Determining Basal Mass Balance of Ice Shelves Using Simulation-Based Inference
- 1Machine Learning in Science, University of Tübingen, Tübingen, Germany (guy.moss@uni-tuebingen.de)
- 2Tübingen AI Center, Tübingen, Germany
- 3Department of Geosciences, University of Tübingen, Tübingen, Germany
- 4Max Planck Institute for Intelligent Systems, Tübingen, Germany
The ice shelves buttressing the Antarctic ice sheet determine its stability. Over half of all mass loss in Antarctica occurs due to ice melting at the water-ice boundary at the base of ice shelves. Different contemporary methods of estimating the spatial distribution of the melting rates do not produce consistent results, and provide no information about decadal to centennial timescales. We explore a new method to infer the spatial distribution of the basal mass balance (BMB) using the internal stratigraphy which may contain additional information not present in other sources such as ice thickness and surface velocities alone. The method estimates the Bayesian posterior distribution of the BMB, and provides us with a principled measure of uncertainty in our estimates.
Our inference procedure is based on simulation-based inference (SBI) [1], a novel machine learning inference method. SBI utilizes artificial neural networks to approximate probability distributions which characterize those parameters that yield data-compatible simulations, without the need of an explicit likelihood function. We demonstrate the validity of our method on a synthetic ice shelf example, and then apply it to Ekström ice shelf, East Antarctica, where we have radar measurements of the internal stratigraphy. The inference procedure relies on a simulator of the dynamics of the ice shelves. For this we use the Shallow Shelf Approximation (SSA) implemented in the Python library Icepack [2], and a time-discretized layer tracing scheme [3]. These detailed simulations, along with available stratigraphic data and the SBI methodology, allows us to compute a spatially-varying posterior distribution of the melting rate. This distribution corroborates existing estimates and extends upon them by quantifying the uncertainty in our inference. This uncertainty should be incorporated in future forecasting of ice shelf dynamics and stability analysis.
[1] Lueckmann et al.: Benchmarking simulation-based inference (2020).
[2] Shapero et al.: icepack: a new glacier flow modeling package in Python, version 1.0. (2021).
[3] Born: Tracer transport in an isochronal ice-sheet model (2017).
How to cite: Moss, G., Višnjević, V., Schröder, C., Macke, J., and Drews, R.: Determining Basal Mass Balance of Ice Shelves Using Simulation-Based Inference, EGU General Assembly 2023, Vienna, Austria, 24–28 Apr 2023, EGU23-6900, https://doi.org/10.5194/egusphere-egu23-6900, 2023.
