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
2211106929,I_kwDOAMm_X86DytBx,8882,"to_zarr silently loses data when using append_dim, if chunks are different to zarr store",140395181,closed,0,,,4,2024-03-27T15:27:02Z,2024-03-29T14:35:51Z,2024-03-29T14:35:51Z,NONE,,,,"### What happened?

When writing a chunked DataArray to an existing zarr store, appending along an existing dimension of the store, I have found that some data are not written if there are multiple array chunks to one zarr chunk. 

I appreciate it is probably bad practice to have different chunksizes in my DataArray and zarr_store, but I think its a realistic scenario that needs to be caught.

This may be related to / the same underlying issue as #8371. Perhaps the checks mentioned in https://github.com/pydata/xarray/issues/8371#issuecomment-1814589157 are somehow getting bypassed? Using zarr's ThreadSynchronizer is the only way I have found to ensure that all the data gets written.

### What did you expect to happen?

I expected that either 

- to_zarr would recognise the different chunk sizes, and re-chunk or wait for all the chunks to be written
- or an error would be raised, given that the results result in loss of data in an unpredictable way

### Minimal Complete Verifiable Example

```Python
import xarray as xr
import numpy as np
from matplotlib import pyplot as plt

x_coords = np.arange(10)
y_coords = np.arange(10)
t_coords = np.array([np.datetime64('2020-01-01').astype('datetime64[ns]')])
data = np.ones((10,10))

for i in range(4):
    plt.subplot(1,4,i+1)
    
    da = xr.DataArray(data.reshape((-1,10,10)),
                      dims = ['time','x','y'],
                      coords = {'x':x_coords, 'y':y_coords, 'time':t_coords},
                     ).chunk({'x':5, 'y':5,'time':1}).rename('foo')
    
    da.to_zarr('foo.zarr', mode='w')
    
    new_time = np.array([np.datetime64('2021-01-01').astype('datetime64[ns]')])
    
    da2 = xr.DataArray(data.reshape((-1,10,10)),
                      dims = ['time','x','y'],
                      coords = {'x':x_coords, 'y':y_coords, 'time':new_time},
                     ).chunk({'x':1, 'y':1,'time':1}).rename('foo')
    
    da2.to_zarr('foo.zarr',append_dim='time', mode='a')
    
    plt.imshow(xr.open_zarr('foo.zarr').isel(time=-1).foo.values)
```


### MVCE confirmation

- [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray.
- [ ] Complete example — the example is self-contained, including all data and the text of any traceback.
- [ ] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result.
- [ ] New issue — a search of GitHub Issues suggests this is not a duplicate.
- [ ] 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?

Output from the plots above:

![image](https://github.com/pydata/xarray/assets/140395181/1982344f-7db8-4e80-a3f3-b031747cacad)


### Environment

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.11.4 | packaged by conda-forge | (main, Jun 10 2023, 18:08:17) [GCC 12.2.0]
python-bits: 64
OS: Linux
OS-release: 5.15.0-1041-azure
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: C.UTF-8
LANG: C.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.14.3
libnetcdf: 4.9.2

xarray: 2024.2.0
pandas: 2.2.1
numpy: 1.26.4
scipy: 1.12.0
netCDF4: 1.6.5
pydap: installed
h5netcdf: 1.3.0
h5py: 3.10.0
Nio: None
zarr: 2.17.1
cftime: 1.6.3
nc_time_axis: 1.4.1
iris: None
bottleneck: 1.3.8
dask: 2024.3.1
distributed: 2024.3.1
matplotlib: 3.8.3
cartopy: 0.22.0
seaborn: 0.13.2
numbagg: None
fsspec: 2024.3.1
cupy: None
pint: 0.23
sparse: 0.15.1
flox: 0.9.5
numpy_groupies: 0.10.2
setuptools: 69.2.0
pip: 24.0
conda: 24.1.2
pytest: 8.1.1
mypy: None
IPython: 8.22.2
sphinx: None


</details>
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