cmip6-cookbook

Examples of analysis of Google Cloud CMIP6 data using Pangeo tools

https://github.com/projectpythia/cmip6-cookbook

Science Score: 64.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
    Found CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
  • Academic publication links
    Links to: zenodo.org
  • Committers with academic emails
    2 of 7 committers (28.6%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (16.0%) to scientific vocabulary

Keywords from Contributors

gridding interactive geocat geocat-examples ncl mesh interpretability sequences generic projection
Last synced: 10 months ago · JSON representation ·

Repository

Examples of analysis of Google Cloud CMIP6 data using Pangeo tools

Basic Info
Statistics
  • Stars: 14
  • Watchers: 1
  • Forks: 11
  • Open Issues: 8
  • Releases: 1
Created almost 4 years ago · Last pushed 10 months ago
Metadata Files
Readme License Citation

README.md

CMIP6 Cookbook

CMIP6 logo

nightly-build Binder DOI

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:

  1. Clone the https://github.com/ProjectPythia/cmip6-cookbook repository:

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

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

All Time
  • Total Commits: 190
  • Total Committers: 7
  • Avg Commits per committer: 27.143
  • Development Distribution Score (DDS): 0.521
Past Year
  • Commits: 14
  • Committers: 2
  • Avg Commits per committer: 7.0
  • Development Distribution Score (DDS): 0.357
Top Committers
Name Email 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
bug (8) content (8) infrastructure (2) hackathon (1) good first issue (1)
Pull Request Labels
dependencies (3) github_actions (3)

Dependencies

.github/workflows/nightly-build.yaml actions
.github/workflows/publish-book.yaml actions
.github/workflows/trigger-book-build.yaml actions
.github/workflows/trigger-delete-preview.yaml actions
.github/workflows/trigger-link-check.yaml actions
.github/workflows/trigger-preview.yaml actions
environment.yml conda
  • 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