Researching and Communicating Our Changing Climate

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Zachary Labe is a postdoc researcher at NOAA GFDL and Princeton University who studies climate variability and change. His research uses tools like artificial intelligence and climate models to disentangle the signal of climate change from natural weather noise. He conducts field work including Arctic expeditions and uses supercomputers to run complex climate models that generate huge amounts of data.

Science

RESEARCHING AND
COMMUNICATING
OUR CHANGING CLIMATE
@ZLabe
Zachary M. Labe
Postdoc in Seasonal-to-Decadal Variability
& Predictability Division at NOAA GFDL
and AOS at Princeton University
6 December 2023
Mercer County
Community College
https://zacklabe.com/

Zack Labe
Pioneer Coal Mine Blue Whale of Catoosa Centralia Underground Fire Roadside America Greenland Sea (81°N)
Enjoy: roadside oddities, diners, and horror movies
Hobbies: gardening, #scicomm ( @ZLabe), hiking, traveling to lighthouses
Graduate work: Arctic – midlatitude climate variability (sea-ice changes)
Recent work: using AI to detect patterns of climate variability/change
Now at GFDL I am thinking about: detection and attribution of extreme events
Hometown – Linglestown,
Pennsylvania
BSc – Atmospheric Science
at Cornell University
PhD – Earth System Science
at UC Irvine
Postdoc – climate variability
using AI at Colorado State U.
Postdoc – climate change
and AI at Princeton/GFDL
he/him

The signal (climate change)
The noise (weather)
MY RESEARCH
DISENTANGLES

…using tools like artificial intelligence and climate models to understand global climate change
The signal (climate change)
The noise (weather)
MY RESEARCH
DISENTANGLES

https://www.ornl.gov/news/ornl-noaa-launch-new-supercomputer-climate-science-research
Home Laptop – 2,000,000,000 instructions per second
Supercomputer – 5,000,000,000,000,000 instructions per second

Adapted from: Kotamarthi, R., Hayhoe, K., Mearns, L., Wuebbles, D., Jacobs, J., & Jurado, J.
(2021). Global Climate Models. In Downscaling Techniques for High-Resolution Climate
Projections: From Global Change to Local Impacts (pp. 19-39). Cambridge: Cambridge University
Press. doi:10.1017/9781108601269.003
CLIMATE MODELS
Horizontal Grid
Vertical Levels
Past/Present/Future
Fully-Coupled System
20-40 Petabytes of data

Senator Sheldon Whitehouse (D-CT) - Time to Wake Up: Climate Progress Speech (3/21/2018)

PLOT BY ED HAWKINS
DON’T BE
SUCH A
SCIENTIST
WE ARE
DATA
SCIENTISTS

Geographers at Clark University and Clark Labs in Worcester, Massachusetts, use geographic information system (GIS) and image processing computer software to produce images that help them understand the impact of humans on the Earth. A spring 2016 exhibit by J. Ronald Eastman, professor of geography and director of Clark Labs, highlights some of these images. The exhibit is sponsored by Clark University's Higgins School of Humanities and Clark Labs.

Using visualization to start climate conversations

Zachary Labe

Curriculum Vitaecolgan

23 November 2022… GFDL Lunchtime Seminar Series Creating visualizations of complex data structures and patterns is an important part of our jobs. We use figures for journal publications, presentations, posters, lab group meetings, science communication, and more. However, creating suitable figures for the task can sometimes be an afterthought during the extensive scientific process. In this seminar, I’ll share examples from climate science to discuss a network of resources available for designing accessible figures in both publications and presentations by leveraging resources that support open science practices. I will also share examples of what not to do, which goes beyond only considering interpretable colormaps, and how to improve these figures moving forward. Finally, by using global mean surface temperature as a case study, I will share some creative instances of using data visualization as a form of storytelling for communicating climate change.

Using explainable machine learning to evaluate climate change projections

Zachary Labe

5 October 2023… Atmosphere and Ocean Climate Dynamics Seminar (Presentation): Using explainable machine learning to evaluate climate change projections, Yale University, New Haven, CT. Remote Presentation. References... 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, https://iopscience.iop.org/article/10.1088/1748-9326/acc81a

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Explainable AI for distinguishing future climate change scenarios

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Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. Explainable AI for distinguishing future climate change scenarios, EGU General Assembly 2024, Vienna, Austria (Apr 2024). Abstract... https://meetingorganizer.copernicus.org/EGU24/EGU24-9110.html References... Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke (2024). Exploring a data-driven approach to identify regions of change associated with future climate scenarios. (submitted; preprint - https://doi.org/10.22541/essoar.171288901.17027965/v1)

Reexamining future projections of Arctic climate linkages

Zachary Labe

10 May 2024… Atmospheric and Oceanic Sciences Student/Postdoc Seminar (Presentation): Reexamining future projections of Arctic climate linkages, Princeton University, USA. References... 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., 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 (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI:10.1029/2020GL088583 Labe, Z.M., May 2020: The effects of Arctic sea-ice thickness loss and stratospheric variability on mid-latitude cold spells. University of California, Irvine. Doctoral Dissertation. 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

Techniques and Considerations for Improving Accessibility in Online Media

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An intro to explainable AI for polar climate science

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26 March 2024… GFDL Polar Climate Interest Group (Presentation): An intro to explainable AI for polar climate science, NOAA GFDL, Princeton, NJ. References: 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, https://doi.org/10.1029/2022EA002348 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, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002464

Water in a Frozen Arctic: Cross-Disciplinary Perspectives

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Explainable AI approach for evaluating climate models in the Arctic

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27 March 2024… IARPC Collaborations, Modelers’ Community of Practice (Presentation): Explainable AI approach for evaluating climate models in the Arctic. Remote Presentation. References... Labe, Z. M., & Barnes, E. A. (2022). Comparison of climate model large ensembles with observations in the Arctic using simple neural networks. Earth and Space Science, 9(7), e2022EA002348, https://doi.org/10.1029/2022EA002348

Explainable neural networks for evaluating patterns of climate change and var...

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12 March 2024… Sharing Science – North American Webinar, Young Earth System Scientists (YESS) Community (Presentation): Explainable neural networks for evaluating patterns of climate change and variability. Remote Presentation. References... 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

Applications of machine learning for climate change and variability

Zachary Labe

23 February 2024… Department of Environmental Sciences Seminar (Presentation): Applications of machine learning for climate change and variability, Rutgers University, New Brunswick, NJ. References: 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 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 Labe, Z. M., Johnson, N. C., & Delworth, T. L. (2024). Changes in United States summer temperatures revealed by explainable neural networks. Earth's Future, DOI:10.1029/2023EF003981

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16 November 2023… Department Seminar (Presentation): Revisiting projections of Arctic climate change linkages, Tongji University, Shanghai, China. Remote Presentation. References: 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., 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 (2020). Warm Arctic, cold Siberia pattern: role of full Arctic amplification versus sea ice loss alone, Geophysical Research Letters, DOI: 10.1029/2020GL088583 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 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

Visualizing climate change through data

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18 November 2023… NJ State Museum Planetarium (Presentation): Visualizing climate change through data, Trenton, NJ. References... 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, https://journals.ametsoc.org/view/journals/clim/36/20/JCLI-D-22-0716.1.xml

Climate change extremes by season in the United States

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11 September 2023… Hershey Horticulture Society (Presentation): Climate change extremes by season in the United States, Hershey, PA, USA. References... 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 Labe, Z.M., T.R. Ault, and R. Zurita-Milla (2016), Identifying Anomalously Early Spring Onsets in the CESM Large Ensemble Project, R. Clim Dyn, DOI:10.1007/s00382-016-3313-2 Labe, Z.M., N.C. Johnson, and T.L Delworth (2023). Changes in United States summer temperatures revealed by explainable neural networks. Preprint. DOI: 10.22541/essoar.168987129.98069596/v1

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22 August 2023… Guest lecture for “Introduction to Global Climate Change (ESS 15)” (Invited): Our changing Arctic in the past and future, University of California, Irvine, CA. Remote Presentation. References... 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, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2019MS001895 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, https://doi.org/10.1029/2022EA002348 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, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL088583

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14 April 2023… Resnick Young Investigators Symposium (Invited Presentation): Creative machine learning approaches for climate change detection, California Institute of Technology (Caltech), USA. https://resnick.caltech.edu/events/resnick-symposium/2023-symposium References: 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 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 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

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Researching and Communicating Our Changing Climate

1. RESEARCHING AND COMMUNICATING OUR CHANGING CLIMATE @ZLabe Zachary M. Labe Postdoc in Seasonal-to-Decadal Variability & Predictability Division at NOAA GFDL and AOS at Princeton University 6 December 2023 Mercer County Community College https://zacklabe.com/

2. 2004

3. Zack Labe Pioneer Coal Mine Blue Whale of Catoosa Centralia Underground Fire Roadside America Greenland Sea (81°N) Enjoy: roadside oddities, diners, and horror movies Hobbies: gardening, #scicomm ( @ZLabe), hiking, traveling to lighthouses Graduate work: Arctic – midlatitude climate variability (sea-ice changes) Recent work: using AI to detect patterns of climate variability/change Now at GFDL I am thinking about: detection and attribution of extreme events Hometown – Linglestown, Pennsylvania BSc – Atmospheric Science at Cornell University PhD – Earth System Science at UC Irvine Postdoc – climate variability using AI at Colorado State U. Postdoc – climate change and AI at Princeton/GFDL he/him

4. The signal (climate change) The noise (weather) MY RESEARCH DISENTANGLES

5. …using tools like artificial intelligence and climate models to understand global climate change The signal (climate change) The noise (weather) MY RESEARCH DISENTANGLES

6. …using tools like artificial intelligence and climate models to understand global climate change The signal (climate change) The noise (weather) MY RESEARCH DISENTANGLES

7. Modeling vs. Field Work

8. R/V Lance – Greenland Sea – May 2017

9. ”Cryo-copter”

10. stuck in the sea ice…

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12. snow pits

13. isbjørn

14. https://www.ornl.gov/news/ornl-noaa-launch-new-supercomputer-climate-science-research Home Laptop – 2,000,000,000 instructions per second Supercomputer – 5,000,000,000,000,000 instructions per second

15. Adapted from: Kotamarthi, R., Hayhoe, K., Mearns, L., Wuebbles, D., Jacobs, J., & Jurado, J. (2021). Global Climate Models. In Downscaling Techniques for High-Resolution Climate Projections: From Global Change to Local Impacts (pp. 19-39). Cambridge: Cambridge University Press. doi:10.1017/9781108601269.003 CLIMATE MODELS Horizontal Grid Vertical Levels Past/Present/Future Fully-Coupled System 20-40 Petabytes of data

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19. Senator Sheldon Whitehouse (D-CT) - Time to Wake Up: Climate Progress Speech (3/21/2018)

20. PLOT BY ED HAWKINS DON’T BE SUCH A SCIENTIST WE ARE DATA SCIENTISTS

21. PLOT BY ED HAWKINS

22. Data visualization is story-telling!

Researching and Communicating Our Changing Climate

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