[Feature]: Refactor `clisccp.py` and `clmodis.py` with `handlers.yaml`

[tomvothecoder]

Is your feature request related to a problem?

clisccp.py and clmodis.py has additional dimensions we need to handle during cmorization and formulas that we need to add to formulas.py.

• Link:
  • https://github.com/E3SM-Project/e3sm_to_cmip/blob/master/e3sm_to_cmip/cmor_handlers/vars/clisccp.py
  • https://github.com/E3SM-Project/e3sm_to_cmip/blob/master/e3sm_to_cmip/cmor_handlers/vars/clmodis.py

Describe the solution you'd like

1. Extend

   e3sm_to_cmip/e3sm_to_cmip/cmor_handlers/handler.py

   Lines 443 to 504 in baaf12c

   	def _get_cmor_axis_ids_and_ips_id(
   	self, ds: xr.Dataset, time_dim: str | None
   	) -> tuple[dict[str, int], int | None]:
   	"""Create the CMOR axes objects, which are set globally in the CMOR module.
   	The CMOR ids for "time" and "lev" should be the starting elements of the
   	axis ID map to align with how they are defined in CMOR tables.
   	Otherwise, CMOR will need to reorder the dimensions and add the message
   	below to the "history" attribute of a variable: "2023-11-02T21:06:00Z
   	altered by CMOR: Reordered dimensions, original order: lat lon time.",
   	which can produce unwanted side-effects with the structure/shape of
   	the final array axes.
   	Parameters
   	----------
   	ds : xr.Dataset
   	The dataset containing axes information.
   	time_dim : str | None
   	An optional time dimension for the output CMIP variable (if set).
   	Returns
   	-------
   	tuple[dict[str, int], int | None]
   	A tuple with the first element being a dictionary (value is the
   	CMOR axis and key is the ID of the CMOR axis), and the second
   	element being the CMOR zfactor ID for ips if the dataset and handler
   	have hybrid sigma levels.
   	Example:
   	({"time": 0, "lev": 1, "lat": 2, "lon": 3}, 4)
   	"""
   	axis_id_map: dict[str, int] = {}
   	cmor_ips_id = None
   	if time_dim is not None:
   	units = ds[time_dim].attrs["units"]
   	axis_id_map["time"] = cmor.axis(time_dim, units=units)
   	if self.levels is not None:
   	axis_id_map["lev"] = self._get_cmor_lev_axis_id(ds)
   	# Datasets will always have a "lat" and "lon" dimension.
   	axis_id_map["lat"] = cmor.axis(
   	"latitude",
   	units=ds["lat"].units,
   	coord_vals=ds["lat"].values,
   	cell_bounds=ds["lat_bnds"].values,
   	)
   	axis_id_map["lon"] = cmor.axis(
   	"longitude",
   	units=ds["lon"].units,
   	coord_vals=ds["lon"].values,
   	cell_bounds=ds["lon_bnds"].values,
   	)
   	if self._has_hybrid_sigma_levels(ds):
   	self._set_cmor_zfactor_for_hybrid_levels(ds, axis_id_map)
   	cmor_ips_id = self._set_and_get_cmor_zfactor_ips_id(axis_id_map)
   	return axis_id_map, cmor_ips_id

Describe alternatives you've considered

No response

Additional context

No response

[chengzhuzhang]

Linking some discussion from #307 (comment)