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
464315727,MDU6SXNzdWU0NjQzMTU3Mjc=,3080,Error in to_netcdf(),22454970,closed,0,,,2,2019-07-04T15:14:33Z,2023-09-16T08:28:12Z,2023-09-16T08:28:12Z,NONE,,,,"#### MCVE Code Sample

In order for the maintainers to efficiently understand and prioritize issues, we ask you post a ""Minimal, Complete and Verifiable Example"" (MCVE): http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports

```python
import xarray as xr
print(xr.__version__)

url = 'https://pavics.ouranos.ca/twitcher/ows/proxy/thredds/dodsC/birdhouse/cccs_portal/indices/Final/BCCAQv2/tg_mean/YS/rcp26/simulations/BCCAQv2+ANUSPLIN300_bcc-csm1-1_historical+rcp26_r1i1p1_1950-2100_tg_mean_YS.nc'
ds = xr.open_dataset(url, chunks={'time':50})
dsSel = ds.sel(lon= -80, lat= 50, method='nearest')
outnc = 'tmp.nc'
dsSel.to_netcdf(outnc)

```

#### Problem Description
This may be related to #2850 but I am unable to save to netcdf after performing a simple ```sel``` for certain .nc files (example file available via opendap link in the code) . Gives following error :
```Traceback (most recent call last):
  File ""/home/travis/.conda/envs/Xarray/lib/python3.6/site-packages/IPython/core/interactiveshell.py"", line 2961, in run_code
    exec(code_obj, self.user_global_ns, self.user_ns)
  File ""<ipython-input-3-492c82b08e81>"", line 8, in <module>
    dsSel.to_netcdf(outnc)
  File ""/home/travis/.conda/envs/Xarray/lib/python3.6/site-packages/xarray/core/dataset.py"", line 1365, in to_netcdf
    compute=compute)
  File ""/home/travis/.conda/envs/Xarray/lib/python3.6/site-packages/xarray/backends/api.py"", line 886, in to_netcdf
    unlimited_dims=unlimited_dims)
  File ""/home/travis/.conda/envs/Xarray/lib/python3.6/site-packages/xarray/backends/api.py"", line 929, in dump_to_store
    unlimited_dims=unlimited_dims)
  File ""/home/travis/.conda/envs/Xarray/lib/python3.6/site-packages/xarray/backends/common.py"", line 269, in store
    self.set_attributes(attributes)
  File ""/home/travis/.conda/envs/Xarray/lib/python3.6/site-packages/xarray/backends/common.py"", line 285, in set_attributes
    self.set_attribute(k, v)
  File ""/home/travis/.conda/envs/Xarray/lib/python3.6/site-packages/xarray/backends/netCDF4_.py"", line 426, in set_attribute
    _set_nc_attribute(self.ds, key, value)
  File ""/home/travis/.conda/envs/Xarray/lib/python3.6/site-packages/xarray/backends/netCDF4_.py"", line 302, in _set_nc_attribute
    obj.setncattr(key, value)
  File ""netCDF4/_netCDF4.pyx"", line 2607, in netCDF4._netCDF4.Dataset.setncattr
  File ""netCDF4/_netCDF4.pyx"", line 1467, in netCDF4._netCDF4._set_att
  File ""netCDF4/_netCDF4.pyx"", line 1733, in netCDF4._netCDF4._ensure_nc_success
AttributeError: NetCDF: String match to name in use
```



#### Expected Output
saved selection to .nc
#### Output of ``xr.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.7 |Anaconda, Inc.| (default, Oct 23 2018, 19:16:44) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 4.15.0-54-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
libhdf5: 1.10.2
libnetcdf: 4.6.1
xarray: 0.12.2
pandas: 0.23.3
numpy: 1.15.4
scipy: 1.2.0
netCDF4: 1.4.0
pydap: None
h5netcdf: 0.6.1
h5py: 2.8.0
Nio: None
zarr: None
cftime: 1.0.3.4
nc_time_axis: None
PseudonetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 1.0.0
distributed: 1.25.2
matplotlib: 3.0.1
cartopy: None
seaborn: None
numbagg: None
setuptools: 39.2.0
pip: 19.1.1
conda: None
pytest: 3.8.1
IPython: 6.5.0
sphinx: 2.1.2
</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3080/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
402413097,MDU6SXNzdWU0MDI0MTMwOTc=,2699,bfill behavior dask arrays with small chunk size,22454970,closed,0,,,4,2019-01-23T20:19:21Z,2021-04-26T13:06:46Z,2021-04-26T13:06:46Z,NONE,,,,"```python
data = np.random.rand(100)
data[25] = np.nan
da = xr.DataArray(data)

#unchunked 
print('output : orig',da[25].values, ' backfill : ',da.bfill('dim_0')[25].values )
output : orig nan  backfill :  0.024710724099643477

#small chunk
da1 = da.chunk({'dim_0':1})
print('output chunks==1 : orig',da1[25].values, ' backfill : ',da1.bfill('dim_0')[25].values )
output chunks==1 : orig nan  backfill :  nan

# medium chunk
da1 = da.chunk({'dim_0':10})
print('output chunks==10 : orig',da1[25].values, ' backfill : ',da1.bfill('dim_0')[25].values )
output chunks==10 : orig nan  backfill :  0.024710724099643477
```




#### Problem description
bfill methods seems to miss nans when dask array chunk size is small. Resulting array still has nan present  (see 'small chunk' section of code)


#### Expected Output
absence of nans
#### Output of ``xr.show_versions()``
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8.final.0
python-bits: 64
OS: Linux
OS-release: 4.15.0-43-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
xarray: 0.11.0
pandas: 0.23.4
numpy: 1.15.4
scipy: None
netCDF4: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: None
PseudonetCDF: None
rasterio: None
iris: None
bottleneck: 1.2.1
cyordereddict: None
dask: 1.0.0
distributed: 1.25.2
matplotlib: None
cartopy: None
seaborn: None
setuptools: 40.6.3
pip: 18.1
conda: None
pytest: None
IPython: None
sphinx: None

","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2699/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
507471865,MDU6SXNzdWU1MDc0NzE4NjU=,3402,reduce() by multiple dims on groupby object ,22454970,closed,0,,,2,2019-10-15T20:42:21Z,2019-10-25T21:01:11Z,2019-10-25T21:01:11Z,NONE,,,,"#### MCVE Code Sample
<!-- In order for the maintainers to efficiently understand and prioritize issues, we ask you post a ""Minimal, Complete and Verifiable Example"" (MCVE): http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports -->

```python
# Your code here
import xarray as xr
import numpy as np
url = 'https://data.nodc.noaa.gov/thredds/dodsC/GCOS/monthly_five_degree/19810101-NODC-L3_GHRSST-SSTblend-GLOB_HadSST2-Monthly_FiveDeg_DayNitAvg_19810101_20071231-v01.7-fv01.0.nc'

ds = xr.open_dataset(url, chunks=dict(time=12))
# reduce() directly on dataArray - THIS IS OK
ds.analysed_sst.reduce(np.percentile, dim=('lat','lon'), q=0.5) # ok

# Group by example
rr = ds.analysed_sst.rolling(min_periods=1, center=True, time=5).construct(""window"")

g = rr.groupby(""time.dayofyear"")
print(g.dims)
test1d = g.reduce(np.percentile, dim=('time'), q=0.5) # ok
testall = g.reduce(np.percentile, dim=xr.ALL_DIMS, q=0.5) # ok

# .reduce() w/ 2dims  on grouby obj not working 
test2d = g.reduce(np.nanpercentile, dim=('time','window'), q=0.5)

```

#### Expected Output
reduced output performed over multiple dimensions (but not xr.ALL_DIMS) on a groupby object

#### Problem Description
<!-- this should explain why the current behavior is a problem and why the expected output is a better solution -->
Using .reduce() on a groupby object is only successful when given a single dimensions or by using xr.ALL_DIMS.  I wish to apply a reduce on a subset of dims (last line of code above) but gives folowing error:
```
Traceback (most recent call last):
  File ""/home/travis/.PyCharmCE2019.2/config/scratches/scratch_20.py"", line 13, in <module>
    test = g.reduce(np.percentile, dim=('time','window'), q=0.5)
  File ""/home/travis/.conda/envs/Xarray/lib/python3.7/site-packages/xarray/core/groupby.py"", line 800, in reduce
    % (dim, self.dims)
ValueError: cannot reduce over dimension ('time', 'window'). expected either xarray.ALL_DIMS to reduce over all dimensions or one or more of ('time', 'lat', 'lon', 'window').
```
Note: Using reduce() on a subset of dims directly on a xr.DataArray seems fine (line 7). 

#### Output of ``xr.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4 (default, Aug 13 2019, 20:35:49) 
[GCC 7.3.0]
python-bits: 64
OS: Linux
OS-release: 4.15.0-65-generic
machine: x86_64
processor: x86_64
byteorder: little
LC_ALL: None
LANG: en_CA.UTF-8
LOCALE: en_CA.UTF-8
libhdf5: 1.10.2
libnetcdf: 4.6.3
xarray: 0.14.0
pandas: 0.25.1
numpy: 1.17.2
scipy: 1.3.1
netCDF4: 1.5.2
pydap: None
h5netcdf: None
h5py: None
Nio: None
zarr: None
cftime: 1.0.3.4
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 2.4.0
distributed: 2.4.0
matplotlib: 3.1.1
cartopy: None
seaborn: None
numbagg: None
setuptools: 41.0.1
pip: 19.2.2
conda: None
pytest: 5.0.1
IPython: None
sphinx: None
</details>
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3402/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
397916846,MDU6SXNzdWUzOTc5MTY4NDY=,2663,Implement quarterly frequencies for cftime ,22454970,closed,0,,,1,2019-01-10T16:40:54Z,2019-03-02T01:41:44Z,2019-03-02T01:41:44Z,NONE,,,,"Currently cftime offsets have no options for quarterly frequencies (QS-DEC ... etc) as in xarray/pandas 
see : http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases

@jwenfai has recently worked extensively to implement xarray/cftime resampling (https://github.com/pydata/xarray/pull/2593)  but this will remain limited as quarterly frequencies are currently not supported under xarray/coding/cftime_offsets.py 

","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2663/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
408924678,MDU6SXNzdWU0MDg5MjQ2Nzg=,2762,PyPi (v11.3) build behind master branch. cftime resampling not working,22454970,closed,0,,,3,2019-02-11T18:51:31Z,2019-02-11T19:43:15Z,2019-02-11T19:10:44Z,NONE,,,,"A pypi  xarray install (v 11.3) gives an error when resampling ncfiles with cftime calendars 
An install from github/master however works fine.

11.3 release notes seem to indicate that it should include the cftime resampling PR","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/2762/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue
375627070,MDU6SXNzdWUzNzU2MjcwNzA=,2527,Undesired automatic conversion of 'dtype' based on variable units 'days'?,22454970,closed,0,,,2,2018-10-30T18:14:38Z,2018-10-31T16:04:08Z,2018-10-31T16:04:08Z,NONE,,,,"```python
import xarray as xr
from glob import os
import numpy as np
import urllib.request
xr.set_options(enable_cftimeindex=True)

url = 'https://github.com/Ouranosinc/xclim/raw/master/tests/testdata/NRCANdaily/nrcan_canada_daily_tasmax_1990.nc'
infile = r'~/nrcan_canada_daily_tasmax_1990.nc'
urllib.request.urlretrieve(url,infile)

freqs = ['MS']  # , 'QS-DEC', 'YS']
ds = xr.open_dataset(infile)
su = (ds.tasmax > 25.0+273.15) * 1.0
for f in freqs:

    output = su.resample(time=f).sum(dim='time')
    output.attrs['units'] = 'days'
    output.attrs['standard_name'] = 'summer_days'
    output.attrs['long_name'] = 'summer days'
    output.attrs['description'] = 'Number of days where daily maximum temperature exceeds 25℃'

    # use original file as template
    ds1 = xr.open_dataset(infile, drop_variables=['tasmax', 'time','time_vectors','ts'])
    ds1.coords['time'] = output.time.values
    ds1['su'] = output

    comp = dict(zlib=True, complevel=5)
    encoding = {var: comp for var in ds1.data_vars}
    print(os.path.basename(infile).replace('.nc', '_SummerDays-' + f) + ' : writing ' + f + ' to netcdf')
    # with dask.config.set(pool=ThreadPool(4)):

    ds1.to_netcdf('~/testNRCANsummerdays.nc', format='NETCDF4', encoding=encoding)

    ds2 = xr.open_dataset('~/testNRCANsummerdays.nc')
    print(ds1)
    print(ds2)
    print(ds1.su.max())
    print(ds2.su.max())


```
#### Problem description

I am calculating a climate index 'summer days' ('su') from daily maximum temperatures.  Everything goes fine but when I reread a newly created output .nc file my 'su' dtype has changed from float to timedelta64.  This seems to be due to the units ('days') that I assign to my newly created variable which when read by xarray must trigger an automatic conversion? If I alter the units to 'days>25C' everything is ok.

Is there a way to avoid this behavior and still keep my units as 'days' which is the CF_standard for this climate index calculation? (note there a large number of cases such as this - wet days, dry-days etc etc all of which have 'days' as the expected unit


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