Dataset, analysis code and model configurations for "Assessment of a finite volume discretization of the horizontal pressure gradient force beneath sloping ice shelves"

Dataset, analysis code and model configurations for "Assessment of a finite volume discretization of the horizontal pressure gradient force beneath sloping ice shelves" by C.K. Yung, R. Hallberg, A. Adcroft and A.K. Morrison. This study uses idealised MOM6 seamount, icemount and simple ice shelf shapes to test the performance of the pressure gradient algorithm in various configurations, stratifications, vertical coordinates and equations of state. This Zenodo repository contains an extended version of github.com/claireyung/IS-PG-MOM6, which includes model output data of the various experiments (in netcdf format) and the MOM6 executable used (according to version https://github.com/claireyung/MOM6/releases/tag/CYPGv2, https://doi.org/10.5281/zenodo.17733830), in addition to the model configuration files and analysis notebooks in the GitHub repository. Please see the README (contained in seamount-icemount-PGtest-jun26-for-zenodo.tar.gz with a copy on Github) for more details. The MOM6 model source code was modified to add an extra diagnostic and an option for perfect initialisation in a specific use case (grounding line of an ice shelf in sigma coordinates). Note: MOM6 (Modular Ocean Model 6) is developed by the NOAA Geophysical Fluid Dynamics Laboratory and is licensed under Apache License Version 2.0, January 2004 https://github.com/mom-ocean/MOM6?tab=Apache-2.0-1-ov-file. MOM6 has many contributors: MOM6 contributors Guidance on compiling and running MOM6 is here https://github.com/NOAA-GFDL/MOM6-examples/wiki CKY acknowledges support from an Australian Government Research Training Program scholarship, the Consortium for Ocean Sea Ice Modelling in Australia (COSIMA), and an ANU Vice Chancellor's HDR Travel Grant. This research was supported by the Australian Research Council (ARC) Special Research Initiative, the Australian Centre for Excellence in Antarctic Science (ACEAS, project number SR200100008). AKM was supported by ARC Discovery Projects DP190100494 and DP250100759. This research was undertaken with the assistance of resources and services from the National Computational Infrastructure (NCI), which is supported by the Australian Government.