CAM-ML: A Fork of The Community Atmosphere Model Implementing a Machine Learning Convection Parameterisation

This Fork of the CAM model is a version in which we are aiming to implement a new convection parameterisation, YOG. The parameterisation is a machine learning implementation using a neural net trained on high-resolution cloud-resolving simulations in the SAM model as described in:\

Yuval, J., O’Gorman, P.A. Stable machine-learning parameterization of subgrid processes for climate modeling at a range of resolutions Nat Commun 11, 3295 (2020). DOI: 10.1038/s41467-020-17142-3
Yuval, J., O'Gorman, P.A., Hill, C.N. Use of Neural Networks for Stable, Accurate and Physically Consistent Parameterization of Subgrid Atmospheric Processes With Good Performance at Reduced Precision Geophysical Research Letters, 48, e2020GL091363 (2021). DOI: 10.1029/2020GL091363

The work is contained in a CAM-ML branch which is based off the cam_cesm2_1_rel_60 tag.

Using this model in a CESM Run

Obtaining CESM

Clone a copy of CESM from git and checkout the cesm2.1.5 tag on which this work is based:

git clone https://github.com/escomp/cesm.git my_cesm_sandbox_2_1
cd my_cesm_sandbox_2_1/
git checkout cesm2.1.5

Setting CAM version in CESM

To use this model in a CESM run you need to modify the Externals.cfg file in the main CESM directory to replace the CAM entry with:

[cam]
branch = CAM-ML
protocol = git
repo_url = https://github.com/m2lines/CAM-ML
local_path = components/cam
externals = Externals_CAM.cfg
required = True

This will pull the CAM-ML branch of this repo in as the CAM component.

You can now run, from within the CESM root directory,

./manage_externals/checkout_externals

to fetch the external components.

Creating and running a case

Details on creating a case can be found here on the NCAR website. For this work we are using the gate III testcase which can be set up by running:

./create_newcase --case <path_to_testcase_directory> --compset FSCAM --res T42_T42 --user-mods-dir ../../components/cam/cime_config/usermods_dirs/scam_gateIII --project NCGD0054

from <cesm_root>/cime/scripts/.

Once this has been done then edit user_nl_cam for the case as detailed below. This is a CAM namelist generated from the default for the case. Add the following lines:

deep_scheme = 'YOG'\ This will be the identifier for our new convection scheme.\ If running a comparison to the ZM scheme also add run_deep_comp = 'on'.
nn_weights = '<PATH/TO/WEIGHTS.nc>'\ The path to the nn weights.
SAM_sounding = '<PATH/TO/SAM/SOUNDING.nc>'\ The path to the SAM sounding for the NN.\ This file is generated using the sounding_to_netcdf.py script in the resources of the NN code.

Also consider adding:

fincl2 = 'ZMDT', 'ZMDQ'
fincl3 = 'YOGDT', 'YOGDQ'

to generate both ZM and YOG output diagnostics.

We can then run ./case.setup and ./case.build.

Note:
By default CESM will place output in /glade/scratch/user/case/ and logs/restart files in /glade/scratch/user/archive/case/. To place all output with logs in archive/case switch 'short term archiving' on by editing env_run.xml in the case directory to change DOUT_S from FALSE to TRUE.

CAM Documentation

CAM Documentation - https://ncar.github.io/CAM/doc/build/html/index.html

CAM6 namelist settings - http://www.cesm.ucar.edu/models/cesm2/settings/current/cam_nml.html

Please see the wiki for information.