An Early Warning Sign of Critical Transition in The Antarctic Ice Sheet -- A Data Driven Tool for Spatiotemporal Tipping Point
This provides a data-driven tool for early warning of ice shelf collapses, which is crucial for climate science and risk assessment, though it is an incremental application of an existing method to a new domain.
The researchers tackled the problem of predicting critical transitions in ice shelves by applying a directed spectral clustering method to Antarctic ice velocity and satellite data, enabling simulated prediction of the Larsen C ice shelf breakup months before it occurred.
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.