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predictgmstrate

Using a neural network to predict changes in the rate of global mean surface temperature warming

https://github.com/zmlabe/predictgmstrate

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 14 DOI reference(s) in README
  • ✓
    Academic publication links
    Links to: wiley.com, nature.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 (7.4%) to scientific vocabulary

Keywords

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

Repository

Using a neural network to predict changes in the rate of global mean surface temperature warming

Basic Info
  • Host: GitHub
  • Owner: zmlabe
  • License: mit
  • Language: Python
  • Default Branch: main
  • Homepage: https://zacklabe.com/
  • Size: 119 MB
Statistics
  • Stars: 4
  • Watchers: 2
  • Forks: 2
  • Open Issues: 0
  • Releases: 2
Topics
climate climate-change climate-models climate-variability explainable-ai internal-variability neural-network
Created over 4 years ago · Last pushed about 3 years ago
Metadata Files
Readme License Citation

README.md

predictGMSTrate DOI

Using a neural network to predict changes in the rate of global mean surface temperature warming

Under construction... [Python 3.7]

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

  • Berkeley Earth Surface Temperature project (BEST) : [DATA]
    • Rohde, R. and Coauthors (2013) Berkeley earth temperature averaging process. Geoinform Geostat Overv. doi:10.4172/2327-4581.1000103 [PUBLICATION]
  • CESM Large Ensemble Project (LENS) : [DATA]
    • Kay, J. E and Coauthors, 2015: The Community Earth System Model (CESM) large ensemble project: A community resource for studying climate change in the presence of internal climate variability. Bull. Amer. Meteor. Soc., 96, 13331349, doi:10.5194/esd-2021-50 [PUBLICATION]
  • CESM2 Large Ensemble Project (LENS2) : [DATA]
    • Rodgers, K. B., Lee, S. S., Rosenbloom, N., Timmermann, A., Danabasoglu, G., Deser, C., ... & Yeager, S. G. (2021). Ubiquity of human-induced changes in climate variability. Earth System Dynamics Discussions, 1-22, doi:10.1175/BAMS-D-13-00255.1 [PUBLICATION]
  • ERA5 : [DATA]
    • Hersbach, H., Bell, B., Berrisford, P., Hirahara, S., Hornyi, A., MuozSabater, J., ... & Simmons, A. (2020). The ERA5 global reanalysis. Quarterly Journal of the Royal Meteorological Society, doi:10.1002/qj.3803 [PUBLICATION]
  • Multi-Model Large Ensemble (SMILE) : [DATA]
    • Deser, C., Phillips, A. S., Simpson, I. R., Rosenbloom, N., Coleman, D., Lehner, F., ... & Stevenson, S. (2020). Deser, C., Lehner, F., Rodgers, K. B., Ault, T., Delworth, T. L., DiNezio, P. N., ... & Ting, M. (2020). Insights from Earth system model initial-condition large ensembles and future prospects. Nature Climate Change, 1-10. doi:10.1038/s41558-020-0731-2 [PUBLICATION]
  • Institute of Atmospheric Physics (IAP) Ocean Heat Content : [DATA]
    • Cheng L., K. Trenberth, J. Fasullo, T. Boyer, J. Abraham, & Zhu, J. (2017): Improved estimates of ocean heat content from 1960 to 2015, Science Advance, doi10.1126/sciadv.1601545 [PUBLICATION]

Publications

  • [1] Labe, Z.M. and E.A. Barnes (2022), Predicting slowdowns in decadal climate warming trends with explainable neural networks. Geophysical Research Letters, DOI:10.1029/2022GL098173 [HTML][SUMMARY][BibTeX]

Conferences

  • [3] Labe, Z.M. and E.A. Barnes. Using artificial neural networks to predict temporary slowdowns in global warming trends, 22nd Conference on Artificial Intelligence for Environmental Science, Denver, CO (Jan 2023) [ABSTRACT][SLIDESHARE]

  • [2] Labe, Z.M. and E.A. Barnes. Temporary slowdowns in decadal warming predictions by a neural network, CLIVAR Climate Dynamics Panel (CDP) annual workshop: External versus internal variability on decadal and longer time scales, Virtual Workshop (Oct 2022) [POSTER][SLIDESHARE]

  • [1] Labe, Z.M. and E.A. Barnes. Decadal warming slowdown predictions by an artificial neural network, 2021 Young Scientist Symposium on Atmospheric Research (YSSAR), Colorado State University, CO (Oct 2021) [SLIDESHARE]

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
Last Year
  • Watch event: 1

Committers

Last synced: over 1 year ago

All Time
  • Total Commits: 55
  • Total Committers: 2
  • Avg Commits per committer: 27.5
  • Development Distribution Score (DDS): 0.018
Past Year
  • Commits: 0
  • Committers: 0
  • Avg Commits per committer: 0.0
  • Development Distribution Score (DDS): 0.0
Top Committers
Name Email Commits
Zachary Labe z****e@r****u 54
Mike Brady m****y 1
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: 1
  • Average time to close issues: N/A
  • Average time to close pull requests: about 5 hours
  • Total issue authors: 0
  • Total pull request authors: 1
  • Average comments per issue: 0
  • Average comments per pull request: 0.0
  • Merged pull requests: 1
  • 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
View more stats
Top Authors
Issue Authors
Pull Request Authors
  • m9brady (1)
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