Under Construction
In this collaborative work, we examine climate change detection and attribution through a data-driven lens and using sets of large ensembles of climate model simulations. In addition to considering methods from deep learning and explainable AI, we also approach climate attribution through counterfactual simulations and storyline approaches to robustly examine extreme events while considering the important influences of internal variability from daily to multidecadal timescales. Further, we are also interested in examining how seasonal-to-decadal climate predictions can be improved and used to inform stakeholders about the probability of extreme and/or persistent temperature and precipitation events occurring on regional scales.
Refereed/Peer-Reviewed:
[2] 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
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[Plain Language Summary]
[1] Thoman, R.L., U. Bhatt, P. Bieniek, B. Brettschneider, M. Brubaker, S. Danielson, Z.M. Labe, R. Lader, W. Meier, G. Sheffield, and J. Walsh (2019): The record low Bering Sea ice extent in 2018: Context, impacts and an assessment of the role of anthropogenic climate change [in “Explaining Extreme Events of 2018 from a Climate Perspective”]. Bull. Amer. Meteor. Soc, DOI:10.1175/BAMS-D-19-0175.1
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[Press Release]
Submitted:
[1] Eischeid, J.K., M.P. Hoerling, X.-W. Quan, A.Kumar, J. Barsugli, Z.M. Labe, K.E. Kunkel, C.J. Schreck III, D.R. Easterling, and T. Zhang (2022). Why has the summertime central U.S. warming hole not disappeared? (submitted)
Presentations:
[15] 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)
[Symposium Event][SlideShare]
[13] 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]
[12] 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).
[SlideShare]
[11] Labe, Z.M., N.C. Johnson, and T.L Delworth. Climate drivers of the recent springtime cooling pattern in northern North America, 36th Conference on Climate Variability and Change, Virtual Attendance (Jan 2023).
[Abstract][SlideShare]
[10] 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, Virtual Attendance (Jan 2023).
[Abstract][SlideShare]
[9] Johnson, N.C., T.L. Delworth, Z.M. Labe, F. Lu, and C.E. McHugh. Accurate seasonal predictions of the 2022 Texas early summer extreme heat nine months in advance, 36th Conference on Climate Variability and Change, Virtual Attendance (Jan 2023).
[Abstract]
[8] J.J. Barsugli, Z.M. Labe, N.C. Johnson, K.E. Kunkel, T. Zhang, D.E. Easterling, J.E. Uehling, J. Eischeid, M. Hoerling, A. Kumar, D. Coates, R. Vose, D. Arndt, C.J. Schreck, and T.L. Delworth. The Texas heat wave of 2022: La Niña, drought, and the emerging climate change signal”, 36th Conference on Climate Variability and Change, Virtual Attendance (Jan 2023).
[Abstract]
[7] Labe, Z.M., N.C. Johnson, and T.L Delworth. Identifying the drivers of the observed springtime cooling trend in northern North America with large ensemble simulations, 2022 American Geophysical Union Annual Meeting, Chicago, IL (Dec 2022).
[Abstract][Poster]
[6] Lehner, F., C. Deser, J. Fasullo, E. Fischer, P. Hitchcock, Z.M. Labe, S. Milinski, M. Röthlisberger, I. Simpson, S. Sippel, and J. Zscheischler. Emergence of multiple feedbacks in the extreme and persistent warmth over Siberia in 2020, 2022 American Geophysical Union Annual Meeting, Chicago, IL (Dec 2022).
[Abstract]
[5] Lehner, F., E. Fischer, Z.M. Labe, S. Milinski, M. Röthlisberger, I. Simpson, S. Sippel, and J. Zscheischler. Evaluating large ensembles for persistent extreme events such as the 2020 temperature anomaly over Siberia, 2021 American Geophysical Union Annual Meeting, Virtual Attendance (Dec 2021).
[Abstract]
[4] 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]
[3] Labe, Z.M. Assessing climate variability and change with explainable neural networks, GFDL, Princeton University, NJ. Remote Presentation (Oct 2021) (Invited).
[SlideShare]
[2] Holman, A., R. Thoman, Z.M. Labe, and J.E. Walsh. Not Just Chance: Ocean and Atmospheric Factors in the Record Low Bering Sea Ice Winter of 2017-2018 and effects on health and safety, 2018 American Geophysical Union Annual Meeting, Washington, DC (Dec 2018).
[Abstract][Code]
[1] Thoman, R. and Z.M. Labe., 2017−18 Sea Ice in Western Alaska during the 2017−18 Season: Historical Context and Possible Drivers, Western Alaska Interdisciplinary Science Conference and Forum, Nome, AK (Mar 2018).
[Code]