Fork Author: Tom Beucler - [email protected] - https://wp.unil.ch/dawn Main Repository Author: Stephan Rasp - [email protected] - https://raspstephan.github.io

Thank you for checking out this fork of the CBRAIN repository (https://github.com/raspstephan/CBRAIN-CAM), dedicated to building physically-constrained and physically-informed climate model parameterizations. This is a working fork in a working repository, which means that recent commits may not always be functional or documented.

If you are looking for the version of the code that corresponds to the PNAS paper. Check out this release: https://github.com/raspstephan/CBRAIN-CAM/releases/tag/PNAS_final

The modified climate model code is available at https://gitlab.com/mspritch/spcam3.0-neural-net (branch: nn_fbp_engy_ess)

(Submitted) Beucler, T., Pritchard, M., Yuval, J., Gupta, A., Peng, L., Rasp, S., Ahmed, F., O'Gorman, P.A., Neelin, J.D., Lutsko, N.J. and Gentine, P.: Climate-Invariant Machine Learning. arXiv preprint arXiv:2112.08440. https://arxiv.org/abs/2112.08440

Beucler, T., Pritchard, M., Rasp, S., Ott, J., Baldi, P., & Gentine, P.: Enforcing Analytic Constraints in Neural-Networks Emulating Physical Systems. Physical Review Letters, 126.9: 098302. Editors’ Suggestion. arXiv pdf https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.126.098302

Brenowitz, N., T. Beucler, M. Pritchard & C. Bretherton: Interpreting and Stabilizing Machine-Learning Parametrizations of Convection. Journal of the Atmospheric Sciences, 77, 4357-4375. https://journals.ametsoc.org/view/journals/atsc/77/12/jas-d-20-0082.1.xml

(Workshop) Beucler, T., Pritchard, M., Gentine, P., & Rasp, S.: Towards Physically-Consistent, Data-Driven Models of Convection. IEEE International Geoscience and Remote Sensing Symposium 2020. [arXiv pdf](https://arxiv.org/abs/2002.08525 https://ieeexplore.ieee.org/document/9324569

(Workshop) Beucler, T., Rasp, S., Pritchard, M., & Gentine, P.: Achieving Conservation of Energy in Neural Network Emulators for Climate Modeling. 2019 International Conference on Machine Learning. https://arxiv.org/abs/1906.06622

S. Rasp, M. Pritchard and P. Gentine, 2018. Deep learning to represent sub-grid processes in climate models https://arxiv.org/abs/1806.04731

P. Gentine, M. Pritchard, S. Rasp, G. Reinaudi and G. Yacalis, 2018. Could machine learning break the convection parameterization deadlock? Geophysical Research Letters. http://doi.wiley.com/10.1029/2018GL078202

The main components of the repository are:

• cbrain: Contains the cbrain module with all code to preprocess the raw data, run the neural network experiments and analyze the data.
• pp_config: Contains configuration files and shell scripts to preprocess the climate model data to be used as neural network inputs
• nn_config: Contains neural network configuration files to be used with run_experiment.py.
• notebooks: Contains Jupyter notebooks used to analyze data. All plotting and data analysis for the papers is done in the subfolder presentation. dev contains development notebooks.
• wkspectra: Contains code to compute Wheeler-Kiladis figures. These were created by Mike S. Pritchard
• save_weights.py: Saves the weights, biases and normalization vectors in text files. These are then used as input for the climate model.