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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "id": "7ec06613-53cd-494c-ade6-8a3a156f77a0",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "50b5d7e7-df4e-4992-a29b-8804b081a320",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "# Demo: Regridding and Plotting with Rooki and Cartopy \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "abd4b497-cdbf-4c29-857c-3017abf9e927",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "0f79862d-7181-4f04-966c-19b5e03a22a5",
+ "metadata": {},
+ "source": [
+ "## Overview\n",
+ "In this notebook, we demonstrate how to use Rooki to regrid CMIP model data and plot it in Cartopy for two examples:\n",
+ "\n",
+ "1. Regrid two CMIP models onto the same grid \n",
+ "1. Coarsen the output for one model"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "4f1db315-fb2d-466d-bd6e-8a4ef18b6cf1",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Prerequisites\n",
+ "\n",
+ "| Concepts | Importance | Notes |\n",
+ "| --- | --- | --- |\n",
+ "| [Intro to intake-esgf](https://projectpythia.org/esgf-cookbook/notebooks/intro-search.html) | Necessary | |\n",
+ "| [Intro to Cartopy](https://foundations.projectpythia.org/core/cartopy/cartopy.html) | Necessary | |\n",
+ "| [Using Rooki to access CMIP6 data](https://projectpythia.org/esgf-cookbook/notebooks/rooki.html) | Helpful | Familiarity with rooki |\n",
+ "| [Understanding of NetCDF](https://foundations.projectpythia.org/core/data-formats/netcdf-cf.html) | Helpful | Familiarity with metadata structure |\n",
+ "\n",
+ "- **Time to learn**: 15 minutes\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "7cbc5d91-db3f-4afd-9093-c3abc7dec82b",
+ "metadata": {},
+ "source": [
+ "---"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "2582d535-9b99-4115-b0ee-7459acd76ec0",
+ "metadata": {},
+ "source": [
+ "## Imports\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "id": "ad4562c9-f5eb-496e-9e17-6453f426e910",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "\n",
+ "import rooki.operators as ops\n",
+ "import matplotlib.pyplot as plt\n",
+ "import matplotlib.colors as mcolors\n",
+ "import cartopy.crs as ccrs\n",
+ "import cartopy.feature as cfeature\n",
+ "\n",
+ "from intake_esgf import ESGFCatalog\n",
+ "from rooki import rooki\n",
+ "from matplotlib.gridspec import GridSpec\n",
+ "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c2b47b1d-db2d-4074-8c92-bb71fa0459a7",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "## Example 1: Regrid two CMIP6 models onto the same grid"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "cc1d512a-68d3-43cf-aac7-6ca233d9ef73",
+ "metadata": {},
+ "source": [
+ "In this example, we want to compare the historical precipitation output between two CMIP models, CESM2 and CanESM5. Here will will look at the annual mean precipitation for 2010. "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "46f5fba3-7410-465c-abdf-4e338855284c",
+ "metadata": {},
+ "source": [
+ "### Access the desired datasets using intake-esgf and rooki"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "c5f4dc65-0dff-4023-880c-f511cbc58666",
+ "metadata": {
+ "tags": []
+ },
+ "source": [
+ "The function and workflow to read in CMPI6 data using `intake-esgf` and `rooki` in the next few cells are adapted from [intake-esgf-with-rooki.ipynb](https://github.com/ProjectPythia/esgf-cookbook/blob/cf69015a464b68ee28cfdd4a27cee4e9d6ca2ca9/notebooks/use-intake-esgf-with-rooki.ipynb). Essentially, we use `intake-esgf` to find the dataset IDs we want and then subset and average them using `rooki`."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "id": "d053a676-2a27-4be0-93c0-eafb9671c0bc",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [],
+ "source": [
+ "def separate_dataset_id(full_dataset):\n",
+ " return full_dataset[0].split(\"|\")[0]\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "id": "095db615-275a-4dbc-8467-833fd7992aed",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "d94f8c4ed5c24cd8b123fac457ee6d00",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ " Searching indices: 0%| |0/2 [ ?index/s]"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "['CMIP6.CMIP.CCCma.CanESM5.historical.r1i1p1f1.Amon.pr.gn.v20190429',\n",
+ " 'CMIP6.CMIP.NCAR.CESM2.historical.r1i1p1f1.Amon.pr.gn.v20190401']"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "cat = ESGFCatalog()\n",
+ "cat.search(\n",
+ " activity_id='CMIP',\n",
+ " experiment_id=[\"historical\",],\n",
+ " variable_id=[\"pr\"],\n",
+ " member_id='r1i1p1f1',\n",
+ " grid_label='gn',\n",
+ " table_id=\"Amon\",\n",
+ " source_id = [ \"CESM2\", \"CanESM5\"]\n",
+ " )\n",
+ "\n",
+ "dsets = [separate_dataset_id(dataset) for dataset in list(cat.df.id.values)]\n",
+ "dsets\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "777f6bc4-f3a8-4110-bc2a-82cbf227ec4e",
+ "metadata": {},
+ "source": [
+ "Subset the data to get the precipitation variable for 2010 and then average by time:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "id": "bf653879-96b5-48e0-be9b-0f0cc08152e2",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading to /tmp/metalink_6_iqo1_6/pr_Amon_CanESM5_historical_r1i1p1f1_gn_20100101-20100101_avg-year.nc.\n",
+ "Downloading to /tmp/metalink_d7uge4gt/pr_Amon_CESM2_historical_r1i1p1f1_gn_20100101-20100101_avg-year.nc.\n"
+ ]
+ }
+ ],
+ "source": [
+ "dset_list = [[]]*len(dsets)\n",
+ "\n",
+ "for i, dset_id in enumerate(dsets):\n",
+ " wf = ops.AverageByTime(\n",
+ " ops.Subset(\n",
+ " ops.Input('pr', [dset_id]),\n",
+ " time='2010/2010'\n",
+ " )\n",
+ " )\n",
+ "\n",
+ " resp = wf.orchestrate()\n",
+ "\n",
+ " # if it worked, add the dataset to our list\n",
+ " if resp.ok:\n",
+ " dset_list[i] = resp.datasets()[0]\n",
+ " \n",
+ " # if it failed, tell us why\n",
+ " else:\n",
+ " print(resp.status)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "e040d078-3981-4246-a10b-c50cf104d8ed",
+ "metadata": {},
+ "source": [
+ "Print the dataset list to get an overview of the metadata structure:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "id": "b2ed096a-2cfc-4e51-9b2a-43b9ee4f103e",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ Size: 37kB\n",
+ "Dimensions: (lat: 64, time: 1, bnds: 2, lon: 128)\n",
+ "Coordinates:\n",
+ " * lat (lat) float64 512B -87.86 -85.1 -82.31 ... 82.31 85.1 87.86\n",
+ " * lon (lon) float64 1kB 0.0 2.812 5.625 8.438 ... 351.6 354.4 357.2\n",
+ " * time (time) object 8B 2010-01-01 00:00:00\n",
+ "Dimensions without coordinates: bnds\n",
+ "Data variables:\n",
+ " lat_bnds (time, lat, bnds) float64 1kB ...\n",
+ " lon_bnds (time, lon, bnds) float64 2kB ...\n",
+ " pr (time, lat, lon) float32 33kB ...\n",
+ " time_bnds (time, bnds) object 16B ...\n",
+ "Attributes: (12/53)\n",
+ " CCCma_model_hash: 3dedf95315d603326fde4f5340dc0519d80d10c0\n",
+ " CCCma_parent_runid: rc3-pictrl\n",
+ " CCCma_pycmor_hash: 33c30511acc319a98240633965a04ca99c26427e\n",
+ " CCCma_runid: rc3.1-his01\n",
+ " Conventions: CF-1.7 CMIP-6.2\n",
+ " YMDH_branch_time_in_child: 1850:01:01:00\n",
+ " ... ...\n",
+ " tracking_id: hdl:21.14100/363e1ebe-46e7-43dc-9feb-a7a4a0c...\n",
+ " variable_id: pr\n",
+ " variant_label: r1i1p1f1\n",
+ " version: v20190429\n",
+ " license: CMIP6 model data produced by The Government ...\n",
+ " cmor_version: 3.4.0, Size: 233kB\n",
+ "Dimensions: (time: 1, lat: 192, lon: 288, nbnd: 2)\n",
+ "Coordinates:\n",
+ " * lat (lat) float64 2kB -90.0 -89.06 -88.12 -87.17 ... 88.12 89.06 90.0\n",
+ " * lon (lon) float64 2kB 0.0 1.25 2.5 3.75 ... 355.0 356.2 357.5 358.8\n",
+ " * time (time) object 8B 2010-01-01 00:00:00\n",
+ "Dimensions without coordinates: nbnd\n",
+ "Data variables:\n",
+ " pr (time, lat, lon) float32 221kB ...\n",
+ " lat_bnds (time, lat, nbnd) float64 3kB ...\n",
+ " lon_bnds (time, lon, nbnd) float64 5kB ...\n",
+ " time_bnds (time, nbnd) object 16B ...\n",
+ "Attributes: (12/45)\n",
+ " Conventions: CF-1.7 CMIP-6.2\n",
+ " activity_id: CMIP\n",
+ " branch_method: standard\n",
+ " branch_time_in_child: 674885.0\n",
+ " branch_time_in_parent: 219000.0\n",
+ " case_id: 15\n",
+ " ... ...\n",
+ " sub_experiment_id: none\n",
+ " table_id: Amon\n",
+ " tracking_id: hdl:21.14100/a2c2f719-6790-484b-9f66-392e62cd0eb8\n",
+ " variable_id: pr\n",
+ " variant_info: CMIP6 20th century experiments (1850-2014) with C...\n",
+ " variant_label: r1i1p1f1]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print(dset_list)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "776f84fd-e329-42e8-bab4-54253636aefc",
+ "metadata": {},
+ "source": [
+ "### Compare the precipitation data between models"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "ee469ea1-e402-4e55-b709-0de01e7875b3",
+ "metadata": {},
+ "source": [
+ "First, let's quickly plot the 2010 annual mean precipitation for each model to see what we're working with. Since precipitation values vary greatly in magnitude, using a log-normalized colormap makes the data easier to visualize. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "id": "e49b55e3-1970-4410-8557-9328f31853fb",
+ "metadata": {
+ "tags": []
+ },
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "