Bayesian Inference of Ice Softness and Basal Sliding Parameters at Langjökull

Abstract

We develop Bayesian statistical models that are designed for the inference of ice softness and basal sliding parameters, important glaciological quantities. These models are applied to Langjökull, the second largest temperate ice cap in Iceland at about 900 squared kilometers in area. The models make use of a relationship between physical parameters and ice velocity as stipulated by a shallow ice approximation that is generally applicable to Langjökull. The posterior distribution for ice softness concentrates around 18.2 × 10−25s−1Pa−3; moreover, spatially varying basal sliding parameters are inferred allowing for the decomposition of velocity into a deformation component and a sliding component, with spatial variation consistent with previous studies. Bayesian computation is conducted with a Gibbs sampling approach. The paper serves as an example of statistical inference for ice softness and basal sliding parameters at temperate, shallow glaciers using surface velocity data.