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
438694589,MDU6SXNzdWU0Mzg2OTQ1ODk=,2932,Facetgrid: colors beyond range (extend) not saturated,7933853,closed,0,,,5,2019-04-30T09:56:46Z,2020-03-29T13:26:43Z,2020-03-29T13:26:42Z,NONE,,,,"#### Code Sample, a copy-pastable example if possible

![Screen Shot 2019-04-30 at 11 59 30](https://user-images.githubusercontent.com/7933853/56954588-70238b00-6b3f-11e9-90ad-81045e500987.png)


Minimal example here:
https://github.com/lvankampenhout/bug-reports/blob/master/Facetgrid_cmap_extend.ipynb

#### Problem description

The extreme colors of neither the pcolormesh or colorbar (using extend='both') are not saturated as they should when faceting.

#### Output of ``xr.show_versions()``

<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.8 |Anaconda custom (x86_64)| (default, Dec 29 2018, 19:04:46) 
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: C
LANG: None
LOCALE: None.None
libhdf5: 1.10.1
libnetcdf: 4.4.1.1

xarray: 0.12.1
pandas: 0.23.4
numpy: 1.14.2
scipy: 0.18.1
netCDF4: 1.3.1
pydap: None
h5netcdf: None
h5py: 2.7.1
Nio: None
zarr: None
cftime: 1.0.0b1
nc_time_axis: None
PseudonetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.0
dask: 0.13.0
distributed: None
matplotlib: 3.0.2
cartopy: 0.16.0
seaborn: 0.7.1
setuptools: 38.5.1
pip: 9.0.1
conda: 4.6.14
pytest: 3.0.5
IPython: 5.1.0
sphinx: 1.5.1
</details>
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548029687,MDU6SXNzdWU1NDgwMjk2ODc=,3681,concat result not correct for particular dataset,7933853,closed,0,,,7,2020-01-10T11:34:06Z,2020-03-13T13:25:13Z,2020-03-13T13:25:13Z,NONE,,,,"

#### MCVE Code Sample
data here: https://www.dropbox.com/sh/8eist9mmlf41mpc/AAB8yp6ERz-b4VYozL8tsj-ma?dl=0

```
import xarray as xr
import numpy as np

print(xr.__version__) #

ds1 = xr.open_dataset('tas_Amon_NorESM1-ME_rcp26_r1i1p1_200601-206012.nc')
ds2 = xr.open_dataset('tas_Amon_NorESM1-ME_rcp26_r1i1p1_206101-210112.nc')

print(np.allclose(ds1.lat , ds2.lat), np.allclose(ds1.lat_bnds , ds2.lat_bnds)) # True, True

ds3 = xr.concat((ds1,ds2), dim='time')

print(ds3.lat.shape == ds1.lat.shape) # False
```

#### Expected Output
```
0.14.1
True True
True
```

since ds3.lat should be identical to ds1.lat

#### Problem Description
I've encountered a particular NetCDF dataset which is not handled correctly by the Xarray `concat` operation. It's a climate dataset with 96 latitude points which has been split into two time segments. After concatenation (dim = 'time') there are suddenly 142 latitude points even though the latitude arrays are completely identical AFAIK. 

As a workaround, I've tried to reindex the result (`ds3`) as follows
```
ds4 = ds3.reindex_like(ds1.drop_dims('time'))
```

but that yields an incomplete field after the year 2061. This can be seen by issuing: 
```
ds4.tas.isel(time=-1).plot()
```
with white areas indicating missing data. There is no missing data in the source files.


#### Output of ``xr.show_versions()``
<details>
INSTALLED VERSIONS
------------------
commit: None
python: 3.6.9 |Anaconda, Inc.| (default, Jul 30 2019, 13:42:17) 
[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]
python-bits: 64
OS: Darwin
OS-release: 17.7.0
machine: x86_64
processor: i386
byteorder: little
LC_ALL: C
LANG: None
LOCALE: None.None
libhdf5: 1.10.4
libnetcdf: 4.6.1

xarray: 0.14.1
pandas: 0.25.1
numpy: 1.16.4
scipy: 1.3.1
netCDF4: 1.4.2
pydap: None
h5netcdf: None
h5py: 2.9.0
Nio: None
zarr: None
cftime: 1.0.0b1
nc_time_axis: None
PseudoNetCDF: None
rasterio: None
cfgrib: None
iris: None
bottleneck: 1.2.1
dask: 2.3.0
distributed: 2.3.2
matplotlib: 3.0.2
cartopy: 0.17.0
seaborn: 0.9.0
numbagg: None
setuptools: 41.0.1
pip: 19.2.2
conda: 4.8.1
pytest: 5.0.1
IPython: 7.7.0
sphinx: 2.1.2
</details>
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