Input data#

Overview#

Data discovery and retrieval is the first step in any evaluation process; ESMValCore uses a semi-automated data finding mechanism with inputs from both the configuration and the recipe file: this means that the user will have to provide the tool with a set of parameters related to the data needed and once these parameters have been provided, the tool will automatically find the right data. We will detail below the data finding and retrieval process and the input the user needs to specify, giving examples on how to use the data finding routine under different scenarios.

Data types#

CMIP data#

CMIP data is widely available via the Earth System Grid Federation (ESGF) and is accessible to users either via automatic download by esmvaltool or through the ESGF data nodes hosted by large computing facilities (like CEDA-Jasmin, DKRZ, etc). This data adheres to, among other standards, the DRS and Controlled Vocabulary standard for naming files and structured paths; the DRS ensures that files and paths to them are named according to a standardized convention. Examples of this convention, also used by ESMValCore for file discovery and data retrieval, include:

  • CMIP6 file: {variable_short_name}_{mip}_{dataset_name}_{experiment}_{ensemble}_{grid}_{start-date}-{end-date}.nc

  • CMIP5 file: {variable_short_name}_{mip}_{dataset_name}_{experiment}_{ensemble}_{start-date}-{end-date}.nc

  • OBS file: {project}_{dataset_name}_{type}_{version}_{mip}_{short_name}_{start-date}-{end-date}.nc

Similar standards exist for the standard paths (input directories); for the ESGF data nodes, these paths differ slightly, for example:

  • CMIP6 path for BADC: ROOT-BADC/{institute}/{dataset_name}/{experiment}/{ensemble}/{mip}/ {variable_short_name}/{grid};

  • CMIP6 path for ETHZ: ROOT-ETHZ/{experiment}/{mip}/{variable_short_name}/{dataset_name}/{ensemble}/{grid}

From the ESMValCore user perspective the number of data input parameters is optimized to allow for ease of use. We detail this procedure in the next section.

Observational data#

Part of observational data is retrieved in the same manner as CMIP data, for example using the OBS root path set to:

OBS: /gws/nopw/j04/esmeval/obsdata-v2

and the dataset:

- {dataset: ERA-Interim, project: OBS6, type: reanaly, version: 1, start_year: 2014, end_year: 2015, tier: 3}

in recipe.yml in datasets or additional_datasets, the rules set in CMOR-DRS are used again and the file will be automatically found:

/gws/nopw/j04/esmeval/obsdata-v2/Tier3/ERA-Interim/OBS_ERA-Interim_reanaly_1_Amon_ta_201401-201412.nc

Since observational data are organized in Tiers depending on their level of public availability, the default directory must be structured accordingly with sub-directories TierX (Tier1, Tier2 or Tier3), even when drs: default.

Datasets in native format#

Some datasets are supported in their native format (i.e., the data is not formatted according to a CMIP data request) through the native6 project (mostly native reanalysis/observational datasets) or through a dedicated project, e.g., ICON (mostly native models). A detailed description of how to include new 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.

Supported native reanalysis/observational datasets#

The following native reanalysis/observational datasets are supported under the native6 project. To use these datasets, put the files containing the data in the directory that you have configured for the rootpath of the native6 project, in a subdirectory called Tier{tier}/{dataset}/{version}/{frequency}/{short_name} (assuming you are using the default DRS for native6). Replace the items in curly braces by the values used in the variable/dataset definition in the recipe.

ERA5 (in netCDF format downloaded from the CDS)#

ERA5 data can be downloaded from the Copernicus Climate Data Store (CDS) using the convenient tool era5cli. For example for monthly data, place the files in the /Tier3/ERA5/version/mon/pr subdirectory of your rootpath that you have configured for the native6 project (assuming you are using the default DRS for native6).

  • Supported variables: cl, clt, evspsbl, evspsblpot, mrro, pr, prsn, ps, psl, ptype, rls, rlds, rsds, rsdt, rss, uas, vas, tas, tasmax, tasmin, tdps, ts, tsn (E1hr/Amon), orog (fx).

  • Tier: 3

Note

According to the description of Evapotranspiration and potential Evapotranspiration on the Copernicus page (https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels-monthly-means?tab=overview): “The ECMWF Integrated Forecasting System (IFS) convention is that downward fluxes are positive. Therefore, negative values indicate evaporation and positive values indicate condensation.”

In the CMOR table, these fluxes are defined as positive, if they go from the surface into the atmosphere: “Evaporation at surface (also known as evapotranspiration): flux of water into the atmosphere due to conversion of both liquid and solid phases to vapor (from underlying surface and vegetation).” Therefore, the ERA5 (and ERA5-Land) CMORizer switches the signs of evspsbl and evspsblpot to be compatible with the CMOR standard used e.g. by the CMIP models.

ERA5 (in GRIB format available on DKRZ’s Levante or downloaded from the CDS)#

ERA5 data in monthly, daily, and hourly resolution is available on Levante in its native GRIB format.

Note

ERA5 data in its native GRIB format can also be downloaded from the Copernicus Climate Data Store (CDS). For example, hourly data on pressure levels is available here. Reading self-downloaded ERA5 data in GRIB format is experimental and likely requires additional setup from the user like setting up the proper directory structure for the input files and/or creating a custom DRS.

To read these data with ESMValCore, use the rootpath /pool/data/ERA5 with DRS DKRZ-ERA5-GRIB in your configuration, for example:

rootpath:
  ...
  native6:
    /pool/data/ERA5: DKRZ-ERA5-GRIB
  ...

The naming conventions for input directories and files for native ERA5 data in GRIB format on Levante are

  • input directories: {family}/{level}/{type}/{tres}/{grib_id}

  • input files: {family}{level}{typeid}_{tres}_*_{grib_id}.grb

All of these facets have reasonable defaults preconfigured in the corresponding extra facets file, which is available here: native6-era5.yml. If necessary, these facets can be overwritten in the recipe.

Thus, example dataset entries could look like this:

datasets:
  - {project: native6, dataset: ERA5, timerange: '2000/2001',
     short_name: tas, mip: Amon}
  - {project: native6, dataset: ERA5, timerange: '2000/2001',
     short_name: cl, mip: Amon, tres: 1H, frequency: 1hr}
  - {project: native6, dataset: ERA5, timerange: '2000/2001',
     short_name: ta, mip: Amon, type: fc, typeid: '12'}

The native ERA5 output in GRIB format is stored on a reduced Gaussian grid. By default, these data are regridded to a regular 0.25°x0.25° grid as recommended by the ECMWF using bilinear interpolation.

To disable this, you can use the facet automatic_regrid: false in the recipe:

datasets:
  - {project: native6, dataset: ERA5, timerange: '2000/2001',
     short_name: tas, mip: Amon, automatic_regrid: false}

  • Supported variables: albsn, cl, cli, clt, clw, hur, hus, o3, prw, ps, psl, rainmxrat27, sftlf, snd, snowmxrat27, ta, tas, tdps, toz, ts, ua, uas, va, vas, wap, zg.

MSWEP#

  • Supported variables: pr

  • Supported frequencies: mon, day, 3hr.

  • Tier: 3

For example for monthly data, place the files in the /Tier3/MSWEP/version/mon/pr subdirectory of your rootpath that you have configured for the native6 project (assuming you are using the default DRS for native6).

Note

For monthly data (V220), the data must be postfixed with the date, i.e. rename global_monthly_050deg.nc to global_monthly_050deg_197901-201710.nc

For more info: http://www.gloh2o.org/

Data for the version V220 can be downloaded from: https://hydrology.princeton.edu/data/hylkeb/MSWEP_V220/.

Supported native models#

The following models are natively supported by ESMValCore. In contrast to the native observational datasets listed above, they use dedicated projects instead of the project native6.

CESM#

ESMValCore is able to read native CESM model output.

Warning

The support for native CESM output is still experimental. Currently, only one variable (tas) is fully supported. Other 2D variables might be supported by specifying appropriate facets in the recipe or extra facets files (see text below). 3D variables (data that uses a vertical dimension) are not supported, yet.

The default naming conventions for input directories and files for CESM are

  • input directories: 3 different types supported:

    • / (run directory)

    • {case}/{gcomp}/hist (short-term archiving)

    • {case}/{gcomp}/proc/{tdir}/{tperiod} (post-processed data)

  • input files: {case}.{scomp}.{type}.{string}*nc

as configured in the config-developer file (using the configuration option drs: default). More information about CESM naming conventions are given here.

Note

The {string} entry in the input file names above does not only correspond to the (optional) $string entry for CESM model output files, but can also be used to read post-processed files. In the latter case, {string} corresponds to the combination $SSTRING.$TSTRING.

Thus, example dataset entries could look like this:

datasets:
  - {project: CESM, dataset: CESM2, case: f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.001, type: h0, mip: Amon, short_name: tas, start_year: 2000, end_year: 2014}
  - {project: CESM, dataset: CESM2, case: f.e21.F1850_BGC.f09_f09_mg17.CFMIP-hadsst-piForcing.001, type: h0, gcomp: atm, scomp: cam, mip: Amon, short_name: tas, start_year: 2000, end_year: 2014}

Variable-specific defaults for the facet gcomp and scomp are given in the extra facets (see next paragraph) for some variables, but this can be overwritten in the recipe.

Similar to any other fix, the CESM fix allows the use of extra facets. By default, the file cesm-mappings.yml is used for that purpose. Currently, this file only contains default facets for a single variable (tas); for other variables, these entries need to be defined in the recipe. Supported keys for extra facets are:

Key

Description

Default value if not specified

gcomp

Generic component-model name

No default (needs to be specified in extra facets or recipe if default DRS is used)

raw_name

Variable name of the variable in the raw input file

CMOR variable name of the corresponding variable

raw_units

Units of the variable in the raw input file

If specified, the value given by the units attribute in the raw input file; otherwise unknown

scomp

Specific component-model name

No default (needs to be specified in extra facets or recipe if default DRS is used)

string

Short string which is used to further identify the history file type (corresponds to $string or $SSTRING.$TSTRING in the CESM file name conventions; see note above)

'' (empty string)

tdir

Entry to distinguish time averages from time series from diagnostic plot sets (only used for post-processed data)

'' (empty string)

tperiod

Time period over which the data was processed (only used for post-processed data)

'' (empty string)
EMAC#

ESMValCore is able to read native EMAC model output.

The default naming conventions for input directories and files for EMAC are

  • input directories: {exp}/{channel}

  • input files: {exp}*{channel}{postproc_flag}.nc

as configured in the config-developer file (using the configuration option drs: default).

Thus, example dataset entries could look like this:

datasets:
  - {project: EMAC, dataset: EMAC, exp: historical, mip: Amon, short_name: tas, start_year: 2000, end_year: 2014}
  - {project: EMAC, dataset: EMAC, exp: historical, mip: Omon, short_name: tos, postproc_flag: "-p-mm", start_year: 2000, end_year: 2014}
  - {project: EMAC, dataset: EMAC, exp: historical, mip: Amon, short_name: ta, raw_name: tm1_p39_cav, start_year: 2000, end_year: 2014}

Please note the duplication of the name EMAC in project and dataset, which is necessary to comply with ESMValCore’s data finding and CMORizing functionalities. A variable-specific default for the facet channel is given in the extra facets (see next paragraph) for many variables, but this can be overwritten in the recipe.

Similar to any other fix, the EMAC fix allows the use of extra facets. By default, the file emac-mappings.yml is used for that purpose. For some variables, extra facets are necessary; otherwise ESMValCore cannot read them properly. Supported keys for extra facets are:

Key

Description

Default value if not specified

channel

Channel in which the desired variable is stored

No default (needs to be specified in extra facets or recipe if default DRS is used)

postproc_flag

Postprocessing flag of the data

'' (empty string)

raw_name

Variable name of the variable in the raw input file

CMOR variable name of the corresponding variable

raw_units

Units of the variable in the raw input file

If specified, the value given by the units attribute in the raw input file; otherwise unknown

Note

raw_name can be given as str or list. The latter is used to support multiple different variables names in the input file. In this case, the prioritization is given by the order of the list; if possible, use the first entry, if this is not present, use the second, etc. This is particularly useful for files in which regular averages (*_ave) or conditional averages (*_cav) exist.

For 3D variables defined on pressure levels, only the pressure levels defined by the CMOR table (e.g., for Amon’s ta: tm1_p19_cav and tm1_p19_ave) are given in the default extra facets file. If other pressure levels are desired, e.g., tm1_p39_cav, this has to be explicitly specified in the recipe using raw_name: tm1_p39_cav or raw_name: [tm1_p19_cav, tm1_p39_cav].

ICON#

ESMValCore is able to read native ICON model output.

The default naming conventions for input directories and files for ICON are

  • input directories: {exp} or {exp}/outdata

  • input files: {exp}_{var_type}*.nc

as configured in the config-developer file (using the configuration option drs: default).

Thus, example dataset entries could look like this:

datasets:
  - {project: ICON, dataset: ICON, exp: icon-2.6.1_atm_amip_R2B5_r1i1p1f1,
     mip: Amon, short_name: tas, start_year: 2000, end_year: 2014}
  - {project: ICON, dataset: ICON, exp: historical, mip: Amon,
     short_name: ta, var_type: atm_dyn_3d_ml, start_year: 2000,
     end_year: 2014}

Please note the duplication of the name ICON in project and dataset, which is necessary to comply with ESMValCore’s data finding and CMORizing functionalities. A variable-specific default for the facet var_type is given in the extra facets (see below) for many variables, but this can be overwritten in the recipe. This is necessary if your ICON output is structured in one variable per file. For example, if your output is stored in files called <exp>_<variable_name>_atm_2d_ml_YYYYMMDDThhmmss.nc, use var_type: <variable_name>_atm_2d_ml in the recipe for this variable.

Usually, ICON reports aggregated values at the end of the corresponding time output intervals. For example, for monthly output, ICON reports the month February as “1 March”. Thus, by default, ESMValCore shifts all time points back by 1/2 of the output time interval so that the new time point corresponds to the center of the interval. This can be disabled by using shift_time: false in the recipe or the extra facets (see below). For point measurements (identified by cell_methods = "time: point"), this is always disabled.

Warning

To get all desired time points, do not use start_year and end_year in the recipe, but rather timerange with at least 8 digits. For example, to get data for the years 2000 and 2001, use timerange: 20000101/20020101 instead of timerange: 2000/2001 or start_year: 2000, end_year: 2001. See Time ranges for more information on the timerange option.

Usually, ESMValCore will need the corresponding ICON grid file of your simulation to work properly (examples: setting latitude/longitude coordinates if these are not yet present, UGRIDization [see below], etc.). This grid file can either be specified as absolute or relative (to the configuration option auxiliary_data_dir) path with the facet horizontal_grid in the recipe or the extra facets (see below), or retrieved automatically from the grid_file_uri attribute of the input files. In the latter case, ESMValCore first searches the input directories specified for ICON for a grid file with that name, and if that was not successful, tries to download the file and cache it. The cached file is valid for 7 days.

ESMValCore can automatically make native ICON data UGRID-compliant when loading the data. The UGRID conventions provide a standardized format to store data on unstructured grids, which is required by many software packages or tools to work correctly and specifically by Iris to interpret the grid as a mesh. An example is the horizontal regridding of native ICON data to a regular grid. While the built-in regridding schemes linear and nearest can handle unstructured grids (i.e., not UGRID-compliant) and meshes (i.e., UGRID-compliant), the area_weighted scheme requires the input data in UGRID format. This automatic UGRIDization is enabled by default, but can be switched off with the facet ugrid: false in the recipe or the extra facets (see below). This is useful for diagnostics that act on the native ICON grid and do not support input data in UGRID format (yet), like the Psyplot diagnostic.

For 3D ICON variables, ESMValCore tries to add the pressure level information (from the variables pfull and phalf) and/or altitude information (from the variables zg and zghalf) to the preprocessed output files. If neither of these variables are available in the input files, it is possible to specify the location of files that include the corresponding zg or zghalf variables with the facets zg_file and/or zghalf_file in the recipe or the extra facets. The paths to these files can be specified absolute or relative (to the configuration option auxiliary_data_dir).

Hint

To use the extract_levels() preprocessor on native ICON data, you need to specify the name of the vertical coordinate (e.g., coordinate: air_pressure) since native ICON output usually provides a 3D air pressure field instead of a simple 1D vertical coordinate. This also works if your files only contain altitude information (in this case, the US standard atmosphere is used to convert between altitude and pressure levels; see Vertical interpolation for details). Example:

preprocessors:
  extract_500hPa_level_from_icon:
    extract_levels:
      levels: 50000
      scheme: linear
      coordinate: air_pressure

Similar to any other fix, the ICON fix allows the use of extra facets. By default, the file icon-mappings.yml is used for that purpose. For some variables, extra facets are necessary; otherwise ESMValCore cannot read them properly. Supported keys for extra facets are:

Key

Description

Default value if not specified

horizontal_grid

Absolute or relative (to auxiliary_data_dir) path to the ICON grid file

If not given, use file attribute grid_file_uri to retrieve ICON grid file (see details above)

latitude

Standard name of the latitude coordinate in the raw input file

latitude

longitude

Standard name of the longitude coordinate in the raw input file

longitude

raw_name

Variable name of the variable in the raw input file

CMOR variable name of the corresponding variable

raw_units

Units of the variable in the raw input file

If specified, the value given by the units attribute in the raw input file; otherwise unknown

shift_time

Shift time points back by 1/2 of the corresponding output time interval

True

ugrid

Automatic UGRIDization of the input data

True

var_type

Variable type of the variable in the raw input file

No default (needs to be specified in extra facets or recipe if default DRS is used)

zg_file

Absolute or relative (to auxiliary_data_dir) path to the the input file that contains zg

If possible, use zg variable provided by the raw input file

zghalf_file

Absolute or relative (to auxiliary_data_dir) path to the the input file that contains zghalf

If possible, use zghalf variable provided by the raw input file

Hint

In order to read cell area files (areacella and areacello), one additional manual step is necessary: Copy the ICON grid file (you can find a download link in the global attribute grid_file_uri of your ICON data) to your ICON input directory and change its name in such a way that only the grid file is found when the cell area variables are required. Make sure that this file is not found when other variables are loaded.

For example, you could use a new var_type, e.g., horizontalgrid for this file. Thus, an ICON grid file located in 2.6.1_atm_amip_R2B5_r1i1p1f1/2.6.1_atm_amip_R2B5_r1i1p1f1_horizontalgrid.nc can be found using var_type: horizontalgrid in the recipe (assuming the default naming conventions listed above). Make sure that no other variable uses this var_type.

If you want to use the area_statistics() preprocessor on regridded ICON data, make sure to not use the cell area files by using the skip: true syntax in the recipe as described in Supplementary variables (ancillary variables and cell measures), e.g.,

datasets:
  - {project: ICON, dataset: ICON, exp: amip,
     supplementary_variables: [{short_name: areacella, skip: true}]}
IPSL-CM6#

Both output formats (i.e. the Output and the Analyse / Time series formats) are supported, and should be configured in recipes as e.g.:

datasets:
  - {simulation: CM61-LR-hist-03.1950, exp: piControl, out: Analyse, freq: TS_MO,
     account: p86caub,  status: PROD, dataset: IPSL-CM6, project: IPSLCM,
     root: /thredds/tgcc/store}
  - {simulation: CM61-LR-hist-03.1950, exp: historical, out: Output, freq: MO,
     account: p86caub,  status: PROD, dataset: IPSL-CM6, project: IPSLCM,
     root: /thredds/tgcc/store}

The Output format is an example of a case where variables are grouped in multi-variable files, which name cannot be computed directly from datasets attributes alone but requires to use an extra_facets file, which principles are explained in Extra Facets, and which content is available here. These multi-variable files must also undergo some data selection.

ACCESS-ESM#

ESMValTool can read native ACCESS-ESM model output.

Warning

This is the first version of ACCESS-ESM CMORizer for ESMValCore. Currently, Supported variables: pr, ps, psl, rlds, tas, ta, va, ua, zg, hus, clt, rsus, rlus.

The default naming conventions for input directories and files for ACCESS output are

  • input directories: {institute}/{sub_dataset}/{exp}/{modeling_realm}/netCDF

  • input files: {sub_dataset}.{special_attr}-*.nc

Hint

We only provide one default input_dir since this is how ACCESS-ESM native data was stored on NCI. Users can modify this path in the Developer configuration file to match their local file structure.

Thus, example dataset entries could look like this:

dataset:
  - {project: ACCESS, mip: Amon, dataset:ACCESS_ESM1_5, sub_dataset: HI-CN-05,
    exp: history, modeling_realm: atm, special_attr: pa, start_year: 1986, end_year: 1986}

Similar to any other fix, the ACCESS-ESM fix allows the use of extra facets. By default, the file access-mappings.yml is used for that purpose. For some variables, extra facets are necessary; otherwise ESMValCore cannot read them properly. Supported keys for extra facets are:

Key

Description

Default value if not specified

raw_name

Variable name of the variable in the raw input file

CMOR variable name of the corresponding variable

modeling_realm

Realm attribute include atm, ice and oce

No default (needs to be specified in extra facets or recipe if default DRS is used)

`special_attr

A special attribute in the filename ACCESS-ESM raw data, it’s related to frequency of raw data

No default

sub_dataset

Part of the ACCESS-ESM raw dataset root, need to specify if you want to use the cmoriser

No default

Data retrieval#

Data retrieval in ESMValCore has two main aspects from the user’s point of view:

  • data can be found by the tool, subject to availability on disk or ESGF;

  • it is the user’s responsibility to set the correct data retrieval parameters;

The first point is self-explanatory: if the user runs the tool on a machine that has access to a data repository or multiple data repositories, then ESMValCore will look for and find the available data requested by the user. If the files are not found locally, the tool can search the ESGF and download the missing files, provided that they are available.

The second point underlines the fact that the user has full control over what type and the amount of data is needed for the analyses. Setting the data retrieval parameters is explained below.

Enabling automatic downloads from the ESGF#

To enable automatic downloads from ESGF, use the configuration option search_esgf: when_missing (use local files whenever possible) or search_esgf: always (always search ESGF for latest version of files and only use local data if it is the latest version). The files will be stored in the directory specified via the configuration option download_dir.

Setting the correct root paths#

The first step towards providing ESMValCore the correct set of parameters for data retrieval is setting the root paths to the data. This is done in the configuration. The two sections where the user will set the paths are rootpath and drs. rootpath contains pointers to CMIP, OBS, default and RAWOBS root paths; drs sets the type of directory structure the root paths are structured by. It is important to first discuss the drs parameter: as we’ve seen in the previous section, the DRS as a standard is used for both file naming conventions and for directory structures.

Explaining drs: CMIP5: or drs: CMIP6:#

Whereas ESMValCore will by default use the CMOR standard for file naming (please refer above), by setting the drs parameter the user tells the tool what type of root paths they need the data from, e.g.:

drs:
  CMIP6: BADC

will tell the tool that the user needs data from a repository structured according to the BADC DRS structure, i.e.:

ROOT/{institute}/{dataset_name}/{experiment}/{ensemble}/{mip}/{variable_short_name}/{grid};

setting the ROOT parameter is explained below. This is a strictly-structured repository tree and if there are any sort of irregularities (e.g. there is no {mip} directory) the data will not be found! BADC can be replaced with DKRZ or ETHZ depending on the existing ROOT directory structure. The snippet

drs:
  CMIP6: default

is another way to retrieve data from a ROOT directory that has no DRS-like structure; default indicates that the data lies in a directory that contains all the files without any structure.

The names of the directories trees that can be used under drs are defined in Developer configuration file.

Note

When using CMIP6: default or CMIP5: default, all the needed files must be in the same top-level directory specified under rootpath. However, it is not recommended to use this, as it makes it impossible for the tool to read the facets from the directory tree. Moreover, this way of organizing data makes it impossible to store multiple versions of the same file because the files typically have the same name for different versions.

Explaining rootpath:#

rootpath identifies the root directory for different data types (ROOT as we used it above):

  • CMIP e.g. CMIP5 or CMIP6: this is the root path(s) to where the CMIP files are stored; it can be a single path, a list of paths, or a mapping with paths as keys and drs names as values; it can point to an ESGF node or it can point to a user private repository. Example for a CMIP5 root path pointing to the ESGF node mounted on CEDA-Jasmin (formerly known as BADC):

    rootpath:
  CMIP5: /badc/cmip5/data/cmip5/output1

    Example for a CMIP6 root path pointing to the ESGF node on CEDA-Jasmin:

    rootpath:
  CMIP6: /badc/cmip6/data/CMIP6

    Example for a mix of CMIP6 root path pointing to the ESGF node on CEDA-Jasmin and a user-specific data repository for extra data:

    rootpath:
  CMIP6:
    /badc/cmip6/data/CMIP6: BADC
    ~/climate_data: ESGF

    Note that this notation combines the rootpath and drs settings, so it is not necessary to specify the directory structure in under drs in this case.

  • OBS: this is the root path(s) to where the observational datasets are stored; again, this could be a single path or a list of paths, just like for CMIP data. Example for the OBS path for a large cache of observation datasets on CEDA-Jasmin:

    rootpath:
  OBS: /gws/nopw/j04/esmeval/obsdata-v2

  • default: this is the root path(s) where the tool will look for data from projects that do not have their own rootpath set.

  • RAWOBS: this is the root path(s) to where the raw observational data files are stored; this is used by esmvaltool data format.

Synda#

If the synda install command is used to download data, it maintains the directory structure as on ESGF. To find data downloaded by synda, use the SYNDA drs parameter.

drs:
  CMIP6: SYNDA
  CMIP5: SYNDA

Dataset definitions in recipe#

Once the correct paths have been established, ESMValCore collects the information on the specific datasets that are needed for the analysis. This information, together with the CMOR convention for naming files (see CMOR-DRS) will allow the tool to search and find the right files. The specific datasets are listed in any recipe, under either the datasets and/or additional_datasets sections, e.g.

datasets:
  - {dataset: HadGEM2-CC, project: CMIP5, exp: historical, ensemble: r1i1p1, start_year: 2001, end_year: 2004}
  - {dataset: UKESM1-0-LL, project: CMIP6, exp: historical, ensemble: r1i1p1f2, grid: gn, start_year: 2004, end_year: 2014}

The data finding feature will use this information to find data for all the variables specified in diagnostics/variables.

Recap and example#

Let us look at a practical example for a recap of the information above: suppose you are using configuration that has the following entries for data finding:

rootpath:  # running on CEDA-Jasmin
  CMIP6: /badc/cmip6/data/CMIP6/CMIP
drs:
  CMIP6: BADC  # since you are on CEDA-Jasmin

and the dataset you need is specified in your recipe.yml as:

- {dataset: UKESM1-0-LL, project: CMIP6, mip: Amon, exp: historical, grid: gn, ensemble: r1i1p1f2, start_year: 2004, end_year: 2014}

for a variable, e.g.:

diagnostics:
  some_diagnostic:
    description: some_description
    variables:
      ta:
        preprocessor: some_preprocessor

The tool will then use the root path /badc/cmip6/data/CMIP6/CMIP and the dataset information and will assemble the full DRS path using information from CMOR-DRS and establish the path to the files as:

/badc/cmip6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/Amon

then look for variable ta and specifically the latest version of the data file:

/badc/cmip6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/Amon/ta/gn/latest/

and finally, using the file naming definition from CMOR-DRS find the file:

/badc/cmip6/data/CMIP6/CMIP/MOHC/UKESM1-0-LL/historical/r1i1p1f2/Amon/ta/gn/latest/ta_Amon_UKESM1-0-LL_historical_r1i1p1f2_gn_195001-201412.nc

Data loading#

Data loading is done using the data load functionality of iris; we will not go into too much detail about this since we can point the user to the specific functionality here but we will underline that the initial loading is done by adhering to the CF Conventions that iris operates by as well (see CF Conventions Document and the search page for CF standard names).

Data concatenation from multiple sources#

Oftentimes data retrieving results in assembling a continuous data stream from multiple files or even, multiple experiments. The internal mechanism through which the assembly is done is via cube concatenation. One peculiarity of iris concatenation (see iris cube concatenation) is that it doesn’t allow for concatenating time-overlapping cubes; this case is rather frequent with data from models overlapping in time, and is accounted for by a function that performs a flexible concatenation between two cubes, depending on the particular setup:

  • cubes overlap in time: resulting cube is made up of the overlapping data plus left and right hand sides on each side of the overlapping data; note that in the case of the cubes coming from different experiments the resulting concatenated cube will have composite data made up from multiple experiments: assume [cube1: exp1, cube2: exp2] and cube1 starts before cube2, and cube2 finishes after cube1, then the concatenated cube will be made up of cube2: exp2 plus the section of cube1: exp1 that contains data not provided in cube2: exp2;

  • cubes don’t overlap in time: data from the two cubes is bolted together;

Note that two cube concatenation is the base operation of an iterative process of reducing multiple cubes from multiple data segments via cube concatenation ie if there is no time-overlapping data, the cubes concatenation is performed in one step.

Use of extra facets in the datafinder#

Extra facets are a mechanism to provide additional information for certain kinds of data. The general approach is described in Extra Facets. Here, we describe how they can be used to locate data files within the datafinder framework. This is useful to build paths for directory structures and file names that require more information than what is provided in the recipe. A common application is the location of variables in multi-variable files as often found in climate models’ native output formats.

Another use case is files that use different names for variables in their file name than for the netCDF4 variable name.

To apply the extra facets for this purpose, simply use the corresponding tag in the applicable DRS inside the config-developer.yml file. For example, given the extra facets in Extra facet example file native6-era5-example.yml, one might write the following.

Listing 3 Example drs use in config-developer.yml#
native6:
  input_file:
    default: '{name_in_filename}*.nc'

The same replacement mechanism can be employed everywhere where tags can be used, particularly in input_dir and input_file.