An assessment of basal melt parameterisations for Antarctic ice shelves

This dataset contains the data and scripts for the publication "An assessment of basal melt parameterisations for Antarctic ice shelves" in The Cryosphere.

Before going into details, here is a reminder that the NEMO runs are called 'OPM+number'. These are the corresponding names given in the manuscript: OPM006=HIGHGETZ, OPM016=WARMROSS, OPM018=COLDAMU and OPM021=REALISTIC.

The zipping has been made so that if you download and unzip everything you will have the following file structure:

===============
raw/

• Ant_MeltingRate.v2.2.nc (from RAW_other.zip): 2D observational estimates of melt rates updated from Rignot et al. (2013) by Jérémie Mouginot (used in Fig. B3)
• DUTRIEUX_2014/ (from RAW_other.zip): observational estimates temperature and salinity profiles for Pine Island Ice Shelf (inferred from Dutrieux et al., 2014 with 'find_click_position_T_from_article.py', used to produce 'T_S_profiles-dutrieux_2014_PIGL.nc') used for Fig. 8
• MASK_METADATA/ (from RAW_other.zip): contains txt and csv files (prepared by N. Jourdain) needed for the masks, and 'IPCC_cryo_div.txt' used for the maps in Fig. 5
• grid_eORCA025_CDO.nc (from RAW_other.zip): grid info needed to interpolate eORCA025 grid to stereographic (provided by Fabien Gillet-Chaulet)
• NEMO_main/NEMO_eORCA025.L121_OPM0*_ANT_STEREO (from RAW_nemo_OPM0*.zip): NEMO simulation output of OPM0* run, cut at 60°S on eORCA025 grid, contains:
  • variables_of_interest: variables_of_interest_*_Ant.nc: variables of interest for the study
  • cavity_melt_*_Ant.nc: ice-shelf melt
  • eORCA025.L121-OPM0*_mesh_mask.nc: geometric constants and masks
• NEMO_appendix/NEMO_OPM0* (from RAW_nemo_appendix.zip): NEMO simulation output of OPM0* run used for the figures in Appendix B.

The NEMO simulations were conducted by Pierre Mathiot.

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interim/

• ANTARCTICA_IS_MASKS/ (from INTERIM_ANTARCTICA_IS_MASKS.zip): contains 'nemo_5km_isf_masks_and_info_and_distance_new_oneFRIS.nc' for each NEMO run and for BedMachine, which contains masks and geometric information needed for the application of the parameterisation and computing plume and box characteristics
  => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'
• BOXES/ (from INTERIM_BOXES.zip): contains 'nemo_5km_boxes_1D_oneFRIS.nc' and 'nemo_5km_boxes_2D_oneFRIS.nc' for each NEMO run and for BedMachine, which contains the variables needed to apply the box parameterisation
  => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'
• PLUMES/ (from INTERIM_PLUMES.zip): contains 'nemo_5km_plume_characteristics_oneFRIS.nc' for each NEMO run and for BedMachine, which contains the variables needed to apply the plume parameterisation
  => produced with 'preprocessing/isf_mask_NEMO.ipynb' and 'preprocessing/isf_mask_BedMachine.ipynb'
• SIMPLE/ (from INTERIM_SIMPLE.zip)
  • nemo_5km_06161821_oneFRIS/: contains netcdf-files summarising the tuned parameters resulting from the cross-validation over ice shelves (CVshelves), the cross-validation over time (CVtime), the tuning over all sample (ALL) and the bootstrap tuning (BT and clusterbootstrap*)
    => summary of results from running the scripts in the folder 'tuning' - for simple parameterisations via 'tuning_cluster_ALL_CV_BT.ipynb' and for the others via 'run_generalized_tuning_from_bash_crossval.py', 'run_generalized_tuning_script.sh', 'group_CV_parameters.ipynb', 'group_BT_parameters.ipynb'
  •  nemo_5km_OPM0*: 'thermal_forcing_term_for_linreg_corrected_oneFRIS.nc', contains the term used for the tuning through linear regression of the simple parameterisations
    => produced via 'prepare_2D_thermal_forcing_simple.ipynb' and 'prepare_1D_thermal_forcing_term_simple_for_linreg.ipynb'
• NEMO_eORCA025.L121_OPM*_ANT_STEREO/ (from INTERIM_geometry_interp_OPM0*.zip): for each NEMO run,
  • corrected_draft_bathy_isf.nc: file containing ice draft and bathymetry corrected by ice draft concentration to account for the biased draft and bathymetry at the grounding line resulting from the interpolation from the native NEMO grid to the stereographic grid (values under ice shelf and NaNs over land
    => produced in 'data_formatting/custom_lsmask.ipynb'
  • custom_lsmask_Ant_stereo_clean.nc: land-sea mask giving 0 = ocean, 1 = shelf, 2 = land
    => produced in 'data_formatting/custom_lsmask.ipynb'
  • isfdraft_conc_Ant_stereo.nc: ice-shelf concentration resulting from the interpolation from the native NEMO grid to the stereographic grid
    => produced in 'data_formatting/custom_lsmask.ipynb'
  • other_mask_vars_Ant_stereo.nc: contains other variables used for the masks
    => produced in 'data_formatting/custom_lsmask.ipynb'
• T_S_PROF/ (from INTERIM_T_S_PROF.zip)
  • dutrieux_2014/: observational estimates temperature and salinity profiles for Pine Island Ice Shelf in one netcdf
    => produced in 'pre_processing/T_S_profiles_Dutrieux14.ipynb'
  • info_chunks.txt: contains corresponding NEMO run and start and end year for each time block used in cross-validation
  • T_S_mean_prof_corrected_km_contshelf_and_offshore_1980-2018_oneFRIS.nc for each NEMO run, which contains temperature and salinity profiles averaged over 5 different regions in front of each ice shelf
    => produced in 'pre_processing/T_S_profile_formatting_with_conversion.ipynb' and 'pre_processing/T_S_profiles_front.ipynb'

===============
processed/MELT_RATE/

• BedMachine_for_comparison (from PROCESSED_BedMachine_for_comparison.zip): contains 'melt_rates_PIGL_dutrieux_time_mean_pattern.nc', which is the mean melt pattern of the melt parameterised using Dutrieux2014 observational temperature and salinity estimates
  => produced in 'apply_params/apply_param_PIGL_dutrieux_BedMachine.ipynb'
• nemo_5km_OPM* (from PROCESSED_nemo_5km_OPM0*.zip): for each NEMO run
  • eval_metrics_1D*.nc : files containing 'melt_1D_Gt_per_y' and 'melt_1D_mean_myr_box1' for all parameterisations using parameters from the cross-validation over shelves (CVshelves), the cross-validation over time (CVtime), the original parameters (orig), and from Favier et al. 2019 (favier) and Jourdain et al. 2020 (lipscomb)
     => produced in 'apply_params/evalmetrics_results_CV.ipynb'
  • diff_melt_param_ref_box1_*.nc: files containing the difference between parameterised and reference melt in box1 for each point to create the left panel of Fig. F1
    => produced in 'apply_pointbypointRMSE_box1_forFigF1.ipynb'
  • melt_rates_1D_NEMO_oneFRIS.nc, melt_rates_2D_NEMO.nc, melt_rates_box1_NEMO_oneFRIS.nc: reference melt rates and integrated melt
    => produced in 'prepare_reference_melt_file.ipynb'

additionally, only in nemo_5km_OPM021:

• melt_rates_2D_NEMO_timmean.nc, melt_rates_2D_boxes_timmean_oneFRIS.nc, melt_rates_2D_plumes_timmean_oneFRIS.nc, melt_rates_2D_simple_timmean_oneFRIS.nc: files containing mean patterns used in Fig. 5 and Fig.
  => produced in 'prepare_data_Figures_5_6.ipynb'

===============
ATTENTION! EXTERNAL DATA NOT CONTAINED IN THIS REPOSITORY:

• Observational estimates of melt rates for Pine Island Ice Shelf (from Shean et al., 2019, for Fig. 8a)
  => This data is currently available upon request from D. Shean and will be soon made open access as well. The DOI will be shared here at that point
• BedMachine data used for Figure 8: It is BedMachine v2 (Morlighem, 2020) and can be found here: https://nsidc.org/data/nsidc-0756/versions/2.
• World Ocean Atlas data for comparisons in Appendix B. The data was downloaded here: https://www.ncei.noaa.gov/access/world-ocean-atlas-2018/bin/woa18.pl. It was then interpolated to the eORCA025 grid and converted to conservative temperature with TEOS10.

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The explanation around the scripts can be found in README.rst with the scripts in SCRIPTS.zip.
Note that these are the scripts needed to produce the results in the paper. You can also find them on Github: https://github.com/ClimateClara/scripts_paper_assessment_basal_melt_param, find the most up-to-date version of the package 'multimelt' here: https://github.com/ClimateClara/multimelt and a version you can install via pip here: https://pypi.org/project/multimelt/

Finally, if anything is unclear, check out the "Methods" section of the paper: https://doi.org/10.5194/tc-2022-32