ModelBiasesANN

Investigation of model biases in historical internal variability using explainable AI

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]
  • Bell, B., Hersbach, H., Simmons, A., Berrisford, P., Dahlgren, P., Horányi, A., ... & Thépaut, J. N. (2021). The ERA5 global reanalysis: Preliminary extension to 1950. Quarterly Journal of the Royal Meteorological Society, doi.org/10.1002/qj.4174 [PUBLICATION]
  • 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]
• 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]
• 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 (2022), Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, DOI:10.1029/2022EA002348 [HTML][SUMMARY][BibTeX]

Conferences

• [5] Labe, Z.M. and E.A. Barnes. Using explainable neural networks for comparing climate model projections, 27th Conference on Probability and Statistics, Virtual Attendance (Jan 2022). [Abstract] [Slides]
• [4] Labe, Z.M. and E.A. Barnes. Evaluating global climate models using simple, explainable neural networks, 2021 American Geophysical Union Annual Meeting, Virtual Attendance (Dec 2021) (Invited). [Abstract] [Slides]
• [3] 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]
• [2] Labe, Z.M. and E.A. Barnes. Climate model evaluation with explainable neural networks, 3rd NOAA Workshop on Leveraging AI in Environmental Sciences, Virtual Workshop (Sep 2021). [Poster]
• [1] Labe, Z.M. and E.A. Barnes. Using explainable neural networks for comparing historical climate model simulations, 2nd Workshop on Knowledge Guided Machine Learning (KGML2021), Virtual Workshop (Aug 2021). [Poster]