Modeling of ice calving and basal melting in Antarctic region using machine learning

Abstract

Accounting for the evolution of Antarctic ice shelves is crucial for predicting future sealevel rise. While their role in buttressing tributary glaciers is recognized, modeling these complex systems has not yet achieved the accuracy of many other components of the Earth and ice sheet systems. Modeling accurately ice shelf evolution faces challenges, particularly with ice calving, responsible for almost half of Antarctica’s ice shelf mass losses. The existing approaches have seen a significant increase in their complexity, making their generalization challenging on a large scale, and they remain confined to specific cases. Even more significant limitations arise from our limited understanding of highly nonlinear processes such as ice shelf damage, which is inherently connected to calving but is not represented in existing methods. The massive influx of satellite imagery data, measuring physical parameters of ice shelves, however, opens the door to new data-driven approaches. These new methods which have largely been underused to model calving, are based on the use of machine learning, and allows capturing highly nonlinear processes by jointly utilizing multi-source observational data. In this study, we employed a data-driven approach to predict calving for Antarctic ice shelves using machine learning models. We chose to predict ice shelf calving using crucial parameters that can influence the rate of iceberg removal from ice shelf faces. These parameters are summarized as: ice shelf basal melting, ice shelf thickness, ice shelf velocity, and sea ice concentration. These were measured from space using multiple sensor remote sensing data, which were analyzed and standardized. The standardization of the dataset includes resampling all variables to the same spatial and temporal resolution, and the the design of an interpolation scheme to assemble a comprehensive dataset. From this work, we assembled two distinct datasets: a data cube incorporating spatial and temporal evolution, and a tabular one integrating spatial information ice shelf-wise near the ice front. We then used the tabular dataset to train a Random Forest model, implementing two cross-validation strategies. The first ensured balanced data representation across folds, respecting spatiotemporal structures in the dataset, while the second involved a random split of the data. Unfortunately, the model failed to accurately predict ice calving in both cases. We attribute this failure to substantial information loss resulting from the spatial information reduction during the construction of the tabular dataset. Although allowing for simpler data representation, the failure of this simple approach suggests the exploration of data driven methods that are able to capture the strong spatial dimension of the dataset (e.g. how changes in the grounding line or shear margins will affect ice shelf calving). For the next steps of this project, which is to be continued beyond the scope of this MSc thesis, we propose using the data cube to feed more complex machine learning methods, such as a Convolutional Neural Network (CNN), which are specifically designed to account for spatial information.