers of extremes of Arctic sea ice reduction, such as observed in the summers of 2007 and 2012, including preconditioning, local feedback mechanisms, oceanic heat transport and the synoptic- and…
A model-data inversion is applied to a very large observational dataset collected in the Southern Ocean north of the Ross Sea during late autumn to early winter, producing estimates of the frequency-dependent rate of dissipation by sea ice.…
Data-driven machine learning models for weather forecasting have made transformational progress in the last 1-2 years, with state-of-the-art ones now outperforming the best physics-based models for a wide range of skill scores. Given the…
Arctic sea ice performs a vital role in global climate and has paramount impacts on both polar ecosystems and coastal communities. In the last few years, multiple deep learning based pan-Arctic sea ice concentration (SIC) forecasting…
Predicting changes in sea ice cover is critical for shipping, ecosystem monitoring, and climate modeling. Current sea ice models, however, predict more ice than is observed in the Arctic, and less in the Antarctic. Improving the fit of…
In this paper, we present a monitoring system that allows increasing road safety by predicting ice formation. The system consists of a network of road weather stations and intelligence data processing program module. The results were…
Machine learning weather models trained on observed atmospheric conditions can outperform conventional physics-based models at short- to medium-range (1-14 day) forecast timescales. Here we take the machine learning weather model ACE2,…