Configuration

Overview

Similar to Dask, ESMValCore provides one single configuration object that consists of a single nested dictionary for its configuration.

Note

In v2.12.0, a redesign process of ESMValTool/Core’s configuration started. Its main aim is to simplify the configuration by moving from many different configuration files for individual components to one configuration object that consists of a single nested dictionary (similar to Dask’s configuration). This change will not be implemented in one large pull request but rather in a step-by-step procedure. Thus, the configuration might appear inconsistent until this redesign is finished. A detailed plan for this new configuration is outlined in Issue #2371.

Specify configuration for esmvaltool command line tool

When running recipes via the command line, configuration options can be specified via YAML files and command line arguments.

YAML files

Configuration options can be specified via YAML files (i.e., *.yaml and *.yml).

A file could look like this (for example, located at ~/.config/esmvaltool/config.yml):

output_dir: ~/esmvaltool_output
search_esgf: when_missing
download_dir: ~/downloaded_data

These files can live in any of the following locations:

1. The directory specified via the --config_dir command line argument.

2. The user configuration directory: by default ~/.config/esmvaltool, but this can be changed with the ESMVALTOOL_CONFIG_DIR environment variable. If ~/.config/esmvaltool does not exist, this will be silently ignored.

ESMValCore searches for all YAML files within each of these directories and merges them together using dask.config.collect(). This properly considers nested objects; see dask.config.update() for details. Preference follows the order in the list above (i.e., the directory specified via command line argument is preferred over the user configuration directory). Within a directory, files are sorted alphabetically, and later files (e.g., z.yml) will take precedence over earlier files (e.g., a.yml).

Warning

ESMValCore will read all YAML files in these configuration directories. Thus, other YAML files in this directory which are not valid configuration files (like the old config-developer.yml files) will lead to errors. Make sure to move these files to a different directory.

To get a copy of the default configuration file, you can run

esmvaltool config get_config_user --path=/target/file.yml

If the option --path is omitted, the file will be copied to ~/.config/esmvaltool/config-user.yml.

Command line arguments

All configuration options can also be given as command line arguments to the esmvaltool executable.

Example:

esmvaltool run --search_esgf=when_missing --max_parallel_tasks=2 /path/to/recipe.yml

Options given via command line arguments will always take precedence over options specified via YAML files.

Specify/access configuration for Python API

When running recipes with the experimental Python API, configuration options can be specified and accessed via the CFG object. For example:

>>> from esmvalcore.config import CFG
>>> CFG['output_dir'] = '~/esmvaltool_output'
>>> CFG['output_dir']
PosixPath('/home/user/esmvaltool_output')

This will also consider YAML configuration files in the user configuration directory (by default ~/.config/esmvaltool, but this can be changed with the ESMVALTOOL_CONFIG_DIR environment variable).

More information about this can be found here.

Top level configuration options

Note: the following entries use Python syntax. For example, Python’s None is YAML’s null, Python’s True is YAML’s true, and Python’s False is YAML’s false.

Option	Description	Type	Default value
auxiliary_data_dir	Directory where auxiliary data is stored. [1]	str	~/auxiliary_data
check_level	Sensitivity of the CMOR check (debug, strict, default relaxed, ignore), see Customizing checker strictness.	str	default
compress_netcdf	Use netCDF compression.	bool	False
config_developer_file	Path to custom Developer configuration file.	str	None (default file)
dask	Dask configuration.	dict	See Default options
diagnostics	Only run the selected diagnostics from the recipe, see Running.	list or str	None (all diagnostics)
download_dir	Directory where downloaded data will be stored. [4]	str	~/climate_data
drs	Directory structure for input data. [2]	dict	{CMIP3: ESGF, CMIP5: ESGF, CMIP6: ESGF, CORDEX: ESGF, obs4MIPs: ESGF}
exit_on_warning	Exit on warning (only used in NCL diagnostic scripts).	bool	False
extra_facets_dir	Additional custom directory for Extra Facets.	list of str	[]
log_level	Log level of the console (debug, info, warning, error).	str	info
logging	Logging configuration.	dict	See Logging configuration
max_datasets	Maximum number of datasets to use, see Running.	int	None (all datasets from recipe)
max_parallel_tasks	Maximum number of parallel processes, see Task priority. [5]	int	None (number of available CPUs)
max_years	Maximum number of years to use, see Running.	int	None (all years from recipe)
output_dir	Directory where all output will be written, see Output.	str	~/esmvaltool_output
output_file_type	Plot file type.	str	png
profile_diagnostic	Use a profiling tool for the diagnostic run. [3]	bool	False
remove_preproc_dir	Remove the preproc directory if the run was successful, see also Preprocessed datasets.	bool	True
resume_from	Resume previous run(s) by using preprocessor output files from these output directories, see ref:running.	list of str	[]
rootpath	Rootpaths to the data from different projects. [2]	dict	{default: ~/climate_data}
run_diagnostic	Run diagnostic scripts, see Running.	bool	True
save_intermediary_cubes	Save intermediary cubes from the preprocessor, see also Preprocessed datasets.	bool	False
search_esgf	Automatic data download from ESGF (never, when_missing, always). [4]	str	never
skip_nonexistent	Skip non-existent datasets, see Running.	bool	False

[1]

The auxiliary_data_dir setting is the path to place any required additional auxiliary data files. This is necessary because certain Python toolkits, such as cartopy, will attempt to download data files at run time, typically geographic data files such as coastlines or land surface maps. This can fail if the machine does not have access to the wider internet. This location allows the user to specify where to find such files if they can not be downloaded at runtime. The example configuration file already contains two valid locations for auxiliary_data_dir directories on CEDA-JASMIN and DKRZ, and a number of such maps and shapefiles (used by current diagnostics) are already there. You will need esmeval group workspace membership to access the JASMIN one (see instructions how to gain access to the group workspace.

Warning

This setting is not for model or observational datasets, rather it is for extra data files such as shapefiles or other data sources needed by the diagnostics.

[2] (1,2)

A detailed explanation of the data finding-related options drs and rootpath is presented in the Data retrieval section. These sections relate directly to the data finding capabilities of ESMValCore and are very important to be understood by the user.

[3]

The profile_diagnostic setting triggers profiling of Python diagnostics, this will tell you which functions in the diagnostic took most time to run. For this purpose we use vprof. For each diagnostic script in the recipe, the profiler writes a .json file that can be used to plot a flame graph of the profiling information by running

vprof --input-file esmvaltool_output/recipe_output/run/diagnostic/script/profile.json

Note that it is also possible to use vprof to understand other resources used while running the diagnostic, including execution time of different code blocks and memory usage.

[4] (1,2)

The search_esgf setting can be used to disable or enable automatic downloads from ESGF. If search_esgf is set to never, the tool does not download any data from the ESGF. If search_esgf is set to when_missing, the tool will download any CMIP3, CMIP5, CMIP6, CORDEX, and obs4MIPs data that is required to run a recipe but not available locally and store it in download_dir using the ESGF directory structure defined in the Developer configuration file. If search_esgf is set to always, the tool will first check the ESGF for the needed data, regardless of any local data availability; if the data found on ESGF is newer than the local data (if any) or the user specifies a version of the data that is available only from the ESGF, then that data will be downloaded; otherwise, local data will be used.

[5]

When using max_parallel_tasks with a value larger than 1 with the Dask threaded scheduler, every task will start num_workers threads. To avoid running out of memory or slowing down computations due to competition for resources, it is recommended to set num_workers such that max_parallel_tasks * num_workers approximately equals the number of CPU cores. The number of available CPU cores can be found by running python -c 'import os; print(len(os.sched_getaffinity(0)))'. See Custom Dask threaded scheduler configuration for information on how to configure num_workers.

Dask configuration

Configure Dask in the dask section.

The preprocessor functions and many of the Python diagnostics in ESMValTool make use of the Iris library to work with the data. In Iris, data can be either real or lazy. Lazy data is represented by dask arrays. Dask arrays consist of many small numpy arrays (called chunks) and if possible, computations are run on those small arrays in parallel. In order to figure out what needs to be computed when, Dask makes use of a ‘scheduler’. The default (thread-based) scheduler in Dask is rather basic, so it can only run on a single computer and it may not always find the optimal task scheduling solution, resulting in excessive memory use when using e.g. the esmvalcore.preprocessor.multi_model_statistics() preprocessor function. Therefore it is recommended that you take a moment to configure the Dask distributed scheduler. A Dask scheduler and the ‘workers’ running the actual computations, are collectively called a ‘Dask cluster’.

Dask profiles

Because some recipes require more computational resources than others, ESMValCore provides the option to define “Dask profiles”. These profiles can be used to update the Dask user configuration per recipe run. The Dask profile can be selected in a YAML configuration file via

dask:
  use: <NAME_OF_PROFILE>

or alternatively in the command line via

esmvaltool run --dask='{"use": "<NAME_OF_PROFILE>"}' recipe_example.yml

Available predefined Dask profiles:

• local_threaded (selected by default): use threaded scheduler without any further options.

• local_distributed: use local distributed scheduler without any further options.

• debug: use synchronous Dask scheduler for debugging purposes. Best used with max_parallel_tasks: 1.

Dask distributed scheduler configuration

Here, some examples are provided on how to use a custom Dask distributed scheduler. Extensive documentation on setting up Dask Clusters is available here.

Note

If not all preprocessor functions support lazy data, computational performance may be best with the threaded scheduler. See Issue #674 for progress on making all preprocessor functions lazy.

Personal computer

Create a distributed.LocalCluster on the computer running ESMValCore using all available resources:

dask:
  use: local_cluster  # use "local_cluster" defined below
  profiles:
    local_cluster:
      cluster:
        type: distributed.LocalCluster

This should work well for most personal computers.

Note

If running this configuration on a shared node of an HPC cluster, Dask will try and use as many resources it can find available, and this may lead to overcrowding the node by a single user (you)!

Shared computer

Create a distributed.LocalCluster on the computer running ESMValCore, with 2 workers with 2 threads/4 GiB of memory each (8 GiB in total):

dask:
  use: local_cluster  # use "local_cluster" defined below
  profiles:
    local_cluster:
      cluster:
        type: distributed.LocalCluster
        n_workers: 2
        threads_per_worker: 2
        memory_limit: 4GiB

this should work well for shared computers.

Computer cluster

Create a Dask distributed cluster on the Levante supercomputer using the Dask-Jobqueue package:

dask:
  use: slurm_cluster  # use "slurm_cluster" defined below
  profiles:
    slurm_cluster:
      cluster:
        type: dask_jobqueue.SLURMCluster
        queue: shared
        account: <YOUR_SLURM_ACCOUNT>
        cores: 8
        memory: 7680MiB
        processes: 2
        interface: ib0
        local_directory: "/scratch/b/<YOUR_DKRZ_ACCOUNT>/dask-tmp"
        n_workers: 24

This will start 24 workers with cores / processes = 4 threads each, resulting in n_workers / processes = 12 Slurm jobs, where each Slurm job will request 8 CPU cores and 7680 MiB of memory and start processes = 2 workers. This example will use the fast infiniband network connection (called ib0 on Levante) for communication between workers running on different nodes. It is important to set the right location for temporary storage, in this case the /scratch space is used. It is also possible to use environmental variables to configure the temporary storage location, if you cluster provides these.

A configuration like this should work well for larger computations where it is advantageous to use multiple nodes in a compute cluster. See Deploying Dask Clusters on High Performance Computers for more information.

Externally managed Dask cluster

To use an externally managed cluster, specify an scheduler_address for the selected profile. Such a cluster can e.g. be started using the Dask Jupyterlab extension:

dask:
  use: external  # Use the `external` profile defined below
  profiles:
    external:
      scheduler_address: "tcp://127.0.0.1:43605"

See here for an example of how to configure this on a remote system.

For debugging purposes, it can be useful to start the cluster outside of ESMValCore because then Dask dashboard remains available after ESMValCore has finished running.

Advice on choosing performant configurations

The threads within a single worker can access the same memory locations, so they may freely pass around chunks, while communicating a chunk between workers is done by copying it, so this is (a bit) slower. Therefore it is beneficial for performance to have multiple threads per worker. However, due to limitations in the CPython implementation (known as the Global Interpreter Lock or GIL), only a single thread in a worker can execute Python code (this limitation does not apply to compiled code called by Python code, e.g. numpy), therefore the best performing configurations will typically not use much more than 10 threads per worker.

Due to limitations of the NetCDF library (it is not thread-safe), only one of the threads in a worker can read or write to a NetCDF file at a time. Therefore, it may be beneficial to use fewer threads per worker if the computation is very simple and the runtime is determined by the speed with which the data can be read from and/or written to disk.

Custom Dask threaded scheduler configuration

The Dask threaded scheduler can be a good choice for recipes using a small amount of data or when running a recipe where not all preprocessor functions are lazy yet (see Issue #674 for the current status).

To avoid running out of memory, it is important to set the number of workers (threads) used by Dask to run its computations to a reasonable number. By default, the number of CPU cores in the machine will be used, but this may be too many on shared machines or laptops with a large number of CPU cores compared to the amount of memory they have available.

Typically, Dask requires about 2 GiB of RAM per worker, but this may be more depending on the computation.

To set the number of workers used by the Dask threaded scheduler, use the following configuration:

dask:
  use: local_threaded  # This can be omitted
  profiles:
    local_threaded:
      num_workers: 4

Default options

By default, the following Dask configuration is used:

dask:
  use: local_threaded  # use the `local_threaded` profile defined below
  profiles:
    local_threaded:
      scheduler: threads
    local_distributed:
      cluster:
        type: distributed.LocalCluster
    debug:
      scheduler: synchronous

All available options

Option	Description	Type	Default value
profiles	Different Dask profiles that can be selected via the use option. Each profile has a name (dict keys) and corresponding options (dict values). See Options for Dask profiles for details.	dict	See Default options
use	Dask profile that is used; must be defined in the option profiles.	str	local_threaded

Options for Dask profiles

Option	Description	Type	Default value
cluster	Keyword arguments to initialize a Dask distributed cluster. Needs the option type, which specifies the class of the cluster. The remaining options are passed as keyword arguments to initialize that class. Cannot be used in combination with scheduler_address.	dict	If omitted, use externally managed cluster if scheduler_address is given or a Dask threaded scheduler otherwise.
scheduler_address	Scheduler address of an externally managed cluster. Will be passed to distributed.Client. Cannot be used in combination with cluster.	str	If omitted, use a Dask distributed cluster if cluster is given or a Dask threaded scheduler otherwise.
All other options	Passed as keyword arguments to dask.config.set().	Any	No defaults.

Logging configuration

Configure what information is logged and how it is presented in the logging section.

Note

Not all logging configuration is available here yet, see Issue #2596.

Configuration file example:

logging:
  log_progress_interval: 10s

will log progress of Dask computations every 10 seconds instead of showing a progress bar.

Command line example:

esmvaltool run --logging='{"log_progress_interval": "1m"}' recipe_example.yml

will log progress of Dask computations every minute instead of showing a progress bar.

Available options:

Option	Description	Type	Default value
log_progress_interval	When running computations with Dask, log progress every log_progress_interval instead of showing a progress bar. The value can be specified in the format accepted by dask.utils.parse_timedelta(). A negative value disables any progress reporting. A progress bar is only shown if max_parallel_tasks: 1.	str or float	0

ESGF configuration

The esmvaltool run command can automatically download the files required to run a recipe from ESGF for the projects CMIP3, CMIP5, CMIP6, CORDEX, and obs4MIPs. The downloaded files will be stored in the directory specified via the configuration option download_dir. To enable automatic downloads from ESGF, use the configuration options search_esgf: when_missing or search_esgf: always.

Note

When running a recipe that uses many or large datasets on a machine that does not have any data available locally, the amount of data that will be downloaded can be in the range of a few hundred gigabyte to a few terrabyte. See Obtaining input data for advice on getting access to machines with large datasets already available.

A log message will be displayed with the total amount of data that will be downloaded before starting the download. If you see that this is more than you would like to download, stop the tool by pressing the Ctrl and C keys on your keyboard simultaneously several times, edit the recipe so it contains fewer datasets and try again.

Configuration file

An optional configuration file can be created for configuring how the tool uses esgf-pyclient to find and download data. The name of this file is ~/.esmvaltool/esgf-pyclient.yml.

Search

Any arguments to pyesgf.search.connection.SearchConnection can be provided in the section search_connection, for example:

search_connection:
  expire_after: 2592000  # the number of seconds in a month

to keep cached search results for a month.

The default settings are:

urls:
  - 'https://esgf.ceda.ac.uk/esg-search'
  - 'https://esgf-node.llnl.gov/esg-search'
  - 'https://esgf-data.dkrz.de/esg-search'
  - 'https://esgf-node.ipsl.upmc.fr/esg-search'
  - 'https://esg-dn1.nsc.liu.se/esg-search'
  - 'https://esgf.nci.org.au/esg-search'
  - 'https://esgf.nccs.nasa.gov/esg-search'
  - 'https://esgdata.gfdl.noaa.gov/esg-search'
distrib: true
timeout: 120  # seconds
cache: '~/.esmvaltool/cache/pyesgf-search-results'
expire_after: 86400  # cache expires after 1 day

Note that by default the tool will try the ESGF index nodes in the order provided in the configuration file and use the first one that is online. Some ESGF index nodes may return search results faster than others, so you may be able to speed up the search for files by experimenting with placing different index nodes at the top of the list.

If you experience errors while searching, it sometimes helps to delete the cached results.

Download statistics

The tool will maintain statistics of how fast data can be downloaded from what host in the file ~/.esmvaltool/cache/esgf-hosts.yml and automatically select hosts that are faster. There is no need to manually edit this file, though it can be useful to delete it if you move your computer to a location that is very different from the place where you previously downloaded data. An entry in the file might look like this:

esgf2.dkrz.de:
  duration (s): 8
  error: false
  size (bytes): 69067460
  speed (MB/s): 7.9

The tool only uses the duration and size to determine the download speed, the speed shown in the file is not used. If error is set to true, the most recent download request to that host failed and the tool will automatically try this host only as a last resort.

Developer configuration file

Most users and diagnostic developers will not need to change this file, but it may be useful to understand its content. It will be installed along with ESMValCore and can also be viewed on GitHub: esmvalcore/config-developer.yml. This configuration file describes the file system structure and CMOR tables for several key projects (CMIP6, CMIP5, obs4MIPs, OBS6, OBS) on several key machines (e.g. BADC, CP4CDS, DKRZ, ETHZ, SMHI, BSC), and for native output data for some models (ICON, IPSL, … see Configuring datasets in native format). CMIP data is stored as part of the Earth System Grid Federation (ESGF) and the standards for file naming and paths to files are set out by CMOR and DRS. For a detailed description of these standards and their adoption in ESMValCore, we refer the user to CMIP data section where we relate these standards to the data retrieval mechanism of the ESMValCore.

Users can get a copy of this file with default values by running

esmvaltool config get_config_developer --path=${TARGET_FOLDER}

If the option --path is omitted, the file will be created in ~/.esmvaltool.

Note

Remember to change the configuration option config_developer_file if you want to use a custom config developer file.

Warning

For now, make sure that the custom config-developer.yml is not saved in the ESMValTool/Core configuration directories (see YAML files for details). This will change in the future due to the redesign of ESMValTool/Core’s configuration.

Example of the CMIP6 project configuration:

CMIP6:
  input_dir:
    default: '/'
    BADC: '{activity}/{institute}/{dataset}/{exp}/{ensemble}/{mip}/{short_name}/{grid}/{version}'
    DKRZ: '{activity}/{institute}/{dataset}/{exp}/{ensemble}/{mip}/{short_name}/{grid}/{version}'
    ETHZ: '{exp}/{mip}/{short_name}/{dataset}/{ensemble}/{grid}/'
  input_file: '{short_name}_{mip}_{dataset}_{exp}_{ensemble}_{grid}*.nc'
  output_file: '{project}_{dataset}_{mip}_{exp}_{ensemble}_{short_name}'
  cmor_type: 'CMIP6'
  cmor_strict: true

Input file paths

When looking for input files, the esmvaltool command provided by ESMValCore replaces the placeholders {item} in input_dir and input_file with the values supplied in the recipe. ESMValCore will try to automatically fill in the values for institute, frequency, and modeling_realm based on the information provided in the CMOR tables and/or extra_facets when reading the recipe. If this fails for some reason, these values can be provided in the recipe too.

The data directory structure of the CMIP projects is set up differently at each site. As an example, the CMIP6 directory path on BADC would be:

'{activity}/{institute}/{dataset}/{exp}/{ensemble}/{mip}/{short_name}/{grid}/{version}'

The resulting directory path would look something like this:

CMIP/MOHC/HadGEM3-GC31-LL/historical/r1i1p1f3/Omon/tos/gn/latest

Please, bear in mind that input_dirs can also be a list for those cases in which may be needed:

- '{exp}/{ensemble}/original/{mip}/{short_name}/{grid}/{version}'
- '{exp}/{ensemble}/computed/{mip}/{short_name}/{grid}/{version}'

In that case, the resultant directories will be:

historical/r1i1p1f3/original/Omon/tos/gn/latest
historical/r1i1p1f3/computed/Omon/tos/gn/latest

For a more in-depth description of how to configure ESMValCore so it can find your data please see CMIP data.

Preprocessor output files

The filename to use for preprocessed data is configured in a similar manner using output_file. Note that the extension .nc (and if applicable, a start and end time) will automatically be appended to the filename.

Project CMOR table configuration

ESMValCore comes bundled with several CMOR tables, which are stored in the directory esmvalcore/cmor/tables. These are copies of the tables available from PCMDI.

For every project that can be used in the recipe, there are four settings related to CMOR table settings available:

• cmor_type: can be CMIP5 if the CMOR table is in the same format as the CMIP5 table or CMIP6 if the table is in the same format as the CMIP6 table.

• cmor_strict: if this is set to false, the CMOR table will be extended with variables from the Custom CMOR tables (by default loaded from the esmvalcore/cmor/tables/custom directory) and it is possible to use variables with a mip which is different from the MIP table in which they are defined. Note that this option is always enabled for derived variables.

• cmor_path: path to the CMOR table. Relative paths are with respect to esmvalcore/cmor/tables. Defaults to the value provided in cmor_type written in lower case.

• cmor_default_table_prefix: Prefix that needs to be added to the mip to get the name of the file containing the mip table. Defaults to the value provided in cmor_type.

Custom CMOR tables

As mentioned in the previous section, the CMOR tables of projects that use cmor_strict: false will be extended with custom CMOR tables. For derived variables (the ones with derive: true in the recipe), the custom CMOR tables will always be considered. By default, these custom tables are loaded from esmvalcore/cmor/tables/custom. However, by using the special project custom in the config-developer.yml file with the option cmor_path, a custom location for these custom CMOR tables can be specified. In this case, the default custom tables are extended with those entries from the custom location (in case of duplication, the custom location tables take precedence).

Example:

custom:
  cmor_path: ~/my/own/custom_tables

This path can be given as relative path (relative to esmvalcore/cmor/tables) or as absolute path. Other options given for this special table will be ignored.

Custom tables in this directory need to follow the naming convention CMOR_{short_name}.dat and need to be given in CMIP5 format.

Example for the file CMOR_asr.dat:

SOURCE: CMIP5
!============
variable_entry:    asr
!============
modeling_realm:    atmos
!----------------------------------
! Variable attributes:
!----------------------------------
standard_name:
units:             W m-2
cell_methods:      time: mean
cell_measures:     area: areacella
long_name:         Absorbed shortwave radiation
!----------------------------------
! Additional variable information:
!----------------------------------
dimensions:        longitude latitude time
type:              real
positive:          down
!----------------------------------
!

It is also possible to use a special coordinates file CMOR_coordinates.dat, which will extend the entries from the default one (esmvalcore/cmor/tables/custom/CMOR_coordinates.dat).

Filter preprocessor warnings

It is possible to ignore specific warnings of the preprocessor for a given project. This is particularly useful for native datasets which do not follow the CMOR standard by default and consequently produce a lot of warnings when handled by Iris. This can be configured in the config-developer.yml file for some steps of the preprocessing chain.

Currently supported preprocessor steps:

• load()

Here is an example on how to ignore specific warnings during the preprocessor step load for all datasets of project EMAC (taken from the default config-developer.yml file):

ignore_warnings:
  load:
    - {message: 'Missing CF-netCDF formula term variable .*, referenced by netCDF variable .*', module: iris}
    - {message: 'Ignored formula of unrecognised type: .*', module: iris}

The keyword arguments specified in the list items are directly passed to warnings.filterwarnings() in addition to action=ignore (may be overwritten in config-developer.yml).

Configuring datasets in native format

ESMValCore can be configured for handling native model output formats and specific reanalysis/observation datasets without preliminary reformatting. These datasets can be either hosted under the native6 project (mostly native reanalysis/observational datasets) or under a dedicated project, e.g., ICON (mostly native models).

Example:

native6:
  cmor_strict: false
  input_dir:
    default: 'Tier{tier}/{dataset}/{version}/{frequency}/{short_name}'
  input_file:
    default: '*.nc'
  output_file: '{project}_{dataset}_{type}_{version}_{mip}_{short_name}'
  cmor_type: 'CMIP6'
  cmor_default_table_prefix: 'CMIP6_'

ICON:
  cmor_strict: false
  input_dir:
    default:
      - '{exp}'
      - '{exp}/outdata'
      - '{exp}/output'
  input_file:
    default: '{exp}_{var_type}*.nc'
  output_file: '{project}_{dataset}_{exp}_{var_type}_{mip}_{short_name}'
  cmor_type: 'CMIP6'
  cmor_default_table_prefix: 'CMIP6_'

A detailed description on how to add support for further native datasets is given here.

Hint

When using native datasets, it might be helpful to specify a custom location for the Custom CMOR tables. This allows reading arbitrary variables from native datasets. Note that this requires the option cmor_strict: false in the project configuration used for the native model output.

References configuration file

The esmvaltool/config-references.yml file contains the list of ESMValTool diagnostic and recipe authors, references and projects. Each author, project and reference referred to in the documentation section of a recipe needs to be in this file in the relevant section.

For instance, the recipe recipe_ocean_example.yml file contains the following documentation section:

documentation:
  authors:
    - demo_le

  maintainer:
    - demo_le

  references:
    - demora2018gmd

  projects:
    - ukesm

These four items here are named people, references and projects listed in the config-references.yml file.

Extra Facets

It can be useful to automatically add extra key-value pairs to variables or datasets in the recipe. These key-value pairs can be used for finding data or for providing extra information to the functions that fix data before passing it on to the preprocessor.

To support this, we provide the extra facets facilities. Facets are the key-value pairs described in Recipe section: datasets. Extra facets allows for the addition of more details per project, dataset, mip table, and variable name.

More precisely, one can provide this information in an extra yaml file, named {project}-something.yml, where {project} corresponds to the project as used by ESMValCore in Recipe section: datasets and “something” is arbitrary.

Format of the extra facets files

The extra facets are given in a yaml file, whose file name identifies the project. Inside the file there is a hierarchy of nested dictionaries with the following levels. At the top there is the dataset facet, followed by the mip table, and finally the short_name. The leaf dictionary placed here gives the extra facets that will be made available to data finder and the fix infrastructure. The following example illustrates the concept.

Listing 1 Extra facet example file native6-era5-example.yml

ERA5:
  Amon:
    tas: {source_var_name: "t2m", cds_var_name: "2m_temperature"}

The three levels of keys in this mapping can contain Unix shell-style wildcards. The special characters used in shell-style wildcards are:

Pattern	Meaning
*	matches everything
?	matches any single character
[seq]	matches any character in seq
[!seq]	matches any character not in seq

where seq can either be a sequence of characters or just a bunch of characters, for example [A-C] matches the characters A, B, and C, while [AC] matches the characters A and C.

For example, this is used to automatically add product: output1 to any variable of any CMIP5 dataset that does not have a product key yet:

Listing 2 Extra facet example file cmip5-product.yml

'*':
  '*':
    '*': {product: output1}

Location of the extra facets files

Extra facets files can be placed in several different places. When we use them to support a particular use-case within the ESMValCore project, they will be provided in the sub-folder extra_facets inside the package esmvalcore.config. If they are used from the user side, they can be either placed in ~/.esmvaltool/extra_facets or in any other directory of the users choosing. In that case, the configuration option extra_facets_dir must be set, which can take a single directory or a list of directories.

The order in which the directories are searched is

1. The internal directory esmvalcore.config/extra_facets

2. The default user directory ~/.esmvaltool/extra_facets

3. The custom user directories given by the configuration option extra_facets_dir

The extra facets files within each of these directories are processed in lexicographical order according to their file name.

In all cases it is allowed to supersede information from earlier files in later files. This makes it possible for the user to effectively override even internal default facets, for example to deal with local particularities in the data handling.

Use of extra facets

For extra facets to be useful, the information that they provide must be applied. There are fundamentally two places where this comes into play. One is the datafinder, the other are fixes.