MOSAiC Sunlight Under Sea Ice

mosaic_sunlight_underseaice is a collection of Jupyter notebooks and python scripts to estimate under-ice sunlight and photosynthetically active radiation (PAR) for GEM2 ice thickness and Magnaprobe snow depth transects collected during the MOSAiC expedition

Level of Support

• This repository is not actively supported by NSIDC but we welcome issue submissions and pull requests in order to foster community contribution.

See the LICENSE for details on permissions and warranties. Please contact nsidc@nsidc.org for more information.

Requirements

You will a python installation for python 3.10. The easiest way to do this is install Anaconda or Miniconda.

Underice sunlight and PAR are estimate using the seaice_rt package, which is a python wrapper for the CESM2 Delta-Eddington sea ice radiative transfer model. You will need to follow the installation instructions for this package.

In addition you will need:

• Jupyter Lab to run the notebooks;
• pandas for reading the transect data;
• numpy;
• matplotlib for plotting the results.
• pytest for testing

Installation

1. Fork the mosaic_sunlight_underseaice repoisitory into your own GitHub user account.
2. Clone this fork onto your local computer. This will create a mosaic_sunlight_underseaice directory.
3. Run the following commands.

cd mosaic_sunlight_underseaice
conda env create -f environment.yml
conda activate mosaic_underice_sunlight

4. Follow the installation instructions for seaice_rt.

Usage

The best way to get started is to run the Jupyter notebook notebooks/getting_started.ipynb.

1. Start Jupyter Lab.
2. Navigate to the notebooks folder.
3. Open getting_started.ipynb.
4. Run the notebook from the drop-down menus Run->Run All Cells. Alternatively, you can run each code cell using Shift+Enter.

Troubleshooting

If the notebook doesn't run. Check that seaice_rt has been installed correctly. Follow the troubleshooting procedure in seaice_rt

License

See LICENSE.

Code of Conduct

See Code of Conduct.

Credit

This software was developed by the National Snow and Ice Data Center with funding from multiple sources.