cmip6-cookbook
Examples of analysis of Google Cloud CMIP6 data using Pangeo tools
Science Score: 64.0%
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✓Academic publication links
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2 of 7 committers (28.6%) from academic institutions -
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Low similarity (16.0%) to scientific vocabulary
Keywords from Contributors
Repository
Examples of analysis of Google Cloud CMIP6 data using Pangeo tools
Basic Info
- Host: GitHub
- Owner: ProjectPythia
- License: apache-2.0
- Language: Jupyter Notebook
- Default Branch: main
- Homepage: https://projectpythia.org/cmip6-cookbook/
- Size: 54.7 MB
Statistics
- Stars: 14
- Watchers: 1
- Forks: 11
- Open Issues: 8
- Releases: 1
Metadata Files
README.md
CMIP6 Cookbook

This Project Pythia Cookbook covers examples of analysis of Google Cloud CMIP6 data using Pangeo tools.
Motivation
From the CMIP6 website:
The simulation data produced by models under previous phases of CMIP have been used in thousands of research papers ... and the multi-model results provide some perspective on errors and uncertainty in model simulations. This information has proved invaluable in preparing high profile reports assessing our understanding of climate and climate change (e.g., the IPCC Assessment Reports).
With such a large amount of model output produced, moving the data around is inefficient. In this collection of notebooks, you will learn how to access cloud-optimized CMIP6 datasets, in addition to a few examples of using that data to analyze some aspects of climate change.
Authors
Ryan Abernathey, Henri Drake, Robert Ford, Max Grover
Contributors
Structure
Foundations
This section includes three variations of accessing CMIP6 data from cloud storage.
Example workflows
There are currently four examples of using this data to - Estimate equilibrium climate sensitivity (ECS) - Plot global mean surface temperature under two different Shared Socioeconomic Pathways - Plot changes in precipitation intensity under the SSP585 scenario - Calculate changes in ocean heat uptake after regridding with xESMF
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
{kbd}Shift+{kbd}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:
- Clone the
https://github.com/ProjectPythia/cmip6-cookbookrepository:
bash
git clone https://github.com/ProjectPythia/cmip6-cookbook.git
1. Move into the cmip6-cookbook directory
bash
cd cmip6-cookbook
1. Create and activate your conda environment from the environment.yml file
bash
conda env create -f environment.yml
conda activate cmip6-cookbook-dev
1. Move into the notebooks directory and start up Jupyterlab
bash
cd notebooks/
jupyter lab
At this point, you can interact with the notebooks! Make sure to check out the "Getting Started with Jupyter" content from the Pythia Foundations material if you are new to Jupyter or need a refresher.
Owner
- Name: Project Pythia
- Login: ProjectPythia
- Kind: organization
- Email: projectpythia@ucar.edu
- Location: United States of America
- Website: projectpythia.org
- Twitter: Project_Pythia
- Repositories: 21
- Profile: https://github.com/ProjectPythia
Community learning resource for Python-based computing in the geosciences
Citation (CITATION.cff)
cff-version: 1.2.0
message: "If you use this cookbook, please cite it as below."
authors:
# add additional entries for each author -- see https://github.com/citation-file-format/citation-file-format/blob/main/schema-guide.md
- family-names: Abernathey
given-names: Ryan
orcid: https://orcid.org/0000-0001-5999-4917 # optional
website: https://github.com/rabernat
affiliation: Columbia University # optional
- family-names: Drake
given-names: Henri
orcid: https://orcid.org/0000-0003-0135-0814
website: https://github.com/hdrake
affiliation: University of California, Irvine
- family-names: Ford
given-names: Robert R.
orcid: https://orcid.org/0000-0001-5483-4965
website: https://github.com/r-ford
affiliation: University at Albany (State University of New York)
- name: "CMIP6 Cookbook contributors" # use the 'name' field to acknowledge organizations
website: "https://github.com/ProjectPythia/cmip6-cookbook/graphs/contributors"
title: "CMIP6 Cookbook"
abstract: "Examples of analysis of Google Cloud CMIP6 data using Pangeo tools."
GitHub Events
Total
- Issues event: 2
- Watch event: 2
- Delete event: 1
- Issue comment event: 6
- Push event: 64
- Pull request event: 5
- Create event: 1
Last Year
- Issues event: 2
- Watch event: 2
- Delete event: 1
- Issue comment event: 6
- Push event: 64
- Pull request event: 5
- Create event: 1
Committers
Last synced: 10 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Robert Ford | 5****d | 91 |
| mgrover1 | m****x@g****m | 56 |
| Julia Kent | 4****t | 22 |
| Brian Rose | b****e@a****u | 18 |
| erogluorhan | e****n@g****m | 1 |
| dependabot[bot] | 4****] | 1 |
| Henri Drake | h****e@u****u | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 10 months ago
All Time
- Total issues: 24
- Total pull requests: 65
- Average time to close issues: 2 months
- Average time to close pull requests: 11 days
- Total issue authors: 6
- Total pull request authors: 6
- Average comments per issue: 1.79
- Average comments per pull request: 2.05
- Merged pull requests: 55
- Bot issues: 0
- Bot pull requests: 5
Past Year
- Issues: 1
- Pull requests: 3
- Average time to close issues: about 20 hours
- Average time to close pull requests: 33 minutes
- Issue authors: 1
- Pull request authors: 2
- Average comments per issue: 1.0
- Average comments per pull request: 1.33
- Merged pull requests: 2
- Bot issues: 0
- Bot pull requests: 1
Top Authors
Issue Authors
- r-ford (13)
- mgrover1 (4)
- brian-rose (3)
- chiaweh2 (1)
- erogluorhan (1)
- ktyle (1)
Pull Request Authors
- mgrover1 (28)
- r-ford (22)
- brian-rose (7)
- dependabot[bot] (7)
- jukent (7)
- hdrake (1)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- act-atmos
- cartopy
- cftime
- dask
- dask-gateway
- esgf-pyclient
- fsspec
- gcsfs
- globus-compute-endpoint
- globus-compute-sdk
- holoviews
- hvplot
- intake
- intake-esm
- jupyter-book
- jupyter_server
- jupyterlab
- matplotlib
- nc-time-axis
- numba >=0.58.0
- numpy
- pandas
- pip
- python <3.12
- seaborn
- tqdm
- xarray
- xesmf
- xhistogram