diff --git a/_toc.yml b/_toc.yml
index 97d53f51..ea322130 100644
--- a/_toc.yml
+++ b/_toc.yml
@@ -15,6 +15,7 @@ parts:
     chapters:
       - file: notebooks/advanced/Parquet_Reference_Storage
       - file: notebooks/advanced/Pangeo_Forge
+      - file: notebooks/advanced/appending
 
   - caption: Generating Reference Files
     chapters:
diff --git a/notebooks/advanced/appending.ipynb b/notebooks/advanced/appending.ipynb
new file mode 100644
index 00000000..95be830d
--- /dev/null
+++ b/notebooks/advanced/appending.ipynb
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Appending to Kerchunk references\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Overview\n",
+    "\n",
+    "In this tutorial we'll show how to append to a pre-existing Kerchunk reference. We'll use the same datasets as in the [NetCDF reference generation](../generating_references/NetCDF.ipynb) example. \n",
+    "\n",
+    "## Prerequisites\n",
+    "| Concepts | Importance | Notes |\n",
+    "| --- | --- | --- |\n",
+    "| [Kerchunk Basics](../foundations/kerchunk_basics) | Required | Core |\n",
+    "| [Multiple Files and Kerchunk](../foundations/kerchunk_multi_file) | Required | Core |\n",
+    "| [Multi-File Datasets with Kerchunk](../case_studies/ARG_Weather.ipynb) | Required | IO/Visualization |\n",
+    "\n",
+    "- **Time to learn**: 45 minutes\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Imports"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import logging\n",
+    "from tempfile import TemporaryDirectory\n",
+    "\n",
+    "import dask\n",
+    "import fsspec\n",
+    "import ujson\n",
+    "import xarray as xr\n",
+    "from distributed import Client\n",
+    "from kerchunk.combine import MultiZarrToZarr\n",
+    "from kerchunk.hdf import SingleHdf5ToZarr"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create Input File List\n",
+    "\n",
+    "Here we are using `fsspec's` glob functionality along with the *`*`* wildcard operator and some string slicing to grab a list of NetCDF files from a `s3` `fsspec` filesystem. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initiate fsspec filesystems for reading\n",
+    "fs_read = fsspec.filesystem(\"s3\", anon=True, skip_instance_cache=True)\n",
+    "\n",
+    "files_paths = fs_read.glob(\n",
+    "    \"s3://smn-ar-wrf/DATA/WRF/DET/2022/12/31/12/WRFDETAR_01H_20221231_12_*\"\n",
+    ")\n",
+    "\n",
+    "# Here we prepend the prefix 's3://', which points to AWS.\n",
+    "file_pattern = sorted([\"s3://\" + f for f in files_paths])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# This dictionary will be passed as kwargs to `fsspec`. For more details, check out the\n",
+    "# `foundations/kerchunk_basics` notebook.\n",
+    "so = dict(mode=\"rb\", anon=True, default_fill_cache=False, default_cache_type=\"first\")\n",
+    "\n",
+    "# We are creating a temporary directory to store the .json reference files\n",
+    "# Alternately, you could write these to cloud storage.\n",
+    "td = TemporaryDirectory()\n",
+    "temp_dir = td.name"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Start a Dask Client\n",
+    "\n",
+    "To parallelize the creation of our reference files, we will use `Dask`. For a detailed guide on how to use Dask and Kerchunk, see the Foundations notebook: [Kerchunk and Dask](../foundations/kerchunk_dask).\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "client = Client(n_workers=8, silence_logs=logging.ERROR)\n",
+    "client"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a `Kerchunk` reference file for the first 24 hours"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "first_24_hrs = file_pattern[:24]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Use Kerchunk's `SingleHdf5ToZarr` method to create a `Kerchunk` index from\n",
+    "# a NetCDF file.\n",
+    "\n",
+    "\n",
+    "def generate_json_reference(fil, output_dir: str):\n",
+    "    with fs_read.open(fil, **so) as infile:\n",
+    "        h5chunks = SingleHdf5ToZarr(infile, fil, inline_threshold=300)\n",
+    "        fname = fil.split(\"/\")[-1].strip(\".nc\")\n",
+    "        outf = f\"{output_dir}/{fname}.json\"\n",
+    "        with open(outf, \"wb\") as f:\n",
+    "            f.write(ujson.dumps(h5chunks.translate()).encode())\n",
+    "        return outf\n",
+    "\n",
+    "\n",
+    "# Generate Dask Delayed objects\n",
+    "tasks = [dask.delayed(generate_json_reference)(fil, temp_dir) for fil in first_24_hrs]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Start parallel processing\n",
+    "import warnings\n",
+    "\n",
+    "warnings.filterwarnings(\"ignore\")\n",
+    "dask.compute(tasks)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Combine .json `Kerchunk` reference files and write a combined `Kerchunk` index\n",
+    "\n",
+    "In the following cell, we are combining all the `.json` reference files that were generated above into a single reference file and writing that file to disk."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a list of reference json files\n",
+    "output_files = [\n",
+    "    f\"{temp_dir}/{f.strip('.nc').split('/')[-1]}.json\" for f in first_24_hrs\n",
+    "]\n",
+    "\n",
+    "# combine individual references into single consolidated reference\n",
+    "mzz = MultiZarrToZarr(\n",
+    "    output_files,\n",
+    "    concat_dims=[\"time\"],\n",
+    "    identical_dims=[\"y\", \"x\"],\n",
+    "    remote_protocol=\"s3\",\n",
+    "    remote_options={\"anon\": True},\n",
+    "    coo_map={\"time\": \"cf:time\"},\n",
+    ")\n",
+    "# save translate reference in memory for later visualization\n",
+    "multi_kerchunk = mzz.translate()\n",
+    "\n",
+    "# Write kerchunk .json record.\n",
+    "output_fname = \"ARG_combined.json\"\n",
+    "with open(f\"{output_fname}\", \"wb\") as f:\n",
+    "    f.write(ujson.dumps(multi_kerchunk).encode())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Append references for the next 24 hours\n",
+    "\n",
+    "We'll now append the references for the next 24 hours. First, we create an individual temporary reference file for each input data file. Then,\n",
+    "we load the original references and append the new references."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# First generate the individual reference files to be appended\n",
+    "\n",
+    "second_24_hrs = file_pattern[24:48]\n",
+    "\n",
+    "# Generate Dask Delayed objects\n",
+    "tasks = [dask.delayed(generate_json_reference)(fil, temp_dir) for fil in second_24_hrs]\n",
+    "\n",
+    "# Generate reference files for the individual NetCDF files\n",
+    "dask.compute(tasks)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load the original references\n",
+    "fs_local = fsspec.filesystem(\"file\")\n",
+    "archive = ujson.load(fs_local.open(output_fname))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Create a list of individual reference files to append to the combined reference\n",
+    "output_files = [\n",
+    "    f\"{temp_dir}/{f.strip('.nc').split('/')[-1]}.json\" for f in second_24_hrs\n",
+    "]\n",
+    "\n",
+    "# Append to the existing reference file\n",
+    "mzz = MultiZarrToZarr.append(\n",
+    "    output_files,\n",
+    "    original_refs=archive,\n",
+    "    concat_dims=[\"time\"],\n",
+    "    identical_dims=[\"y\", \"x\"],\n",
+    "    remote_protocol=\"s3\",\n",
+    "    remote_options={\"anon\": True},\n",
+    "    coo_map={\"time\": \"cf:time\"},\n",
+    ")\n",
+    "\n",
+    "multi_kerchunk = mzz.translate()\n",
+    "\n",
+    "# Write kerchunk .json record.\n",
+    "output_fname = \"ARG_combined.json\"\n",
+    "with open(f\"{output_fname}\", \"wb\") as f:\n",
+    "    f.write(ujson.dumps(multi_kerchunk).encode())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Opening Reference Dataset with Fsspec and Xarray\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "storage_options = {\n",
+    "    \"remote_protocol\": \"s3\",\n",
+    "    \"skip_instance_cache\": True,\n",
+    "}  # options passed to fsspec\n",
+    "open_dataset_options = {\"chunks\": {}}  # opens passed to xarray\n",
+    "\n",
+    "ds = xr.open_dataset(\n",
+    "    \"ARG_combined.json\",\n",
+    "    engine=\"kerchunk\",\n",
+    "    storage_options=storage_options,\n",
+    "    open_dataset_options=open_dataset_options,\n",
+    ")\n",
+    "\n",
+    "ds"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "kerchunk-cookbook",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.13"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}