  Our recently developed tool, called Directed Affinity Segmentation was
originally designed for data-driven discovery of coherent sets in fluidic
systems. Here we interpret that it can also be used to indicate early warning
signs of critical transitions in ice shelves as seen from remote sensing data.
We apply a directed spectral clustering methodology, including an asymmetric
affinity matrix and the associated directed graph Laplacian, to reprocess the
ice velocity data and remote sensing satellite images of the Larsen C ice
shelf. Our tool has enabled the simulated prediction of historical events from
historical data, fault lines responsible for the critical transitions leading
to the break up of the Larsen C ice shelf crack, which resulted in the A68
iceberg. Such benchmarking of methods using data from the past to forecast
events that are now also in the past is sometimes called post-casting,
analogous to forecasting into the future. Our method indicated the coming
crisis months before the actual occurrence.
