# Overview¶

After config-user.yml, the recipe.yml is the second file the user needs to pass to esmvaltool as command line option, at each run time point. Recipes contain the data and data analysis information and instructions needed to run the diagnostic(s), as well as specific diagnostic-related instructions.

Broadly, recipes contain a general section summarizing the provenance and functionality of the diagnostics, the datasets which need to be run, the preprocessors that need to be applied, and the diagnostics which need to be run over the preprocessed data. This information is provided to ESMValTool in four main recipe sections: Documentation, Datasets, Preprocessors and Diagnostics, respectively.

## Recipe section: documentation¶

The documentation section includes:

• The recipe’s author’s user name (authors, matching the definitions in the References configuration file)

• A description of the recipe (description, written in MarkDown format)

• A list of scientific references (references, matching the definitions in the References configuration file)

• the project or projects associated with the recipe (projects, matching the definitions in the References configuration file)

For example, the documentation section of recipes/recipe_ocean_amoc.yml is the following:

documentation:
description: |
Recipe to produce time series figures of the derived variable, the
Atlantic meridional overturning circulation (AMOC).
This recipe also produces transect figures of the stream functions for
the years 2001-2004.

authors:
- demo_le

maintainer:
- demo_le

references:
- demora2018gmd

projects:
- ukesm


Note

Note that all authors, projects, and references mentioned in the description section of the recipe need to be included in the config-references.yml file. The author name uses the format: surname_name. For instance, John Doe would be: doe_john. This information can be omitted by new users whose name is not yet included in config-references.yml.

## Recipe section: datasets¶

The datasets section includes dictionaries that, via key-value pairs, define standardized data specifications:

• dataset name (key dataset, value e.g. MPI-ESM-LR or UKESM1-0-LL)

• project (key project, value CMIP5 or CMIP6 for CMIP data, OBS for observational data, ana4mips for ana4mips data, obs4mips for obs4mips data, EMAC for EMAC data)

• experiment (key exp, value e.g. historical, amip, piControl, RCP8.5)

• mip (for CMIP data, key mip, value e.g. Amon, Omon, LImon)

• ensemble member (key ensemble, value e.g. r1i1p1, r1i1p1f1)

• time range (e.g. key-value start_year: 1982, end_year: 1990. Please note that yaml interprets numbers with a leading 0 as octal numbers, so we recommend to avoid them. For example, use 128 to specify the year 128 instead of 0128.)

• model grid (native grid grid: gn or regridded grid grid: gr, for CMIP6 data only).

For example, a datasets section could be:

datasets:
- {dataset: CanESM2, project: CMIP5, exp: historical, ensemble: r1i1p1, start_year: 2001, end_year: 2004}
- {dataset: UKESM1-0-LL, project: CMIP6, exp: historical, ensemble: r1i1p1f2, start_year: 2001, end_year: 2004, grid: gn}
- {dataset: EC-EARTH3, alias: custom_alias, project: CMIP6, exp: historical, ensemble: r1i1p1f1, start_year: 2001, end_year: 2004, grid: gn}


It is possible to define the experiment as a list to concatenate two experiments. Here it is an example concatenating the historical experiment with rcp85

datasets:
- {dataset: CanESM2, project: CMIP5, exp: [historical, rcp85], ensemble: r1i1p1, start_year: 2001, end_year: 2004}


It is also possible to define the ensemble as a list when the two experiments have different ensemble names. In this case, the specified datasets are concatenated into a single cube:

datasets:
- {dataset: CanESM2, project: CMIP5, exp: [historical, rcp85], ensemble: [r1i1p1, r1i2p1], start_year: 2001, end_year: 2004}


ESMValTool also supports a simplified syntax to add multiple ensemble members from the same dataset. In the ensemble key, any element in the form (x:y) will be replaced with all numbers from x to y (both inclusive), adding a dataset entry for each replacement. For example, to add ensemble members r1i1p1 to r10i1p1 you can use the following abbreviated syntax:

datasets:
- {dataset: CanESM2, project: CMIP5, exp: historical, ensemble: "r(1:10)i1p1", start_year: 2001, end_year: 2004}


It can be included multiple times in one definition. For example, to generate the datasets definitions for the ensemble members r1i1p1 to r5i1p1 and from r1i2p1 to r5i1p1 you can use:

datasets:
- {dataset: CanESM2, project: CMIP5, exp: historical, ensemble: "r(1:5)i(1:2)p1", start_year: 2001, end_year: 2004}


Please, bear in mind that this syntax can only be used in the ensemble tag. Also, note that the combination of multiple experiments and ensembles, like exp: [historical, rcp85], ensemble: [r1i1p1, “r(2:3)i1p1”] is not supported and will raise an error.

Note that this section is not required, as datasets can also be provided in the Diagnostics section.

## Recipe section: preprocessors¶

The preprocessor section of the recipe includes one or more preprocessors, each of which may call the execution of one or several preprocessor functions.

Each preprocessor section includes:

• A preprocessor name (any name, under preprocessors);

• A list of preprocessor steps to be executed (choose from the API);

• Any or none arguments given to the preprocessor steps;

• The order that the preprocessor steps are applied can also be specified using the custom_order preprocessor function.

The following snippet is an example of a preprocessor named prep_map that contains multiple preprocessing steps (Horizontal regridding with two arguments, Time manipulation with no arguments (i.e., calculating the average over the time dimension) and Multi-model statistics with two arguments):

preprocessors:
prep_map:
regrid:
target_grid: 1x1
scheme: linear
climate_statistics:
operator: mean
multi_model_statistics:
span: overlap
statistics: [mean]


Note

In this case no preprocessors section is needed the workflow will apply a default preprocessor consisting of only basic operations like: loading data, applying CMOR checks and fixes (CMORization and dataset-specific fixes) and saving the data to disk.

Preprocessor operations will be applied using the default order as listed in Preprocessor functions. Preprocessor tasks can be set to run in the order they are listed in the recipe by adding custom_order: true to the preprocessor definition.

## Recipe section: diagnostics¶

The diagnostics section includes one or more diagnostics. Each diagnostic section will include:

• the variable(s) to preprocess, including the preprocessor to be applied to each variable;

• the diagnostic script(s) to be run;

• a description of the diagnostic and lists of themes and realms that it applies to;

• an optional additional_datasets section.

### The diagnostics section defines tasks¶

A (simplified) example diagnostics section could look like

diagnostics:
diagnostic_name:
description: Air temperature tutorial diagnostic.
themes:
- phys
realms:
- atmos
variables:
variable_name:
short_name: ta
preprocessor: preprocessor_name
mip: Amon
scripts:
script_name:
script: examples/diagnostic.py


Note that the example recipe above contains a single diagnostic section called diagnostic_name and will result in two tasks:

• a preprocessing task called diagnostic_name/variable_name that will preprocess air temperature data for each dataset in the Datasets section of the recipe (not shown).

• a diagnostic task called diagnostic_name/script_name

The path to the script provided in the script option should be either the absolute path to the script, or the path relative to the esmvaltool/diag_scripts directory.

Depending on the installation configuration, you may get an error of “file does not exist” when the system tries to run the diagnostic script using relative paths. If this happens, use an absolute path instead.

Some tasks require the result of other tasks to be ready before they can start, e.g. a diagnostic script needs the preprocessed variable data to start. Thus each tasks has zero or more ancestor tasks. By default, each diagnostic task in a diagnostic section has all variable preprocessing tasks in that same section as ancestors. However, this can be changed using the ancestors keyword. Note that wildcard expansion can be used to define ancestors.

diagnostics:
diagnostic_1:
variables:
airtemp:
short_name: ta
preprocessor: preprocessor_name
mip: Amon
scripts:
script_a:
script: diagnostic_a.py
diagnostic_2:
variables:
precip:
short_name: pr
preprocessor: preprocessor_name
mip: Amon
scripts:
script_b:
script: diagnostic_b.py
ancestors: [diagnostic_1/script_a, precip]


The example recipe above will result in four tasks:

• a preprocessing task called diagnostic_1/airtemp

• a diagnostic task called diagnostic_1/script_a

• a preprocessing task called diagnostic_2/precip

• a diagnostic task called diagnostic_2/script_b

the preprocessing tasks do not have any ancestors, while the diagnostic_a.py script will receive the preprocessed air temperature data (has ancestor diagnostic_1/airtemp) and the diagnostic_b.py script will receive the results of diagnostic_a.py and the preprocessed precipitation data (has ancestors diagnostic_1/script_a and diagnostic_2/precip).

Tasks are assigned a priority, with tasks appearing earlier on in the recipe getting higher priority. The tasks will be executed sequentially or in parellel, depending on the setting of max_parallel_tasks in the User configuration file. When there are fewer than max_parallel_tasks running, tasks will be started according to their priority. For obvious reasons, only tasks that are not waiting for ancestor tasks can be started. This feature makes it possible to reduce the processing time of recipes with many tasks, by placing tasks that take relatively long near the top of the recipe. Of course this only works when settings max_parallel_tasks to a value larger than 1. The current priority and run time of individual tasks can be seen in the log messages shown when running the tool (a lower number means higher priority).

### Variable and dataset definitions¶

To define a variable/dataset combination that corresponds to an actual variable from a dataset, the keys in each variable section are combined with the keys of each dataset definition. If two versions of the same key are provided, then the key in the datasets section will take precedence over the keys in variables section. For many recipes it makes more sense to define the start_year and end_year items in the variable section, because the diagnostic script assumes that all the data has the same time range.

### Diagnostic and variable specific datasets¶

The additional_datasets option can be used to add datasets beyond those listed in the Datasets section. This is useful if specific datasets need to be used only by a specific diagnostic or variable, i.e. it can be added both at diagnostic level, where it will apply to all variables in that diagnostic section or at individual variable level. For example, this can be a good way to add observational datasets, which are usually variable-specific.

### Running a simple diagnostic¶

The following example, taken from recipe_ocean_example.yml, shows a diagnostic named diag_map, which loads the temperature at the ocean surface between the years 2001 and 2003 and then passes it to the prep_map preprocessor. The result of this process is then passed to the ocean diagnostic map script, ocean/diagnostic_maps.py.

diagnostics:

diag_map:
description: Global Ocean Surface regridded temperature map
variables:
tos: # Temperature at the ocean surface
preprocessor: prep_map
start_year: 2001
end_year: 2003
scripts:
Global_Ocean_Surface_regrid_map:
script: ocean/diagnostic_maps.py


### Passing arguments to a diagnostic script¶

The diagnostic script section(s) may include custom arguments that can be used by the diagnostic script; these arguments are stored at runtime in a dictionary that is then made available to the diagnostic script via the interface link, independent of the language the diagnostic script is written in. Here is an example of such groups of arguments:

scripts:
autoassess_strato_test_1: &autoassess_strato_test_1_settings
script: autoassess/autoassess_area_base.py
title: "Autoassess Stratosphere Diagnostic Metric MPI-MPI"
area: stratosphere
control_model: MPI-ESM-LR
exp_model: MPI-ESM-MR
obs_models: [ERA-Interim]  # list to hold models that are NOT for metrics but for obs operations


In this example, apart from specifying the diagnostic script script: autoassess/autoassess_area_base.py, we pass a suite of parameters to be used by the script (area, control_model etc). These parameters are stored in key-value pairs in the diagnostic configuration file, an interface file that can be used by importing the run_diagnostic utility:

from esmvaltool.diag_scripts.shared import run_diagnostic

# write the diagnostic code here e.g.
def run_some_diagnostic(my_area, my_control_model, my_exp_model):
"""Diagnostic to be run."""
if my_area == 'stratosphere':
diag = my_control_model / my_exp_model
return diag

def main(cfg):
"""Main diagnostic run function."""
my_area = cfg['area']
my_control_model = cfg['control_model']
my_exp_model = cfg['exp_model']
run_some_diagnostic(my_area, my_control_model, my_exp_model)

if __name__ == '__main__':

with run_diagnostic() as config:
main(config)


This way a lot of the optional arguments necessary to a diagnostic are at the user’s control via the recipe.

If the user wants to test a newly-developed my_first_diagnostic.py which is not yet part of the ESMValTool diagnostics library, he/she do it by passing the absolute path to the diagnostic:

diagnostics:

myFirstDiag:
description: John Doe wrote a funny diagnostic
variables:
tos: # Temperature at the ocean surface
preprocessor: prep_map
start_year: 2001
end_year: 2003
scripts:
JoeDiagFunny:
script: /home/users/john_doe/esmvaltool_testing/my_first_diagnostic.py


This way the user may test a new diagnostic thoroughly before committing to the GitHub repository and including it in the ESMValTool diagnostics library.

### Re-using parameters from one script to another¶

Due to yaml features it is possible to recycle entire diagnostics sections for use with other diagnostics. Here is an example:

scripts:
cycle: &cycle_settings
script: perfmetrics/main.ncl
plot_type: cycle
time_avg: monthlyclim

In this example the hook &cycle_settings can be used to pass the cycle: parameters to grading: via the shortcut <<: *cycle_settings.