DOI: 10.5194/tc-15-1731-2021
论文题名: Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty
作者: Babaniyi O. ; Nicholson R. ; Villa U. ; Petra N.
刊名: Cryosphere
ISSN: 19940416
出版年: 2021
卷: 15, 期: 4 起始页码: 1731
结束页码: 1750
语种: 英语
英文关键词: basal ice
; ice sheet
; model
; rheology
; uncertainty analysis
英文摘要: We consider the problem of inferring the basal sliding coefficient field for an uncertain Stokes ice sheet forward model from synthetic surface velocity measurements. The uncertainty in the forward model stems from unknown (or uncertain) auxiliary parameters (e.g., rheology parameters). This inverse problem is posed within the Bayesian framework, which provides a systematic means of quantifying uncertainty in the solution. To account for the associated model uncertainty (error), we employ the Bayesian approximation error (BAE) approach to approximately premarginalize simultaneously over both the noise in measurements and uncertainty in the forward model. We also carry out approximative posterior uncertainty quantification based on a linearization of the parameter-to-observable map centered at the maximum a posteriori (MAP) basal sliding coefficient estimate, i.e., by taking the Laplace approximation. The MAP estimate is found by minimizing the negative log posterior using an inexact Newton conjugate gradient method. The gradient and Hessian actions to vectors are efficiently computed using adjoints. Sampling from the approximate covariance is made tractable by invoking a low-rank approximation of the data misfit component of the Hessian. We study the performance of the BAE approach in the context of three numerical examples in two and three dimensions. For each example, the basal sliding coefficient field is the parameter of primary interest which we seek to infer, and the rheology parameters (e.g., the flow rate factor or the Glen's flow law exponent coefficient field) represent so-called nuisance (secondary uncertain) parameters. Our results indicate that accounting for model uncertainty stemming from the presence of nuisance parameters is crucial. Namely our findings suggest that using nominal values for these parameters, as is often done in practice, without taking into account the resulting modeling error, can lead to overconfident and heavily biased results. We also show that the BAE approach can be used to account for the additional model uncertainty at no additional cost at the online stage. © 2021 Author(s). Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/164646
Appears in Collections: 气候变化与战略
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作者单位: School of Mathematical Sciences, Rochester Institute of Technology, Rochester, NY 14623, United States; Department of Engineering Science, University of Auckland, Auckland, 1010, New Zealand; Electrical and Systems Engineering, Washington University in St. Louis, St. Louis, MO 63130, United States; Department of Applied Mathematics, University of California, Merced, Merced, CA 95343, United States
Recommended Citation:
Babaniyi O.,Nicholson R.,Villa U.,et al. Inferring the basal sliding coefficient field for the Stokes ice sheet model under rheological uncertainty[J]. Cryosphere,2021-01-01,15(4)