FESOM Arctic Ocean sea ice concentration and thickness 1995-2014, links to movies in mp4 format

Northern Hemisphere sea ice from a Finite-Element Sea-Ice Ocean Model (FESOM) 4.5 km resolution simulation carried out by researchers from Alfred Wegener Institute (AWI), Germany. Concentration is shown with color; thickness is shown with shading. A global 1 degree mesh is used, with the "Arctic Ocean" locally refined to 4.5 km. South of CAA and Fram Strait the resolution is not refined in this simulation. The animation indicates that the 4.5 km model resolution helps to represent the small scale sea ice features, although much higher resolution is required to fully resolve the ice leads. The animation is created by Michael Böttinger from DKRZ (https://www.dkrz.de).

Direct visualization of solute locations in laboratory ice samples, links to videos

Many important chemical reactions occur in polar snow, where solutes may be present in several reservoirs, including at the air-ice interface and in liquid-like regions within the ice matrix. Some recent laboratory studies suggest chemical reaction rates may differ in these two reservoirs. While investigations have examined where solutes are found in natural snow and ice, similar research has not identified solute locations in laboratory samples, nor the possible factors controlling solute segregation. To address this, we examined solute locations in ice samples prepared from either aqueous cesium chloride (CsCl) or Rose Bengal solutions that were frozen using several different methods. Samples frozen in a laboratory freezer had the largest liquid-like inclusions and air bubbles, while samples frozen in a custom freeze chamber had somewhat smaller air bubbles and inclusions; in contrast, samples frozen in liquid nitrogen showed much smaller concentrated inclusions and air bubbles, only slightly larger than the resolution limit of our images (~2 µm). Freezing solutions in plastic versus glass vials had significant impacts on the sample structure, perhaps because the poor heat conductivity of plastic vials changes how heat is removed from the sample as it cools. Similarly, the choice of solute had a significant impact on sample structure, with Rose Bengal solutions yielding smaller inclusions and air bubbles compared to CsCl solutions frozen using the same method. Additional experiments using higher-resolution imaging of an ice sample show that CsCl moves in a thermal gradient, supporting the idea that the solutes in ice are present in liquid-like regions. Our work shows that the structure of laboratory ice samples, including the location of solutes, is sensitive to freezing method, sample container, and solute characteristics, requiring careful experimental design and interpretation of results.

30 years of synoptic observations from Neumayer Station with links to datasets

The analysis of research data plays a key role in data-driven areas of science. Varieties of mixed research data sets exist and scientists aim to derive or validate hypotheses to find undiscovered knowledge. Many analysis techniques identify relations of an entire dataset only. This may level the characteristic behavior of different subgroups in the data. Like automatic subspace clustering, we aim at identifying interesting subgroups and attribute sets. We present a visual-interactive system that supports scientists to explore interesting relations between aggregated bins of multivariate attributes in mixed data sets. The abstraction of data to bins enables the application of statistical dependency tests as the measure of interestingness. An overview matrix view shows all attributes, ranked with respect to the interestingness of bins. Complementary, a node-link view reveals multivariate bin relations by positioning dependent bins close to each other. The system supports information drill-down based on both expert knowledge and algorithmic support. Finally, visual-interactive subset clustering assigns multivariate bin relations to groups. A list-based cluster result representation enables the scientist to communicate multivariate findings at a glance. We demonstrate the applicability of the system with two case studies from the earth observation domain and the prostate cancer research domain. In both cases, the system enabled us to identify the most interesting multivariate bin relations, to validate already published results, and, moreover, to discover unexpected relations.