Using neural networks to detect effects of rapid climate mitigation
Zachary Labe - Research Website - @ZLabe
Scripts/: Main Python scripts/functions used in data analysis and plottingrequirements.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.
- 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]
- [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]
- [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]
- [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]