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Computer simulations of the Earth’s climate and weather generate huge amounts of data. These data are often persisted on HPC systems or in the cloud across multiple data assets of a variety of formats (netCDF, zarr, etc…). Finding, investigating, loading these data assets into compute-ready data containers costs time and effort. The data user needs to know what data sets are available, the attributes describing each data set, before loading a specific data set and analyzing it.

Finding, investigating, loading these assets into data array containers such as xarray can be a daunting task due to the large number of files a user may be interested in. Intake-esm aims to address these issues by providing necessary functionality for searching, discovering, data access/loading.


intake-esm is a data cataloging utility built on top of intake, pandas, and xarray, and it’s pretty awesome!

  • Opening an ESM collection definition file: An ESM (Earth System Model) collection file is a JSON file that conforms to the ESM Collection Specification. When provided a link/path to an esm collection file, intake-esm establishes a link to a database (CSV file) that contains data assets locations and associated metadata (i.e., which experiment, model, the come from). The collection JSON file can be stored on a local filesystem or can be hosted on a remote server.

    In [1]: import intake
    In [2]: col_url = ""
    In [3]: col = intake.open_esm_datastore(col_url)
    In [4]: col
    Out[4]: <pangeo-cmip6 catalog with 4287 dataset(s) from 282905 asset(s)>
  • Search and Discovery: intake-esm provides functionality to execute queries against the catalog:

    In [5]: col_subset =
       ...:     experiment_id=["historical", "ssp585"],
       ...:     table_id="Oyr",
       ...:     variable_id="o2",
       ...:     grid_label="gn",
       ...: )
    In [6]: col_subset
    Out[6]: <pangeo-cmip6 catalog with 18 dataset(s) from 138 asset(s)>
  • Access: when the user is satisfied with the results of their query, they can ask intake-esm to load data assets (netCDF/HDF files and/or Zarr stores) into xarray datasets:

      In [7]: dset_dict = col_subset.to_dataset_dict(zarr_kwargs={"consolidated": True})
      --> The keys in the returned dictionary of datasets are constructed as follows:
      |███████████████████████████████████████████████████████████████| 100.00% [18/18 00:10<00:00]

See documentation for more information.


Intake-esm can be installed from PyPI with pip:

python -m pip install intake-esm

It is also available from conda-forge for conda installations:

conda install -c conda-forge intake-esm


If you encounter any errors or problems with intake-esm, please open an issue at the GitHub main repository.