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
608923222,MDU6SXNzdWU2MDg5MjMyMjI=,4014,Add NetCDF3 dtype coercion for unsigned integer types,1700203,closed,0,,,2,2020-04-29T09:52:08Z,2020-05-20T17:08:24Z,2020-05-20T17:08:24Z,CONTRIBUTOR,,,,"`xr.Dataset.to_netcdf` does not seem to support writing data with unsigned integer dtypes, `uint32`, `uint64` etc.. This seems to be the case for both scipy-based output formats, `NETCDF3_CLASSIC` and `NETCDF3_64BIT`.

It seems like dtype coercions for `int64` and `bool` are done automatically for NetCDF3 in the [`xarray.netcdf3`](https://github.com/pydata/xarray/blob/33a66d6380c26a59923922ee11e8ffcf0b4f379f/xarray/backends/netcdf3.py#L31) module.
Shouldn't data of signed dtypes then *also* be coerced, i.e. to their signed equivalent?

#### MCVE Code Sample
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```python
import numpy as np
import xarray as xr

da = xr.DataArray(np.array([1,2,3], dtype='uint64'))

# The following all fail:
da.to_netcdf(""foo"")  # default format: scipy NETCDF3_CLASSIC
da.to_netcdf(""bar"", format='NETCDF3_64BIT')
da.astype('uint32').to_netcdf(""baz"")
da.astype('uint16').to_netcdf(""spam"")

# This works:
da.astype('int64').to_netcdf(""working64"")  # is coerced
da.astype('int32').to_netcdf(""working32"")  # works natively
```

*Importantly,* this is with the netcdf4 python package **not** being installed, in which case that package would be used for writing rather than scipy's netcdf.

#### Expected Output
NetCDF3 file is written with an appropriately coerced data format, e.g. as done with `int64`.

Alternatively, writing data fails for _all_ dtypes that would natively be unsupported, including `int64` and `bool`.

#### Problem Description
Given that the infrastructure for coercion is [already in place](https://github.com/pydata/xarray/blob/33a66d6380c26a59923922ee11e8ffcf0b4f379f/xarray/backends/netcdf3.py#L37-L57), it seems more consistent to me to apply coercion to all cases where it would lead to `to_netcdf` method calls _succeeding_ rather than failing, not only to `int64` and `bool`.

Ideally, coercion would happen towards another unsigned integer type.
However, writing `uint32` seems not to be possible, so it's not a 64bit/32bit issue.
While the [NetCDF Format Specification](https://www.unidata.ucar.edu/software/netcdf/docs/file_format_specifications.html#classic_format_spec) declares as only unsigned integer type `NON_NEG`, which I presume to be equivalent to `uint32`, writing unsigned integers seems not possible via scipy's NetCDF3 writer. Thus, the only viable coercion, as far as I see, would be to signed equivalents.

I see no new cast safety implications here, because the existing `coerce_nc3_dtype` function already checks if the original and cast arrays compare equivalently.


#### Versions

<details><summary>Output of <tt>xr.show_versions()</tt></summary>

INSTALLED VERSIONS
------------------
commit: None
python: 3.7.6 (default, Dec 30 2019, 19:38:26) 
[Clang 11.0.0 (clang-1100.0.33.16)]
python-bits: 64
OS: Darwin
OS-release: 19.3.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: en_US.UTF-8
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: None

xarray: 0.15.1
pandas: 1.0.1
numpy: 1.18.3
scipy: 1.4.1
netCDF4: None
pydap: None
h5netcdf: None
h5py: 2.10.0
Nio: None
zarr: None
cftime: 1.0.4.2
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: None
dask: 2.10.1
distributed: 2.10.0
matplotlib: 3.1.3
cartopy: None
seaborn: None
numbagg: None
setuptools: 45.1.0
pip: 20.0.2
conda: None
pytest: 5.3.5
IPython: 7.12.0
sphinx: 2.4.1

</details>
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