Sensitivity Analysis of Pine Island Glacier ice flow using ISSM and DAKOTA
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Sensitivity Analysis of Pine Island Glacier ice flow using ISSM and DAKOTA

  • Author(s): Morlighem, M;
  • Larour, E;
  • Schiermeier, J;
  • Rignot, E;
  • Seroussi, H;
  • Paden, J
  • et al.

Assessing output errors of ice flow models is a major challenge that needs to be addressed if we are to increase our confidence level in projections of mass balance in Antarctica and Greenland. Major inputs to ice flow models include geometry (ice thickness and surface elevation), constitutive laws and boundary conditions (geothermal flux, basal drag coefficient, surface temperature). These inputs can be either measured, in which case they carry errors due to instruments, or inferred using inverse methods (such as basal drag which is inverted using InSAR surface velocities) in which case they carry additional errors generated by the inversion process itself. In both cases, these input errors will result in uncertainties that propagate throughout a forward model, and that influence output diagnostics. In order to estimate the resulting error margins on diagnostics such as mass flux, we develop a new framework based on the Design Analysis Kit for Optimization and Terascale Applications (DAKOTA), which we interface to the Ice Sheet System Model (ISSM). We present results on the Pine Island Glacier, West Antarctica, for which we evaluate error margins of mass flux across the whole glacier, given currently known error margins on ice thickness, basal friction and ice hardness. Our results suggest errors in these inputs propagate linearly through the ice flow model, providing a way to 1) calibrate measurement requirements for field campaigns collecting data such as bedrock or surface topography 2) quantify uncertainties in projections of mass balance and 3) assess the sensitivity of model outputs to input parameters. This new error propagation model should help quantify confidence levels that we assign to model projections for the mass balance of Antarctica and Greenland, which will ultimately improve our projections of future sea level rise in a warming climate.

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