Preprints
https://doi.org/10.5194/gmd-2021-90
https://doi.org/10.5194/gmd-2021-90

Submitted as: model description paper 29 Mar 2021

Submitted as: model description paper | 29 Mar 2021

Review status: this preprint is currently under review for the journal GMD.

fenics_ice 1.0: A framework for quantifying initialisation uncertainty fortime-dependent ice-sheet models

Conrad P. Koziol1, Joe A. Todd1, Daniel N. Goldberg1, and James R. Maddison2 Conrad P. Koziol et al.
  • 1School of GeoSciences, Univ. of Edinburgh, City of Edinburgh, United Kingdom
  • 2School of Mathematics, Univ. of Edinburgh, City of Edinburgh, United Kingdom

Abstract. Mass loss due to dynamic changes in ice sheets is a significant contributor to sea level rise, and this contribution is expected to increase in the future. Numerical codes simulating the evolution of ice sheets can potentially quantify this future contribution. However, the uncertainty inherent in these models propagates into projections of sea level rise, and hence is crucial to understand. Key variables of ice sheet models, such as basal drag or ice stiffness, are typically initialized using inversion methodologies to ensure that models match present observations. Such inversions often involve tens or hundreds of thousands of parameters, with unknown uncertainties and dependencies. The computationally intensive nature of inversions along with their high number of parameters mean traditional methods such as Monte Carlo are expensive for uncertainty quantification. Here we develop a framework to estimate the posterior uncertainty of inversions, and project them onto sea level change projections over the decadal timescale. The framework treats parametric uncertainty as multivariate Gaussian, and exploits the equivalence between the Hessian of the model and the inverse covariance of the parameter set. The former is computed efficiently via algorithmic differentiation, and the posterior covariance is propagated in time using a time-dependent model adjoint to produce projection error bars. This work represents an important step in quantifying the internal uncertainty of projections of ice-sheet models.

Conrad P. Koziol et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-90', Anonymous Referee #1, 22 Apr 2021
  • RC2: 'Comment on gmd-2021-90', Anonymous Referee #2, 03 Jun 2021

Conrad P. Koziol et al.

Model code and software

fenics_ice Joe A Todd, Conrad P Koziol, James R Maddison, Daniel N Goldberg https://doi.org/10.5281/zenodo.4633106

Conrad P. Koziol et al.

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Short summary
Sea-level change due to the loss of ice sheets presents great risk for coastal communities. Models are used to forecast ice loss, but their evolution depend strongly on properties which are hidden from observation and must be inferred from satellite observations. Common methods for doing so do not allow for quantification of the uncertainty inherent, or how it will affect forecasts. We provide a framework for quantifying how this "initialisation uncertainty" affects ice loss forecasts.