issues: 1655569401

What happened?

When reading a netcdf file wich has been created with fill_value=None (default) those data is not masked. If one is writing back to disk this manifests.

What did you expect to happen?

Values should be masked.

There seems to be a simple solution:

On read apply the netcdf default fill_value in the variables attributes before decoding if no _FillValue attribute is set. After decoding we could change that to np.nan for floating point types.

Minimal Complete Verifiable Example

```Python import numpy as np import netCDF4 as nc import xarray as xr

with nc.Dataset("test-no-missing-01.nc", mode="w") as ds: x = ds.createDimension("x", 5) test = ds.createVariable("test", "f4", ("x",), fill_value=None) test[:4] = np.array([0.0, np.nan, 1.0, 8.0], dtype="f4") with nc.Dataset("test-no-missing-01.nc") as ds: print(ds["test"]) print(ds["test"][:])

with xr.open_dataset("test-no-missing-01.nc").load() as roundtrip: print(roundtrip) print(roundtrip["test"].attrs) print(roundtrip["test"].encoding) roundtrip.to_netcdf("test-no-missing-02.nc") with nc.Dataset("test-no-missing-02.nc") as ds: print(ds["test"]) print(ds["test"][:]) ```

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.

Relevant log output

```Python <class 'netCDF4._netCDF4.Variable'> float32 test(x) unlimited dimensions: current shape = (5,) filling on, default _FillValue of 9.969209968386869e+36 used [0.0 nan 1.0 8.0 --]

<xarray.Dataset> Dimensions: (x: 5) Dimensions without coordinates: x Data variables: test (x) float32 0.0 nan 1.0 8.0 9.969e+36 {} {'zlib': False, 'szip': False, 'zstd': False, 'bzip2': False, 'blosc': False, 'shuffle': False, 'complevel': 0, 'fletcher32': False, 'contiguous': True, 'chunksizes': None, 'source': 'test-no-missing-01.nc', 'original_shape': (5,), 'dtype': dtype('float32')} <class 'netCDF4._netCDF4.Variable'> float32 test(x) _FillValue: nan unlimited dimensions: current shape = (5,) filling on [0.0 -- 1.0 8.0 9.969209968386869e+36] ```

Anything else we need to know?

The issue is similar to #7722 but is more intricate, as now the status of certain data values change from masked to some netcdf specific default value.

This is when only parts of the source dataset have been written to. Then the default fill_value get's delivered to the user but it is not backed by an _FillValue attribute.

Environment

INSTALLED VERSIONS ------------------ commit: None python: 3.11.0 | packaged by conda-forge | (main, Jan 14 2023, 12:27:40) [GCC 11.3.0] python-bits: 64 OS: Linux OS-release: 5.14.21-150400.24.55-default machine: x86_64 processor: x86_64 byteorder: little LC_ALL: None LANG: de_DE.UTF-8 LOCALE: ('de_DE', 'UTF-8') libhdf5: 1.14.0 libnetcdf: 4.9.2 xarray: 2023.3.0 pandas: 1.5.3 numpy: 1.24.2 scipy: 1.10.1 netCDF4: 1.6.3 pydap: None h5netcdf: 1.1.0 h5py: 3.8.0 Nio: None zarr: 2.14.2 cftime: 1.6.2 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: None dask: 2023.3.1 distributed: 2023.3.1 matplotlib: None cartopy: None seaborn: None numbagg: None fsspec: 2023.3.0 cupy: 11.6.0 pint: 0.20.1 sparse: None flox: None numpy_groupies: None setuptools: 67.6.0 pip: 23.0.1 conda: None pytest: 7.2.2 mypy: None IPython: 8.11.0 sphinx: None