Kerchunk and Xarray-Datatree - Kerchunk Cookbook

Overview¶

In this tutorial we are going to use a large collection of pre-generated Kerchunk reference files and open them with Xarray’s new DataTree functionality. This chapter is heavily inspired by this blog post.

About the Dataset¶

This collection of reference files were generated from the NASA NEX-GDDP-CMIP6 (Global Daily Downscaled Projections) dataset. A version of this dataset is hosted on s3 as a collection of NetCDF files.

Prerequisites¶

Concepts	Importance	Notes
Kerchunk Basics	Required	Core
Multiple Files and Kerchunk	Required	Core
Kerchunk and Dask	Required	Core
Multi-File Datasets with Kerchunk	Required	IO/Visualization
Xarray-Datatree Overview	Required	IO

Time to learn: 30 minutes

Motivation¶

In total the dataset is roughly 12TB in compressed blob storage, with a single NetCDF file per yearly timestep, per variable. Downloading this entire dataset for analysis on a local machine would difficult to say the least. The collection of Kerchunk reference files for this entire dataset is only 272 Mb, which is about 42,000 times smaller!

Imports¶

import dask
import hvplot.xarray  # noqa
import pandas as pd
import xarray as xr
from xarray import DataTree
from distributed import Client
from fsspec.implementations.reference import ReferenceFileSystem

Read the reference catalog¶

The NASA NEX-GDDP-CMIP6 dataset is organized by GCM, Scenario and Ensemble Member. Each of these Scenario/GCM combinations is represented as a combined reference file, which was created by merging across variables and concatenating along time-steps. All of these references are organized into a simple .csv catalog in the schema:

GCM/Scenario	url

Organzing with Xarray-Datatree¶

Not all of the GCM/Scenario reference datasets have shared spatial coordinates and many of the have slight differences in their calendar and thus time dimension. Because of this, these cannot be combined into a single Xarray-Dataset. Fortunately Xarray-Datatree provides a higher level abstraction where related Xarray-Datasets are organized into a tree structure where each dataset corresponds to a leaf.

# Read the reference catalog into a Pandas DataFrame
cat_df = pd.read_csv(
    "s3://carbonplan-share/nasa-nex-reference/reference_catalog_nested.csv"
)
# Convert the DataFrame into a dictionary
catalog = cat_df.set_index("ID").T.to_dict("records")[0]

Load Reference Datasets into Xarray-DataTree¶

In the following cell we create a function load_ref_ds, which can be parallelized via Dask to load Kerchunk references into a dictionary of Xarray-Datasets.

def load_ref_ds(url: str):
    fs = ReferenceFileSystem(
        url,
        remote_protocol="s3",
        target_protocol="s3",
        remote_options={"anon": True},
        target_options={"anon": True},
        lazy=True,
    )
    return xr.open_dataset(
        fs.get_mapper(),
        engine="zarr",
        backend_kwargs={"consolidated": False},
        chunks={"time": 300},
    )


tasks = {id: dask.delayed(load_ref_ds)(url) for id, url in catalog.items()}

Use Dask Distributed to load the Xarray-Datasets from Kerchunk reference files¶

Using Dask, we are loading 164 reference datasets into memory. Since they are are Xarray datasets the coordinates are loaded eagerly, but the underlying data is still lazy.

client = Client(n_workers=8)
client

catalog_computed = dask.compute(tasks)

2025-09-19 00:41:14,964 - distributed.worker - ERROR - Compute Failed
Key:       load_ref_ds-f5ca11dc-3cf6-4501-845a-54a9dada3edc
State:     executing
Task:  <Task 'load_ref_ds-f5ca11dc-3cf6-4501-845a-54a9dada3edc' load_ref_ds(...)>
Exception: 'ValueError("Reference-FS\'s target filesystem must have same value of asynchronous")'
Traceback: '  File "/tmp/ipykernel_4299/3963997076.py", line 10, in load_ref_ds\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/xarray/backends/api.py", line 760, in open_dataset\n    backend_ds = backend.open_dataset(\n        filename_or_obj,\n    ...<2 lines>...\n        **kwargs,\n    )\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/xarray/backends/zarr.py", line 1654, in open_dataset\n    store = ZarrStore.open_group(\n        filename_or_obj,\n    ...<10 lines>...\n        cache_members=cache_members,\n    )\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/xarray/backends/zarr.py", line 714, in open_group\n    ) = _get_open_params(\n        ~~~~~~~~~~~~~~~~^\n        store=store,\n        ^^^^^^^^^^^^\n    ...<9 lines>...\n        zarr_format=zarr_format,\n        ^^^^^^^^^^^^^^^^^^^^^^^^\n    )\n    ^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/xarray/backends/zarr.py", line 1896, in _get_open_params\n    zarr_group = zarr.open_group(store, **open_kwargs)\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/api/synchronous.py", line 531, in open_group\n    sync(\n    ~~~~^\n        async_api.open_group(\n        ^^^^^^^^^^^^^^^^^^^^^\n    ...<12 lines>...\n        )\n        ^\n    )\n    ^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/core/sync.py", line 163, in sync\n    raise return_result\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/core/sync.py", line 119, in _runner\n    return await coro\n           ^^^^^^^^^^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/api/asynchronous.py", line 845, in open_group\n    store_path = await make_store_path(store, mode=mode, storage_options=storage_options, path=path)\n                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/storage/_common.py", line 380, in make_store_path\n    store = FsspecStore.from_mapper(store_like, read_only=_read_only)\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/storage/_fsspec.py", line 204, in from_mapper\n    fs = _make_async(fs_map.fs)\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/storage/_fsspec.py", line 58, in _make_async\n    return fsspec.AbstractFileSystem.from_json(json.dumps(fs_dict))\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/spec.py", line 1480, in from_json\n    return json.loads(blob, cls=FilesystemJSONDecoder)\n           ~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/json/__init__.py", line 359, in loads\n    return cls(**kw).decode(s)\n           ~~~~~~~~~~~~~~~~^^^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/json/decoder.py", line 345, in decode\n    obj, end = self.raw_decode(s, idx=_w(s, 0).end())\n               ~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/json/decoder.py", line 361, in raw_decode\n    obj, end = self.scan_once(s, idx)\n               ~~~~~~~~~~~~~~^^^^^^^^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/json.py", line 97, in custom_object_hook\n    return AbstractFileSystem.from_dict(dct)\n           ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/spec.py", line 1556, in from_dict\n    return cls(\n        *json_decoder.unmake_serializable(dct.pop("args", ())),\n        **json_decoder.unmake_serializable(dct),\n    )\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/spec.py", line 81, in __call__\n    obj = super().__call__(*args, **kwargs)\n  File "/home/runner/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/implementations/reference.py", line 770, in __init__\n    raise ValueError(\n    ...<2 lines>...\n    )\n'

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Cell In[5], line 1
----> 1 catalog_computed = dask.compute(tasks)

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/dask/base.py:681, in compute(traverse, optimize_graph, scheduler, get, *args, **kwargs)
    678     expr = expr.optimize()
    679     keys = list(flatten(expr.__dask_keys__()))
--> 681     results = schedule(expr, keys, **kwargs)
    683 return repack(results)

Cell In[3], line 10, in load_ref_ds()
      1 def load_ref_ds(url: str):
      2     fs = ReferenceFileSystem(
      3         url,
      4         remote_protocol="s3",
   (...)      8         lazy=True,
      9     )
---> 10     return xr.open_dataset(
     11         fs.get_mapper(),
     12         engine="zarr",
     13         backend_kwargs={"consolidated": False},
     14         chunks={"time": 300},
     15     )

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/xarray/backends/api.py:760, in open_dataset()
    748 decoders = _resolve_decoders_kwargs(
    749     decode_cf,
    750     open_backend_dataset_parameters=backend.open_dataset_parameters,
   (...)    756     decode_coords=decode_coords,
    757 )
    759 overwrite_encoded_chunks = kwargs.pop("overwrite_encoded_chunks", None)
--> 760 backend_ds = backend.open_dataset(
    761     filename_or_obj,
    762     drop_variables=drop_variables,
    763     **decoders,
    764     **kwargs,
    765 )
    766 ds = _dataset_from_backend_dataset(
    767     backend_ds,
    768     filename_or_obj,
   (...)    779     **kwargs,
    780 )
    781 return ds

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/xarray/backends/zarr.py:1654, in open_dataset()
   1652 filename_or_obj = _normalize_path(filename_or_obj)
   1653 if not store:
-> 1654     store = ZarrStore.open_group(
   1655         filename_or_obj,
   1656         group=group,
   1657         mode=mode,
   1658         synchronizer=synchronizer,
   1659         consolidated=consolidated,
   1660         consolidate_on_close=False,
   1661         chunk_store=chunk_store,
   1662         storage_options=storage_options,
   1663         zarr_version=zarr_version,
   1664         use_zarr_fill_value_as_mask=None,
   1665         zarr_format=zarr_format,
   1666         cache_members=cache_members,
   1667     )
   1669 store_entrypoint = StoreBackendEntrypoint()
   1670 with close_on_error(store):

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/xarray/backends/zarr.py:714, in open_group()
    688 @classmethod
    689 def open_group(
    690     cls,
   (...)    707     cache_members: bool = True,
    708 ):
    709     (
    710         zarr_group,
    711         consolidate_on_close,
    712         close_store_on_close,
    713         use_zarr_fill_value_as_mask,
--> 714     ) = _get_open_params(
    715         store=store,
    716         mode=mode,
    717         synchronizer=synchronizer,
    718         group=group,
    719         consolidated=consolidated,
    720         consolidate_on_close=consolidate_on_close,
    721         chunk_store=chunk_store,
    722         storage_options=storage_options,
    723         zarr_version=zarr_version,
    724         use_zarr_fill_value_as_mask=use_zarr_fill_value_as_mask,
    725         zarr_format=zarr_format,
    726     )
    728     return cls(
    729         zarr_group,
    730         mode,
   (...)    739         cache_members=cache_members,
    740     )

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/xarray/backends/zarr.py:1896, in _get_open_params()
   1892     if _zarr_v3():
   1893         # we have determined that we don't want to use consolidated metadata
   1894         # so we set that to False to avoid trying to read it
   1895         open_kwargs["use_consolidated"] = False
-> 1896     zarr_group = zarr.open_group(store, **open_kwargs)
   1898 close_store_on_close = zarr_group.store is not store
   1900 # we use this to determine how to handle fill_value

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/api/synchronous.py:531, in open_group()
    454 def open_group(
    455     store: StoreLike | None = None,
    456     *,
   (...)    467     use_consolidated: bool | str | None = None,
    468 ) -> Group:
    469     """Open a group using file-mode-like semantics.
    470 
    471     Parameters
   (...)    528         The new group.
    529     """
    530     return Group(
--> 531         sync(
    532             async_api.open_group(
    533                 store=store,
    534                 mode=mode,
    535                 cache_attrs=cache_attrs,
    536                 synchronizer=synchronizer,
    537                 path=path,
    538                 chunk_store=chunk_store,
    539                 storage_options=storage_options,
    540                 zarr_version=zarr_version,
    541                 zarr_format=zarr_format,
    542                 meta_array=meta_array,
    543                 attributes=attributes,
    544                 use_consolidated=use_consolidated,
    545             )
    546         )
    547     )

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/core/sync.py:163, in sync()
    160 return_result = next(iter(finished)).result()
    162 if isinstance(return_result, BaseException):
--> 163     raise return_result
    164 else:
    165     return return_result

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/core/sync.py:119, in _runner()
    114 """
    115 Await a coroutine and return the result of running it. If awaiting the coroutine raises an
    116 exception, the exception will be returned.
    117 """
    118 try:
--> 119     return await coro
    120 except Exception as ex:
    121     return ex

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/api/asynchronous.py:845, in open_group()
    842 if chunk_store is not None:
    843     warnings.warn("chunk_store is not yet implemented", ZarrRuntimeWarning, stacklevel=2)
--> 845 store_path = await make_store_path(store, mode=mode, storage_options=storage_options, path=path)
    846 if attributes is None:
    847     attributes = {}

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/storage/_common.py:380, in make_store_path()
    376     if path:
    377         raise ValueError(
    378             "'path' was provided but is not used for FSMap store_like objects. Specify the path when creating the FSMap instance instead."
    379         )
--> 380     store = FsspecStore.from_mapper(store_like, read_only=_read_only)
    381 else:
    382     raise TypeError(f"Unsupported type for store_like: '{type(store_like).__name__}'")

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/storage/_fsspec.py:204, in from_mapper()
    180 @classmethod
    181 def from_mapper(
    182     cls,
   (...)    185     allowed_exceptions: tuple[type[Exception], ...] = ALLOWED_EXCEPTIONS,
    186 ) -> FsspecStore:
    187     """
    188     Create a FsspecStore from a FSMap object.
    189 
   (...)    202     FsspecStore
    203     """
--> 204     fs = _make_async(fs_map.fs)
    205     return cls(
    206         fs=fs,
    207         path=fs_map.root,
    208         read_only=read_only,
    209         allowed_exceptions=allowed_exceptions,
    210     )

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/zarr/storage/_fsspec.py:58, in _make_async()
     56     fs_dict = json.loads(fs.to_json())
     57     fs_dict["asynchronous"] = True
---> 58     return fsspec.AbstractFileSystem.from_json(json.dumps(fs_dict))
     60 if fsspec_version < parse_version("2024.12.0"):
     61     raise ImportError(
     62         f"The filesystem '{fs}' is synchronous, and the required "
     63         "AsyncFileSystemWrapper is not available. Upgrade fsspec to version "
     64         "2024.12.0 or later to enable this functionality."
     65     )

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/spec.py:1480, in from_json()
   1459 """
   1460 Recreate a filesystem instance from JSON representation.
   1461 
   (...)   1476 at import time.
   1477 """
   1478 from .json import FilesystemJSONDecoder
-> 1480 return json.loads(blob, cls=FilesystemJSONDecoder)

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/json/__init__.py:359, in loads()
    357 if parse_constant is not None:
    358     kw['parse_constant'] = parse_constant
--> 359 return cls(**kw).decode(s)

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/json/decoder.py:345, in decode()
    340 def decode(self, s, _w=WHITESPACE.match):
    341     """Return the Python representation of ``s`` (a ``str`` instance
    342     containing a JSON document).
    343 
    344     """
--> 345     obj, end = self.raw_decode(s, idx=_w(s, 0).end())
    346     end = _w(s, end).end()
    347     if end != len(s):

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/json/decoder.py:361, in raw_decode()
    352 """Decode a JSON document from ``s`` (a ``str`` beginning with
    353 a JSON document) and return a 2-tuple of the Python
    354 representation and the index in ``s`` where the document ended.
   (...)    358 
    359 """
    360 try:
--> 361     obj, end = self.scan_once(s, idx)
    362 except StopIteration as err:
    363     raise JSONDecodeError("Expecting value", s, err.value) from None

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/json.py:97, in custom_object_hook()
     95 if "cls" in dct:
     96     if (obj_cls := self.try_resolve_fs_cls(dct)) is not None:
---> 97         return AbstractFileSystem.from_dict(dct)
     98     if (obj_cls := self.try_resolve_path_cls(dct)) is not None:
     99         return obj_cls(dct["str"])

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/spec.py:1556, in from_dict()
   1553 dct.pop("cls", None)
   1554 dct.pop("protocol", None)
-> 1556 return cls(
   1557     *json_decoder.unmake_serializable(dct.pop("args", ())),
   1558     **json_decoder.unmake_serializable(dct),
   1559 )

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/spec.py:81, in __call__()
     79     return cls._cache[token]
     80 else:
---> 81     obj = super().__call__(*args, **kwargs)
     82     # Setting _fs_token here causes some static linters to complain.
     83     obj._fs_token_ = token

File ~/micromamba/envs/kerchunk-cookbook/lib/python3.13/site-packages/fsspec/implementations/reference.py:770, in __init__()
    768     self.fss[k] = AsyncFileSystemWrapper(f, asynchronous=self.asynchronous)
    769 elif self.asynchronous ^ f.asynchronous:
--> 770     raise ValueError(
    771         "Reference-FS's target filesystem must have same value "
    772         "of asynchronous"
    773     )

ValueError: Reference-FS's target filesystem must have same value of asynchronous

Build an Xarray-Datatree from the dictionary of datasets¶

dt = DataTree.from_dict(catalog_computed[0])

Accessing the Datatree¶

A Datatree is a collection of related Xarray datasets. We can access individual datasets using UNIX syntax. In the cell below, we will access a single dataset from the datatree.

dt["ACCESS-CM2/ssp585"]

# or

dt["ACCESS-CM2"]["ssp585"]

Convert a Datatree node to a Dataset¶

dt["ACCESS-CM2"]["ssp585"].to_dataset()

Operations across a Datatree¶

A Datatree contains a collection of datasets with related coordinates and variables. Using some in-built methods, we can analyze it as if it were a single dataset. Instead of looping through hundreds of Xarray datasets, we can apply operations across the Datatree. In the example below, we will lazily create a time-series.

ts = dt.mean(dim=["lat", "lon"])

Visualize a single dataset with HvPlot¶

display(  # noqa
    dt["ACCESS-CM2/ssp585"].to_dataset().pr.hvplot("lon", "lat", rasterize=True)
)

Shut down the Dask cluster¶

client.shutdown()

Using Pre-Generated References

Use xrefcoord to Generate Coordinates

Using Pre-Generated References

Kerchunk, hvPlot, and Datashader: Visualizing datasets on-the-fly