CMIP6 Cookbook
This Project Pythia Cookbook covers examples of analysis of Google Cloud CMIP6 data using Pangeo tools.
https://github.com/projectpythia/cmip6-cookbook
Category: Sustainable Development
Sub Category: Education
Keywords from Contributors
transforms archiving measur optimize compose generic conversion grid climate-model animals
Last synced: about 21 hours ago
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Repository metadata
Examples of analysis of Google Cloud CMIP6 data using Pangeo tools
- Host: GitHub
- URL: https://github.com/projectpythia/cmip6-cookbook
- Owner: ProjectPythia
- License: apache-2.0
- Created: 2022-06-27T17:01:34.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2024-10-17T21:10:16.000Z (6 months ago)
- Last Synced: 2025-04-19T14:10:05.757Z (9 days ago)
- Language: Jupyter Notebook
- Homepage: https://projectpythia.org/cmip6-cookbook/
- Size: 33.9 MB
- Stars: 14
- Watchers: 1
- Forks: 10
- Open Issues: 6
- Releases: 1
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Citation: CITATION.cff
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:git clone https://github.com/ProjectPythia/cmip6-cookbook.git -
Move into the
cmip6-cookbookdirectorycd cmip6-cookbook -
Create and activate your conda environment from the
environment.ymlfileconda env create -f environment.yml conda activate cmip6-cookbook-dev -
Move into the
notebooksdirectory and start up Jupyterlabcd 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.
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."
Owner metadata
- Name: Project Pythia
- Login: ProjectPythia
- Email: [email protected]
- Kind: organization
- Description: Community learning resource for Python-based computing in the geosciences
- Website: projectpythia.org
- Location: United States of America
- Twitter: Project_Pythia
- Company:
- Icon url: https://avatars.githubusercontent.com/u/75807555?v=4
- Repositories: 21
- Last ynced at: 2023-03-03T22:51:31.899Z
- Profile URL: https://github.com/ProjectPythia
GitHub Events
Total
- Issues event: 2
- Watch event: 2
- Issue comment event: 3
- Push event: 3
- Pull request event: 2
Last Year
- Issues event: 2
- Watch event: 2
- Issue comment event: 3
- Push event: 3
- Pull request event: 2
Committers metadata
Last synced: 6 days ago
Total Commits: 176
Total Committers: 6
Avg Commits per committer: 29.333
Development Distribution Score (DDS): 0.506
Commits in past year: 10
Committers in past year: 3
Avg Commits per committer in past year: 3.333
Development Distribution Score (DDS) in past year: 0.4
| Name | Commits | |
|---|---|---|
| Robert Ford | 5****d | 87 |
| mgrover1 | m****x@g****m | 56 |
| Brian Rose | b****e@a****u | 18 |
| Julia Kent | 4****t | 13 |
| dependabot[bot] | 4****] | 1 |
| Henri Drake | h****e@u****u | 1 |
Committer domains:
- uci.edu: 1
- albany.edu: 1
Issue and Pull Request metadata
Last synced: 2 days ago
Total issues: 45
Total pull requests: 118
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: 1.89
Average comments per pull request: 2.07
Merged pull request: 100
Bot issues: 0
Bot pull requests: 7
Past year issues: 6
Past year pull requests: 7
Past year average time to close issues: about 20 hours
Past year average time to close pull requests: about 1 hour
Past year issue authors: 2
Past year pull request authors: 3
Past year average comments per issue: 1.33
Past year average comments per pull request: 1.14
Past year merged pull request: 7
Past year bot issues: 0
Past year bot pull requests: 0
Top Issue Authors
- r-ford (26)
- mgrover1 (7)
- brian-rose (6)
- chiaweh2 (2)
- erogluorhan (2)
- ktyle (2)
Top Pull Request Authors
- mgrover1 (53)
- r-ford (32)
- brian-rose (14)
- jukent (10)
- dependabot[bot] (7)
- hdrake (2)
Top Issue Labels
- bug (17)
- content (16)
- infrastructure (4)
- hackathon (2)
- good first issue (2)
Top 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
Score: 4.787491742782046