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-Investigating interhemispheric precipitation changes over the past millennium
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This Project Pythia Cookbook covers paleoclimate model-data comparison using spatio-temporal pattern obtained using Principal Component Analysis (PCA).
-Motivation
-Paleoclimate observations obtained from tree rings or the geochemical composition of speleothems, ice and sediments provide an out-of-sample validation of climate models. However, comparing the output of climate models directly with the paleoclimate observations is difficult: (1) they are often expressed in different quantities (i.e., temperature vs ring width), (2) paleoclimate observations have large time uncertainties, (3) paleoclimate observations often incorporate more than one environmental signal (i.e., temperature AND moisture).
-Recently, Steinman et al. (2022) used PCA analysis to compare model and data output. Here, we use a similar approach with the CESM Last Millennium simulation and proxy records stored on the LiPDverse. This repository contains paleoclimate datasets that have been curated by the community and are archived in a standard format, facilitating analysis.
-Authors
- -Contributors
- -Structure
-This Cookbook is broken down into three main sections. The first section performs the PCA analysis on the output of the CESM LME simulation. The second section performs the same analysis on paleoclimate observations. The last section compares the two analyses.
-PCA analysis on proxy observations
-The section shows the query used to obtain relevant proxy datasets from a graphDB mirroring the LiPDverse data, filtering for records that cover at least 1500 of the last 2000 years with a resolution better than 60 years, and running PCA.
-PCA analysis on the CESM Last Millennium Analysis¶
-The section walks through fetching the data from JetStream2, calculating the precipitation d18O and running PCA.
-Model Data Comparison
-Visualizing the output of the PCA analyses.
-Running the Notebooks
-You can either run the notebook using Binder or on your local machine.
-Running on Binder
-The simplest way to interact with a Jupyter Notebook is through -Binder, which enables the execution of a -Jupyter Book in the cloud. The details of how this works are not -important for now. All you need to know is how to launch a Pythia -Cookbooks chapter via Binder. Simply navigate your mouse to -the top right corner of the book chapter you are viewing and click -on the rocket ship icon, (see figure below), and be sure to select -“launch Binder”. After a moment you should be presented with a -notebook that you can interact with. I.e. you’ll be able to execute -and even change the example programs. You’ll see that the code cells -have no output at first, until you execute them by pressing -Shift+Enter. Complete details on how to interact with -a live Jupyter notebook are described in Getting Started with -Jupyter.
-Running on Your Own Machine
-If you are interested in running this material locally on your computer, you will need to follow this workflow:
--
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Clone the
-https://github.com/ProjectPythia/paleoPCA-Cookbookrepository:--git clone https://github.com/ProjectPythia/paleoPCA-Cookbook.git -
-Move into the
-paleoPCA-Cookbookdirectory--cd paleoPCA-Cookbook -
-Create and activate your conda environment from the
-environment.ymlfile--conda env create -f environment.yml -conda activate paleoPCA-Cookbook -
-Move into the
-notebooksdirectory and start up Jupyterlab--cd notebooks/ -jupyter lab -
-
-![\"Project](\"images/ProjectPythia_Logo_Final-01-Blue.svg\"){kind=logo}
\n",
- "![\"LinkedEarth](\"https://github.com/LinkedEarth/Logos/blob/master/LinkedEarth_medium.png?raw=true\")
"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "# Investigating interhemispheric precipitation changes over the past millennium"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Overview\n",
- "\n",
- "This CookBook demonstrates how to compare paleoclimate model output and proxy observations using EOF to identify large-scale spatio-temporal patterns in the data. It is inspired from a study by [Steinman et al. (2022)](https://doi.org/10.1073/pnas.2120015119) although it reuses different datasets. "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Prerequisites\n",
- "\n",
- "| Concepts | Importance | Notes |\n",
- "| --- | --- | --- |\n",
- "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
- "| [Intro to Matplotlib](https://foundations.projectpythia.org/core/matplotlib.html) | Necessary | |\n",
- "| [Intro to Pandas](https://foundations.projectpythia.org/core/pandas.html) | Necessary | |\n",
- "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Necessary | |\n",
- "| [Using xarray](https://foundations.projectpythia.org/core/xarray.html) | Necessary | Familiarity with understanding opening multiple files and merging |\n",
- "|EOF (PCA) Analysis - See Chapter 12 of [this book](https://figshare.com/articles/book/Data_Analysis_in_the_Earth_Environmental_Sciences/1014336)|Helpful|Familiarity with the concepts is helpful for interpretation of the results|\n",
- "|[Using Pyleoclim for Paleoclimate Data](http://linked.earth/PyleoTutorials/intro.html)|Helpful||\n",
- "|[SPARQL](https://www.w3.org/TR/sparql11-query/)|Familiarity|Query language for graph database|\n",
- "\n",
- "- **Time to learn**: 40 min."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "---"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## Imports"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 315,
- "metadata": {},
- "outputs": [],
- "source": [
- "#To deal with model data\n",
- "import s3fs\n",
- "import fsspec\n",
- "import xarray as xr\n",
- "import glob\n",
- "\n",
- "#To deal with proxy data\n",
- "import pandas as pd\n",
- "import numpy as np\n",
- "import json\n",
- "import requests\n",
- "import pandas as pd\n",
- "import io\n",
- "import ast\n",
- "\n",
- "#To deal with analysis\n",
- "import pyleoclim as pyleo\n",
- "from eofs.xarray import Eof\n",
- "\n",
- "#Plotting and mapping\n",
- "import cartopy.crs as ccrs\n",
- "import matplotlib.pyplot as plt\n",
- "import nc_time_axis\n",
- "from matplotlib import gridspec\n",
- "from matplotlib.colors import Normalize"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "## PCA analysis on proxy observations\n",
- "\n",
- "After looking at the model data, let's have a look at the proxy datasets."
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Query to remote database\n",
- "\n",
- "The first step is the query the [LinkedEarth](https://linkedearth.graphdb.mint.isi.edu/) Graph Database for relevant datasets for comparison with the model. \n",
- "\n",
- "The database uses the SPARQL language for queries. We are filtering the database for the following criteria:\n",
- "* Datasets bounded by 27°S-27°N and 70°W-150°W\n",
- "* Datasets from the [Pages2k](https://lipdverse.org/project/pages2k/), [Iso2k](https://lipdverse.org/project/iso2k/), [CoralHydro2k](https://essd.copernicus.org/articles/15/2081/2023/essd-15-2081-2023-discussion.html) and [SISAL](https://essd.copernicus.org/articles/12/2579/2020/) working groups. These working groups identified archived datasets that were sensitive to temperature and the isotopic composition of precipication (precipitation $\\delta{18}$O) and curated them for use in a standardized database.\n",
- "* Timeseries within these datasets representing precipitation.\n",
- "\n",
- "We asked for the following information back:\n",
- "* The name of the dataset\n",
- "* Geographical Location of the record expressed in latitude and longitude\n",
- "* The type of archive (e.g., speleothem, Lake sediment) the measurements were made on\n",
- "* The name of the variable\n",
- "* The values and units of the measurements\n",
- "* The time information (values and units) associated with the variable of interest.\n",
- "\n",
- "The following cell points to the query API and creates the query itself."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 224,
- "metadata": {},
- "outputs": [],
- "source": [
- "url = 'https://linkedearth.graphdb.mint.isi.edu/repositories/LiPDVerse-dynamic'\n",
- "\n",
- "query = \"\"\"PREFIX le: