Welcome to the repository for the Community Firn Model (CFM).
The CFM is a comprehensive firn-model framework. It is designed to be modular, which allows the user to choose which physical processes in firn she or he would like to model. Its base function is to model firn densification and heat transfer in firn.
The CFM is meant to be useful for a variety of applications, including ice core science and estimations of ice-sheet surface-elevation change.
A few novel aspects of the CFM include:
- The user can choose which firn densification physics she/he would like to use for a model run.
- A firn-air model is optionally coupled to the firn-densification model, which allows studies of firn-air transport under non-steady-state firn conditions.
- Simulations of meltwater percolation, refreezing, and runoff. This feature currently only uses a basic bucket scheme. Work is underway to model meltwater percolation using Richard's equation and/or a dual domain scheme.
- Ability to simulate diffusion of water isotopes in firn.
Funding for this project has come from the National Science Foundation (NSF) and NASA.
Full documentation available here: https://communityfirnmodel.readthedocs.io/en/latest/
Documentation is still (always) a work in progress, and in the past few years I have been especially negligent of it. Please let me know of glaring omissions, and send me an email if you can't figure something out.
The CFM is coded in python. To use the software, you need to clone the repository to your computer.
The CFM can be run from the command line using the main.py script. It is also relatively easy to create a separate script or jupyter notebook to configure and run the CFM; this can make it easier to do a large number of runs with similar parameters.
All of the details for a model run are specifed in a .json file; the repository includes example.json. The values for each key in the .json file can be altered for a particular model run. The model is run from the command line using:
python main.py example_df.json -n
or
python main.py example_csv.json -n
The df version forces the model using climate data stored in a pandas dataframe, while the csv version uses climate data from csv files (old/original behavior, which will continue to be supported).
Starting with version 3.0.0, the repository also includes a script (run_CFM_example.py) and a jupyter notebook (run_CFM_example_notebook.ipynb) that allow the user to configure and run the CFM from the same script (or notebook). They work for example runs or can be edited for a specific use case.
More details can be found in the full documentation.
Starting with version 3.0.0, there are two example .json files as well as a new .py script and notebook that can be used to run CFM examples. (See previous section, 'Running the CFM'.) Further instructions for using run_CFM_example.py are in the script itself.
The CFMinput_example directory contains example forcing files to run the CFM. It includes .csv files and a .pkl file, which contains a pandas dataframe with climate data. The source for these forcings is MERRA2 data for Summit, Greenland (72.5 N, -38.75 W) or DYE-2 Greenland (66.5 N, -46.25 W).
The .csv files were created using the data in the .pkl file; effectively I am running RCMpkl_to_spin.py, which returns a dictionary full of arrays of climate variables and saving those arrays as csv files. I do this in a jupyter notebook called CFM_create_examples.ipynb. I am not including that on the repository but would be happy to share.
I also create an artificial surface isotope record using the skin temperature. I use equations from Jouzel and Merlivat, 1984 to do so. These forcings only go into the .csv files.
The CFMoutput_example directory contains results generated when running the CFM using these forcings with the settings prescribed in example.json. The only thing I change in example.json between the two example runs is "input_type": "csv" or "dataframe"; and "resultsFolder": "CFMoutput_example/csv" or "CFMoutput_example/df".
The CFM automatically moves the .json file for a particular run to the directory containing the results, so the exact .json files for the example runs are included in the CFMoutput_example/csv and CFMoutput_example/df subdirectories. You could move those .json files to CFM_main and then e.g. run CFM using >>>python main.py example_df.json. (Note that this will overwrite the results in those example results directories.)
The CFM is coded in python 3. It is not backwards compatible with python 2.
We recommend the Anaconda python distribution, which includes all of the python packages needed to run the CFM. The specific packages you will need are: numpy, scipy, h5py, and pandas. If you want to plot your results, you will need matplotlib.
The software does not include techinal support, but we are happy to help provide assistance if we can. Please submit questions and/or bug reports to Max Stevens: maxstev@uw.edu or maxstev@umd.edu
If there is a feature you would like to see (e.g. simulating different processes; writing different outputs), please let us us know and we can try to integrate that for you in the next release.
We welcome contributions to the CFM. If you are interested in adding code, it is best to get in touch (maxstev@umd.edu) to discuss the contribution and then to submit a pull request.
If you use the CFM for your research, please cite your use. The correct citation is:
Stevens, C. M., Verjans, V., Lundin, J. M. D., Kahle, E. C., Horlings, A. N., Horlings, B. I., and Waddington, E. D.: The Community Firn Model (CFM) v1.0, Geosci. Model Dev., 13, 4355–4377, https://doi.org/10.5194/gmd-13-4355-2020, 2020.
If you are using the CFM, please consider sending a note (maxstev@umd.edu) to let us know that you are using it. We try to keep track of user numbers, which helps us keep the project going.
MIT © C. Max Stevens
The CFM is open-source software, but we ask that you please cite your use.
C. Max Stevens Vincent Verjans Emma Kahle Vasileios Gkinis Ilyse (Brita) Horlings Annika Horlings Brooke Medley Jessica Lundin Huong Vo Ed Waddington Falk Orachewski Ben Smith