The first example works because there's no index.

In the second example, a PandasIndex is created from the scalar value (wrapped in a sequence) so that concat works on the "time" dimension (required since the logic has moved to Index.concat). See https://github.com/pydata/xarray/pull/5692#discussion_r820581305.

The problem is when creating a PandasIndex we call pd.Index, which doesn't seem to create the right kind of index given the value type:

```python array = da.isel(time=0).values value = array.item() seq = np.array([value], dtype=array.dtype) pd.Index(seq, dtype=array.dtype)

Float64Index([1.0], dtype='float64')

```

So in the example above you end-up with different index types, which xr.align doesn't like:

```python concat.indexes["time"]

Index([1356998400000000000, 2013-01-01 06:00:00], dtype='object', name='time')

da.indexes["time"]

DatetimeIndex(['2013-01-01 00:00:00', '2013-01-01 06:00:00'], dtype='datetime64[ns]', name='time', freq=None)

concat.indexes["time"].equals(da.indexes["time"])

False

```

I'm not very satisfied with the current solution in concat but I'm not sure what we should do here:

• Special case for datetime, and other value types?
• Review the approach used to concatenate scalar coordinates (no-index) and indexed array coordinates?
• Depreciate concatenating a mix of scalar coordinates and indexed coordinates?