diff --git a/doc/jupyter/Demo/Demo_6_ENSO.ipynb b/doc/jupyter/Demo/Demo_6_ENSO.ipynb
index 638ddea3f..74c4c4671 100644
--- a/doc/jupyter/Demo/Demo_6_ENSO.ipynb
+++ b/doc/jupyter/Demo/Demo_6_ENSO.ipynb
@@ -2,6 +2,7 @@
  "cells": [
   {
    "cell_type": "markdown",
+   "id": "f02518f6",
    "metadata": {},
    "source": [
     "# ENSO"
@@ -9,10 +10,15 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1bb7a1ac",
    "metadata": {},
    "source": [
     "This notebook provides an overview of running the ENSO metrics. More information can be found in the [README]( ). Example parameter files are located in the [PMP sample setups]( ). \n",
     "\n",
+    "**Reference**: Planton, Y., E. Guilyardi, A. T. Wittenberg, J. Lee, P. J. Gleckler, T. Bayr, S. McGregor, M. J. McPhaden, S. Power, R. Roehrig, A. Voldoire, 2020: Evaluating El Niño in climate models with the CLIVAR 2020 ENSO metrics package. Bulletin of the American Meteorological Society. [doi: 10.1175/BAMS-D-19-0337.1](https://doi.org/10.1175/BAMS-D-19-0337.1)\n",
+    "\n",
+    "Description for individual metrics can be found at https://github.com/CLIVAR-PRP/ENSO_metrics/wiki.\n",
+    "\n",
     "### Requirements\n",
     "\n",
     "The first two sections of this notebook help to set up the demo data and environment.\n",
@@ -24,6 +30,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "7a6383bf",
    "metadata": {},
    "source": [
     "## Download demo data"
@@ -31,6 +38,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "cdc4b9e2",
    "metadata": {},
    "source": [
     "The ENSO metric requires a different set of sample data than the rest of the PMP metrics. This section of the notebook will download that data to your chosen location and generate a basic parameter file."
@@ -39,6 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": 1,
+   "id": "da5d715f",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -51,6 +60,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ec2d5a6a",
    "metadata": {},
    "source": [
     "If you want to change the location where the demo data and output are stored, you can do so here:"
@@ -59,6 +69,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
+   "id": "8e7cc829",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -70,6 +81,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "f2386574",
    "metadata": {},
    "source": [
     "Then download the data. The total sample data size is 10.8 GB. This will take several minutes."
@@ -78,6 +90,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
+   "id": "520aa1c5",
    "metadata": {},
    "outputs": [
     {
@@ -101,6 +114,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "11aa0d9f",
    "metadata": {},
    "source": [
     "After downloading the data, we generate the parameter file for this demo."
@@ -109,6 +123,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
+   "id": "bf31cbd1",
    "metadata": {},
    "outputs": [
     {
@@ -128,6 +143,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "81ab1e49",
    "metadata": {},
    "source": [
     "## Environment"
@@ -135,6 +151,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "647d09fa",
    "metadata": {},
    "source": [
     "[ENSO Metrics package](https://github.com/CLIVAR-PRP/ENSO_metrics) and [scipy](https://www.scipy.org/) installations are needed. This section will clone the ENSO Metrics repository and *install ENSO Metrics and scipy in your current conda environment*. Set the `enso_install_location` below to chose where to clone the ENSO Metrics repository."
@@ -143,6 +160,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
+   "id": "d632f1ab",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -151,6 +169,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ed720426",
    "metadata": {},
    "source": [
     "To clone and install the ENSO Metrics package, un-comment the next cell and run it (delete quotes in lines 1 & 6)."
@@ -159,6 +178,7 @@
   {
    "cell_type": "code",
    "execution_count": 6,
+   "id": "aecd5bcc",
    "metadata": {
     "scrolled": true
    },
@@ -186,6 +206,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "137de9c7",
    "metadata": {},
    "source": [
     "To install scipy, un-comment the next cell and run it (delete quotes in lines 1 & 3)."
@@ -194,6 +215,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
+   "id": "046c7468",
    "metadata": {},
    "outputs": [
     {
@@ -215,6 +237,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "ab7ac9e8",
    "metadata": {},
    "source": [
     "## Usage"
@@ -222,6 +245,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "44577389",
    "metadata": {},
    "source": [
     "The ENSO driver can be run from the command line as `enso_driver.py`. In this notebook, we will use bash cell magic (cells beginning with `%%bash`) to run the ENSO driver as a subprocess."
@@ -229,6 +253,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "b9059757",
    "metadata": {},
    "source": [
     "For help, type:  \n",
@@ -240,6 +265,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
+   "id": "c113d824",
    "metadata": {},
    "outputs": [
     {
@@ -324,6 +350,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "0701dc96",
    "metadata": {},
    "source": [
     "### Basic example"
@@ -331,6 +358,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "46928faf",
    "metadata": {},
    "source": [
     "Parameters for the ENSO Metrics can be set on the command line or using a parameter file. This first example will use a parameter file, which is shown below."
@@ -339,6 +367,7 @@
   {
    "cell_type": "code",
    "execution_count": 9,
+   "id": "70a6b070",
    "metadata": {},
    "outputs": [
     {
@@ -386,6 +415,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "92d0e015",
    "metadata": {},
    "source": [
     "The next cell runs the ENSO driver using the basic parameter file. This may take several minutes."
@@ -394,6 +424,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
+   "id": "5f8ef3f9",
    "metadata": {
     "scrolled": false
    },
@@ -642,6 +673,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "1da57cf2",
    "metadata": {},
    "source": [
     "This run saved metrics to two files:  \n",
@@ -655,6 +687,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
+   "id": "32ffb4a4",
    "metadata": {},
    "outputs": [
     {
@@ -978,6 +1011,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "d25a4fb7",
    "metadata": {},
    "source": [
     "### ENSO Metrics Collections\n",
@@ -995,6 +1029,7 @@
   {
    "cell_type": "code",
    "execution_count": 12,
+   "id": "931a02c5",
    "metadata": {},
    "outputs": [
     {
@@ -1179,6 +1214,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "da0956e3",
    "metadata": {},
    "source": [
     "All of the results (netCDF and JSON) are located in the output directory, which uses the metrics collection name."
@@ -1187,6 +1223,7 @@
   {
    "cell_type": "code",
    "execution_count": 13,
+   "id": "d297d2e5",
    "metadata": {},
    "outputs": [
     {
@@ -1209,6 +1246,7 @@
   },
   {
    "cell_type": "markdown",
+   "id": "2cd7ff92",
    "metadata": {},
    "source": [
     "Finally, this example runs the remaining metrics collection ENSO_proc:"
@@ -1217,6 +1255,7 @@
   {
    "cell_type": "code",
    "execution_count": 14,
+   "id": "4ec6cdde",
    "metadata": {},
    "outputs": [
     {
@@ -1582,7 +1621,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.8.6"
+   "version": "3.8.10"
   }
  },
  "nbformat": 4,