Timing of emergence of CONUS summertime temperatures using explainable neural networks

Zachary Labe - Research Website - @ZLabe

• 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.

• 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]
• CESM1 Large Ensemble Project (LENS1) : [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:10.1175/BAMS-D-13-00255.1 [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]
• GFDL FLOR: Forecast-oriented Low Ocean Resolution : [DATA]
  • Vecchi, G. A., Delworth, T., Gudgel, R., Kapnick, S., Rosati, A., Wittenberg, A. T., ... & Zhang, S. (2014). On the seasonal forecasting of regional tropical cyclone activity. Journal of Climate, 27(21), 7994-8016. doi:10.1175/JCLI-D-14-00158.1 [PUBLICATION]
• GFDL SPEAR: Seamless System for Prediction and EArth System Research : [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]
• Sixth Version of Model for Interdisciplinary Research on Climate (MIROC6) : [DATA]
  • Tatebe, H., Ogura, T., Nitta, T., Komuro, Y., Ogochi, K., Takemura, T., ... & Kimoto, M. (2019). Description and basic evaluation of simulated mean state, internal variability, and climate sensitivity in MIROC6. Geoscientific Model Development, 12(7), 2727-2765. doi:10.5194/gmd-12-2727-2019 [PUBLICATION]
• NOAA Monthly U.S. Climate Gridded Dataset (NClimGrid) : [DATA]
  • Vose, R. S., Applequist, S., Squires, M., Durre, I., Menne, M. J., Williams, C. N., Jr., Fenimore, C., Gleason, K., & Arndt, D. (2014). Improved Historical Temperature and Precipitation Time Series for U.S. Climate Divisions, Journal of Applied Meteorology and Climatology, 53(5), 1232-1251. doi:10.1175/JAMC-D-13-0248.1 [PUBLICATION]
• NOAA-CIRES-DOE Twentieth Century Reanalysis (20CRv3) : [DATA]
  • Slivinski, L. C., Compo, G. P., Whitaker, J. S., Sardeshmukh, P. D., Giese, B. S., McColl, C., ... & Wyszyński, P. (2019). Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. Quarterly Journal of the Royal Meteorological Society, 145(724), 2876-2908. doi:10.1002/qj.3598 [PUBLICATION]

• [1] Labe, Z.M., N.C. Johnson, and T.L. Delworth (2024), Changes in United States summer temperatures revealed by explainable neural networks. Earth's Future, DOI:10.1029/2023EF003981 [HTML][SUMMARY][BibTeX]

• [5] 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).
• [4] Labe, Z.M., N.C. Johnson, and T.L. Delworth. Distinguishing the regional emergence of United States summer temperatures between observations and climate model large ensembles, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024). [Abstract][Slides]
• [3] Labe, Z.M., Climate change extremes by season in the United States, Hershey Horticulture Society, Hershey, PA (Sep 2023). [Slides]
• [2] 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]
• [1] Labe, Z.M. Forced climate signals with explainable AI and large ensembles, Atmospheric and Oceanic Sciences Student/Postdoc Seminar, Princeton University, NJ, USA (Feb 2023). [Slides]