Monitoring the Arctic Sea Surface Salinity (SSS) from space is crucial to understand the global water cycle and the ocean dynamics is limited due to nonhomogeneous and sparse in situ data. Two gridded satellite SSS products have been derived from European Space Agency’s Soil Moisture and Ocean Salinity mission (SMOS). The uncertainties of these two SSS products in the Arctic are quantified against other two SSS products in the Copernicus Marine Environment Monitoring Services and other in situ datasets.

In order to assure the security of navigation and offshore activities scientists have to predict with precision the type and the location of sea ices. Getting information about cold intensity and ocean-atmosphere dynamics of these polar regions are also motivations. The ways to describe sea ices are numerous but this article focuses only on type.

Over recent years, the vision of Open Science has emerged as a new paradigm of transparent, data-driven science capable of accelerating competitiveness and innovation. The embodiment of this vision in Europe is the European Open Science Cloud (EOSC), first proposed by the European Commission in April 2016 as part of the Communication on the ‘European Cloud Initiative’, one of the pillars of the Digital Single Market Strategy.

Numerical models solved on adaptive moving meshes have become increasingly prevalent in recent years. Motivating problems include the study of fluids in a Lagrangian frame and the presence of highly localized structures such as shock waves or interfaces. In the former case, Lagrangian solvers move the nodes of the mesh with the dynamical flow; in the latter, mesh resolution is increased in the proximity of the localized structure. Mesh adaptation can include remeshing, a procedure that adds or removes mesh nodes according to specific rules reflecting constraints in the numerical solver.

In the first part of this presentation I will give an overview over the high-resolution studies and capabilities, the Climate Prediction and Dynamics Group has been working with so far. This part will highlight results from projects during the last years. Among these are the successful studies for the Bergen harbour authority, the TRAKT-2018 project and the study on the mitigation of domestic-wood-heating related pollution.

Repeated climate stress events may cause fundamental shifts in species compositions or ecosystem functioning. However, few studies document such shifts. One reason for higher stability of ecosystems than previously expected may be ecological stress memory of vegetation. The study of memory effects of large-scale vegetation may therefore aid in predictions of future changes in biome distributions and resilience assessments on ecosystem or even species level. Such information is invaluable for management oriented decision support systems.

Sentinel-1A and 1B operate in Extra Wide swath dual-polarization mode over the Arctic Seas, and the two-satellite constellation provides the most frequent SAR observation of the Arctic sea ice ever. However, the use of Sentinel-1 for sea ice classification has not been popular because of relatively higher level of system noise and radiometric calibration issues. By taking advantage of my recent development on Sentinel-1 image noise correction, we suggest a fully automated SAR image-based sea ice classification scheme which can provide a potential near-real time service of sea ice charting.

A regional ocean model for Fram Strait allows to understand the variability and structure of acoustic tomography arrivals. The eddy-permitting model (52 vertical layers and 4.5 km horizontal resolution) was evaluated using long-term moored hydrography data and time series of depth-range averaged temperature obtained from the inversion of acoustic tomography measurements. Geometric ray modelling on the ocean model fields reproduces the measured arrival structure of the acoustic tomography experiment.

Sea ice type and age are one of the basic indicators of Arctic sea ice state. For a sea ice model to simulate sea ice type or age faithfully, both sea ice dynamics and thermodynamics need to be represented well. In contrast to sea ice thickness, ice age and type have been able to be retrieved from satellite observations relatively reliably for more than a decade. In this study we are using neXtSIM – ‘next generation sea ice model’ that uses Maxwell-elasto-brittle rheology to simulate sea ice motion and a thermodynamical model that accounts for healing of damaged ice through freezing.