Deep learning the flow law of Antarctic ice shelves.

Antarctic ice shelves buttress the grounded ice sheet, mitigating global sea level rise. However, fundamental mechanical properties, such as the ice flow law and viscosity structure, remain under debate. In this work, by leveraging remote-sensing data and physics-informed deep learning, we provide evidence over several ice shelves that the flow law follows a grain size–sensitive composite rheology in the compression zone. In the extension zone, we found that ice exhibits anisotropic properties. We constructed ice shelf–wide anisotropic viscosity maps that capture the suture zones, which inhibit rift propagation. The inferred stress exponent near the grounding zone dictates the grounding-line ice flux and grounding line stability, whereas the inferred viscosity maps inform the prediction of rifts. Both are essential for predicting the future mass loss of the Antarctic Ice Sheet. Editor's summary: It is well known that ice flow from the Antarctic Ice Sheet into the ocean is slowed by the buttressing effect of ice shelves, but the fundamental mechanical properties of these structures are poorly understood. Wang et al. applied a combination of remote-sensing data and deep learning to uncover flow laws that govern the many different Antarctic ice shelves, information that is essential for predicting future mass loss from the Antarctic Ice Sheet (see the Perspective by Riel). —Jesse Smith [ABSTRACT FROM AUTHOR]