Reconciling ice dynamics and bed topography with a versatile and fast ice thickness inversion

• Published in: The Cryosphere
• Main Authors: Frank, Thomas, Pelt, Ward J. J., Kohler, Jack
• Format: Text
• Language: English
• Published: 2023
• Subjects: Kronebreen; Svalbard; glacier; Ice cap; Tidewater
• Online Access: https://doi.org/10.5194/tc-17-4021-2023; https://tc.copernicus.org/articles/17/4021/2023/

We present a novel thickness inversion approach that leverages satellite products and state-of-the-art ice flow models to produce distributed maps of sub-glacial topography consistent with the dynamic state of a given glacier. While the method can use any complexity of ice flow physics as represented in ice dynamical models, it is computationally cheap and does not require bed observations as input, enabling applications on both local and large scales. Using the mismatch between observed and modelled rates of surface elevation change ( d h / d t <svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="31pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="6475a6b5411bf125092ba330d7bb10e8"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="tc-17-4021-2023-ie00001.svg" width="31pt" height="14pt" src="tc-17-4021-2023-ie00001.png"/></svg:svg> ) as the misfit functional, iterative point-wise updates to an initial guess of bed topography are made, while mismatches between observed and modelled velocities are used to simultaneously infer basal friction. The final product of the inversion is not only a map of ice thickness, but is also a fully spun-up glacier model that can be run forward without requiring any further model relaxation. Here we present the method and use an artificial ice cap built inside a numerical model to test it and conduct sensitivity experiments. Even under a range of perturbations, the method is stable and fast. We also apply the approach to the tidewater glacier Kronebreen on Svalbard and finally benchmark it on glaciers from the Ice Thickness Models Intercomparison eXperiment (ITMIX, Farinotti et al. , 2017 ) , where we find excellent performance. Ultimately, our method shown here represents a fast way of inferring ice thickness where the final output forms a consistent picture of model physics, input observations and bed topography.