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.

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.

Sea ice is a key component in the global climate system. Satellite remote sensing is the major approach to the basin-scale observation of sea ice, informing the community of key findings including accelerated shrinkage and drastic thinning of the sea ice cover. As the major method for the estimation of sea ice thickness, satellite altimetry is hindered by the snow cover over the sea ice, which introduces large uncertainty to the sea ice thickness retrieval. Meanwhile, the snow over is a direct indicator of polar hydrological cycle, and a key modulating factor of air-ice-sea interaction.

Because of the interaction between ocean wave and sea ice, reliable models for wave propagation in the ice-covered region is critical to sea ice morphology. We present calibration of a viscoelastic type wave-in-ice model with wave, wind, and ice data collected from the Beaufort and Chukchi seas in the autumn of 2015. The data were from multiple sources of in-situ and remote sensing measurements in the marginal ice zone during the ice advance season.