`missing_dims` option for aggregation methods like `mean` and `std`

I work a lot with climate model output and often loop over several models, of which some have a 'member' dimension and others don't.

I end up writing many lines like this:

for ds in model_datasets:
    if 'member_id' in ds.dims:
        ds = ds.mean('member_id)

Which often makes for very lengthy code blocks.

I recently noticed that .isel() actually has a nifty keyword argument 'missing_dims', which enables the user to apply isel and it just doesn't do anything when the dimension is not present.

I'd love to be able to do:

for ds in model_datasets:
    ds = ds.mean('member_id', missing_dims='ignore')

Is there a way to implement this generally for xarray aggregation methods (mean/max/min/std/...). Or is there a reason this should be avoided?

Start by comparing the existing isel missing_dims behavior with aggregation methods such as mean, max, min, and std. Determine the supported scope for a common option and define done as consistent handling of dimensions that are present and absent, with tests covering both cases.