diff --git a/doc/examples/multidimensional-coords.ipynb b/doc/examples/multidimensional-coords.ipynb
index f095d1137de..f7471f05e5d 100644
--- a/doc/examples/multidimensional-coords.ipynb
+++ b/doc/examples/multidimensional-coords.ipynb
@@ -175,7 +175,7 @@
     "# define a label for each bin corresponding to the central latitude\n",
     "lat_center = np.arange(1, 90, 2)\n",
     "# group according to those bins and take the mean\n",
-    "Tair_lat_mean = ds.Tair.groupby_bins(\"xc\", lat_bins, labels=lat_center).mean(\n",
+    "Tair_lat_mean = ds.Tair.groupby_bins(\"yc\", lat_bins, labels=lat_center).mean(\n",
     "    dim=xr.ALL_DIMS\n",
     ")\n",
     "# plot the result\n",
@@ -186,7 +186,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "The resulting coordinate for the `groupby_bins` operation got the `_bins` suffix appended: `xc_bins`. This help us distinguish it from the original multidimensional variable `xc`.\n",
+    "The resulting coordinate for the `groupby_bins` operation got the `_bins` suffix appended: `yc_bins`. This help us distinguish it from the original multidimensional variable `yc`.\n",
     "\n",
     "**Note**: This group-by-latitude approach does not take into account the finite-size geometry of grid cells. It simply bins each value according to the coordinates at the cell center. Xarray has no understanding of grid cells and their geometry. More precise geographic regridding for xarray data is available via the [xesmf](https://xesmf.readthedocs.io) package."
    ]