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
2136832627,I_kwDOAMm_X85_XXpz,8755,to_zarr removes global attributes in destination dataset,8998112,open,0,,,12,2024-02-15T15:32:10Z,2024-04-29T19:22:41Z,,NONE,,,,"### What happened?

Adding new variables to a zarr dataset with to_zarr() always removes the existing global attributes. New global attributes in the source dataset are not always added to the destination dataset depending on how to_zarr() is called.

### What did you expect to happen?

I would expect that existing global attributes would always be preserved. If there are new global attributes I would expect them to be added to the existing global attributes instead of replacing all existing global attributes.

### Minimal Complete Verifiable Example

```Python
import xarray as xr
from pyproj import CRS

local_zarr = 'sample.zarr'

ds_sample = xr.tutorial.load_dataset(""air_temperature"")

# Make a local copy
ds_sample.to_zarr(local_zarr, mode='w')
ds_sample = xr.open_dataset(local_zarr, engine='zarr', 
                            backend_kwargs={'consolidated':True}, chunks={}, 
                            decode_coords=True)

# Create CRS metadata
crs_meta = CRS.from_epsg(4326).to_cf()

ds_new = xr.Dataset(data_vars={""crs"": ([], 1, crs_meta)})
ds_new.attrs['note'] = 'please add this'

# Add all variables from ds_new to the zarr
# NOTE: This adds the new global attribute but also removes
#       all existing global attributes
ds_new.to_zarr(local_zarr, mode='a')

# Add selected variable(s) to zarr dataset
# NOTE: This does not copy new global attributes 
#       and removes all existing global attributes
# ds_new['crs'].to_zarr(local_zarr, mode='a')

# Re-open local zarr store
ds_sample = xr.open_dataset(local_zarr, engine='zarr', 
                            backend_kwargs={'consolidated':True}, chunks={}, 
                            decode_coords=True)

ds_sample
```


### 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](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), 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

<details>

INSTALLED VERSIONS
------------------
commit: None
python: 3.11.0 | packaged by conda-forge | (main, Jan 14 2023, 12:26:40) [Clang 14.0.6 ]
python-bits: 64
OS: Darwin
OS-release: 22.6.0
machine: arm64
processor: arm
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: ('en_US', 'UTF-8')
libhdf5: 1.12.2
libnetcdf: 4.8.1

xarray: 2024.1.1
pandas: 2.2.0
numpy: 1.26.4
scipy: 1.12.0
netCDF4: 1.6.0
pydap: installed
h5netcdf: 1.3.0
h5py: 3.8.0
Nio: None
zarr: 2.17.0
cftime: 1.6.3
nc_time_axis: None
iris: None
bottleneck: 1.3.7
dask: 2024.2.0
distributed: 2024.2.0
matplotlib: 3.8.2
cartopy: 0.22.0
seaborn: None
numbagg: None
fsspec: 2023.12.2
cupy: None
pint: 0.23
sparse: None
flox: None
numpy_groupies: None
setuptools: 69.0.3
pip: 24.0
conda: None
pytest: 8.0.0
mypy: None
IPython: 8.21.0
sphinx: None

</details>
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