The integrated ice sheet response to stochastic iceberg calving

Iceberg calving is one of the dominant sources of ice loss from the Antarctic and Greenland Ice sheets. Iceberg calving is still one of the most poorly understood aspects of ice sheet dynamics due to its variability at a wide range of spatial and temporal scales. Despite this variability, current large-scale ice sheet models assume that calving can be represented as a deterministic flux. Failure to parameterize calving accurately in predictive models could lead to large errors in warming-induced sea-level rise. In this study, we introduce stochastic calving within a one-dimensional depth-integrated tidewater glacier and ice shelf models to determine how changes in the calving style and size distribution of calving events cause changes in glacier state. We apply stochastic variability in the calving rate by drawing the calving rate from two different probability distributions.e also quantify the time scale on which individual calving events need to be resolved within a stochastic calving model to accurately simulate the probabilistic distribution of glacier state. We find that incorporating stochastic calving with a glacier model with or without buttressing ice shelves changes the simulated mean glacier state, due to nonlinearities in glacier terminus dynamics. This has important implications for the intrinsic biases in current ice sheet models, none of which include stochastic processes. Additionally, changes in calving frequency, without changes in total calving flux, lead to substantial changes in the distribution of glacier state. This new approach to modeling calving provides a framework for ongoing work to implement stochastic calving capabilities in large-scale ice sheet models, which should improve our capability to make well-constrained predictions of future ice sheet change.