Sorry for taking so long to get to this @JuliaN - I was sick last week.
I think this difference is due to the files for dates prior to 2000, which you are including when using Intake-ESM, but not when you open directly with xr.open_mfdataset. Are these files formatted differently that the later dates?
If I restrict the Intake-esm approach to files post 2000, I can run your analysis without issue:
esmds = intake.open_esm_datastore("~/catalog/panant.json", columns_with_iterables=["variable"])
shelf_mask = xr.open_dataset('/g/data/ik11/grids/Antarctic_slope_contour_1000m_MOM6_005deg.nc')['contour_masked_above']
area = esmds.search(variable='areacello', path=[".*output021.*"]).to_dask()['areacello']
def preprocess(ds):
return ds.sel(yh=shelf_mask['yh'], z_l=slice(None,500))
ds = esmds.search(
variable=["thetao", "volcello"],
frequency="1mon",
filename=".*ocean_month_z",
start_date="^2.*" # 2000s+ - this will be easier/nicer when date-based searching is implemented
).to_dask(
xarray_open_kwargs=dict(
chunks="auto"
),
preprocess=preprocess
)
temp = ds['thetao']
volc = ds['volcello']
thik = ds['volcello']/area
temp.isel(z_l=0, time=0).where(shelf_mask==0).plot()