diff --git a/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb b/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
index 29e27fc1147ae86d788b89aaf79b6c4ccf2bfcf2..58161e5ad0b7268bc9b395e155db170d92319bf4 100644
--- a/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
+++ b/workflows/examples/create_collection_from_zarr_conus404-daily.ipynb
@@ -194,6 +194,16 @@
     "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "239d3b00-77f9-4178-954b-ba81a2b34512",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crs_var = 'crs'"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -202,7 +212,7 @@
    "outputs": [],
    "source": [
     "# use pyproj to automatically extract crs info\n",
-    "crs = pyproj.CRS.from_cf(ds.crs.attrs)\n",
+    "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
     "\n",
     "# alternatively, create the appropriate cartopy projection\n",
     "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
@@ -821,7 +831,7 @@
     "vars_dict={}\n",
     "for v in vars:\n",
     "    unit = stac_helpers.get_unit(ds, v)\n",
-    "    var_type = stac_helpers.get_var_type(ds, v)\n",
+    "    var_type = stac_helpers.get_var_type(ds, v, crs_var)\n",
     "    long_name = stac_helpers.get_long_name(ds, v)\n",
     "    vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})"
    ]
diff --git a/workflows/examples/create_item_from_zarr_conus404-daily.ipynb b/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
index 250d7781293cd412339ac5f60cac26d0e7a24749..ad517d534a202cf8c06b1d087f4fbe54bd6612c6 100644
--- a/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
+++ b/workflows/examples/create_item_from_zarr_conus404-daily.ipynb
@@ -178,6 +178,16 @@
     "If there is no crs info that can be automatically extracted from the dataset with pyproj, you will need to manually identify the crs and create a crs object. This reference list of cartopy projections may be a helpful resource: https://scitools.org.uk/cartopy/docs/latest/reference/projections.html"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6bae3ab0-b0cc-4486-aaaf-209c4d30ec1f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "crs_var = 'crs'"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -186,7 +196,7 @@
    "outputs": [],
    "source": [
     "# use pyproj to automatically extract crs info\n",
-    "crs = pyproj.CRS.from_cf(ds.crs.attrs)\n",
+    "crs = pyproj.CRS.from_cf(ds[crs_var].attrs)\n",
     "\n",
     "# alternatively, create the appropriate cartopy projection\n",
     "# crs = ccrs.LambertConformal(central_longitude=crs_info.longitude_of_central_meridian, \n",
@@ -808,7 +818,7 @@
     "vars_dict={}\n",
     "for v in vars:\n",
     "    unit = stac_helpers.get_unit(ds, v)\n",
-    "    var_type = stac_helpers.get_var_type(ds, v)\n",
+    "    var_type = stac_helpers.get_var_type(ds, v, crs_var)\n",
     "    long_name = stac_helpers.get_long_name(ds, v)\n",
     "    vars_dict[v] = pystac.extensions.datacube.Variable({'dimensions':list(ds[v].dims), 'type': var_type, 'description': long_name, 'unit': unit})"
    ]