html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/4598#issuecomment-731743161,https://api.github.com/repos/pydata/xarray/issues/4598,731743161,MDEyOklzc3VlQ29tbWVudDczMTc0MzE2MQ==,6628425,2020-11-22T12:49:53Z,2020-11-22T12:49:53Z,MEMBER,"I see, yeah, timezones with `cftime` are tricky.  Why are you using `cftime_range` to populate the time variable though in this case?  Why not `pandas.date_range`?  

The problematic variable in this dataset is `""tau""`.  If we drop that variable the dates are automatically decoded to pandas-compatible times (perhaps that's not an option for you though):
```
In [1]: import xarray as xr

In [2]: xr.open_dataset('https://tds.hycom.org/thredds/dodsC/GLBy0.08/latest', drop_variables=[""tau""])
Out[2]:
<xarray.Dataset>
Dimensions:            (depth: 40, lat: 4251, lon: 4500, time: 101)
Coordinates:
  * lat                (lat) float64 -80.0 -79.96 -79.92 ... 89.92 89.96 90.0
  * lon                (lon) float64 0.0 0.07996 0.16 0.24 ... 359.8 359.8 359.9
  * depth              (depth) float64 0.0 2.0 4.0 6.0 ... 3e+03 4e+03 5e+03
  * time               (time) datetime64[ns] 2020-11-16T12:00:00 ... 2020-11-29
    time_run           (time) datetime64[ns] ...
Data variables:
    time_offset        (time) datetime64[ns] ...
    surf_el            (time, lat, lon) float32 ...
    water_u            (time, depth, lat, lon) float32 ...
    water_u_bottom     (time, lat, lon) float32 ...
    water_v            (time, depth, lat, lon) float32 ...
    water_v_bottom     (time, lat, lon) float32 ...
    water_temp         (time, depth, lat, lon) float32 ...
    water_temp_bottom  (time, lat, lon) float32 ...
    salinity           (time, depth, lat, lon) float32 ...
    salinity_bottom    (time, lat, lon) float32 ...
Attributes:
    classification_level:      UNCLASSIFIED
    distribution_statement:    Approved for public release. Distribution unli...
    downgrade_date:            not applicable
    classification_authority:  not applicable
    institution:               Fleet Numerical Meteorology and Oceanography C...
    source:                    HYCOM archive file
    history:                   archv2ncdf2d ;\nFMRC Best Dataset
    comment:                   p-grid
    field_type:                instantaneous
    Conventions:               CF-1.4, NAVO_netcdf_v1.1
    _CoordSysBuilder:          ucar.nc2.dataset.conv.CF1Convention
    cdm_data_type:             GRID
    featureType:               GRID
    location:                  Proto fmrc:GLBy0.08_930_FMRC
```","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,748229907
https://github.com/pydata/xarray/issues/4598#issuecomment-731740688,https://api.github.com/repos/pydata/xarray/issues/4598,731740688,MDEyOklzc3VlQ29tbWVudDczMTc0MDY4OA==,6628425,2020-11-22T12:30:37Z,2020-11-22T12:30:37Z,MEMBER,"We do not have anything in xarray that works for `cftime` scalars currently, but we do have a `to_datetimeindex` method on `CFTimeIndex`:
```
In [1]: import xarray as xr

In [2]: times = xr.cftime_range(""2000"", periods=2)

In [3]: times.to_datetimeindex()
Out[3]: DatetimeIndex(['2000-01-01', '2000-01-02'], dtype='datetime64[ns]', freq=None)
```
Which functionality are you looking for in xarray that pandas `Timestamp` objects provide, but `cftime` objects do not?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,748229907