Columns

This section presents a definition and examples for each column of a xscen DataCatalog. The entries for the columns are based on CMIP6 metadata and the ES-DOC controlled vocabulary (https://github.com/ES-DOC/pyessv-archive). Some columns might be left empty (with a NaN), but id, domain, processing_level and xrfreq are mandatory. These four columns are what xscen uses by default to guess which entries can be merged together : all entries with the same unique combination of the four columns will be combined in a single Dataset when any of the first three functions listed here are used.

• id: Unique Dataset ID generated by xscen based on a subset of columns. By default, it is based on xscen.catalog.ID_COLUMNS.

  • E.g. “ERA_ecmwf_ERA5_ERA5-Land_NAM”, “CMIP6_ScenarioMIP_CMCC_CMCC-ESM2_ssp245_r1i1p1f1_global”

• type: Type of data.

  • E.g. “forecast”, “station-obs”, “gridded-obs”, “reconstruction”, “simulation”

• processing_level: Level of post-processing reached.

  • E.g. “raw”, “extracted”, “regridded”, “biasadjusted”

• bias_adjust_institution: Institution that computed the bias adjustment.

  • E.g. “Ouranos”, “PCIC”

• bias_adjust_project: Name of the project that computed the bias adjustment.

  • E.g. “ESPO-R5”, “BCCAQv2”, “ESPO6”

• bias_adjust_reference: Name of the reference that was used in the training of the bias adjustment. Only specified when there is more than one by project.

  E.g. “RDRSv21”, “CaSRv32”, “ERA5-Land”

• mip_era: CMIP generation associated with the data.

  • E.g. “CMIP6”, “CMIP5”

• activity: Model Intercomparison Project (MIP) associated with the data. This is the same as activity_id in CMIP6 data. CMIP is the activity for the historical experiment and the DECK experiments. It can also be “MRCC5” for simulations that are not part of CORDEX.

  • E.g. “CMIP”, “CORDEX”, “HighResMIP”

• driving_model: For RCM data, name of the driver. For CMIP6, this is only includes the model name. In CMIP5, following the driving_model convention from ES-DOC, this was in the format “institution-model”.

  • E.g. “CanESM5”, “CCCma-CanESM2”

• driving_member: For RCM data, member (realisation) of the driver. This looks like “rAiBpCfD” for CMIP6 (without the “fD” for CMIP5)..

  • E.g. “r1i1p1f1”

• institution: Institution associated with the source.

  • E.g. “CCCma”, “Ouranos”, “ECMWF”

• source: For simulation types, this is the model. For GCMs, this is the name of the model (source_id in CMIP6 and rcm_name in ES-DOC for CORDEX-CMIP5). For reconstruction types, this is the name of the dataset.

  • E.g. “CanESM5”, “CRCM5”, “ERA5”, “ERA5-Land, ERA5-Preliminary”

• experiment: Name of the experiment of the model.

  • E.g. “historical”, “ssp245”, “rcp85”

• member: Name of the realisation. For CMIP6 RCMs, this is the RCM member “rX”, the realization part of the version_realization CORDEX-CMIP6 spec. In CMIP5, this is the member associated with the driver (i.e. driving_member).

  • E.g. “r1i1p1f1”

• xrfreq: Pandas/xarray frequency.

  • E.g. “YS”, “QS-DEC”

• frequency: Frequency in letters (CMIP6 format).

  • E.g. “yr”,”qtr”

• variable: Variables in the dataset. It can be a Tuple.

  • E.g. “tasmax”, (“tasmax”, “tasmin”, “pr”)

• domain: Name of the region covered by the dataset. It can also contain information on the grid.

  • E.g. “global”, “NAM”, “ARC-44”, “ARC-22”

• date_start: First date of the dataset. This usually is a Datetime object with a ms resolution.

  • E.g. “2022-06-03 00:00:00”

• date_end: Last date of the dataset. This usually is a Datetime object with a ms resolution.

  • E.g. “2022-06-03 00:00:00”

• version: Version of the dataset. For RCMs, this is the version of the version_realization CORDEX-CMIP6 spec.

  • E.g. “1.0” “v1”

• format: Format of the dataset.

  • E.g. “zarr”, “nc”

• path: Path to the dataset.

  • E.g. “/some/path/to/the/data.zarr”