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detectmitigate

Using neural networks to detect effects of rapid climate mitigation

https://github.com/zmlabe/detectmitigate

Science Score: 59.0%

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

  • ○
    CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 11 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: wiley.com, zenodo.org
  • ✓
    Committers with academic emails
    1 of 2 committers (50.0%) from academic institutions
  • ○
    Institutional organization owner
  • ○
    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (9.6%) to scientific vocabulary

Keywords

climate-change climate-migration climate-mitigation-options climate-model climate-modeling-experiments climate-variability explainable-ai internal-variability machine-learning neural-networks
Last synced: 6 months ago · JSON representation

Repository

Using neural networks to detect effects of rapid climate mitigation

Basic Info
  • Host: GitHub
  • Owner: zmlabe
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage: https://zacklabe.com/
  • Size: 81.2 MB
Statistics
  • Stars: 5
  • Watchers: 2
  • Forks: 2
  • Open Issues: 0
  • Releases: 2
Topics
climate-change climate-migration climate-mitigation-options climate-model climate-modeling-experiments climate-variability explainable-ai internal-variability machine-learning neural-networks
Created almost 3 years ago · Last pushed 11 months ago
Metadata Files
Readme License Citation

README.md

DetectMitigate DOI

Using neural networks to detect effects of rapid climate mitigation

Under construction... [Python 3.9]

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 processing.

Data

  • GFDL SPEAR: Seamless System for Prediction and EArth System Research : [GFDL PORTAL][RAW DATA][PROCESSED DATA]
    • Delworth, T. L., Cooke, W. F., Adcroft, A., Bushuk, M., Chen, J. H., Dunne, K. A., ... & Zhao, M. (2020). SPEAR: The next generation GFDL modeling system for seasonal to multidecadal prediction and projection. Journal of Advances in Modeling Earth Systems, 12(3), e2019MS001895. doi:10.1029/2019MS001895 [PUBLICATION]

Publications

  • [2] Labe, Z.M., T.L. Delworth, N.C. Johnson, B-T. Jong, C.E. McHugh, W.F. Cooke, and L. Jia (2025). Large reductions in United States heat extremes found in overshoot simulations with SPEAR, EarthArXiv, DOI:10.31223/X5TX4P (submitted) [PREPRINT]
  • [1] Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke (2024). Exploring a data-driven approach to identify regions of change associated with future climate scenarios. Journal of Geophysical Research: Machine Learning and Computation, DOI:10.1029/2024JH000327 [HTML][SUMMARY][BibTeX]

Conferences/Presentations

  • [8] Labe, Z.M., T.L. Delworth, N.C. Johnson, L. Jia, W.F. Cooke, B.-T. Jong, and C.E. McHugh. Greater reduction in U.S. heat extreme days in overshoot simulations with GFDL SPEAR, 38th Conference on Climate Variability and Change, New Orleans, LA (Jan 2025). [Abstract][Poster]
  • [7] Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. Explainable AI for distinguishing future climate change scenarios, EGU General Assembly 2024, Vienna, Austria (Apr 2024). [Abstract]
  • [6] Labe, Z.M. Applications of machine learning for climate change and variability, Department of Environmental Sciences Seminar, Rutgers University, New Brunswick, NJ, USA (Feb 2023) (Invited).
  • [5] Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. A data-driven approach to identifying key regions of change associated with future climate scenarios, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). [Abstract][Slides]
  • [4] Labe, Z.M. Using explainable machine learning to evaluate climate change projections, Atmosphere and Ocean Climate Dynamics Seminar, Yale University, CT, USA (Oct 2023) (Invited-Remote). [Slides]
  • [3] Labe, Z.M., N.C. Johnson, and T.L Delworth. A data-driven approach to identifying key regions of climate change in GFDL SPEAR, GFDL Poster Session with NOAA Research, Princeton, NJ, USA (Apr 2023). [Poster]
  • [2] Labe, Z.M. Creative machine learning approaches for climate change detection, Resnick Young Investigators Symposium, California Institute of Technology (Caltech), CA, USA (Apr 2023) (Invited). [Abstract][Slides]
  • [1] Labe, Z.M. Using explainable AI to identify key regions of climate change in GFDL SPEAR large ensembles, GFDL Lunchtime Seminar Series, Princeton, NJ, USA (Mar 2023). [Slides]

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.

GitHub Events

Total
  • Watch event: 1
  • Push event: 5
  • Fork event: 1
Last Year
  • Watch event: 1
  • Push event: 5
  • Fork event: 1

Committers

Last synced: about 2 years ago

All Time
  • Total Commits: 37
  • Total Committers: 2
  • Avg Commits per committer: 18.5
  • Development Distribution Score (DDS): 0.189
Past Year
  • Commits: 37
  • Committers: 2
  • Avg Commits per committer: 18.5
  • Development Distribution Score (DDS): 0.189
Top Committers
Name Email Commits
Zachary Labe z****e@u****u 30
Zachary Labe z****e@g****m 7
Committer Domains (Top 20 + Academic)
uci.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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