Offline Land Model Testbed (OLMT) Contact: Dan Ricciuto ([email protected])

Updated 2/25/2025

The purpose of the Offline Land Model Testbed (OLMT) is to simplify the workflows for single site, regional and ensemble offline ELM simulations, which are otherwise cumbersome using only CIME. We have been working on a new version with an improved interface. The user now can run a simulation using a single python run script that will perform the entire workflow. This usually involves setting up three cases for biogeochemistry-enabled runs: the ad spinup, final spinup and transient simulations. It is also possible to add additional cases beginning in later years where we apply treatment effects or otherwise modify forcings. For example, in the SPRUCE study we have 10 experimental treatments (different levels of temperature and CO2 modifications) that begin in 2015. For those point simulations, we have a single run script that launches and manages 13 cases.

For each case, the runscript will perform the create_newcase, case setup, and submission. The case.build will be performed on the first case only, and then the same executable will be used for following cases. When submitting cases, the correct dependencies will be applied, such that the second case will start running after the first has finished, etc.

Users can customize the simulations to run single points, a list of lat/lon coordinates, rectangular regions or global simulations. OLMT assumes surface, land use and domain data already exist (using the defaults for specified compsets and resolution) and will extract points or regions from these data. The user can also set custom files, for example ultra high-resolution files created from kilocraft. For single point runs, the PFT and soil information can be set to match observations, for example at AmeriFlux sites.

Finally, OLMT also has the capability to perform ensemble simulations. Users can specify a list of parameters, with allowable ranges for each. Random samples can then be created. Alternatively, the user can provide their own files with different parameter combinations. When this ensemble option is enabled, the cases will be set up in the same way as above, but then multiple copies of run directories will be created. We then use another MPI-enabled python script to manage the multiple simulations in parallel. Users can also specify a list of output variables and time frequency for which to postprocess. A matrix of output values for all ensemble members is then created at the end of the simulation, which can then be used in Uncertainty quantification applications discussed in the other epics.

Please see the wiki page for instructions and examples.