\n",
"\n",
"# PaleoHack: Putting EarthCube tools in the hands of paleogeoscientists\n",
"\n",
"## Authors\n",
"\n",
"**Deborah Khider1, Julien Emile-Geay2, Alexander James2, Feng Zhu3**\n",
"\n",
"1 Information Sciences Institute, University of Southern California\n",
"2 Department of Earth Sciences, University of Southern California\n",
"3 Nanjing University of Information Science and Technology\n",
"\n",
"* Author1 = {\"name\": \"Deborah Khider\", \"affiliation\": \"Information Sciences Institute, University of Southern California\", \"email\": \"khider@usc.edu\", \"orcid\": \"0000-0001-7501-8430\"}\n",
"* Author2 = {\"name\": \"Julien Emile-Geay\", \"affiliation\": \"Department of Earth Sciences, University of Southern California\", \"email\": \"julieneg@usc.edu\", \"orcid\": \"0000-0001-5920-4751\"}\n",
"* Author3 = {\"name\": \"Alexander James\", \"affiliation\": \"Department of Earth Sciences, University of Southern California\", \"email\": \"akjames@usc.edu\", \"orcid\": \"0000-0001-8561-3188\"}\n",
"* Author4 = {\"name\": \"Feng Zhu\", \"affiliation\": \"Nanjing University of Information Science and Technology\", \"email\": \"fzhu@nuist.edu\", \"orcid\": \"0000-0002-9969-2953\"}"
]
},
{
"cell_type": "markdown",
"id": "fdafdc60",
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"toc": true
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"source": [
"
\n",
"\n",
"This notebook features two tutorials based on paleoclimate records obtained from both non-LiPD (csv and a record generated from the [climlab](https://climlab.readthedocs.io/en/latest/) library [6]) and LiPD-formatted datasets to illustrate how Pyleoclim can be used with various data formats. It walks through basic data visualization and analysis of the records using coherence, correlation, and causality. During hackahtons, participants are asked to answer specific questions in the notebooks, designed to help them learn Pyleoclim as well as critically thinking about their scientific objectives. For the purpose of this presentation, the answers will be provided in cells labeled as `answers`. \n",
"\n",
"## Results\n",
"\n",
"The primary objective of this notebook is to serve as a learning tool for scientists engaged in paleoclimate research at all career stage. It is also an introduction in the use of the Pyleoclim software package.\n",
"\n",
"## Funding\n",
"\n",
"Award1 = {\"agency\": \"US National Science Foundation\", \"award_code\": \"1541029\", \"award_URL\": https://www.nsf.gov/awardsearch/showAward?AWD_ID=1541029&HistoricalAwards=false}\n",
"\n",
"Award2 = {\"agency\": \"JP Morgan\"}\n",
"\n",
"Award3 = {\"agency\": \"US National Science Foundation\", \"award_code\": \"2002556\", \"award_URL\": https://www.nsf.gov/awardsearch/showAward?AWD_ID=2002556&HistoricalAwards=false}\n",
"\n",
"Award3 = {\"agency\": \"US National Science Foundation\", \"award_code\": \"2126510\", \"award_URL\": https://www.nsf.gov/awardsearch/showAward?AWD_ID=2126510&HistoricalAwards=false}\n",
"\n",
"## Keywords\n",
"\n",
"keywords=[\"Paleoclimate\",\"LiPD\",\"Python\",\"Coherence\",\"Correlation\",\"Causality']\n",
"\n",
"## Citation\n",
"\n",
"Khider D., Emile-Geay, J., James, A., Zhu, F. (2022). PaleoHack: Putting EarthCube tools in the hands of paleogeoscientists. Jupyter Notebook. \n",
"\n",
"## Acknowledgements\n",
"\n",
"The LinkedEarth team would like to thank the EarthCube Technology and Architecture Committee for their support in developing and deploying an introduction Python tutorial and [2i2c](https://2i2c.org) for providing JupyterHub capabilities during the hackathons.\n",
"\n",
"# Setup\n",
"\n",
"## Library import\n",
"\n",
"We will import 6 libraries:\n",
"\n",
"* numpy, to facilitate operations with:\n",
"* [climlab](https://climlab.readthedocs.io/en/latest/), a Python package for process-oriented modeling, which we will use to compute insolation curves for specific orbital timescales.\n",
"* pandas, to read csv\n",
"* xarray to read and analyze gridded data\n",
"* cartopy, seaborn and matplotlib for visualizations\n",
"* Pyleoclim"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "fa95d884",
"metadata": {},
"outputs": [],
"source": [
"# Data manipulation and analysis\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import xarray as xr\n",
"import pandas as pd\n",
"import cartopy.crs as ccrs\n",
"import cartopy.feature as cfeature\n",
"from cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\n",
"import seaborn as sns\n",
"\n",
"#relevant climlab libraries\n",
"from climlab import constants as const\n",
"from climlab.solar.orbital import OrbitalTable\n",
"from climlab.solar.insolation import daily_insolation\n",
"\n",
"#pyleoclim and set its style to 'web'\n",
"import pyleoclim as pyleo\n",
"pyleo.set_style('web')"
]
},
{
"cell_type": "markdown",
"id": "e1c9f5fd",
"metadata": {},
"source": [
"# Data import\n",
"\n",
"We will import the following datasets throughout the notebook:\n",
"* Tutorial 1: \n",
" * A composite record of Chinese speleothem $\\delta^{18}O$ (`Sanbao_composite.csv`) [4]. The data is in tabular format with the first column representing age and the second column the $\\delta^{18}O$. \n",
" * This record will be compared to a generated insolation curve from the climlab package [5]. \n",
"* Tutorial 2: \n",
" * A $\\delta^{18}O$ speleothem record from Crystal Cave, California ([6]), stored in LiPD format\n",
" * the Met Office Hadley Centre's sea surface temperature dataset (HadSST.4.0.1.0) a monthly global field of SST on a 5° latitude by 5° longitude grid from 1850 to date, stored in NetCDF [7]. "
]
},
{
"cell_type": "markdown",
"id": "7e57a0d9",
"metadata": {},
"source": [
"# Data Processing and Analysis\n",
"\n",
"## Tutorial 1: Forcing and response\n",
"\n",
"A common task in paleoclimatology is to relate a record (or several of them) to what is thought to be their forcing. A classic example comes $\\delta^{18}O$ records from Chinese speleothems, broadly interpreted as reflecting continental-scale monsoon circulations. In this notebook we focus on a composite of three Chinese speleothem records (Sanbao, Hulu, and Dongge caves) [4] and its relationship to insolation.\n",
"\n",
"The composite isn't available in LiPD form, so we create a `Series` object by hand:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "1468851a",
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"outputs": [
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