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 <https://github.com/pgriewank/SAMSIM>_. 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 </scripts_simulation/download_ERA_forcing_data.py>. This data was converted to the .txt-format necessary as input for SAMSIM, with the script: convert_nctotxt.py </scripts_simulation/convert_nctotxt.py>

For the analysis of the simplification of temperature and salinity (Section 4), the following were used: * run_simplifications.py </data/run_simplifications.py>_ (different options can be given in the file), using functions from simplification_functions.py </scripts_simulation/simplification_functions.py>: produces dat-files as input for MEMLS for each timestep * write_input_netcdf.py </scripts_simulation/write_input_netcdf.py>: transformes the single .dat-files into one netcdf-file. * run_memls.py </scripts_simulation/run_memls.py>, using memls_functions.py </scripts_simulation/memls_functions.py>: simulates the brightness temperatures with MEMLS and stores the results in .dat-files * write_output_netcdf.py </scripts_simulation/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 </scripts_simulation/run_simplifications_layers.py>_ (can change the amount of layers as an option in the beginning), using functions from simplification_functions.py </scripts_simulation/simplification_functions.py>: produces dat-files as input for MEMLS for each timestep and each interest in layering * write_input_netcdf_layers.py </scripts_simulation/write_input_netcdf_layers.py>: transformes the single .dat-files into one netcdf-file, additional sorting by layers * run_memls_layers.py </scripts_simulation/run_memls_layers.py>, using memls_functions.py </scripts_simulation/memls_functions.py>: simulates the brightness temperatures with MEMLS qnd stores the results in .dat-files * write_output_netcdf_layers.py </scripts_simulation/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 <https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=DKRZ_LTA_033_ds00005>_.

Producing figures

The final processing and visualization was done using the following scripts: * Figure 2: Figure2.ipynb </scripts_figures/Figure2.ipynb>_ * Figure 3, 5, 6: Figures_3_4_6_7.ipynb </scripts_figures/Figures_3_4_6_7.ipynb>_ * Figure 4: Figure5.py </scripts_figures/Figure5.py>_ * Values in Table 3: Table_layers.ipynb </scripts_figures/Table_layers.ipynb>_

Signed: Clara Burgard, 02.09.2020