.. _findingdata: ********** 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 ========== .. _CMOR-DRS: 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: .. code-block:: yaml OBS: /gws/nopw/j04/esmeval/obsdata-v2 and the dataset: .. code-block:: yaml - {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: .. code-block:: /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``. .. _read_native_datasets: 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 :ref:`here `. .. hint:: When using native datasets, it might be helpful to specify a custom location for the :ref:`custom_cmor_tables`. This allows reading arbitrary variables from native datasets. Note that this requires the option ``cmor_strict: false`` in the :ref:`project configuration ` used for the native model output. .. _read_native_obs: 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 :ref:`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 :ref:`recipe `. .. _read_native_era5_nc: 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. .. _read_native_era5_grib: 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 :ref:`DRS `. To read these data with ESMValCore, use the :ref:`rootpath ` ``/pool/data/ERA5`` with :ref:`DRS ` ``DKRZ-ERA5-GRIB`` in your configuration, for example: .. code-block:: yaml 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 :ref:`extra facets` configuration file, which is available here: :download:`extra_facets_native6.yml `. If necessary, these facets can be overwritten in the recipe. Thus, example dataset entries could look like this: .. code-block:: yaml 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: .. code-block:: yaml 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``. .. _read_native_mswep: 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/. .. _read_native_models: 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``. .. _read_cesm: 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 :ref:`config-developer file ` (using the :ref:`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.$TSTRING``. Thus, example dataset entries could look like this: .. code-block:: yaml datasets: - {project: CESM, dataset: CESM2, case: f.e21.FHIST_BGC.f09_f09_mg17.CMIP6-AMIP.001, type: h0, mip: Amon, short_name: tas, start_year: 2000, end_year: 2014} - {project: CESM, dataset: CESM2, case: f.e21.F1850_BGC.f09_f09_mg17.CFMIP-hadsst-piForcing.001, type: h0, gcomp: atm, scomp: cam, mip: Amon, short_name: tas, start_year: 2000, end_year: 2014} Variable-specific defaults for the facet ``gcomp`` and ``scomp`` are given as extra facets (see next paragraph) for some variables, but these can be overwritten in the recipe. Similar to any other fix, the CESM fix allows the use of :ref:`extra facets`. The configuration file :download:`extra_facets_cesm.yml ` contains the defaults. Currently, this file only contains default facets for a single variable (`tas`); for other variables, these entries need to be defined in the recipe. Supported keys for extra facets are: ==================== ====================================== ================================= Key Description Default value if not specified ==================== ====================================== ================================= ``gcomp`` Generic component-model name No default (needs to be specified as extra facets or in recipe if default DRS is used) ``raw_name`` Variable name of the variable in the CMOR variable name of the raw input file corresponding variable ``raw_units`` Units of the variable in the raw If specified, the value given by input file the ``units`` attribute in the raw input file; otherwise ``unknown`` ``scomp`` Specific component-model name No default (needs to be specified as extra facets or in recipe if default DRS is used) ``string`` Short string which is used to further ``''`` (empty string) identify the history file type (corresponds to ``$string`` or ``$SSTRING.$TSTRING`` in the CESM file name conventions; see note above) ``tdir`` Entry to distinguish time averages ``''`` (empty string) from time series from diagnostic plot sets (only used for post-processed data) ``tperiod`` Time period over which the data was ``''`` (empty string) processed (only used for post-processed data) ==================== ====================================== ================================= .. _read_emac: 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 :ref:`config-developer file ` (using the :ref:`configuration option ` ``drs: default``). Thus, example dataset entries could look like this: .. code-block:: yaml 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 :ref:`extra facets`. The configuration file :download:`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 No default (needs to be specified is stored 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 CMOR variable name of the raw input file corresponding variable ``raw_units`` Units of the variable in the raw If specified, the value given by input file the ``units`` attribute in the raw input file; otherwise ``unknown`` ``reset_time_bounds`` Boolean if time bounds are deleted, ``False`` and automatically recalculated by iris ===================== ====================================== ================================= .. 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]``. .. _read_icon: 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 the :ref:`config-developer file ` (using the :ref:`configuration option ` ``drs: default``). 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: .. code-block:: yaml 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: .. code-block:: yaml 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 ``__atm_2d_ml_YYYYMMDDThhmmss.nc``, use ``var_type: _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 :ref:`timerange_examples` 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 :ref:`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 :ref:`mesh `. An example is the horizontal regridding of native ICON data to a regular grid. While the :ref:`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 :ref:`configuration option ` ``auxiliary_data_dir``). .. hint:: To use the :func:`~esmvalcore.preprocessor.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 :ref:`Vertical interpolation` for details). Example: .. code-block:: yaml 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 :ref:`extra facets`. The configuration file :download:`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 If not given, use file attribute ``auxiliary_data_dir``) ``grid_file_uri`` to retrieve ICON path to the ICON grid file grid file (see details above) ``lat_var`` Variable name of the latitude ``clat`` coordinate in the raw input file/grid file ``lon_var`` Variable name of the longitude ``clon`` coordinate in the raw input file/grid file ``pfull_var`` Variable name of the pressure at ``pfull`` (``dataset: ICON``) or full levels in the raw input ``pres`` (``dataset: ICON-XPP``) file ``phalf_var`` Variable name of the pressure at ``phalf`` half levels in the raw input file ``raw_name`` Variable name of the CMOR variable name of the variable in the raw input corresponding variable file ``raw_units`` Units of the variable in the If specified, the value given by raw input file the ``units`` attribute in the raw input file; otherwise ``unknown`` ``shift_time`` Shift time points back by 1/2 of ``True`` the corresponding output time interval ``ugrid`` Automatic UGRIDization of ``True`` the input data ``var_type`` Variable type of the No default (needs to be specified variable in the raw input as extra facets or in recipe if file default DRS is used) ``zg_file`` Absolute or relative (to If possible, use geometric height ``auxiliary_data_dir``) path to at full levels provided by the raw the the input file that contains input file the geometric height at full levels ``zg_var`` Variable name of the geometric ``zg`` height at full levels in the raw input file ``zghalf_file`` Absolute or relative (to If possible, use geometric height ``auxiliary_data_dir``) path to at half levels provided by the raw the the input file that contains input file the geometric height at half levels ``zghalf_var`` Variable name of the geometric ``zghalf`` height at half levels in 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 :func:`~esmvalcore.preprocessor.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 :ref:`preprocessors_using_supplementary_variables`, e.g., .. code-block:: yaml datasets: - {project: ICON, dataset: ICON, exp: amip, supplementary_variables: [{short_name: areacella, skip: true}]} .. _read_ipsl-cm6: 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.: .. code-block:: yaml 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} .. _ipslcm_extra_facets_example: 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 :ref:`config-extra-facets`. The configuration file :download:`extra_facets_ipslcm.yml ` contains the default extra facets. These multi-variable files must also undergo some data selection. .. _read_access-esm: 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 :ref:`config-developer` to match their local file structure. Thus, example dataset entries could look like this: .. code-block:: yaml 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 :ref:`extra facets`. The configuration file :download:`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 CMOR variable name of the raw input file corresponding variable ``modeling_realm`` Realm attribute includes `atm`, `ice`, No default (needs to be and `oce` specified as extra facets or in recipe if default DRS is used) ``freq_attribute`` A special attribute in the filename No default `ACCESS-ESM` raw data, related to the frequency of raw data ``sub_dataset`` Part of the ACCESS-ESM raw dataset root, No default needs to be specified if you want to use the cmoriser ``ocean_grid_path`` Path to load the grid data for ACCESS No default ocean variables ==================== ========================================== ==================================== .. _data-retrieval: 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 :ref:`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 :ref:`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. .. _config_option_drs: 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.: .. code-block:: yaml 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 .. code-block:: yaml 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 :ref:`config-developer`. .. 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. .. _config_option_rootpath: 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): .. code-block:: yaml rootpath: CMIP5: /badc/cmip5/data/cmip5/output1 Example for a CMIP6 root path pointing to the ESGF node on CEDA-Jasmin: .. code-block:: yaml 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: .. code-block:: yaml 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: .. code-block:: yaml 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. .. code-block:: yaml 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. .. code-block:: yaml 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: .. code-block:: yaml 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: .. code-block:: yaml - {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.: .. code-block:: yaml 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: .. code-block:: bash /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: .. code-block:: bash /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: .. code-block:: bash /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 .. _observations: 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. .. _extra-facets-data-finder: 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 :ref:`config-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 :ref:`config-developer`. For example, given the extra facets .. code-block:: yaml projects: native6: extra_facets: ERA5: Amon: tas: source_var_name: t2m a corresponding entry in the developer configuration file could look like: .. code-block:: yaml :caption: Contents of ``config-developer.yml`` native6: input_file: default: '{source_var_name}_*.nc' The same replacement mechanism can be employed everywhere where tags can be used, particularly in ``input_dir``, ``input_file``, and ``output_file``.