Regridding to reference dataset fails sometimes

[schlunma]

Not an urgent issue, but something I noticed:

Regridding to a reference dataset, e.g.

regrid_to_ref:
  regrid:
    target_grid: GFDL-CM3
    scheme: linear

invokes a simple iris.load_cube

https://github.com/ESMValGroup/ESMValTool/blob/43598ee48a327fc3ef2a6e53768324d02ad61863/esmvaltool/preprocessor/_regrid.py#L169

on the raw nc file, which may fail in rare cases if the file contains multiple variables (e.g rsdt of GFDL-CM3), which leads to errors like this:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "~/anaconda3/envs/esmvaltool/lib/python3.6/site-packages/iris/__init__.py", line 376, in load_cube
    raise iris.exceptions.ConstraintMismatchError(str(e))
iris.exceptions.ConstraintMismatchError: failed to merge into a single cube.
  cube.long_name differs: 'Length of average period' != 'End time for average period'
  cube.var_name differs: 'average_DT' != 'average_T2'
  cube.units differs: Unit('days') != Unit('days since 1860-01-01 00:00:00', calendar='gregorian')

I think we need an advanced loading function here.

[bouweandela]

Yes, if the dataset needs cmorization before the grid can be read it doesn't work. This should probably be fixed similar to how it is done for vertical levels in the function esmvaltool.preprocessor._regrid.get_reference_levels.

[valeriupredoi]

this needs just the MxN grid dimensionality so a single iris.load(file, constraints=constraint) where constraint is on the var_name:

    constraint = iris.Constraint(
        cube_func=(lambda c: c.var_name == VARIABLE))

should do fine, no need to CMORize twice the same file

[bouweandela]

That would work for this particular case but not in general.

[valeriupredoi]

what sort of general case you thinking about @bouweandela

[bouweandela]

Where the cmorization error is different from containing multiple variables in a single file.