Regional modeling of the Shirase drainage basin, East Antarctica: full Stokes vs. shallow ice dynamics

Seddik, Hakime; Greve, Ralf; Zwinger, Thomas; Sugiyama, Shin

doi:10.5194/tc-11-2213-2017

Cited by 11 publications

(24 citation statements)

References 47 publications

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“…Ice acteristically used for three-dimensional, thermomechanical modeling in ISSM (Seroussi et al, 2013), further comparisons can be made to those models that agree well with simulated ice volume, ice divide basal temperature, and ice divide thickness from the 25-layer P1 model. In Table 2, the absolute value of the percent difference is shown between each individual model simulation and that using the 25-layer P1 model.…”

Section: Icementioning

confidence: 95%

“…Aside from comparison of the results to EISMINT 2, we run a simulation using the P1 vertical interpolation on a model with 25 layers. This model will serve as the benchmark to compare the other simulations to, with a 25-layer P1 model being representative of what is typically used in the setup for GrIS-wide simulations in ISSM (Seroussi et al, 2013). We note that for the stress balance computation, we use the P1 vertical interpolation, while the thermal computation makes use of the higher-order vertical elements.…”

Section: Single-dome Experiments Setupmentioning

confidence: 99%

“…The models are initialized with bed topography from Bed-Machine Greenland v3 (Morlighem et al, 2017) and ice surface elevation from the GMIP DEM (Greenland Mapping Project Digital Elevation Model) of Howat et al (2014). The surface mass balance and surface temperatures are taken from Ettema et al (2009), and the geothermal heat flux relies on a setup identical to Seroussi et al (2013). The underlying geothermal heat flux from Shapiro and Ritzwoller (2004) is used; however, values of 20 and 60 mWm −2 are added at the Dye3 and GRIP sites, respectively, after Seroussi et al (2013).…”

Section: Gris Model Setupmentioning

confidence: 99%

“…The surface mass balance and surface temperatures are taken from Ettema et al (2009), and the geothermal heat flux relies on a setup identical to Seroussi et al (2013). The underlying geothermal heat flux from Shapiro and Ritzwoller (2004) is used; however, values of 20 and 60 mWm −2 are added at the Dye3 and GRIP sites, respectively, after Seroussi et al (2013). These modifications follow an exponential decay from the particular sites with a radius of 250 km.…”

Section: Gris Model Setupmentioning

confidence: 99%

“…At the ice surface, air temperature is imposed, while the geothermal heat flux is applied at the base. For full details outlining the thermal model used in ISSM, we direct the reader to Seroussi et al (2013) and Larour et al (2012). Lastly, the spatially varying basal drag coefficient is determined using inverse methods (Morlighem et al, 2010;Larour et al, 2012), providing the best match between modeled and InSAR surface velocities from Rignot and Mouginot (2012).…”

Section: Gris Model Setupmentioning

confidence: 99%

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Implementation of higher-order vertical finite elements in ISSM v4.13 for improved ice sheet flow modeling over paleoclimate timescales

et al. 2018

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Abstract. Paleoclimate proxies are being used in conjunction with ice sheet modeling experiments to determine how the Greenland ice sheet responded to past changes, particularly during the last deglaciation. Although these comparisons have been a critical component in our understanding of the Greenland ice sheet sensitivity to past warming, they often rely on modeling experiments that favor minimizing computational expense over increased model physics. Over Paleoclimate timescales, simulating the thermal structure of the ice sheet has large implications on the modeled ice viscosity, which can feedback onto the basal sliding and ice flow. To accurately capture the thermal field, models often require a high number of vertical layers. This is not the case for the stress balance computation, however, where a high vertical resolution is not necessary. Consequently, since stress balance and thermal equations are generally performed on the same mesh, more time is spent on the stress balance computation than is otherwise necessary. For these reasons, running a higher-order ice sheet model (e.g., Blatter-Pattyn) over timescales equivalent to the paleoclimate record has not been possible without incurring a large computational expense. To mitigate this issue, we propose a method that can be implemented within ice sheet models, whereby the vertical interpolation along the z axis relies on higher-order polynomials, rather than the traditional linear interpolation. This method is tested within the Ice Sheet System Model (ISSM) using quadratic and cubic finite elements for the vertical interpolation on an idealized case and a realistic Greenland configuration. A transient experiment for the ice thickness evolution of a single-dome ice sheet demonstrates improved accuracy using the higher-order vertical interpolation compared to models using the linear vertical interpolation, despite having fewer degrees of freedom. This method is also shown to improve a model's ability to capture sharp thermal gradients in an ice sheet particularly close to the bed, when compared to models using a linear vertical interpolation. This is corroborated in a thermal steady-state simulation of the Greenland ice sheet using a higher-order model. In general, we find that using a higher-order vertical interpolation decreases the need for a high number of vertical layers, while dramatically reducing model runtime for transient simulations. Results indicate that when using a higher-order vertical interpolation, runtimes for a transient ice sheet relaxation are upwards of 5 to 7 times faster than using a model which has a linear vertical interpolation, and this thus requires a higher number of vertical layers to achieve a similar result in simulated ice volume, basal temperature, and ice divide thickness. The findings suggest that this method will allow higher-order models to be used in studies investigating ice sheet behavior over paleoclimate timescales at a fraction of the computational cost than would otherwise be needed for a model using a linear vertical interpolation.

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Section: Icementioning

confidence: 95%

Section: Single-dome Experiments Setupmentioning

confidence: 99%