diff --git a/core/xarray/computation-masking.ipynb b/core/xarray/computation-masking.ipynb
index 3e3c29f5..c4cc2875 100644
--- a/core/xarray/computation-masking.ipynb
+++ b/core/xarray/computation-masking.ipynb
@@ -335,11 +335,10 @@
    "id": "5d176ad8-15f1-4ecc-ab3e-898cef3b4e18",
    "metadata": {},
    "source": [
-    "<div class=\"admonition alert alert-info\">\n",
+    "```{admonition} Info\n",
+    ":class: tip\n",
     "\n",
-    "In the above code example, we are extracting components of date/time data by way of the time coordinate's `.dt` attribute. This attribute is a `DatetimeAccessor` object that contains additional attributes for units of time, such as hour, day, and year. Since we are splitting the data into monthly data, we use the `month` attribute of .dt in this example. (In addition, there exists similar functionality in Pandas; see the [official documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.month.html) for details.)\n",
-    "    \n",
-    "   </div>"
+    "In the above code example, we are extracting components of date/time data by way of the time coordinate's `.dt` attribute. This attribute is a `DatetimeAccessor` object that contains additional attributes for units of time, such as hour, day, and year. Since we are splitting the data into monthly data, we use the `month` attribute of .dt in this example. (In addition, there exists similar functionality in Pandas; see the [official documentation](https://pandas.pydata.org/docs/reference/api/pandas.Series.dt.month.html) for details.)"
    ]
   },
   {
@@ -505,12 +504,10 @@
    "id": "d9f768be-a960-4417-bb1e-9785ca9ca4ea",
    "metadata": {},
    "source": [
-    "<div class=\"admonition alert alert-warning\">\n",
-    "   \n",
+    "```{admonition} Info\n",
+    ":class: warning\n",
     "\n",
-    "Many geoscientific algorithms perform operations over data contained in many different grid cells. However, if the grid cells are not equivalent in size, the operation is not scientifically valid by default. Fortunately, this can be fixed by weighting the data in each grid cell by the size of the cell. Weighting data in Xarray is simple, as Xarray has a built-in weighting method, known as [`.weighted()`](https://xarray.pydata.org/en/stable/user-guide/computation.html#weighted-array-reductions).\n",
-    "\n",
-    "</div>\n"
+    "Many geoscientific algorithms perform operations over data contained in many different grid cells. However, if the grid cells are not equivalent in size, the operation is not scientifically valid by default. Fortunately, this can be fixed by weighting the data in each grid cell by the size of the cell. Weighting data in Xarray is simple, as Xarray has a built-in weighting method, known as [`.weighted()`](https://xarray.pydata.org/en/stable/user-guide/computation.html#weighted-array-reductions).\n"
    ]
   },
   {
diff --git a/core/xarray/dask-arrays-xarray.ipynb b/core/xarray/dask-arrays-xarray.ipynb
index 4f8e4996..02ee0dc4 100644
--- a/core/xarray/dask-arrays-xarray.ipynb
+++ b/core/xarray/dask-arrays-xarray.ipynb
@@ -167,12 +167,10 @@
    "id": "ebbafe88-bb79-436c-aa3b-a9c5f31ff1ec",
    "metadata": {},
    "source": [
-    "<div class=\"admonition alert alert-info\">\n",
-    "    <p class=\"admonition-title\" style=\"font-weight:bold\">Specifying Chunks</p>\n",
-    "    In this tutorial, we specify Dask Array chunks in a block shape. However, there are many additional ways to specify chunks; see <a href=\"https://docs.dask.org/en/latest/array-chunks.html\">this documentation</a> for more details.\n",
+    "```{admonition} Specifying Chunks\n",
+    ":class: tip\n",
     "\n",
-    "</div>\n",
-    "\n"
+    "In this tutorial, we specify Dask Array chunks in a block shape. However, there are many additional ways to specify chunks; see [this documentation](https://docs.dask.org/en/latest/array-chunks.html) for more details."
    ]
   },
   {