id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
1804983457,I_kwDOAMm_X85rldyh,7987,Existing chunks not being respected on to_zarr(),99441529,closed,0,,,2,2023-07-14T14:30:20Z,2023-11-24T22:15:04Z,2023-11-24T22:15:04Z,NONE,,,,"### What is your issue?

Hi folks, I'm not sure what I'm doing wrong here. Context: I have a dataset, adding a coordinate variable with some specified chunking, and then that chunking is reset when writing to_zarr() and opening from disk. Even if I call unify_chunks() before writing or explicitly set the `preferred_chunks` encoding, the chunks get reset upon write. I have managed to force the chunks by explicitly setting `ds.end_date.encoding['chunks']= (2,)` before writing (note that before this, _no encoding was set_, so this isn't the old issue of previous encoding overwriting the dask chunks). It took a while to find this, and I don't understand why the existing behavior disregards the existing chunks on single-dimension variables or coordinates. The issue is that with this default behavior, the dataset written to disk has inconsistent dimensions on read, throws an error, and requires a call to unify_chunks() to be usable. 

Reproducer below:

versions: 
xarray 2023.6.0
dask 2023.6.0
zarr 2.14.2


```
import pandas as pd
import numpy as np
import xarray as xr
from datetime import datetime
import dask.array as da

from pandas.tseries.offsets import MonthEnd

# create toy dataset
dates = [datetime(2021, 1, 1), datetime(2021, 2, 1), datetime(2021, 3, 1), datetime(2021, 4, 1)]
ds = xr.Dataset(
    data_vars=dict(
        tas=([""time"", ""lat"", ""lon"",], 
            np.array([300]) * np.ones((len(dates), 4, 4)),
        ),
    ),
    coords=dict(
        time=dates,
        lat=np.array([10, 11, 12, 13]),
        lon=np.array([20, 21, 22, 23]),
    ),
)

# chunk dataset
ds = ds.chunk(chunks={""lat"": 2, ""lon"": 2, 'time': 2})

# add end date coordinate
time_df = pd.DataFrame({""time"": list(ds['time'].values)})
time_df['end_date'] = time_df['time'] + MonthEnd(0)
ds = ds.assign_coords(
    end_date=(""time"", da.from_array(time_df['end_date'].values)))

print(""---data before unify chunks--- \n"", ds.end_date.data)
ds = ds.unify_chunks() # surely you'll respect my chunking
print(""---data before writing to s3--- \n"", ds.end_date.data)
ds.time.encoding['preferred_chunks']= {'time': 2} # please use this chunking?
ds.end_date.encoding['preferred_chunks']= {'time': 2} # pretty please use this chunking?


# write data to s3
data_path = ""/tmp/mydata.zarr""
ds.to_zarr(data_path, mode='w')

# read data back from s3
ds_check = xr.open_zarr(data_path)

print(""---data after writing to disk and reading back in--- \n"", ds_check.end_date.data)
```

output from code above:
```
---data before unify chunks--- 
 dask.array<array, shape=(4,), dtype=datetime64[ns], chunksize=(4,), chunktype=numpy.ndarray>
---data before writing to s3--- 
 dask.array<rechunk-merge, shape=(4,), dtype=datetime64[ns], chunksize=(2,), chunktype=numpy.ndarray>
---data after writing to disk and reading back in--- 
 dask.array<open_dataset-end_date, shape=(4,), dtype=datetime64[ns], chunksize=(4,), chunktype=numpy.ndarray>
 ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7987/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue