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Ice-sheet models can be used to forecast ice losses from Antarctica and Greenland, but to fully quantify the risks associated with sea-level rise, probabilistic forecasts are needed. These require estimates of the probability density function (PDF) for various model parameters (e.g. the basal drag coefficient and ice viscosity). To infer such parameters from satellite observations it is common to use inverse methods. Two related approaches are in use: (1) minimization of a cost function that describes the misfit to the observations, often accompanied by explicit or implicit regularization, or (2) use of Bayes' theorem to update prior assumptions about the probability of parameters. Both approaches have much in common and questions of regularization often map onto implicit choices of prior probabilities that are made explicit in the Bayesian framework. In both approaches questions can arise that seem to demand subjective input. One way to specify prior PDFs more objectively is by deriving transformation group priors that are invariant to symmetries of the problem, and then maximizing relative entropy, subject to any additional constraints. Here we investigate the application of these methods to the derivation of priors for a Bayesian approach to an idealized glaciological inverse problem. [ABSTRACT FROM AUTHOR]

The article offers information on a study conducted by the authors in which they calculated Antarctica's Antarctic Peninsula ice sheet's (APIS) contribution to sea level by 2200. It states that the calculation was done using an ice-sheet model which had a technique for computing ice fluxes based on observed altimetry and surface velocity. It mentions that APIS due to anomalies in surface mass-balance came out to be negative 0.5.

The article discusses a study which formulated an inverse Robin problem for Stokes flow in an attempt to specify the initial state of an ice-sheet model to motivate further numerical and theoretical calculations. The algorithm was used to invert for the basal drag coefficient and ice viscosity, but its ability to recover viscosity and basal drag coefficient from realistic glaciological data was not explored. The authors suggested the use of a more sophisticated algorithm to lessen the cost function, such as conjugate gradients.