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

Observational datasets CMORized by ESMValTool are organized in Tiers depending on their level of public availability.

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 as defined in the file:

Listing 1 Contents of data-local-esmvaltool.yml#
projects:
  # Data that can be read in its native format by ESMValCore.
  native6:
    data:
      local:
        type: "esmvalcore.local.LocalDataSource"
        rootpath: ~/climate_data
        dirname_template: "Tier{tier}/{dataset}/{version}/{frequency}/{short_name}"
        filename_template: "*.nc"

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 described above).

  • 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.

To read these data with ESMValCore, use the data definition for the native6 project:

Listing 2 Contents of data-hpc-dkrz.yml#
      # ERA5 data in GRIB format:
      # https://docs.dkrz.de/doc/dataservices/finding_and_accessing_data/era_data/index.html#pool-data-era5-file-and-directory-names
      dkrz-era5:
        type: "esmvalcore.local.LocalDataSource"
        rootpath: /pool/data/ERA5
        dirname_template: "{family}/{level}/{type}/{tres}/{grib_id}"
        filename_template: "{family}{level}{typeid}_{tres}_*_{grib_id}.grb"

To use this configuration, run esmvaltool config copy data-hpc-dkrz.yml.

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 configuration file, which is available here: extra_facets_native6.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 described above).

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:

Listing 3 Contents of data-native-cesm.yml#
# Read data from the CESM model in its native format.
projects:
  CESM:
    data:
      run: &cesm
        type: "esmvalcore.local.LocalDataSource"
        rootpath: ~/climate_data
        dirname_template: "" # run directory
        filename_template: "{case}.{scomp}.{type}.{string}*nc"
      short-term-archive:
        <<: *cesm
        dirname_template: "{case}/{gcomp}/hist" # short-term archiving
      postprocessed:
        <<: *cesm
        dirname_template: "{case}/{gcomp}/proc/{tdir}/{tperiod}" # postprocessed data

To use this configuration, run esmvaltool config copy data-native-cesm.yml and adapt the rootpath to your system. 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 as extra facets (see next paragraph) for some variables, but these can be overwritten in the recipe.

Similar to any other fix, the CESM fix allows the use of extra facets. The configuration file extra_facets_cesm.yml contains the defaults. 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 as extra facets or in 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 as extra facets or in 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:

Listing 4 Contents of data-native-emac.yml#
# Read data from the EMAC model in its native format.
projects:
  EMAC:
    data:
      emac:
        type: "esmvalcore.local.LocalDataSource"
        rootpath: ~/climate_data
        dirname_template: "{exp}/{channel}"
        filename_template: "{exp}*{channel}{postproc_flag}.nc"
        ignore_warnings:
          - message: "Ignored formula of unrecognised type: .*"
            module: iris
          - message: "Ignoring formula terms variable .* referenced by data variable .* via variable .*"
            module: iris
          - message: "Missing CF-netCDF formula term variable .*, referenced by netCDF variable .*"
            module: iris
          - message: "NetCDF variable .* contains unknown cell method .*"
            module: iris

To use this configuration, run esmvaltool config copy data-native-emac.yml and adapt the rootpath to your system.

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. The configuration file extra_facets_emac.yml contains the defaults. 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 as extra facets or in 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

reset_time_bounds

Boolean if time bounds are deleted, and automatically recalculated by iris

False

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 as default extra facets. 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}, {exp}/outdata, or {exp}/output

  • input files: {exp}_{var_type}*.nc

as configured in:

Listing 5 Contents of data-native-icon.yml#
# Read data from the ICON model in its native format.
projects:
  ICON:
    data:
      icon: &icon
        type: "esmvalcore.local.LocalDataSource"
        rootpath: ~/climate_data
        dirname_template: "{exp}"
        filename_template: "{exp}_{var_type}*.nc"
      icon-outdata:
        <<: *icon
        dirname_template: "{exp}/outdata"
      icon-output:
        <<: *icon
        dirname_template: "{exp}/output"

To use this configuration, run esmvaltool config copy data-native-icon.yml and adapt the rootpath to your system.

Currently, two different versions of ICON are supported:

  1. ICON-A, which is based on ECHAM physics (deprecated): select via dataset: ICON.

  2. ICON-XPP, which is based on NWP physics (preferred; model code can be downloaded from DKRZ’s GitLab): select via dataset: ICON-XPP.

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, timerange: 20010101/20020101}
  - {project: ICON, dataset: ICON-XPP, exp: historical, mip: Amon,
     short_name: ta, timerange: 20010101/20020101}

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, for example:

datasets:
  - {project: ICON, dataset: ICON-XPP, exp: historical, mip: Amon,
     short_name: ta, var_type: atm_dyn_3d_ml, timerange: 20010101/20020101}

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 as extra facet (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 as extra facet (see below). This is useful for diagnostics that act on the native ICON grid and do not support input data in UGRID format (yet).

For 3D ICON variables, ESMValCore tries to add the pressure level information and/or altitude information to the preprocessed output files. If the names of these variables differ from the default values, the facets pfull_var, phalf_var, zg_var, and zghalf_var can be specified in the recipe or as extra facets. If neither of these variables are available in the input files, it is possible to specify the location of files that include the corresponding altitude information with the facets zg_file and/or zghalf_file in the recipe or as 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. The configuration file extra_facets_icon.yml contains the defaults. 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)

lat_var

Variable name of the latitude coordinate in the raw input file/grid file

clat

lon_var

Variable name of the longitude coordinate in the raw input file/grid file

clon

pfull_var

Variable name of the pressure at full levels in the raw input file

pfull (dataset: ICON) or pres (dataset: ICON-XPP)

phalf_var

Variable name of the pressure at half levels in the raw input file

phalf

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 as extra facets or in recipe if default DRS is used)

zg_file

Absolute or relative (to auxiliary_data_dir) path to the the input file that contains the geometric height at full levels

If possible, use geometric height at full levels provided by the raw input file

zg_var

Variable name of the geometric height at full levels in the raw input file

zg

zghalf_file

Absolute or relative (to auxiliary_data_dir) path to the the input file that contains the geometric height at half levels

If possible, use geometric height at half levels provided by the raw input file

zghalf_var

Variable name of the geometric height at half levels in the raw input file

zghalf

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}

and data can be found by running esmvaltool config copy data-native-ipslcm.yml and adapting the rootpath to your system.

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 the usage Extra Facets. The configuration file extra_facets_ipslcm.yml contains the default extra facets. 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

as configured in:

Listing 6 Contents of data-native-access.yml#
# Read data from the ACCESS model in its native format.
projects:
  ACCESS:
    data:
      access-sub-dataset:
        type: "esmvalcore.local.LocalDataSource"
        rootpath: ~/climate_data
        dirname_template: "{dataset}/{sub_dataset}/{exp}/{modeling_realm}/netCDF"
        filename_template: "{sub_dataset}.{freq_attribute}-*.nc"
      access-ocean:
        type: "esmvalcore.local.LocalDataSource"
        rootpath: ~/climate_data
        dirname_template: "{dataset}/{sub_dataset}/{exp}/{modeling_realm}/netCDF"
        filename_template: "ocean_{freq_attribute}.nc-*"

To use this configuration, run esmvaltool config copy data-native-access.yml and adapt the rootpath to your system.

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. The configuration file extra_facets_access.yml contains the defaults. 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 includes atm, ice, and oce

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

freq_attribute

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

No default

sub_dataset

Part of the ACCESS-ESM raw dataset root, needs to be specified if you want to use the cmoriser

No default

ocean_grid_path

Path to load the grid data for ACCESS ocean variables

No default

Data retrieval#

Please go to Data sources for instructions and background on how to configure data retrieval.

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 filename_template or dirname_template in esmvalcore.local.LocalDataSource.

For example, given the extra facets

projects:
  native6:
    extra_facets:
      ERA5:
        Amon:
          tas:
            source_var_name: t2m

a corresponding entry in the configuration file could look like:

Listing 7 Contents of data-local-esmvaltool.yml#
  # Data that can be read in its native format by ESMValCore.
  native6:
    data:
      local:
        type: "esmvalcore.local.LocalDataSource"
        rootpath: ~/climate_data
        dirname_template: "Tier{tier}/{dataset}/{version}/{frequency}/{short_name}"
        filename_template: "*.nc"

The same replacement mechanism can be employed everywhere where tags can be used, particularly in dirname_template and filename_template in esmvalcore.local.LocalDataSource, and in output_file in config-developer.yml.