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Abstract. A new calving algorithm was developed in the glacier model Elmer/Ice that allows unrestricted calving and terminus advance in 3D. The algorithm used the meshing software Mmg to implement anisotropic remeshing and allow mesh adaptation at each timestep. The development of the algorithm along with the implementation of the crevasse depth law produced a new full-Stokes calving model capable of simulating calving and terminus advance across an array of complex geometries. Using a synthetic tidewater glacier geometry the model was tested to highlight the non-physical parameters that can alter calving. For a system with no clear attractor, model timestep and mesh resolution are shown to alter the simulated calving. In particular vertical mesh resolution had a large impact, increasing calving, as the frontal bending stresses are better resolved. However, when the system had a strong attractor, provided by basal pinning points, non-physical parameters have a limited affect on the terminus evolution. The new algorithm is capable of implementing unlimited terminus advance and retreat as well as unrestricted calving geometries, applying any melt field to the front, use in conjunction with any calving law or potentially advecting variables downstream. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]