diff --git a/intermediate/xarray_and_dask.ipynb b/intermediate/xarray_and_dask.ipynb
index 4de7a1ce..52b7decf 100644
--- a/intermediate/xarray_and_dask.ipynb
+++ b/intermediate/xarray_and_dask.ipynb
@@ -230,9 +230,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
@@ -246,16 +243,13 @@
    "source": [
     "### Exercise\n",
     "\n",
-    "Try calling `mean.values` and `mean.data`. Do you understand the difference?"
+    "Try calling `ds.air.values` and `ds.air.data`. Do you understand the difference?"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
@@ -331,7 +325,7 @@
     "2. `.load()` replaces the dask array in the xarray object with a numpy array.\n",
     "   This is equivalent to `ds = ds.compute()`\n",
     "   \n",
-    "**Tip:** There is a third option : \"persisting\". `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So `ds.air.persist()` still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. You will see a persistent task on the dashboard. See the [dask user guide](https://docs.dask.org/en/latest/api.html#dask.persist) for more on persisting\n"
+    "**Tip:** There is a third option : \"persisting\". `.persist()` loads the values into distributed RAM. The values are computed but remain distributed across workers. So `ds.air.persist()` still returns a dask array. This is useful if you will be repeatedly using a dataset for computation but it is too large to load into local memory. You will see a persistent task on the dashboard. See the [dask user guide](https://docs.dask.org/en/latest/api.html#dask.persist) for more on persisting"
    ]
   },
   {
@@ -347,9 +341,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
@@ -446,7 +437,10 @@
     "\n",
     "You can use any kind of Dask cluster. This step is completely independent of\n",
     "xarray. While not strictly necessary, the dashboard provides a nice learning\n",
-    "tool."
+    "tool.\n",
+    "\n",
+    "By default, Dask uses the current working directory for writing temporary files.\n",
+    "We choose to use a temporary scratch folder `local_directory='/tmp'` in the example below instead."
    ]
   },
   {
@@ -464,10 +458,17 @@
     "# if os.environ.get('JUPYTERHUB_USER'):\n",
     "#    dask.config.set(**{\"distributed.dashboard.link\": \"/user/{JUPYTERHUB_USER}/proxy/{port}/status\"})\n",
     "\n",
-    "client = Client(local_directory='/tmp')\n",
+    "client = Client()\n",
     "client"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -483,9 +484,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [],
@@ -539,9 +537,6 @@
    "cell_type": "code",
    "execution_count": null,
    "metadata": {
-    "jupyter": {
-     "outputs_hidden": true
-    },
     "tags": []
    },
    "outputs": [],