Refereed/Peer-Reviewed:
If you do not have access to a study listed here, please feel free to send me an email, and I will be happy to share!
2024
[26] Bushuk, M., S. Ali, D. Bailey, Q. Bao, L. Batte, U.S. Bhatt, E. Blanchard-Wrigglesworth, E. Blockley, G. Cawley, J. Chi, F. Counillon, P. Goulet Coulombe, R. Cullather, F.X. Diebold, A. Dirkson, E. Exarchou, M. Gobel, W. Gregory, V. Guemas, L. Hamilton, B. He, S. Horvath, M. Ionita, J. E. Kay, E. Kim, N. Kimura, D. Kondrashov, Z.M. Labe, W. Lee, Y.J. Lee, C. Li, X. Li, Y. Lin, Y. Liu, W. Maslowski, F. Massonnet, W.N. Meier, W.J. Merryfield, H. Myint, J.C. Acosta Navarro, A. Petty, F. Qiao, D. Schroder, A. Schweiger, Q. Shu, M. Sigmond, M. Steele, J. Stroeve, N. Sun, S. Tietsche, M. Tsamados, K. Wang, J. Wang, W. Wang, Y. Wang, Y. Wang, J. Williams, Q. Yang, X. Yuan, J. Zhang, and Y. Zhang (2024). Predicting September Arctic sea ice: A multi-model seasonal skill comparison. Bulletin of the American Meteorological Society, DOI:10.1175/BAMS-D-23-0163.1
[HTML][Code]
[25] Zhang, Y., B.M. Ayyub, J.F. Fung, and Z.M. Labe (2024). Incorporating extreme event attribution into climate change adaptation for civil infrastructure: Methods, benefits, and research needs. Resilient Cities and Structures, DOI:10.1016/j.rcns.2024.03.002
[HTML][BibTeX]
[24] 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
[HTML][BibTeX][Code]
[Plain Language Summary]
2023
[23] Timmermans, M.-L. and Z.M. Labe (2023). Sea surface temperature [in “Arctic Report Card 2023”], NOAA, DOI:10.25923/e8jc-f342
[HTML][BibTeX][Code]
[Press Release][NOAA Climate(dot)gov]
[22] 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
[HTML][BibTeX][Code]
[Press Release]
[21] 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
[HTML][BibTeX]
[Fox 32 Chicago][NOAA CPO][NOAA Climate(dot)gov][Wired]
[20] 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
[HTML][BibTeX][Code]
[Blog][Plain Language Summary][CNN]
[19] 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
[HTML][BibTeX][Code]
[Plain Language Summary]
2022
[18] Timmermans, M.-L. and Z.M. Labe (2022). Sea surface temperature [in “Arctic Report Card 2022”], NOAA, DOI:10.25923/p493-2548
[HTML][BibTeX][Code]
[Press Release]
[17] 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
[HTML][BibTeX][Code][Data]
[Press Release][DOE Research Highlight]
[16] 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
[HTML][BibTeX][Code]
[Plain Language Summary][CNN]
[15] 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
[HTML][BibTeX][Code]
[Press Release]
[14] 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][BibTeX][Code]
[Plain Language Summary]
[13] 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
[HTML][BibTeX][Code]
[Plain Language Summary][DOE Research Highlight]
2021
[12] Timmermans, M.-L. and Z.M. Labe (2021). Sea surface temperature [in “Arctic Report Card 2021”], NOAA, DOI:10.25923/2y8r-0e49
[HTML][BibTeX][Code]
[Press Release]
[11] 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
[HTML][BibTeX][Code]
[Press Release]
[10] 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][BibTeX][Code]
[Plain Language Summary][Data Skeptic Podcast]
[9] 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
[HTML][BibTeX][Code]
[Plain Language Summary][CLIVAR Research Highlight]
2020
[8] Timmermans, M.-L. and Z.M. Labe (2020). Sea surface temperature [in “Arctic Report Card 2020”], NOAA, DOI:10.25923/v0fs-m920
[HTML][BibTeX][Code]
[Press Release]
[7] 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
[HTML][BibTeX][Code]
[Press Release]
[6] 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
[HTML][BibTeX][Code]
[Plain Language Summary][CBS News][Science Magazine][The Washington Post]
2019
[5] 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
[HTML][BibTeX][Code]
[Press Release]
[4] 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
[HTML][BibTeX][Code]
[Plain Language Summary]
2016-2018
[3] 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
[HTML][BibTeX][Code]
[Plain Language Summary][Arctic Today]
[2] 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
[HTML][BibTeX][Code]
[Plain Language Summary]
[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
[HTML][BibTeX][Code]
[Plain Language Summary][Cornell Press Release][The Cornell Daily Sun][Earther][National Phenology Network]
Submitted manuscripts:
[5] Schreck III, C.M., D.R. Easterling, J.J. Barsugli, D.A. Coates, A. Hoell, N.C. Johnson, K.E. Kunkel, Z.M. Labe, J. Uehling, R.S. Vose, and X. Zhang (2024). A Rapid Response Process for Evaluating Causes of Extreme Temperature Events in the United States: The 2023 Texas/Louisiana Heatwave as a Prototype. (submitted)
[4] 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]
[3] Kalashnikov, D., F. Davenport, Z. Labe, P. Loikith, J. Abatzoglou, and D. Singh (2024). Predicting cloud-to-ground lightning in the Western United States from the large-scale environment using explainable neural networks. (submitted)
[2] Timmermans, M.-L. and Z.M. Labe (2024). [The Arctic] Sea surface temperature [in “State of the Climate in 2023”]. (submitted)
[Code]
[1] Witt, J.K., Z.M. Labe, and B.A. Clegg (2024). An Alternative to the “Cone of Uncertainty” that is Flexible, Intuitive, and Desirable for Communicating Hurricane Forecasts. (submitted)
[Code]
[Plain Language Summary][CNN]
Non-refereed/Other:
[12] Labe, Z.M., August 2023: Pan-Arctic Ice Ocean Modeling and Assimilation System (PIOMAS). NCAR Climate Data Guide. Expert Contributor.
[Article]
[11] Labe, Z.M., July 2022: State of the Arctic 2022. Polar Bears International. Blog Post.
[Article]
[10] Labe, Z.M., August 2021: Sharing data-driven stories of Arctic climate change. WMO/WWRP Year of Polar Prediction. PolarPredictNews.
[HTML][PDF]
[9] Peings, Y., Z.M. Labe, and G. Magnusdottir, August 2021: How reproducible is the response to +2°C Arctic sea-ice loss in a large ensemble of simulations? CLIVAR Research Highlight.
[Article]
[8] Labe, Z.M., July 2021: State of the Arctic. Polar Bears International. Blog Post.
[Article]
[7] Labe, Z.M., February 2021: Telling stories with data. School of Global Environmental Sustainability, HumanNature Blog. Blog Post.
[Article]
[6] Labe, Z.M., September 2020: A Sign of the Future: Summer 2020 in the Arctic. Polar Bears International. Blog Post.
[Article]
[5] Labe, Z.M., July 2020: State of the Arctic in 2020. Polar Bears International. Blog Post.
[Article]
[4] 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.
[PDF]
[3] Labe, Z.M., November 2019: Understanding Our Changing Arctic. Polar Bears International. Annual Magazine.
[PDF]
[2] Labe, Z.M., August 2017: Sea Ice Thickness Data Sets: Overview & Comparison Table. NCAR Climate Data Guide. Expert Contributor.
[Article]
[1] Labe, Z.M., April 2015: Anomalously Early Onset of Spring in the CESM Large Ensemble. Cornell University. Undergraduate Honors Thesis.
[PDF][Code]
Conferences Abstracts/Posters:
[52] Vanek, S., E. Adasheva, L Ashokkumar, M.N. Helmberger, Z.M. Labe, O. Lauter, and M.A. Shadab. Exploring the past, present, and future of USAPECS: Lessons from a decade of supporting early career research across national and international polar networks, Arctic Congress 2024, Bodø, Norway (Jun 2024). [Abstract]
[51] 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][SlideShare]
[50] Labe, Z.M., T.L. Delworth, N.C. Johnson, and W.F. Cooke. A data-driven approach to identifying key regions of change associated with future climate scenarios, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024).
[Abstract][SlideShare]
[49] Schreck, C.J., J. Barsugli, D.A. Coates, D.R. Easterling, K.E. Kunkel, Z.M. Labe, J.E. Uehling, R. Vose, and X. Zhang. Comparing the causes and unusualness of the Texas heatwaves in 2022 and 2023, 37th Conference on Climate Variability and Change, Baltimore, MD (Jan 2024).
[Abstract]
[48] Kalashnikov, D.A., F.V. Davenport, Z.M. Labe, P.C. Loikith, J. Abatzoglou, and D. Singh. Using deep learning to predict cloud-to-ground lightning in the western United States, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024).
[Abstract]
[47] Labe, Z.M., N.C. Johnson, and T.L. Delworth. Distinguishing the regional emergence of United States summer temperatures between observations and climate model large ensembles, 23rd Conference on Artificial Intelligence for Environmental Science, Baltimore, MD (Jan 2024).
[Abstract][SlideShare]
[46] Ashokkumar, L, Z.M. Labe, M.A. Shadab, O. Lauter and E.P. Schreiber. Advancing inclusion, diversity, equity, and accessibility (IDEA) in the polar sciences by USAPECS, 2023 American Geophysical Union Annual Meeting, San Francisco, CA (Dec 2023).
[Abstract][Recording]
[45] Meem, T.J., Z.M. Labe, W.F. Cooke, T.L. Delworth, and V. Ramaswamy. Role of anthropogenic aerosols on the South Asian summer monsoon in a high-resolution large ensemble, 2023 American Geophysical Union Annual Meeting, San Francisco, CA (Dec 2023).
[Abstract]
[44] Kalashnikov, D.A., F.V. Davenport, Z.M. Labe, P.C. Loikith, J. Abatzoglou, and D. Singh. Using deep learning to predict cloud-to-ground lightning in the western United States, 2023 American Geophysical Union Annual Meeting, San Francisco, CA (Dec 2023).
[Abstract]
[43] Johnson, N.C., L. Jia, Z.M. Labe, T.L. Delworth, F. Lu, and C.E. McHugh. Sources of seasonal extreme heat predictability diagnosed from the GFDL seamless System for Prediction and EArth System Research (SPEAR), 2023 American Geophysical Union Annual Meeting, San Francisco, CA (Dec 2023).
[Abstract]
[42] 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]
[41] 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]
[40] 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]
[39] 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]
[38] Labe, Z.M., E.A. Barnes, and J. Hurrell. Detecting the regional emergence of climate signals with machine learning in a set of stratospheric aerosol injection simulations, 2022 American Geophysical Union Annual Meeting, Chicago, IL (Dec 2022).
[Abstract][Poster][Code]
[37] 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]
[36] Ashokkumar, L., L. Weinberg, Z.M. Labe, E. Schreiber, A. Taitt, and M. Dryak. Progress and challenges by early career polar scientists (USAPECS) in addressing inclusivity, diversity, equity, and accessibility, 2022 American Geophysical Union Annual Meeting, Chicago, IL (Dec 2022).
[Abstract][Recording]
[35] Po-Chedley, S., E.A. Barnes, C. Bonfils, J. Fasullo, Z.M. Labe, B. Santer, and N. Siler. Substantial contribution of internal variability to satellite-era tropospheric warming inferred from CMIP6 large ensembles, 2022 American Geophysical Union Annual Meeting, Chicago, IL (Dec 2022).
[Abstract]
[34] 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]
[33] Bushuk, M., et al. (including Z.M. Labe). A multi-model comparison of September Arctic sea ice seasonal prediction skill, 2022 American Geophysical Union Annual Meeting, Chicago, IL (Dec 2022).
[Abstract]
[32] Witt, J.K., Z.M. Labe, and B. Clegg. Comparisons of Perceptions of Risk for Visualizations Using Animated Risk Trajectories Versus Cones of Uncertainty, Human Factors and Ergonomics Society (HFES) 66th International Annual Meeting, Atlanta, GA (Oct 2022).
[Abstract]
[31] Po-Chedley, S., E.A. Barnes, C. Bonfils, J. Fasullo, Z.M. Labe, B. Santer, and N. Siler. Internal Variability and Forcing Influence Model-satellite Differences in the Rate of Tropical Tropospheric Warming, Asia Oceania Geosciences Society 19th Annual Meeting, Virtual Conference (Aug 2022).
[30] Po-Chedley, S., E.A. Barnes, C. Bonfils, J. Fasullo, Z.M. Labe, B. Santer, and N. Siler. Internal variability influences model-satellite differences in the rate of tropical tropospheric warming, US CLIVAR: The Pattern Effect: Coupling of SST Patterns, Radiative Feedbacks, and Climate Sensitivity Workshop, Boulder, CO (May 2022).
[Poster]
[29] 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][SlideShare]
[28] Myint, H. and Z.M. Labe. Predicting September Arctic sea-ice using a hierarchy of statistical models, 21st Annual Student Conference: Polar Meteorology, Virtual Attendance (Jan 2022).
[Abstract]
[27] Witt, J.K., Z.M. Labe, B. Clegg, and A. Warden. An alternative to the “Cone of Uncertainty” that is flexible, intuitive, and desirable for communicating hurricane forecasts, 17th Symposium on Societal Applications: Policy, Research and Practice, Virtual Attendance (Jan 2022).
[Abstract]
[26] 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).
[Abstract][SlideShare]
[25] 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][SlideShare]
[24] 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]
[23] 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]
[22] 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).
[SlideShare]
[21] 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]
[20] 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]
[19] 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).
[SlideShare]
[18] Peings, Y., Z.M. Labe, and G. Magnusdottir. Arctic-midlatitude linkages: role of sea ice loss versus full Arctic amplification. US CLIVAR PPAI Panel Webinar Series, Virtual Talk (Apr 2021).
[Recording]
[17] Magnusdottir, G., Z.M. Labe, and Y. Peings. The warm Arctic, cold Siberia pattern: role of the full Arctic amplification versus sea ice loss alone. Polar Amplification Model Intercomparison (PAMIP) Virtual Workshop, Virtual Conference (Mar 2021).
[16] Peings, Y., Z.M. Labe, and G. Magnusdottir. Influence of internal variability: how to ensure results are robust? Polar Amplification Model Intercomparison (PAMIP) Virtual Workshop, Virtual Conference (Mar 2021).
[15] Peings, Y., Z.M. Labe, and G. Magnusdottir. Arctic-midlatitude linkages: role of sea ice loss versus full Arctic Amplification, Arctic Science Summit Week 2021, Virtual Conference (Mar 2021).
[14] 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][SlideShare][Summary][Code]
[13] Magnusdottir, G., Z.M. Labe, and Y. Peings. The midlatitude response to Arctic sea-ice decline, compared to the response to the full effects of Arctic amplification, 34th Conference on Climate Variability and Change, Virtual Conference (Jan 2021).
[Abstract][Code]
[12] Peings, Y., G. Magnusdottir., and Z.M. Labe. Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss?, 34th Conference on Climate Variability and Change, Virtual Conference (Jan 2021).
[Abstract][Code]
[11] Magnusdottir, G., Z.M. Labe, and Y. Peings. How does the atmospheric response to Arctic sea-ice decline compare to the full effect of the Arctic Amplification?, 2020 American Geophysical Union Annual Meeting, Virtual Conference (Dec 2020).
[Abstract][Code]
[10] Peings, Y., Z.M. Labe, and G. Magnusdottir. Are 100 ensemble members enough to capture the remote atmospheric response to +2°C Arctic sea ice loss?, 2020 American Geophysical Union Annual Meeting, Virtual Conference (Dec 2020).
[Abstract][Code]
[9] Labe, Z.M., Y. Peings, and G. Magnusdottir. Detection of Signal in the Large-Scale Circulation Response to Arctic Sea-Ice Decline, 33rd Conference on Climate Variability and Change, Boston, MA (Jan 2020).
[Abstract][SlideShare][Code]
[8] Magnusdottir, G., Y. Peings, and Z.M. Labe. Response to sea-ice loss under the Polar Amplification MIP protocol in extended ensembles of simulations, 2019 American Geophysical Union Annual Meeting, San Francisco, CA (Dec 2019).
[Abstract][Code]
[7] Magnusdottir, G., Y. Peings, and Z.M. Labe. Impact of the QBO on the response to Arctic sea ice loss. Polar Amplification Model Intercomparison (PAMIP) Workshop, Devon, UK (Jun 2019).
[Slides]
[6] Labe, Z.M., G. Magnusdottir, and Y. Peings. Linking the Quasi-Biennial Oscillation and Projected Arctic Sea-Ice Loss to Stratospheric Variability in Early Winter, 20th Conference on Middle Atmosphere, Phoenix, AZ (Jan 2019).
[Abstract][SlideShare][Code]
[5] 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]
[5] Magnusdottir, G., Z.M. Labe, and Y. Peings. The role of the stratosphere, including the QBO, in Arctic to mid-latitude teleconnections associated with sea-ice forcing, 2018 American Geophysical Union Annual Meeting, Washington, DC (Dec 2018).
[Abstract][Code]
[4] Labe, Z.M. Loss of Arctic sea ice thickness affects the large-scale atmosphere, Arctic System Change Workshop at NCAR, Boulder, CO (Apr 2018).
[Poster][Code]
[3] Labe, Z.M., G. Magnusdottir, and H.S. Stern. Arctic Sea Ice Thickness Variability and the Large-scale Atmospheric Circulation Using Satellite Observations, PIOMAS, and the CESM Large Ensemble, 14th Conference on Polar Meteorology and Oceanography, Seattle, WA (Jan 2017).
[Abstract] [Poster][Code]
[2] Labe, Z.M., G. Magnusdottir, and H.S. Stern. Making the most of Arctic sea ice thickness observations, Symposium on Recent Advances in Data Science, University of California, Irvine (Oct 2016).
[Poster][Code]
[1] Labe, Z.M. and T.R. Ault. Anomalously Early Onset of Spring in the CESM Large Ensemble Project, 95th American Meteorological Society Annual Meeting, Phoenix, AZ (Jan 2015).
[Abstract] [Poster][Code]