predictGMSTrate

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, 1333–1349, 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., Horányi, A., Muñoz‐Sabater, 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]