The ensemble_generator test group (compass.landice.tests.ensemble_generator) creates an ensemble of MALI simulations with different parameter values. The ensemble framework sets up a user-defined number of simulations with parameter values selected by uniform sampling or from a space-filling Sobol sequence (see ensemble_generator).


The shared config options for the ensemble_generator test group are described in ensemble_generator in the User’s Guide.


The class compass.landice.tests.ensemble_generator.EnsembleMember defines a step for a single ensemble member (model run). The constructor stores the run number and name, as well as the parameter values to be used and the python package where the namelist, streams, and albany input file can be found. By setting up an ensemble_member this way, this class can be flexibly called from any ensemble test case variants that one would like to define in the future.

The setup method actually sets up the run by first setting up a baseline configuration and then modifying parameter values using the parameter values defined when the constructor was called. There are operations to set parameters:

  • basal friction exponent

  • scaling factor on muFriction

  • scaling factor on stiffnessFactor

  • von Mises threshold stress

  • calving speed limit

  • gamma0 melt sensitivity parameter in ISMIP6-AIS ice-shelf basal melting parameterization

  • target ice-shelf basal melt rate for ISMIP6-AIS ice-shelf basal melting parameterization. In the model setup, the deltaT thermal forcing bias adjustment is adjusted to obtain the target melt rate for a given gamma0

Each parameter can be activated or disabled as a free parameter. If disabled, whatever values specified in namelist and input files will be used.

Because changing the exponent requires modifying the input file to adjust muFriction to yield the same basal shear stress as the original file, there is also an operation to set the output filename in the streams file, where the file to be used and modified was specified in the cfg file for the test case. The default input file is renamed to indicate it was modified. This is updated automatically in the streams file, and it seeks to avoid potential confusion if a user were to use this file for another purpose.

Similarly, because changing gamma0 and deltaT require modifying a basal melt parameter file, the baseline file path needs to be specified in the config and that file is copied, renamed, and modified in each run directory.

Additionally, a job script is written for the run so that the run can be submitted as a slurm job independent of other runs in the ensemble. This also allows a user to easily run a single ensemble member by submitting the job script within the run step work directory.

Finally, a symlink to the compass load script is added to the run work directory, which compass does not do by default.

The run method creates a graph file and runs MALI.

There is a function _adjust_friction_exponent that modifies the friction exponent in the albany_input.yaml file and adjusts muFriction in the input file to maintain an unchanged basal shear stress. Similarly, there is a function _adjust_basal_melt_params that modifes gamma0 and deltaT in a basal melt parameter file.


The class compass.landice.tests.ensemble_generator.EnsembleManager defines a step for managing the entire ensemble. The constructor and setup methods perform minimal operations. The run method submits each run in the ensemble as a slurm job. Eventually the ensemble_manager will be able to assess if runs need restarts and modify them to be submitted as such.


The compass.landice.tests.ensemble_generator.spinup_ensemble.SpinupEnsemble uses the framework described above to set up an ensemble of spinup or historical simulations from a common initial condition. The constructor simply adds the ensemble manager as the only step. This allows the test case to be listed by compass list without having all ensemble members listed in a verbose listing. Because there may be dozens of ensemble members, it is better to wait to have them added until the setup phase. Also, by waiting until configure to define the ensemble members, it is possible to have the start and end run numbers set in the config, because the config is not parsed by the constructor.

The configure method is where most of the work happens. Here, the start and end run numbers are read from the config, a parameter array is generated, and the parameters to be varied and over what range are defined. The values for each parameter are passed to the EnsembleMember constructor to define each run. Finally, each run is now added to the test case as a step to run, because they were not automatically added by compass during the test case constructor phase.

The run step simply sets up a graph file and runs the model.

The ensemble manager handles “running” ensemble members by submitting them as slurm jobs. This is a major difference in how this test case functions from most compass test cases. The visualization script plot_ensemble.py is symlinked in the test case work directory and can be run manually to assess the status of the ensemble, but there is not a formal analysis step that can be run through compass.


The compass.landice.tests.ensemble_generator.branch_ensemble.BranchEnsemble sets up an ensemble of runs each of which are branched from an ensemble member of a previously run spinup ensemble. The constructor adds the ensemble_manager as a step, as with the spinup_ensemble.

The configure method searches over the range of runs requested and assesses if the corresponding spinup_ensemble member reached the requested branch time. If so, and if the branch_ensemble memebr directory does not already exist, that run is added as a step. Within each run (step), the restart file from the branch year is copied to the branch run directory. The time stamp is reassigned to 2015 (this could be made a cfg option in the future). Also copied over are the namelist and albany_input.yamlm files. The namelist is updated with settings specific to the branch ensemble, and a streams file specific to the branch run is added. Finally, details for managing runs are set up, including a job script.

As in the spinup_ensemble, the run step just runs the model.