I want to bring up an issue that has tripped up my workflow with large climate models many times. I am dealing with large data arrays of vertical cell thickness. These are 4d arrays (x, y, z, time) but I would define them as coordinates, not data_variables in the xarrays data model (e.g. they should not be multiplied by a value if a dataset is multiplied). These sort of coordinates might become more prevalent with newer ocean models like MOM6

Whenever I assign these arrays as coordinates operations on the arrays seem to trigger computation, whereas they don't if I set them up as data_variables. The example below shows this behavior. Is this a bug or done on purpose? Is there a workaround to keep these vertical thicknesses as coordinates?

``` import xarray as xr import numpy as np import dask.array as dsa

create dataset with with vertical thickness dz as data variable

data = xr.DataArray(dsa.random.random([30, 50, 200, 1000]), dims=['x','y', 'z', 't']) dz = xr.DataArray(dsa.random.random([30, 50, 200, 1000]), dims=['x','y', 'z', 't']) ds = xr.Dataset({'data':data, 'dz':dz})

another dataset with dz as coordinate

ds_new = xr.Dataset({'data':data}) ds_new.coords['dz'] = dz ```

%%time test = ds['data'] * ds['dz'] CPU times: user 1.94 ms, sys: 19.1 ms, total: 21 ms Wall time: 21.6 ms

%%time test = ds_new['data'] * ds_new['dz'] CPU times: user 17.4 s, sys: 1.98 s, total: 19.4 s Wall time: 12.5 s

Output of xr.show_versions()