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Head shot of Zachary Labe

Zachary Labe

Postdoctoral Research Associate
Atmospheric and Oceanic Sciences, Princeton University
Geophysical Fluid Dynamics Laboratory (GFDL), Seasonal-to-Decadal Variability and Predictability Division

Google Scholar
Web of Science



My current work explores the intersection of large-scale climate variability and change, extreme events, large ensembles, decadal prediction, and data science methods. In addition to academic research, I am very passionate about improving science communication, accessibility, and outreach through engaging data visualizations.


  • Ph.D. in Earth System Science – University of California, Irvine – May 2020
  • M.Sc. in Earth System Science – University of California, Irvine – September 2017
  • B.Sc. in Atmospheric Science – Cornell University – May 2015 (Distinction in Research)


  • 2022-Present: Postdoctoral Research Associate
    • Program in Atmospheric and Oceanic Sciences, Princeton University, NJ
    • NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ
  • 2020-2022: Postdoctoral Researcher
    • Department of Atmospheric Science, Colorado State University, CO
  • 2015-2020: Graduate Research Assistant
    • Department of Earth System Science, University of California, Irvine, CA
  • 2014-2015: Undergraduate Research Assistant
    • Department of Earth and Atmospheric Science, Cornell University, NY


  • Member – Diversity, Equity, Inclusivity, and Accessibility Committee (NOAA GFDL)
  • Member – Fresh Eyes on CMIP (WCRP Working Group)
  • Associate Editor – Journal of Climate
  • Contributing Editor – Carbon Brief
  • Guest Editor – Special Issue for Atmospheric Science Letters
  • Board Member – United States Association of Polar Early Career Scientists (USAPECS)
  • Board of Advisors – NCAR Climate Data Guide


(GFDL Bibliography)

  • 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
  • Timmermans, M.-L. and Z.M. Labe (2023). Sea surface temperature [in “Arctic Report Card 2023”], NOAA, DOI:10.25923/e8jc-f342
  • Timmermans, M.-L. and Z.M. Labe (2023). [The Arctic] Sea surface temperature [in “State of the Climate in 2022”]. Bull. Amer. Meteor. Soc., DOI:10.1175/BAMS-D-23-0079.1
  • 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, T. Zhang, J. Uehling, and X. Zhang (2023). Why has the summertime central U.S. warming hole not disappeared? Journal of Climate, DOI:10.1175/JCLI-D-22-0716.1
  • Witt, J.K., Z.M. Labe, A.C. Warden, and B.A. Clegg (2023). Visualizing uncertainty in hurricane forecasts with animated risk trajectories. Weather, Climate, and Society, DOI:10.1175/WCAS-D-21-0173.1
  • Labe, Z.M., E.A. Barnes, and J.W. Hurrell (2023). Identifying the regional emergence of climate patterns in the ARISE-SAI-1.5 simulations. Environmental Research Letters, DOI:10.1088/1748-9326/acc81a
  • Timmermans, M.-L. and Z.M. Labe (2022). Sea surface temperature [in “Arctic Report Card 2022”], NOAA, DOI:10.25923/p493-2548
  • Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, and B.D. Santer (2022). Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming. Proceedings of the National Academy of Sciences, DOI:10.1073/pnas.2209431119
  • Witt, J.K., Z.M. Labe, and B.A. Clegg (2022). Comparisons of perceptions of risk for visualizations using animated risk trajectories versus cones of uncertainty. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, DOI:10.1177/1071181322661308
  • Timmermans, M.-L. and Z.M. Labe (2022). [The Arctic] Sea surface temperature [in “State of the Climate in 2021”]. Bull. Amer. Meteor. Soc., DOI:10.1175/BAMS-D-22-0082.1
  • 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
  • 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
  • Timmermans, M.-L. and Z.M. Labe (2021). Sea surface temperature [in “Arctic Report Card 2021”], NOAA, DOI:10.25923/2y8r-0e49
  • Timmermans, M.-L. and Z.M. Labe (2021). [The Arctic] Sea surface temperature [in “State of the Climate in 2020”]. Bull. Amer. Meteor. Soc., DOI:10.1175/BAMS-D-21-0086.1
  • 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
  • Peings, Y., Z.M. Labe, and G. Magnusdottir (2021), Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss? Journal of Climate, DOI:10.1175/JCLI-D-20-0613.1
  • Timmermans, M.-L. and Z.M. Labe (2020). Sea surface temperature [in “Arctic Report Card 2020”], NOAA, DOI:10.25923/v0fs-m920
  • Timmermans, M.-L., Z.M. Labe, and C. Ladd (2020). [The Arctic] Sea surface temperature [in “State of the Climate in 2019”], Bull. Amer. Meteor. Soc., DOI:10.1175/BAMS-D-20-0086.1
  • Labe, Z.M., Y. Peings, and G. Magnusdottir (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583
  • 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
  • Labe, Z.M., Y. Peings, and G. Magnusdottir (2019). The effect of QBO phase on the atmospheric response to projected Arctic sea ice loss in early winter, Geophysical Research Letters, DOI:10.1029/2019GL083095
  • Labe, Z.M., Y. Peings, and G. Magnusdottir (2018), Contributions of ice thickness to the atmospheric response from projected Arctic sea ice loss, Geophysical Research Letters, DOI:10.1029/2018GL078158
  • Labe, Z.M., G. Magnusdottir, and H.S. Stern (2018), Variability of Arctic sea ice thickness using PIOMAS and the CESM Large Ensemble, Journal of Climate, DOI:10.1175/JCLI-D-17-0436.1
  • Labe, Z.M., T.R. Ault, and R. Zurita-Milla (2017), Identifying Anomalously Early Spring Onsets in the CESM Large Ensemble Project, Climate Dynamics, DOI:10.1007/s00382-016-3313-2




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