Uncertainty Quantification for Large-Scale Ice Sheet Modeling

Technical Report · Fri Feb 05 04:00:00 EST 2016

DOI:https://doi.org/10.2172/1237006· OSTI ID:1237006

Ghattas, Omar ^[1]

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1. Univ. of Texas, Austin, TX (United States); The University of Texas at Austin

This report summarizes our work to develop advanced forward and inverse solvers and uncertainty quantification capabilities for a nonlinear 3D full Stokes continental-scale ice sheet flow model. The components include: (1) forward solver: a new state-of-the-art parallel adaptive scalable high-order-accurate mass-conservative Newton-based 3D nonlinear full Stokes ice sheet flow simulator; (2) inverse solver: a new adjoint-based inexact Newton method for solution of deterministic inverse problems governed by the above 3D nonlinear full Stokes ice flow model; and (3) uncertainty quantification: a novel Hessian-based Bayesian method for quantifying uncertainties in the inverse ice sheet flow solution and propagating them forward into predictions of quantities of interest such as ice mass flux to the ocean.

• Research Organization: Univ. of Texas, Austin, TX (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Advanced Scientific Computing Research (ASCR) (SC-21)
• DOE Contract Number: SC0002710
• OSTI ID: 1237006
• Report Number(s): DOE-UTAUSTIN--0002710
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
97 MATHEMATICS AND COMPUTING