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

Conrad P. Koziol, Joe A. Todd, Daniel N. Goldberg, James R Maddison

Research output: Contribution to journalArticlepeer-review


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.
Original languageEnglish
JournalGeoscientific Model Development
Publication statusPublished - 24 Sep 2021


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