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InternalSignal DOI

ANN detecting signal from internal variability

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]
  • 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]
  • 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:10.1175/BAMS-D-13-00255.1 [PUBLICATION]
  • CESM Large Ensemble Single Forcing (LENS-X) : [Data]
    • Deser, C., Phillips, A. S., Simpson, I. R., Rosenbloom, N., Coleman, D., Lehner, F., ... & Stevenson, S. (2020). Isolating the evolving contributions of anthropogenic aerosols and greenhouse gases: A new CESM1 large ensemble community resource. Journal of Climate. doi:10.1175/JCLI-D-20-0123.1 [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]

Publications

  • [1] Labe, Z.M. and E.A. Barnes (2021), Detecting climate signals using explainable AI with single-forcing large ensembles. Journal of Advances in Modeling Earth Systems, DOI:10.1029/2021MS002464 [HTML][SUMMARY][BibTeX]

Conferences/Presentations

  • [5] Labe, Z.M. and E.A. Barnes. Using neural networks to explore regional climate patterns in single-forcing large ensembles, 2021 American Geophysical Union Annual Meeting, Virtual Attendance (Dec 2021) (Invited). [Slides]
  • [4] Labe, Z.M. and E.A. Barnes. Exploring climate model large ensembles with explainable neural networks, WCRP workshop on attribution of multi-annual to decadal changes in the climate system, Virtual Workshop (Sep 2021). [Slides]
  • [3] Labe, Z.M. and E.A. Barnes. Climate Signals in CESM1 Single-Forcing Large Ensembles Revealed by Explainable Neural Networks, 26th Annual CESM Workshop, Virtual Workshop (Jun 2021). [Slides]
  • [2] Labe, Z.M. Revealing climate change signals with explainable AI, 2021 Spring Postdoctoral Research Symposium, Remote Presentation at Colorado State University (Mar 2021). [Slides]
  • [1] Labe, Z.M. and E.A. Barnes. Disentangling Climate Forcing in Multi-Model Large Ensembles Using Neural Networks, 20th Conference on Artificial Intelligence for Environmental Science, Virtual Conference (Jan 2021). [Abstract] [Slides]