Dynamic Observations of the Densification of Greenland Firn under Compression using a microcomputed X-ray tomography, June 2017

• Main Authors: Ian Baker, Yuan Li
• Format: Dataset
• Language: unknown
• Published: Arctic Data Center 2020
• Subjects: firn; microcomputed X-ray tomography; dynamic observation; microstructure; densification; Greenland
• Online Access: https://search.dataone.org/view/urn:uuid:0901e3b2-1541-48c3-9247-224011adf7ae

The present dataset includes 2-Dimensional (2-D) micro computed tomography (CT) reconstructions obtained from depths of 10-80 meters (m) firn from Summit, Greenland using the micro CT in 2017; comparison before the strain with after the strain of 4% from reconstructed 3-Dimensional (3-D) images (horizontal cross section) from the micro CT for 30 m firn specimen; stress as a function of strain for depths of 10-80 m during interrupted compressive tests; plots of density, the area-equivalent circle diameter (ECDa – a measure of the feature pore-size), the structure thickness (S.Th - a measure of the feature grain-size), total porosity, closed porosity, the specific surface area (SSA) and the structure model index (SMI - a measure of the prevalent ice curvature) respectively as a function of strain for depths of 10-80 m; grain size obtained using the micro CT as a function of depth or strain; SMI evolution with depth for before the test for depths of 10-80 m; 2-D reconstructions obtained using the micro CT 0% strain to 4% strain for the 30 m specimen; semi-empirical model for the lattice diffusion and the grain boundary diffusion, and the mean relationship between the density and stress for all tests; comparison of the density calculated from micro CT data with density obtained from measurement of the mass-volume method at each depth; the fitted linear slope of each microstructural parameter with increasing strain for depths of 10-80 m; the fitted mean density and the fraction of lattice diffusion α evaluated using the semi-empirical model under the corresponding stresses. We share this dataset with other glaciologists, material scientists, experimental geophysicists, and all other researchers and engineers in order to improve our understanding on the scientific issue of snow, firn and ice.