{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ClimateMatchAcademy/course-content/blob/main/tutorials/W1D4_Paleoclimate/instructor/W1D4_Tutorial2.ipynb)   <a href=\"https://kaggle.com/kernels/welcome?src=https://raw.githubusercontent.com/{ORG}/course-content/main/tutorials/W1D4_Paleoclimate/instructor/W1D4_Tutorial2.ipynb\" target=\"_blank\"><img alt=\"Open in Kaggle\" src=\"https://kaggle.com/static/images/open-in-kaggle.svg\"/></a>"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "# **Tutorial 2: Reconstructing Past Changes in Ocean Climate**\n",
    "**Week 1, Day 4, Paleoclimate**\n",
    "\n",
    "**Content creators:** Sloane Garelick\n",
    "\n",
    "**Content reviewers:** Yosmely Bermúdez, Dionessa Biton, Katrina Dobson, Maria Gonzalez, Will Gregory, Nahid Hasan, Sherry Mi, Beatriz Cosenza Muralles, Brodie Pearson, Jenna Pearson, Chi Zhang, Ohad Zivan \n",
    "\n",
    "**Content editors:** Yosmely Bermúdez, Zahra Khodakaramimaghsoud, Jenna Pearson, Agustina Pesce, Chi Zhang, Ohad Zivan\n",
    "\n",
    "**Production editors:** Wesley Banfield, Jenna Pearson, Chi Zhang, Ohad Zivan\n",
    "\n",
    "**Our 2023 Sponsors:** NASA TOPS and Google DeepMind"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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"
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"
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    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "# **Tutorial Objectives**\n",
    "\n",
    "In the previous days, you learned about the El Niño–Southern Oscillation (ENSO), and have explored how satellite data can be employed to track this phenomenon during the instrumental period. In this tutorial, you will explore how oxygen isotopes of corals can record changes in temperature associated with the phase of ENSO even further back in time.\n",
    "\n",
    "By the end of this tutorial you will be able to:\n",
    "\n",
    "*   Understand the types of marine proxies that are used to reconstruct past climate\n",
    "*   Create a stacked plot and warming stripe to visualize ENSO temperature reconstructions\n",
    "\n",
    "\n",
    "\n",
    "### **An Overview of Isotopes in Paleoclimate**\n",
    "\n",
    "In this tutorial, and many of the remaining tutorials in this day, you will be looking at data of hydrogen and oxygen isotopes (δD and δ<sup>18</sup>O). As you learned in the video, isotopes are forms of the same element that contain the same numbers of protons but different numbers of neutrons. The two oxygen isotopes that are most commonly used in paleoclimate are oxygen 16 (<sup>16</sup>O), which is the which is the **\"lighter\"** oxygen isotope, and oxygen 18 (<sup>16</sup>O), which is the **\"heavier\"** oxygen isotope. The two hydrogen isotopes that are most commonly used in paleoclimate are hydrogen (H), which is the **\"lighter\"** oxygen isotope, and deuterium (D), which is the **\"heavier\"** oxygen isotope. \n",
    "\n",
    "![image-3.png](attachment:image-3.png)\n",
    "\n",
    "Credit: [NASA Climate Science Investigations](https://www.ces.fau.edu/nasa/module-3/how-is-temperature-measured/isotopes.php)\n",
    "\n",
    "![image-2.png](attachment:image-2.png)\n",
    "\n",
    "Credit: [NASA Climate Science Investigations](https://www.ces.fau.edu/nasa/module-3/how-is-temperature-measured/isotopes.php)\n",
    "\n",
    "Changes in the ratio of the heavy to light isotope can reflect changes in different climate variables, depending on geographic location and the material being measured. The ratio represented in delta notation (δ) and in units of per mille (‰), and is calculated using the equation below (the same applies to the ratio of the heavy and light hydrogen isotopes):\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "The terminology for discussing δ<sup>18</sup>O and δD can be a bit confusing and there are multiple ways to reference the same trends in the data. The most common terms used to describe isotopic compositions are **\"depleted\"** and **\"enriched\"**. These terms refer to the relative amout of the heavy isotopes. Therefore, a \"more depleted\" isotopic value is more depleted in the heavy isotope (i.e., there is less of the heavy isotope), whereas a \"more enriched\" isotopic value is more enriched in the heavy isotope (i.e., there is more of the heavy isotope). Other terms that are sometimes used to describe whether isotopes are depleted or enriched are **\"more negative\"** or **\"more positive\"**. Isotopic values can be both positive and negative, so using \"more negative\" and \"more positive\" can be a bit confusing. For example, if we have isotopic values are of -15‰ and -9‰, the value of -9‰ is \"more enriched\" (i.e., has more of the heavy isotope) or \"more positive\" (i.e., is closer to zero and positive values) than -15‰. Finally, the terms **\"smaller\"** or **\"larger\"** isotopic values can also be used to reference isotopes.\n",
    "\n",
    "Additional information about the use of isotopes in paleoclimate can be found [here](https://earthobservatory.nasa.gov/features/Paleoclimatology_OxygenBalance).\n",
    "\n"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "# **Setup**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "# imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pooch\n",
    "import os\n",
    "import tempfile\n",
    "import cartopy\n",
    "import pyleoclim as pyleo\n",
    "import matplotlib.pyplot as plt\n",
    "import cartopy\n",
    "import cartopy.crs as ccrs\n",
    "import cartopy.feature as cfeature\n",
    "from matplotlib import patches"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##  Video 1: Speaker Introduction\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "cellView": "form",
    "execution": {},
    "tags": [
     "hide-input"
    ]
   },
   "outputs": [],
   "source": [
    "# @title Video 1: Speaker Introduction\n",
    "# Tech team will add code to format and display the video"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "# helper functions\n",
    "\n",
    "\n",
    "def pooch_load(filelocation=None, filename=None, processor=None):\n",
    "    shared_location = \"/home/jovyan/shared/Data/tutorials/W1D4_Paleoclimate\"  # this is different for each day\n",
    "    user_temp_cache = tempfile.gettempdir()\n",
    "\n",
    "    if os.path.exists(os.path.join(shared_location, filename)):\n",
    "        file = os.path.join(shared_location, filename)\n",
    "    else:\n",
    "        file = pooch.retrieve(\n",
    "            filelocation,\n",
    "            known_hash=None,\n",
    "            fname=os.path.join(user_temp_cache, filename),\n",
    "            processor=processor,\n",
    "        )\n",
    "\n",
    "    return file"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "# Section 1: Assessing Variability Related to El Niño Using Pyleoclim Series\n",
    "\n",
    "ENSO is a recurring climate pattern involving changes in SST in the central and eastern tropical Pacific Ocean.  As we learned in the introductory video, oxygen isotopes ([&delta;<sup>18</sup>O](https://en.wikipedia.org/wiki/Δ18O)) of corals are a commonly used proxy for reconstructing changes in tropical Pacific SST and ENSO.  Long-lived corals are well-suited for studying paleo-ENSO variability because they store decades to centuries of sub-annually resolved proxy information in the tropical Pacific. The oxygen isotopes of corals are useful for studying ENSO because they record changes in sea-surface temperature (SST), with more positive values of &delta;<sup>18</sup>O corresponding to colder SSTs, and vice-versa.\n",
    "\n",
    "One approach for detecting ENSO from coral isotope data is applying a 2- to 7-year bandpass filter to the &delta;<sup>18</sup>O records to highlight ENSO-related variability and compare (quantitatively) the bandpassed coral records to the Oceanic Niño Index (ONI) you learned about in Day 2 and Day 3. While we won't be going into this amount of detail, you may utilize the methods sections of these papers as a guide: [Cobb et al.(2003)](https://www.nature.com/articles/nature01779), [Cobb et al.(2013)](https://www.science.org/doi/10.1126/science.1228246). In this tutorial we will be looking at the &delta;<sup>18</sup>O records and comparing to a plot of the ONI without this band-pass filtering, in part to highlight why the filtering is needed."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "## Section 1.1: Load coral oxygen isotope proxy reconstructions\n",
    "\n",
    "The two coral records we'll look at are from [Palmyra Atoll](https://en.wikipedia.org/wiki/Palmyra_Atoll) and [Line Islands](https://en.wikipedia.org/wiki/Line_Islands), both of which are in the tropical central Pacific Ocean.\n",
    "\n",
    "Let's plot these approximate locations as well as the Niño 3.4 region you are familiar with from the first three days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "# select data for the month of interest\n",
    "# data = precip.sel(time='1979-01-01', method='nearest')\n",
    "\n",
    "# initate plot with the specific figure size\n",
    "fig = plt.figure(figsize=(9, 6))\n",
    "\n",
    "# set base map projection\n",
    "ax = plt.axes(projection=ccrs.Robinson(central_longitude=180))\n",
    "\n",
    "# add background image to show land and sea\n",
    "ax.stock_img()\n",
    "\n",
    "# add coastlines\n",
    "ax.add_feature(cfeature.COASTLINE)\n",
    "\n",
    "# add in rectangle showing Nino 3.4 region\n",
    "rectangle = patches.Rectangle(\n",
    "    (170, -5),\n",
    "    50,\n",
    "    10,\n",
    "    transform=ccrs.Geodetic(),\n",
    "    edgecolor=\"k\",\n",
    "    facecolor=\"none\",\n",
    "    linewidth=3,\n",
    ")\n",
    "ax.add_patch(rectangle)\n",
    "\n",
    "rx, ry = rectangle.get_xy()\n",
    "cx = rx + rectangle.get_width() / 2.0\n",
    "cy = ry + rectangle.get_height() / 2.0\n",
    "\n",
    "# add labels\n",
    "ax.annotate(\n",
    "    \"Nino 3.4\",\n",
    "    (cx - 10, cy),\n",
    "    color=\"w\",\n",
    "    transform=ccrs.PlateCarree(),\n",
    "    weight=\"bold\",\n",
    "    fontsize=10,\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    ")\n",
    "\n",
    "# add the proxy locations\n",
    "ax.scatter(\n",
    "    [-162.078333], [5.883611], transform=ccrs.Geodetic(), s=50, marker=\"v\", color=\"w\"\n",
    ")\n",
    "ax.scatter([-157.2], [1.7], transform=ccrs.Geodetic(), s=50, marker=\"v\", color=\"w\")\n",
    "\n",
    "# add labels\n",
    "ax.annotate(\n",
    "    \"Palmyra Atoll\",\n",
    "    (-170, 10),\n",
    "    color=\"w\",\n",
    "    transform=ccrs.Geodetic(),\n",
    "    weight=\"bold\",\n",
    "    fontsize=10,\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    ")\n",
    "\n",
    "ax.annotate(\n",
    "    \"Line Islands\",\n",
    "    (-144, -1),\n",
    "    color=\"w\",\n",
    "    transform=ccrs.Geodetic(),\n",
    "    weight=\"bold\",\n",
    "    fontsize=10,\n",
    "    ha=\"center\",\n",
    "    va=\"center\",\n",
    ")\n",
    "\n",
    "# change the map view to zoom in on central Pacific\n",
    "ax.set_extent((120, 300, -25, 25), crs=ccrs.PlateCarree())"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "To analyze and visualize paleoclimate proxy time series, we will be using [Pyleoclim](https://pyleoclim-util.readthedocs.io/en/latest/). Pycleoclim is specifically designed for the analysis of paleoclimate data. The package is designed around object-oriented `Series`, which can be directly manipulated for plotting and time series-appropriate analysis and operation. \n",
    "\n",
    "The `Series` object describes the fundamentals of a time series. To create a Pyleoclim `Series`, we first need to load the data set, and then specify values for its various properties:\n",
    "\n",
    "*   `time`: Time values for the time series\n",
    "*   `value`: Paleo values for the time series\n",
    "*   `time_name` (optional): Name of the time vector, (e.g., 'Time', 'Age'). This is used to label the x-axis on plots\n",
    "*   `time_unit` (optional): The units of the time axis (e.g., 'years')\n",
    "*   `value_name` (optional): The name of the paleo variable (e.g., 'Temperature')\n",
    "*   `value_unit` (optional): The units of the paleo variable (e.g., 'deg C')\n",
    "*   `label` (optional): Name of the time series (e.g., 'Nino 3.4')\n",
    "*   `clean_ts` (optional): If True (default), remove NaNs and set an increasing time axis\n",
    "\n",
    "A common data format for datasets downloaded from the NOAA Paleoclimate Database is a templated text file, which contains helpful data and metadata. \n",
    "\n",
    "Take a look at our two datasets [here](https://www.ncei.noaa.gov/pub/data/paleo/coral/east_pacific/palmyra_2003.txt) and [here](https://www.ncei.noaa.gov/pub/data/paleo/coral/east_pacific/cobb2013-fan-modsplice-noaa.txt).\n",
    "\n",
    "The functionality in python allows us to ignore all of the information at the beginning of the text files, and we can load the data directly into a `pandas.DataFrame` using `.read_csv()`.\n",
    "\n",
    "### Section 1.1.1: Load Palmyra coral data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "executionInfo": {
     "elapsed": 1031,
     "status": "ok",
     "timestamp": 1681584691666,
     "user": {
      "displayName": "Sloane Garelick",
      "userId": "04706287370408131987"
     },
     "user_tz": 240
    }
   },
   "outputs": [],
   "source": [
    "# download the data using the url\n",
    "filename_Palmyra = \"palmyra_2003.txt\"\n",
    "url_Palmyra = (\n",
    "    \"https://www.ncei.noaa.gov/pub/data/paleo/coral/east_pacific/palmyra_2003.txt\"\n",
    ")\n",
    "data_path = pooch_load(\n",
    "    filelocation=url_Palmyra, filename=filename_Palmyra\n",
    ")  # open the file\n",
    "\n",
    "# from the data set, we only want the data related to Modern Living Coral.\n",
    "# this data is between row 6190 and 7539 of the dataset\n",
    "rows = [int(row) for row in np.linspace(6190, 7539, 7539 - 6190 + 1)]\n",
    "\n",
    "# use pandas to read in the csv file\n",
    "palmyra = pd.read_csv(\n",
    "    data_path,\n",
    "    skiprows=lambda x: x\n",
    "    not in rows,  # number of rows to skip based on definition of rows above\n",
    "    sep=\"\\s+\",  # how the data values are seperated (delimited) : '\\s+' = space\n",
    "    encoding=\"ISO-8859-1\",\n",
    "    names=[\"CalendarDate\", \"d180\"],\n",
    "    header=None,\n",
    ")\n",
    "\n",
    "palmyra.head()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "Now that we have the data in a dataframe, we can pull the relevant columns of this datframe into a `Series` object in Pyleoclim, which will allow us to organize the relevant metadata so that we can get a well-labeled, publication-quality plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "ts_palmyra = pyleo.Series(\n",
    "    time=palmyra[\"CalendarDate\"],\n",
    "    value=palmyra[\"d180\"],\n",
    "    time_name=\"Calendar date\",\n",
    "    time_unit=\"Years\",\n",
    "    value_name=r\"$d18O$\",\n",
    "    value_unit=\"per mille\",\n",
    "    label=\"Palmyra Coral\",\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "Since we want to compare datasets based on different measurements (coral &delta;<sup>18</sup>O and the ONI, i.e., a temperature anomaly), it's helpful to standardize the data by removing it's estimated mean and dividing by its estimated standard deviation. Thankfully Pyleoclim has a [function](https://pyleoclim-util.readthedocs.io/en/v0.7.4/core/ui.html#pyleoclim.core.ui.Series.standardize) to do that for us."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "palmyra_stnd = ts_palmyra.standardize()\n",
    "palmyra_stnd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "### Section 1.1.2: Load Line Island coral data\n",
    "\n",
    "We will repeat these steps for the other dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "# Download the data using the url\n",
    "filename_cobb2013 = \"cobb2013-fan-modsplice-noaa.txt\"\n",
    "url_cobb2013 = \"https://www.ncei.noaa.gov/pub/data/paleo/coral/east_pacific/cobb2013-fan-modsplice-noaa.txt\"\n",
    "data_path2 = pooch_load(\n",
    "    filelocation=url_cobb2013, filename=filename_cobb2013\n",
    ")  # open the file\n",
    "\n",
    "# From the data set, we only want the data related to Modern Living Coral.\n",
    "# So this data is between row 6190 and 7539 of the dataset\n",
    "rows = [int(row) for row in np.linspace(127, 800, 800 - 127 + 1)]\n",
    "line = pd.read_csv(\n",
    "    data_path2,\n",
    "    skiprows=lambda x: x not in rows,\n",
    "    sep=\"\\s+\",\n",
    "    encoding=\"ISO-8859-1\",\n",
    "    names=[\"age\", \"d18O\"],\n",
    "    header=None,\n",
    ")\n",
    "\n",
    "line.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "ts_line = pyleo.Series(\n",
    "    time=line[\"age\"],\n",
    "    value=line[\"d18O\"],\n",
    "    time_name=\"Calendar date\",\n",
    "    time_unit=\"Years\",\n",
    "    value_name=r\"$d18O$\",\n",
    "    value_unit=\"per mille\",\n",
    "    label=\"Line Island Coral\",\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "line_stnd = ts_line.standardize()\n",
    "line_stnd"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "# Section 2: Plot the Data Using Multiseries\n",
    "\n",
    "We will utilize the built in features of a [multiseries](https://pyleoclim-util.readthedocs.io/en/latest/core/api.html#multipleseries-pyleoclim-multipleseries) object to plot our coral proxy data side by side. To create a `pyleo.MultipleSeries`, we first create a list with our `pyleo.Series` objects and then pass this into a `pyleo.MultipleSeries`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "# combine into a list\n",
    "nino_comparison = [palmyra_stnd, line_stnd]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "# create multiseries\n",
    "nino = pyleo.MultipleSeries(nino_comparison, name=\"El Nino Comparison\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "executionInfo": {
     "elapsed": 942,
     "status": "ok",
     "timestamp": 1681584722114,
     "user": {
      "displayName": "Sloane Garelick",
      "userId": "04706287370408131987"
     },
     "user_tz": 240
    }
   },
   "outputs": [],
   "source": [
    "# plot the time series of both datasets\n",
    "fig, ax = nino.stackplot(time_unit=\"year\", xlim=[1960, 2000], colors=[\"b\", \"g\"])"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "## Questions 2: Climate Connection\n",
    "\n",
    "Recall that as SST becomes colder, &delta;<sup>18</sup>O becomes more positive, and vice versa. Compare the figure below of the ONI to the time series of coral &delta;<sup>18</sup>O you plotted above and answer the questions below.\n",
    "\n",
    "![](https://climatedataguide.ucar.edu/sites/default/files/styles/extra_large/public/2022-03/indices_oni_2_2_lg.png?itok=Zew3VK_4)\n",
    "\n",
    "Credit: [UCAR](https://climatedataguide.ucar.edu/sites/default/files/styles/extra_large/public/2022-03/indices_oni_2_2_lg.png?itok=Zew3VK_4)\n",
    "\n",
    "1. Do the ENSO events recorded by the ONI agree with the coral data?\n",
    "2. What are some considerations you can think of when comparing proxies such as this to the ONI?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {}
   },
   "outputs": [],
   "source": [
    "# to_remove explanation\n",
    "\n",
    "\"\"\"\n",
    "1. In general when there is a recorded ENSO event in the ONI, there is an agreement with the coral records.  During El Nino Phases captured by the ONI (where SSTs are anomolously warm) we typically see a more negative d18O and vice versa for La Nina events.\n",
    "2. ENSO is an interannual occurence, happening every 2-7 years. Within the d18O records there are high frequency signals. One thing (among many) we could do is remove these by filtering. This would help resolve our dataset into something more comparable to what the ONI is tracking.\n",
    "\"\"\";"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "# **Section 1.3: Make Warming Stripes Plot**"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "We can also make a warming stripe for this data `Series` using the [`.stripes()`](https://pyleoclim-util.readthedocs.io/en/latest/core/api.html#pyleoclim.core.multipleseries.MultipleSeries.stripes) method, where darker red stripes indicate a warmer eastern Pacific and possibly an El Niño phase, and darker blue stripes indicate a cooler eastern Pacific and possibly La Niña phase. Can you see the trend present in the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "execution": {},
    "executionInfo": {
     "elapsed": 409,
     "status": "ok",
     "timestamp": 1681584728212,
     "user": {
      "displayName": "Sloane Garelick",
      "userId": "04706287370408131987"
     },
     "user_tz": 240
    }
   },
   "outputs": [],
   "source": [
    "fig, ax = nino.stripes(\n",
    "    ref_period=(1960, 1990), time_unit=\"year\", show_xaxis=True, cmap=\"RdBu\"\n",
    ")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "# **Summary**\n",
    "In this tutorial, we discovered how oxygen isotopes within corals serve as a valuable archive, recording changes in temperature associated with ENSO phases.\n",
    "\n",
    "During our explorations, \n",
    "- We ventured into the study of proxy-based coral &delta;<sup>18</sup>O records, gaining insights into the rich historical climate data encapsulated within these marine structures.\n",
    "- We compared these records to noted ENSO events over a few decades, offering us a glimpse into the dynamic nature of this influential climate phenomenon."
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {
    "execution": {}
   },
   "source": [
    "# **Resources**\n",
    "\n",
    "Code for this tutorial is based on existing notebooks from LinkedEarth that uses the `Pyleoclim` package to [assess variability in the El Nino](https://github.com/LinkedEarth/PyleoTutorials/blob/main/notebooks/L0_a_quickstart.ipynb). \n",
    "\n",
    "Data from the following sources are used in this tutorial:\n",
    "\n",
    "\n",
    "* Cobb,K., et al., Highly Variable El Niño–Southern Oscillation Throughout the Holocene.Science 339, 67-70(2013). https://doi.org/10.1126/science.1228246 accessible [here]( https://www.ncei.noaa.gov/pub/data/paleo/coral/east_pacific/palmyra_2003.txt)\n",
    "\n",
    "* Cobb, K., Charles, C., Cheng, H. et al. El Niño/Southern Oscillation and tropical Pacific climate during the last millennium. Nature 424, 271–276 (2003). https://doi.org/10.1038/nature01779 accessible [here]( https://www.ncei.noaa.gov/pub/data/paleo/coral/east_pacific/cobb2013-fan-modsplice-noaa.txt)"
   ]
  }
 ],
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