Geology datasets in North America, Greenland and surrounding areas for use with ice sheet models

• Published in: Earth System Science Data
• Main Authors: Gowan, Evan J., Niu, Lu, Knorr, Gregor, Lohmann, Gerrit
• Format: Article in Journal/Newspaper
• Language: English
• Published: Copernicus Publications 2019
• Subjects: article; Verlagsveröffentlichung; Greenland; Ice Sheet; Iceland
• Online Access: https://doi.org/10.5194/essd-11-375-2019; https://noa.gwlb.de/receive/cop_mods_00002957; https://noa.gwlb.de/servlets/MCRFileNodeServlet/cop_derivate_00002915/essd-11-375-2019.pdf; https://essd.copernicus.org/articles/11/375/2019/essd-11-375-2019.pdf

The ice–substrate interface is an important boundary condition for ice sheet modelling. The substrate affects the ice sheet by allowing sliding through sediment deformation and accommodating the storage and drainage of subglacial water. We present three datasets on a 1 : 5 000 000 scale with different geological parameters for the region that was covered by the ice sheets in North America, including Greenland and Iceland. The first dataset includes the distribution of surficial sediments, which is separated into continuous, discontinuous and predominantly rock categories. The second dataset includes sediment grain size properties, which is divided into three classes: clay, silt and sand, based on the dominant grain size of the fine fraction of the glacial sediments. The third dataset is the generalized bedrock geology. We demonstrate the utility of these datasets for governing ice sheet dynamics by using an ice sheet model with a simulation that extends through the last glacial cycle. In order to demonstrate the importance of the basal boundary conditions for ice sheet modelling, we changed the shear friction angle to account for a weaker substrate and found changes up to 40 % in ice thickness compared to a reference run. Although incorporation of the ice–bed boundary remains model dependent, our dataset provides an observational baseline for improving a critical weakness in current ice sheet modelling (https://doi.org/10.1594/PANGAEA.895889, Gowan et al., 2018b).