The integrated ice sheet response to stochastic iceberg calving

Abstract Iceberg calving is a major source of ice loss from the Antarctic and Greenland ice sheets. However, it is still one of the most poorly understood aspects of ice sheet dynamics, in part due to its variability at a wide range of spatial and temporal scales. Despite this variability, most current large-scale ice sheet models assume that calving can be represented as a deterministic flux. In this study, we describe an approach to modeling calving as a stochastic process, using a one-dimensional depth-integrated marine-terminating glacier model as a demonstration. We show that for glaciers where calving occurs more frequently than the typical model time steps (days-months), stochastic calving schemes sampling a binomial distribution accurately simulate the probabilistic distribution of glacier state. We also find that incorporating stochastic calving into simulations of a glacier with a buttressing ice shelf changes the simulated mean glacier state, due to nonlinearities in ice shelf dynamics. Relatedly, we find that changes in calving frequency, without changes in the mean calving flux, can cause ice shelf retreat. This new stochastic approach can be implemented in large-scale ice sheet models, which should improve our capability to quantify uncertainty in predictions of future ice sheet change.