What happened?

When writing a coord with a datetime type, the chunking on the coord is ignored, and the whole coord is written as a single chunk. (or at least it can be, I haven't done enough to confirm whether it'll always be...)

This can be quite inconvenient. Any attempt to write to that dataset from a distributed process will have errors, since each process will be attempting to write another process's data, rather than only its region. And less severely, the chunks won't be unified.

Minimal Complete Verifiable Example

```Python ds = xr.tutorial.load_dataset('air_temperature')

( ds.chunk() .expand_dims(a=1000) .assign_coords( time2=lambda x: x.time, time_int=lambda x: (("time"), np.full(ds.sizes["time"], 1)), ) .chunk(time=10) .to_zarr("foo.zarr", mode="w") )

xr.open_zarr('foo.zarr')

Note the chunksize=(2920,) vs chunksize=(10,)!

<xarray.Dataset> Dimensions: (a: 1000, time: 2920, lat: 25, lon: 53) Coordinates: * lat (lat) float32 75.0 72.5 70.0 67.5 65.0 ... 22.5 20.0 17.5 15.0 * lon (lon) float32 200.0 202.5 205.0 207.5 ... 322.5 325.0 327.5 330.0 * time (time) datetime64[ns] 2013-01-01 ... 2014-12-31T18:00:00 time2 (time) datetime64[ns] dask.array<chunksize=(2920,), meta=np.ndarray> # here time_int (time) int64 dask.array<chunksize=(10,), meta=np.ndarray> # here Dimensions without coordinates: a Data variables: air (a, time, lat, lon) float32 dask.array<chunksize=(1000, 10, 25, 53), meta=np.ndarray> Attributes: Conventions: COARDS description: Data is from NMC initialized reanalysis\n(4x/day). These a... platform: Model references: http://www.esrl.noaa.gov/psd/data/gridded/data.ncep.reanaly... title: 4x daily NMC reanalysis (1948)

xr.open_zarr('foo.zarr').chunks

ValueError Traceback (most recent call last) Cell In[13], line 1 ----> 1 xr.open_zarr('foo.zarr').chunks

File /opt/homebrew/lib/python3.9/site-packages/xarray/core/dataset.py:2567, in Dataset.chunks(self) 2552 @property 2553 def chunks(self) -> Mapping[Hashable, tuple[int, ...]]: 2554 """ 2555 Mapping from dimension names to block lengths for this dataset's data, or None if 2556 the underlying data is not a dask array. (...) 2565 xarray.unify_chunks 2566 """ -> 2567 return get_chunksizes(self.variables.values())

File /opt/homebrew/lib/python3.9/site-packages/xarray/core/common.py:2013, in get_chunksizes(variables) 2011 for dim, c in v.chunksizes.items(): 2012 if dim in chunks and c != chunks[dim]: -> 2013 raise ValueError( 2014 f"Object has inconsistent chunks along dimension {dim}. " 2015 "This can be fixed by calling unify_chunks()." 2016 ) 2017 chunks[dim] = c 2018 return Frozen(chunks)

ValueError: Object has inconsistent chunks along dimension time. This can be fixed by calling unify_chunks().

```

MVCE confirmation

• [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
• [X] Complete example — the example is self-contained, including all data and the text of any traceback.
• [X] Verifiable example — the example copy & pastes into an IPython prompt or Binder notebook, returning the result.
• [X] New issue — a search of GitHub Issues suggests this is not a duplicate.
• [X] Recent environment — the issue occurs with the latest version of xarray and its dependencies.

Relevant log output

No response

Anything else we need to know?

No response

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.9.18 (main, Nov 2 2023, 16:51:22) [Clang 14.0.3 (clang-1403.0.22.14.1)] python-bits: 64 OS: Darwin OS-release: 22.6.0 machine: arm64 processor: arm byteorder: little LC_ALL: en_US.UTF-8 LANG: None LOCALE: ('en_US', 'UTF-8') libhdf5: 1.12.2 libnetcdf: None xarray: 2023.10.1 pandas: 2.1.1 numpy: 1.26.1 scipy: 1.11.1 netCDF4: None pydap: None h5netcdf: 1.1.0 h5py: 3.8.0 Nio: None zarr: 2.16.0 cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None iris: None bottleneck: 1.3.7 dask: 2023.5.0 distributed: 2023.5.0 matplotlib: 3.6.0 cartopy: None seaborn: 0.12.2 numbagg: 0.6.0 fsspec: 2022.8.2 cupy: None pint: 0.22 sparse: 0.14.0 flox: 0.8.1 numpy_groupies: 0.9.22 setuptools: 68.2.2 pip: 23.3.1 conda: None pytest: 7.4.0 mypy: 1.6.1 IPython: 8.14.0 sphinx: 5.2.1