CF coordinates get forgotten after operation modifying coordinates

Start by reproducing the shown DataArray example and compare d.cf.coordinates with d.mean("time").cf.coordinates. Trace how CF coordinates are identified before and after the time reduction; done means the reduced result keeps TLON as longitude and TLAT as latitude without adding ULON or ULAT.

I have a DataArray that's similar, but not quite the same as the popds example dataset (the axes aren't labelled, and it has a time dimension):

<xarray.DataArray 'Tair_m' (time: 1, nj: 1080, ni: 1440)>
array([[[nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan],
        ...,
        [nan, nan, ..., nan, nan],
        [nan, nan, ..., nan, nan]]], dtype=float32)
Coordinates:
  * time     (time) datetime64[ns] 2255-01-01
    TLON     (nj, ni) float32 nan nan nan nan nan nan ... nan nan nan nan nan
    TLAT     (nj, ni) float32 nan nan nan nan nan nan ... nan nan nan nan nan
    ULON     (nj, ni) float32 nan nan nan nan nan nan ... nan nan nan nan nan
    ULAT     (nj, ni) float32 nan nan nan nan nan nan ... nan nan nan nan nan
Dimensions without coordinates: nj, ni
Attributes:
    units:          C
    long_name:      air temperature
    cell_measures:  area: tarea
    cell_methods:   time: mean
    time_rep:       averaged

And of course, the coordinates get correctly decoded:

>>> d.cf.coordinates
{'longitude': ['TLON'], 'latitude': ['TLAT']}

But if I take the time mean of this dataset, the correct coordinates get forgotten:

>>> d.mean("time").cf.coordinates
{'longitude': ['TLON', 'ULON'], 'latitude': ['TLAT', 'ULAT']}