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sai

Using explainable to identify regional climate signals to stratospheric aerosol injection

https://github.com/zmlabe/sai

Science Score: 77.0%

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
    Found 8 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: wiley.com, zenodo.org
  • ✓
    Committers with academic emails
    1 of 1 committers (100.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (8.7%) to scientific vocabulary

Keywords

aerosols climate-change climate-models climate-variability explainable-ai internal-variability large-ensembles neural-network
Last synced: 6 months ago · JSON representation ·

Repository

Using explainable to identify regional climate signals to stratospheric aerosol injection

Basic Info
  • Host: GitHub
  • Owner: zmlabe
  • Language: Python
  • Default Branch: main
  • Homepage: https://zacklabe.com/
  • Size: 605 KB
Statistics
  • Stars: 3
  • Watchers: 2
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Topics
aerosols climate-change climate-models climate-variability explainable-ai internal-variability large-ensembles neural-network
Created almost 4 years ago · Last pushed almost 3 years ago
Metadata Files
Readme Citation

README.md

SAI DOI

Using explainable AI to identify regional climate signals in response to stratospheric aerosol injection

Under construction... [Python 3.7]

Project

Check out AI Methods for Solar Radiation Management Research (DARPA‐PA‐21‐04‐02) - https://github.com/eabarnes1010/actm-sai-csu

Contact

Zachary Labe - Research Website - @ZLabe

Description

  • Scripts/: Main Python scripts/functions used in data analysis and plotting
  • requirements.txt: List of environments and modules associated with the most recent version of this project. A Python Anaconda3 Distribution was used for our analysis. Tools including NCL, CDO, and NCO were also used for initial data manipulation.

Data

  • Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE) : [DATA]

    • Richter, J., Visioni, D., MacMartin, D., Bailey, D. A., Lee, W., Woodhouse, S., ... & Lamarque, J. F. (2021, December). Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosols (ARISE-SAI) simulations. In AGU Fall Meeting 2021. AGU. [ABSTRACT]
    • Richter, J. H., Visioni, D., MacMartin, D. G., Bailey, D. A., Rosenbloom, N., Dobbins, B., ... & Lamarque, J. F. (2022). Assessing Responses and Impacts of Solar climate intervention on the Earth system with stratospheric aerosol injection (ARISE-SAI): protocol and initial results from the first simulations. Geoscientific Model Development, 15(22), 8221-8243., doi:10.5194/gmd-15-8221-2022 [PUBLICATION]

  • Whole Atmosphere Community Climate Model Version 6 (WACCM6) : [DATA]

    • Danabasoglu, G., Lamarque, J. F., Bacmeister, J., Bailey, D. A., DuVivier, A. K., Edwards, J., ... & Strand, W. G. (2020). The community earth system model version 2 (CESM2). Journal of Advances in Modeling Earth Systems, 12(2), e2019MS001916., doi:10.1029/2019MS001916 [PUBLICATION]
    • Gettelman, A., Mills, M. J., Kinnison, D. E., Garcia, R. R., Smith, A. K., Marsh, D. R., ... & Randel, W. J. (2019). The whole atmosphere community climate model version 6 (WACCM6). Journal of Geophysical Research: Atmospheres, 124(23), 12380-12403., doi:10.1029/2019JD030943 [PUBLICATION]

Publications

  • [1] Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023), Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a [HTML][SUMMARY][BibTeX]

Conferences

  • [1] Labe, Z.M., E.A. Barnes, and J.W. Hurrell. Detecting the regional emergence of climate signals with machine learning in a set of stratospheric aerosol injection simulations, 2022 American Geophysical Union Annual Meeting, Chicago, IL (Dec 2022) [ABSTRACT][POSTER]

Owner

  • Name: Zachary Labe
  • Login: zmlabe
  • Kind: user
  • Location: Princeton, NJ
  • Company: Princeton University & NOAA GFDL

I’m a climate scientist trying to visualize the signal from a lot of noise.

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Labe"
  given-names: "Zachary"
  orcid: "https://orcid.org/0000-0002-6394-7651"
- family-names: "Barnes"
  given-names: "Elizabeth"
title: "SAI"
version: 1.0
date-released: 2022-03-08
url: "https://github.com/zmlabe/SAI"

GitHub Events

Total
  • Watch event: 1
Last Year
  • Watch event: 1

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 30
  • Total Committers: 1
  • Avg Commits per committer: 30.0
  • Development Distribution Score (DDS): 0.0
Past Year
  • Commits: 11
  • Committers: 1
  • Avg Commits per committer: 11.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Zachary Labe z****e@r****u 30
Committer Domains (Top 20 + Academic)
rams.colostate.edu: 1

Issues and Pull Requests

Last synced: about 2 years ago

All Time
  • Total issues: 0
  • Total pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Total issue authors: 0
  • Total pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Past Year
  • Issues: 0
  • Pull requests: 0
  • Average time to close issues: N/A
  • Average time to close pull requests: N/A
  • Issue authors: 0
  • Pull request authors: 0
  • Average comments per issue: 0
  • Average comments per pull request: 0
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
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