Rapid melting of some ice shelves from below has spurred new research into boundary layer turbulence in the sub-ice-shelf ocean cavity. We fill a gap in existing turbulence studies by exploring the factors that control ice-shelf melting when ambient ocean currents are strong, where past studies have focused on more quiescent ocean conditions.
We use the results from our very-high-resolution simulations to examine the fidelity of ice-shelf melting predictions in typical, coarse-resolution ocean models. We find that while these models may capture the sensitivity of ice-shelf melting to ocean temperature, the complex boundary layer structure and its effects on melting are neglected, necessitating major revision to existing melt parameterizations.
Today, ice-shelf melting is driven primarily from below and controlled by the turbulence in the ice-shelf ocean boundary layer flowing along the ice-shelf base. Turbulence in this setting is still poorly constrained by observations and numerical models, but research in this area has accelerated in recent decades due to the recognition that ice-shelf melting can accelerate ice loss and thus sea-level rise. In this study, we adapted an existing large-eddy model for the unique ice-shelf ocean boundary layer. Our simulations are consistent with the commonly used linear relationship between ocean temperature and ice-shelf melting at low ocean temperatures and high shear due to ocean currents. In addition, we find that ice-shelf melting increases with ice-shelf basal slopes, due to enhanced turbulence at higher slopes. Interestingly, our simulations evolve into intermittent turbulence despite fast ocean currents, as reported by similar studies. The competing processes of current shear, which generates turbulence, and ice-shelf melting, which dampens turbulence by generating stable stratification, make the task of predicting ice-shelf melting challenging and intimately linked with fundamental questions in stratified turbulence.
This research was supported by the Scientific Discovery through Advanced Computing (SciDAC) program funded by the US Department of Energy (DOE), Office of Science, Biological and Environmental Research and Advanced Scientific Computing Research (ASCR) programs.