Scripts used to produce the data and figures for the ARC3O Part 1

What's this?

These are the scripts used to compute data and produce figures for the paper:

Burgard, C., Notz, D., Pedersen, L. T., and Tonboe, R. T.: The Arctic Ocean Observation Operator for 6.9 GHz (ARC3O) – Part 1: How to obtain sea ice brightness temperatures at 6.9 GHz from climate model output, The Cryosphere, 14, 2369–2386, https://doi.org/10.5194/tc-14-2369-2020, 2020.

Computing data

The reference sea-ice evolution was computed with the model SAMSIM, which can be downloaded on github here. The version used for the analysis was downloaded on March 10th, 2017.

The forcing data for SAMSIM was downloaded from the ERA-Interim dataset, with the script: download_ERA_forcing_data.py. This data was converted to the .txt-format necessary as input for SAMSIM, with the script: convert_nctotxt.py

For the analysis of the simplification of temperature and salinity (Section 4), the following were used:

• run_simplifications.py (different options can be given in the file), using functions from simplification_functions.py: produces dat-files as input for MEMLS for each timestep
• write_input_netcdf.py: transformes the single .dat-files into one netcdf-file.
• run_memls.py, using memls_functions.py: simulates the brightness temperatures with MEMLS and stores the results in .dat-files
• write_output_netcdf.py: writes the MEMLS output to netcdf

For the sensitivity study looking at the influence of the number of layers (Section 5), the following were used:

• run_simplifications_layers.py (can change the amount of layers as an option in the beginning), using functions from simplification_functions.py: produces dat-files as input for MEMLS for each timestep and each interest in layering
• write_input_netcdf_layers.py: transformes the single .dat-files into one netcdf-file, additional sorting by layers
• run_memls_layers.py, using memls_functions.py: simulates the brightness temperatures with MEMLS qnd stores the results in .dat-files
• write_output_netcdf_layers.py: writes the MEMLS output to netcdf

The data and its documentation can be found on the DKRZ long term archive here.

Producing figures

The final processing and visualization was done using the following scripts:

• Figure 2: Figure2.ipynb
• Figure 3, 5, 6: Figures_3_4_6_7.ipynb
• Figure 4: Figure5.py
• Values in Table 3: Table_layers.ipynb

Signed: Clara Burgard, 02.09.2020