Abstract

Abstract. Among the most important challenges faced by ice flow models is how to represent basal and rheological conditions, which are challenging to obtain from direct observations. A common practice is to use numerical inversions to calculate estimates for the unknown properties, but there are many possible methods and not one standardised approach. As such, every ice flow model has a unique initialisation procedure. Here we compare the outputs of inversions from three different ice flow models, each employing a variant of adjoint-based optimisation to calculate basal sliding coefficients and flow rate factors using the same observed surface velocities and ice thickness distribution. The region we focus on is the Amundsen Sea Embayment in West Antarctica, the subject of much investigation due to rapid changes in the area over recent decades. We find that our inversions produce similar distributions of basal sliding across all models, despite using different techniques, implying that the methods used are highly robust and represent the physical equations without much influence by individual model behaviours. Transferring the products of inversions between models results in time-dependent simulations displaying variability on the order of or lower than existing model intercomparisons. Focusing on contributions to sea level, the highest variability we find in simulations run in the same model with different inversion products is 32 %, over a 40-year period, a difference of 3.67 mm. There is potential for this to be improved with further standardisation of modelling processes, and the lowest variability within a single model is 13 %, or 1.82 mm over 40 years. While the successful transfer of inversion outputs from one model to another requires some extra effort and technical knowledge of the particular models involved, it is certainly possible and could indeed be useful for future intercomparison projects.

Highlights

  • Many ice flow models use inverse methods to calculate initial conditions for properties of the ice for which directly observed data do not exist or are of poor quality

  • Inversion is an iterative process which starts from an initial guess and obtains improved values for the unknown property based on its relationship to a well-observed property, such as surface velocity

  • Our aim was to investigate the transferability of inversions between models, and for this we examine the results in sets of three, comparing the outcomes produced in the same forward model using the three different inversion outputs

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Summary

Introduction

Many ice flow models use inverse methods to calculate initial conditions for properties of the ice for which directly observed data do not exist or are of poor quality. Inversion is an iterative process which starts from an initial guess and obtains improved values for the unknown property based on its relationship to a well-observed property, such as surface velocity. This process is generally undertaken for at least one of the following properties: ice rheology (flow rate factor, A), basal sliding and bed topography.

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