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
540399695,MDU6SXNzdWU1NDAzOTk2OTU=,3644,Handling of non iso8601 format time,13421074,closed,0,,,3,2019-12-19T15:52:19Z,2019-12-19T16:46:18Z,2019-12-19T16:46:18Z,NONE,,,,"#### MCVE Code Sample
```
import pandas as pd
a = '2019-04-02 22:00:02 0:00'
pd.Timestamp(a)
```


#### Expected Output
Timestamp('2019-04-02 22:00:02')

#### Actual Output
Timestamp('2019-04-02 00:00:00')

#### Problem Description
The default for decoding time is to use pandas Timestamp (_decode_datetime_with_pandas), however it appears that the pandas Timestamp function only handles iso8601 time format when passed as a string.  **Should there be a check on the format of the string before passing ref_date to Timestamp to ensure this does not return erroneous times without warning?**

The code sample above is a simple example where a format acceptable by udunits and used by the Atmospheric Radiation Measurement program, returns an erroneous time (00:00:00 instead of 22:00:02).  use_cftime is an option and this is my fault for not knowing, but I did not know this was even an issue until we had time units that start at something other than 00:00 utc like:

time:units = ""seconds since 2019-04-02 22:00:02 0:00"" ;

Thank you ahead of time for review of this issue.  I've search a lot over the past couple days to see if a related issue was open but had no luck.  Apologies if I missed something.

#### Output of ``xr.show_versions()``
<details>
# Paste the output here xr.show_versions() here
INSTALLED VERSIONS
------------------
commit: None
python: 3.7.4 (default, Aug 13 2019, 15:17:50) 
[Clang 4.0.1 (tags/RELEASE_401/final)]
python-bits: 64
OS: Darwin
OS-release: 18.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: None
LANG: en_US.UTF-8
LOCALE: en_US.UTF-8
libhdf5: 1.10.4
libnetcdf: 4.6.1

xarray: 0.13.0
pandas: 0.25.2
numpy: 1.17.2
scipy: 1.3.1
netCDF4: 1.4.2
pydap: None
h5netcdf: 0.7.4
h5py: 2.9.0
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: 2.6.0
distributed: 2.6.0
matplotlib: 3.1.1
cartopy: 0.17.0
seaborn: 0.9.0
numbagg: None
setuptools: 41.4.0
pip: 19.3.1
conda: 4.7.12
pytest: 5.2.1
IPython: 7.8.0
sphinx: 2.2.0
</details>
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