Vulnerability of Antarctica’s ice shelves to meltwater-driven fracture
This data set includes the result presented in Lai et al. (2020), including the 125m-resolution fracture map, the spatial distribution of fracture depths and the initial flaw sizes calculated using linear elastic fracture mechanics (LEFM) according to the stress fields and ice-shelf thickness. We ca...
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| Format: | Dataset |
| Language: | English |
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U.S. Antarctic Program (USAP) Data Center
2020
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| Online Access: | https://dx.doi.org/10.15784/601335 https://www.usap-dc.org/view/dataset/601335 |
| Summary: | This data set includes the result presented in Lai et al. (2020), including the 125m-resolution fracture map, the spatial distribution of fracture depths and the initial flaw sizes calculated using linear elastic fracture mechanics (LEFM) according to the stress fields and ice-shelf thickness. We calculated the dimensionelss stress (Rxx_bar, defined in Lai et al. (2020)) which governs fracture behaviors. We obtained a dimensionless stress criteria which determines the ice-shelf areas vulnerable to hydrofracture if inundated with melt water (Rxx_bar >Rxx*_bar). Input data source: The input sources are "SUMER Antarctic Ice-shelf Buttressing, Version 1" (https://doi.org/10.5067/FWHORAYVZCE7; Fürst et al. (2016)), "MOA 2009" (https://doi.org/10.7265/N5KP8037; Haran et al. (2014), Scambos et al. (2007)), and "Bedmap2" (https://www.bas.ac.uk/project/bedmap-2/; Fretwell et al. (2013)). Data processing: The dimensionless stress Rxx_bar is calculated using the along-flow strain rates exx (calculated from the result of Fürst et al. (2016)), the viscosity calculated from surface temperature (from the regional climate model RACMO2.3p2), and the ice-shelf thickness (from Bedmap2). For di_dry, di_water, ds, exx, Rxx_bar, PSI, non-ice shelf areas (ocean or ice sheet) are marked NaN, using the same mask applied by Fürst et al. (2016). The code for training a neural-network to identify fracture locations is available at https://github.com/chingyaolai/Antarctic-fracture-detection Grid Specifications: All parameters except for "frac_loc_125m" are generated on the same grid used by Fürst et al. (2016) with a grid resolution of 1 km and grid dimensions (x: 5501 pixels and y: 5501 pixels). The x and y coordinate of the center of the upper-left pixel are -2,750,000 m and 2,750,000 m, respectively. The x and y coordinate are provided. "frac_loc_125m" is produced on the same grid as MOA 2009 (Haran et al. (2014)) with a grid resolution of 125 m and grid dimensions (x: 48333 pixels and y: 41779 pixels). The x and y coordinate of the center of the upper-left pixel are -3,174,450 m and 2,406,325 m, respectively. Parameters: frac_loc_125m: Fracture locations classified by the neural network on the MOA 2009 125m map. Fracture and non-fracture locations are denoted 1 and 0, respectively. No Unit. di_water: Depth of the initial flaws required to destabilize fractures fully filled with water (hydrofractures). Places marked 0 are where no hydrofractures can form. Unit [m] di_dry: Depth of the initial flaws required to form dry fractures. Places marked 0 are where no fractures can form. Unit [m] ds: Depth of the stable dry fractures. Places marked 0 are where no dry fractures can form. Places marked -9999 are where dry fractures are predicted to be unstable. Unit [m] exx: Along-flow strain rates calculated, according to Glen's law, with the along-flow stress and the viscosity factor derived by Fürst et al. (2016) from data assimilation with the Elmer/Ice ice flow model. Unit [1/year] Rxx_bar: Dimensionless extensional stress, defined as Rxx/(rhoi g H). The regions vulnerable to hydrofracture satisfie Rxx_bar > Rxx*_bar. No unit. PSI: Places marked 1 are the "passive shelf ice" identified by Fürst et al. (2016). Other locations are marked 0. No unit. frac_loc_1km: Parameter "frac_loc_125m" downscaled to 1km resolution (see Lai et al. (2020)). No unit. x: x coordinate. Unit [m] y: y coordinate. Unit [m] |
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