Publications
Publications
*denotes conference proceedings, newsletters, reports, white papers
**denotes book chapters
Rebecca Baiman, Andrew C. Winters, Kirsten J. Mayer, Clairisse A. Reiher (under review): Disentangling Regional Drivers of Top Antarctic Snowfall Days with a Convolutional Neural Network
Mayer, Kirsten J., Kathrine Dagon, Maria J. Molina (under review): Can Transfer Learning be Used to Identify Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability?, https://doi.org/10.48550/arXiv.2409.10755
**Mayer, Kirsten J. Sebastian Lerch, Catherine de Burgh-Day, and Marybeth C. Arcodia (under review): Machine Learning for S2S Prediction, Chapter in "Sub-seasonal to Seasonal Prediction: The Gap Between Weather and Climate Forecasting", second edition
Mayer, Kirsten J., E. A. Barnes, and J. W. Hurrell: Future Seasonal Surface Temperature Predictability with and without ARISE-Stratospheric Aerosol Injection-1.5, Environmental Research: Climate, https://doi.org/10.1088/2752-5295/ad9b43
Wirz, C.D., Sutter, C., Demuth, J. L., Mayer, Kirsten J., Chapman, W. E., Cains, M. G., Radford, J., Przybylo, V., Evans, A., Martin, T., Gaudet, L. C., Sulia, K., Bostrom, A., Gagne, D. J., Bassill, N., Schumacher, A., and Thorncroft, C. Increasing the reproducibility, replicability, and evaluability of supervised AI/ML in the earth systems science by leveraging social science methods, AGU Earth and Space Science, https://doi.org/10.1029/2023EA003364
Mayer, Kirsten J., William E. Chapman [equal contribution], and William A. Manriquez: Exploring the Relative Importance of the MJO and ENSO to North Pacific Subseasonal Predictability, Geophysical Research Letters, https://doi.org/10.1029/2024GL108479
*Mayer, Kirsten J., Katherine Dagon, and Maria J. Molina: Identifying Tropical State-Dependent Bias Relevant to Midlatitude Subseasonal Predictability with Explainable Neural Networks, S2S Prediction Project Newsletter No. 23, link to newsletter.
Arcodia, Marybeth, Elizabeth A. Barnes, Kirsten J. Mayer, Jiwoo Lee, Ana Ordonez, Min-Seop Ahn: Assessing Decadal Variability of Subseasonal Forecasts of Opportunity using Explainable AI, Environmental Research: Climate, https://doi.org/10.1088/2752-5295/aced60
Mayer, Kirsten J.: Application of Neural Networks to Subseasonal to Seasonal Predictability in Present and Future Climates, Ph.D. Dissertation.
Mayer, Kirsten J. and Elizabeth A. Barnes: Quantifying the Effect of Climate Change on Midlatitude Subseasonal Prediction Skill Provided by the Tropics, Geophysical Research Letters, https://doi.org/10.1029/2022GL098663.
Mayer, Kirsten J. and Elizabeth A. Barnes: Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network, Geophysical Research Letters, https://doi.org/10.1029/2020GL092092.
*Mayer, Kirsten J. and Elizabeth A. Barnes 2021: Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network. Extended Summary, Climate Prediction S&T Digest, 45th NOAA Climate Diagnostics and Prediction Workshop, Virtual Online, DOC/NOAA, 24. https://repository.library.noaa.gov/view/noaa/29968.
Merryfield, William J. et al. (including Mayer, Kirsten J.): Subseasonal to Decadal Prediction: Filling the Weather-Climate Gap, BAMS: https://doi.org/10.1175/BAMS-D-19-0037.A.
*Dagon et al. (including Mayer, Kirsten J.): Machine learning to extend and understand the sources and limits of water cycle predictability on subseasonal-to-decadal timescales in the Earth system, AI4ESP White Paper on Earth System Predictability, doi: 10.2172/1769744
*Barnes, Elizabeth A., Kirsten J. Mayer, Benjamin Toms, Zane Martin and Emily Gordon: Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks, keynote talk and conference paper at NeurIPS AI4EARTH 2020, https://arxiv.org/abs/2012.07830.
Mayer, Kirsten J. and Elizabeth A. Barnes: Subseasonal Midlatitude Prediction Skill Following QBO and MJO Activity, Weather and Climate Dynamics, https://doi.org/10.5194/wcd-1-247-2020.
Merryfield, William J. et al. (including Mayer, Kirsten J.): Current and Emerging Developments in Subseasonal to Decadal Prediction, BAMS: https://doi.org/10.1175/BAMS-D-19-0037.1