ESM Runscripts - Using the Workflow Manager


Starting with Release 6.0, esm_runscripts allows the user to define additional subjobs for data processing, arrange them in clusters, and set the order of execution of these and the standard runjob parts in a flexible and short way from the runscript. This is applicable for both pre- and postprocessing, but especially useful for iterative coupling jobs, like e.g. coupling pism to vilma (see below). In this section we explain the basic concept, and the keywords that have to be set in the runscript to make use of this feature.

Subjobs of a normal run

Even before the addition of the workflow manager, the run jobs of esm_runscript were split into different subjobs, even though that was mostly hidden from the user’s view. Before Release 6.0, these subjobs were:

compute --> tidy_and_resubmit (incl. wait_and_observe + resubmit next run)

Technically, wait_and_observe was part of the tidy_and_resubmit job, as was the resubmission, including above only for the purpose of demonstrating the difference to the new standard workflow, which is now (post-Release 6.0):

newrun --> prepcompute --> compute --> observe_compute --> tidy (+ resubmit next run)

Other than before adding the workflow manager, these standard subjobs are all separated and independant subjobs, each submitted (or started) by the previous subjob in one of three ways (see below). The splitting of the old compute job into newrun, prepcompute and compute on one side, and tidy_and_resubmit into observe and tidy, was necessary to enable the user to insert coupling subjobs for iterative coupling at the correct places. Here is what each of the standard subjobs does:




Initializes a new experiment, only very basic stuff, like creating (empty) folders needed by any of the following subjobs. NEEDS TO BE THE FIRST SUBJOB OF ANY EXPERIMENT.


Prepares the compute job. All the (Python) functionality that needs to be run, up to the job submission. Includes copying files, editing namelists, write batch scripts, etc.


Actual model integration, nothing else. No Python codes involved.


Python job running at the same time as compute, checking if the compute job is still running, looking for some known errors for monitoring / job termination.


Sorts the produced outputs, restarts and log files into the correct folders, checks for missing and unknown files, builds coupler restart files if not present

It is important to understand that none of this has to be edited by the users, this is the default setup.

Keywords available for defining additional data processing subjobs

The workflow manager is intended to include shell scripted data processing jobs into the esm_runscripts workflow, so several things have to be defined:

  • Name of the script to be run

  • Name of the python script used for setting up the environment

  • Name of the folder in which both of the above scripts can be found

  • Information on how often the subjob should be called

  • Information on between which other subjobs the new subjob should be inserted into the workflow

  • In case it isn’t clear: Which subjob should resubmit the next run.

The keywords used to define that are:




Chapter headline in a model’s section, indicating that alterations to the standard workflow will be defined here


Section in the workflow chapter, containing the information on additional subjob_clusters. A subjob_cluster is a collection of subjobs run from the same batch script. Each subjob needs to belong to one cluster, if none is defined, each subjob will automatically get assigned to its own cluster. Each entry in subjob_clusters is a dict, with the outermost key being the (arbitrary) name of the cluster.


Section in the workflow chapter, containing the information on additional subjobs.

run_after / run_before

Entry in spevifications of a subjob_cluster, to define before or after which other cluster of the workflow this cluster is supposed to run. Only one of the two should be specified. Can also be used in the specifications of subjobs if these subjobs get a corresponding cluster auto-assigned.






Example 1: Adding an additional postprocessing subjob

In the case of a simple echam postprocessing job, the corresponding section in the runscript could look like this:

        [...other information...]

                next_run_triggered_by: tidy

                                nproc: 1
                                run_after: tidy

Example 2: Adding an additional preprocessing subjob

A preprocessing job basically is configured the same way as a postprocessing job, but the run_after entry is repl

Example 3: Adding a iterative coupling job

Writing a runscript for iterative coupling using the workflow manager requires some more changes. The principal idea is that each coupling step consists of two data processing jobs, one pre- and one postprocessing job. This is done this way as to make the coupling modular, and enable the modeller to easily replace one of the coupled components by a different implementation. This is of course up to the user to decide, but we generally advise to do so, and the iterative couplings distributed with ESM-Tools are organized this way.