Scripts used for the publication "Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks." ===================================================================================================================================================

These are the scripts that were developed and used for the publication: Burgard, C., Jourdain, N. C., Mathiot, P., Smith, R.S., Schäfer, R., Caillet, J., Finn, T. S. and Johnson, J.E.: "Emulating present and future simulations of melt rates at the base of Antarctic ice shelves with neural networks." Journal of Advances in Modeling Earth Systems, https://essopenarchive.org/users/559280/articles/644074-emulating-present-and-future-simulations-of-melt-rates-at-the-base-of-antarctic-ice-shelves-with-neural-networks in press, 2023.

Useful functions are grouped in the package nn_funcs. To install them and use them in further scripts, don't forget to run

pip install .

The scripts to format the data and produce the figures can be found in the folder scripts_and_notebooks.

Note - In the scripts, the NEMO runs are called 'OPM+number'. Here are the corresponding names given in the manuscript: OPM006=HIGHGETZ, OPM016=WARMROSS, OPM018=COLDAMU and OPM021=REALISTIC. Also 'bf663' is the REPEAT1970 run and 'bi646' is the 4xCO2 run

Initial data formatting (from raw NEMO output to interesting variables gridded on stereographic grid)

The scripts for the initial formatting and of the data, prepare the ice-shelf masks, the box and plume characteristics, and the temperature and salinity profiles can be found in scripts_and_notebooks/data_formatting.

The training data is the same as used in Burgard et al. (2022). See: https://github.com/ClimateClara/scripts_paper_assessment_basal_melt_param

To format the testing data from Smith et al. (2021): Start with data_formatting_smith.sh, then move to custom_lsmask_Smith.ipynb and finally to regridding_vars_cdoSmith.ipynb. At this point you have the relevant NEMO fields on a stereographic grid.

isf_mask_NEMO_Smith.ipynb prepare masks of ice shelves, and plume and box characteristics, on the NEMO grid respectively.

prepare_reference_melt_file_Smith.ipynb prepares 2D and 1D metrics of the melt in NEMO for future comparison to the results of the parameterisations and neural network.

T_S_profile_formatting_with_conversion_Smith.ipynb converts the 3D fields from absolute salinity to practical salinity.

T_S_profiles_front_Smith.ipynb prepares the average temperature and salinity profiles in front of the ice shelf.

Conduct the preprocessing for the neural network

The script for the preprocessing of the data to be fed as input for neural networks. To run in the following order:

• prepare_2D_T_S_trainingruns.ipynb, compute_bedrock_slope.ipynb, prepare_mean_std_hydroinput.ipynb
• prepare_input_csv_extrap_chunks.ipynb, prepare_inputdata_crossval.ipynb or prepare_indata_parallely.py
• prepare_inputdata_whole_dataset.ipynb
• prepare_2D_T_S_Smith.ipynb, compute_bedrock_slope_Smith.ipynb, prepare_mean_std_hydroinput_Smith.ipynb
• prepare_input_csv_Smith.ipynb
• shuffle_variables_Smith.ipynb

Conduct the training of the neural networks

The scripts to conduct the cross-validation, the best-estimate tuning and the tuning on different bootstrap samples can be found in scripts_and_notebooks/training.

run_cross_validation_NN_experiments.ipynb for cross validation, run_training_whole_dataset.ipynb for the "final" training used in the test.

Apply the neural network

The scripts to run the neural networks can be found in scripts_and_notebooks/postprocessing.

• compute_1D_evalmetrics_directly_experiments.ipynb: Evaluation metrics for cross validation
• compute_2D_NN_experiments_CV.ipynb: 2D output for cross-validation
• compute_1D_NN_Smith_deepensemble.ipynb: Evaluation metrics testing dataset
• compute_2D_deepensemble_Smith.py: 2D output testing dataset
• script_to_apply_classic_param_Smith.ipynb: Application of classic basal melt parameterisations to testing dataset
• compute_2D_evalmetrics_shuffling_deepensemble_Smith.py: 2D output for analysis of permute-and-predict
• compute_1D_evalmetrics_shuffling_deepensemble_Smith.py: Evaluation metrics for analysis of permute-and-predict

Final analysis and figures

The scripts to finalise the figures can be found in scripts_and_notebooks/figures.