MITgcm Antarctic Slope Front process-oriented model (MITgcm_ASF)

This release contains the Matlab scripts used to generate, run and analyze the MITgcm Antarctic Slope Front (MITgcm_ASF) process-oriented simulations, as well as the configurations of the simulations. The products of MITgcm_ASF simulations and the data we used to make figures for the manuscript are available via: https://doi.org/10.15144/S4MW21 The source code of the Massachusetts Institute of Technology General Circulation Model (MITgcm) is available via: http://mitgcm.org. Guidelines on reproducing MITgcm_ASF simulations: Start each simulation with a 10-year integration at low resolution. Before running each simulation, you need to substitute &OBCS_PARM04 with &OBCS_PARM05 in the file input/data.obcs, and substitute &EXF_NML_05 with &EXF_NML_OBCS in the file input/data.exf. Initialize the high-resolution simulations from the corresponding low-resolution simulations by the Matlab script higherRes.m in the folder MITgcm_ASF-csi/newexp. Each high-resolution simulation is run for a further 10 years, with a 5-year spin-up and a 5-year analysis period. Before running each simulation, you need to substitute &OBCS_PARM04 with &OBCS_PARM05 in the file input/data.obcs, substitute &EXF_NML_05 with &EXF_NML_OBCS in the file input/data.exf. Initialize the high-resolution production run from the corresponding high-resolution spin-up run, using the Matlab script initialize.m in the folder MITgcm_ASF-csi/newexp. When using the LAYERS package, you need to substitute numperlist = 1 with numperlist = 2 in the file code/DIAGNOSTICS_SIZE.h before running the simulations. A reduced-order model of ice-ocean mechanical interactions in the ASC: https://doi.org/10.5281/zenodo.7631435 If you have any questions on running simultions using MITgcm_ASF configurations, please feel free to contact Yidongfang Si via ysi@g.ucla.edu.This work is supported by the Faculty Early Career Development Program of the National Science Foundation, under award numbers OCE-1751386 and OPP-2023244, as well as awards OCE-2048590 and OPP-1643445. This work used the Extreme Science and Engineering Discovery Environment (XSEDE), which is supported by the National Science Foundation grant number ACI-1548562. We thank the MITgcm team for their contribution to numerical modeling and making their code available. Y. Si acknowledges the scholarship provided by China Scholarship Council that supports her study at UCLA