issue_comments: 866314326

~encoding is where that information is stored between reading a dataset in from disk and saving it back out again.~ _encode_coordinates can take a default value from either of encoding or attrs, but a falsy value will be overwritten. Setting .attrs["coordinates"] = " " should work.

```python

import numpy as np, xarray as xr data = xr.DataArray(np.random.randn(2, 3), dims=("x", "y"), coords={"x": [10, 20]}) ds = xr.Dataset({"foo": data, "bar": ("x", [1, 2]), "fake": 10}) ds = ds.assign_coords({"reftime":np.array("2004-11-01T00:00:00", dtype=np.datetime64)}) ds = ds.assign({"test": 1}) ds.test.encoding["coordinates"] = " " ds.to_netcdf("file.nc") bash $ ncdump -h file.nc netcdf file { dimensions: x = 2 ; y = 3 ; variables: int64 x(x) ; double foo(x, y) ; foo:_FillValue = NaN ; foo:coordinates = "reftime" ; int64 bar(x) ; bar:coordinates = "reftime" ; int64 fake ; fake:coordinates = "reftime" ; int64 reftime ; reftime:units = "days since 2004-11-01 00:00:00" ; reftime:calendar = "proleptic_gregorian" ; int64 test ; test:coordinates = " " ; } `` As mentioned above, the XArray data model associates coordinates with dimensions rather than with variables, so any time you read the dataset back in again, thetestvariable will gainreftimeas a coordinate, because the dimensions ofreftime(()), are a subset of the dimensions oftest(also()`).

Not producing a coordinates attribute for variables mentioned in another variable's bounds attribute (or a few other attributes, for that matter) would be entirely doable within the function linked above, and should be straightforward if you want to make a PR for that.

Making realization and the bounds show up in ds.coords rather than ds.data_vars may also skip setting the coordinates attribute, though I'm less sure of that. It would, however, add realization to the coordinates attributes of every other data_var unless you overrode that, which may not be what you want.