Publications

bold face denotes members of the Barnes Research Group* denotes conference proceedings** denotes newsletters, reports, white papers, data sets

POSTED/SUBMITTED/IN REVIEW

  • Barnes, Elizabeth A., James Hurrell and Lantao Sun: Detecting changes in global extremes under the GLENS-SAI climate intervention strategy, submitted 06/2022, preprint available soon.

  • Keys, Patrick, Elizabeth A. Barnes, Noah S. Diffenbaugh, James W. Hurrell and Curtis M. Bell: Potential for Perceived Failure of Stratospheric Aerosol Injection Deployment, submitted 06/2022, preprint available at https://doi.org/10.31223/X5805S

  • Labe, Zachary M. and Elizabeth A. Barnes: Comparison of climate model large ensembles with observations in the Arctic using simple neural networks, revised to Earth and Space Science, 06/2022, preprint available at https://www.essoar.org/doi/abs/10.1002/essoar.10510977.2

  • Diffenbaugh, Noah and Elizabeth A. Barnes: Data-driven predictions of the time remaining until critical global warming thresholds are reached, submitted 04/2022.

  • Po-Chedley, Stephen, John T. Fasullo, Nicholas Siler, Elizabeth A. Barnes, Zachary M. Labe, Céline J. W. Bonfils, Benjamin D. Santer: Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming, submitted 04/2022.

  • Witt, Jessica K., Danielle Albers Szafir, Zachary M. Labe, and Elizabeth A. Barnes: Perceiving Internal Climate Variability: Signaling Change through Animation Decreases Performance, submitted to IEEE Transactions on Visualization & Computer Graphics, 03/2022

  • Gordon, Emily M. and Elizabeth A. Barnes: Incorporating Uncertainty into a Regression Neural Network Enables Identification of Decadal State-Dependent Predictability, submitted to Geophysical Research Letters, 03/2022, preprint available at https://www.essoar.org/doi/abs/10.1002/essoar.10510836.1

  • Mamalakis, Antonios, Elizabeth A. Barnes and Imme Ebert-Uphoff: Investigating the fidelity of Explainable Artificial Intelligence methods for applications of Convolutional Neural Networks in Geoscience, submitted to Artificial Intelligence for the Earth Systems (AMS), 02/2022, preprint available https://arxiv.org/abs/2202.03407.

ACCEPTED/PUBLISHED/POSTED

2022

  • Rader, Jamin K., Elizabeth A. Barnes, Imme Ebert-Uphoff, and Chuck Anderson: Detection of forced change within combined climate fields using explainable neural networks, accepted by the Journal of Advances in Modeling Earth Systems, 05/2022, preprint available at https://www.essoar.org/doi/10.1002/essoar.10509261.2

  • **Arcodia, Marybeth, Elizabeth Barnes, Charlotte Connolly, Frances Davenport, Zaibeth Carlo Frontera, Emily Gordon, Daniel Hueholt, Antonios Mamalakis and Elina Valkonen: Applied Machine Learning Tutorial for Earth Scientists, Zenodo: https://doi.org/10.5281/zenodo.6686879. Github: https://github.com/eabarnes1010/ml_tutorial_csu.

  • Barnes, Elizabeth A., Randal J. Barnes, Zane K. Martin, and Jamin K. Rader: This Looks Like That There: Interpretable neural networks for image tasks when location matters, submitted to Artificial Intelligence for the Earth Systems, 01/2022, preprint available at https://www.essoar.org/doi/10.1002/essoar.10509984.2

  • 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, preprint available at https://doi.org/10.1029/2022GL098663

  • **Mamalakis, Antonios, Imme Ebert-Uphoff and Elizabeth A. Barnes: CSU Synthetic Attribution Benchmark Dataset", Version 1.0, Radiant MLHub. https://doi.org/10.34911/rdnt.8snx6c

  • Mamalakis, Antonios, Imme Ebert-Uphoff and Elizabeth A. Barnes: Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset, Environmental Data Science, https://doi.org/10.1017/eds.2022.7. Associated dataset at RadiantEarth: https://doi.org/10.34911/rdnt.8snx6c

  • Labe, Zachary M. and Elizabeth A. Barnes: Predicting slowdowns in decadal climate warming trends with explainable neural networks, Geophysical Research Letters, https://doi.org/10.1029/2022GL098173

  • Martin, Zane, Elizabeth A. Barnes, and Eric D. Maloney: Using simple, explainable neural networks to predict the Madden-Julian oscillation, in press, Journal of Advances in Modeling Earth Systems, https://agupubs.onlinelibrary.wiley.com/doi/abs/10.1029/2021MS002774

  • Hsiao, Wei-Ting, Elizabeth A. Barnes, Eric Maloney, Stefan Tulich, Juliana Dias and George Kiladis: Role of the Tropics and its Extratropical Teleconnections in State-Dependent Improvements of U.S. West Coast UFS Precipitation Forecasts, Geophysical Research Letters, http://doi.org/10.1029/2021GL096447

  • Mamalakis, Antonios, Imme Ebert-Uphoff and Elizabeth A. Barnes: Explainable Artificial Intelligence in Meteorology and Climate Science: Model fine-tuning, calibrating trust and learning new science, invited book chapter, https://link.springer.com/chapter/10.1007/978-3-031-04083-2_16.

2021

  • Jones, Jhordanne, Michael Bell, Philip Klotzbach, and Elizabeth A. Barnes: Wintertime Rossby Wave Breaking Persistence in Extended-range Seasonal Forecasts of Atlantic Tropical Cyclone Activity, Journal of Climate, https://doi.org/10.1175/JCLI-D-21-0213.1.

  • Barnes, Elizabeth A. and Randal J. Barnes: Controlled abstention neural networks for identifying skillful predictions for regression problems, Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2021MS002575.

  • Barnes, Elizabeth A. and Randal J. Barnes: Controlled abstention neural networks for identifying skillful predictions for classification problems, Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2021MS002573.

  • Gordon, Emily M., Elizabeth A. Barnes, and James Hurrell: Oceanic harbingers of Pacific Decadal Oscillation predictability in CESM2 detected by neural networks, Geophysical Research Letters, https://doi.org/10.1029/2021GL095392

  • **Barnes, Elizabeth A., Randal J. Barnes and Nicolas Gordillo: Adding Uncertainty to Neural Network Regression Tasks in the Geosciences, https://arxiv.org/abs/2109.07250

  • Jenney, Andrea, Dave Randall and Elizabeth Barnes, 2021: Mechanisms driving MJO teleconnection changes with warming in CMIP6, Weather and Climate Dynamics, https://wcd.copernicus.org/articles/2/653/2021/

  • Irrgang, C., N. Boers, M. Sonnewald, Elizabeth A. Barnes, C. Kadow, J. Staneva, and J. Saynisch-Wagner, 2021: Towards Neural Earth System Modelling by integrating Artificial Intelligence in Earth System Science, Nature Machine Intelligence, https://rdcu.be/cuyuV

  • Labe, Zachary M. and Elizabeth A. Barnes: Detecting climate signals using explainable AI with single-forcing large ensembles, Journal of Advances in Modeling Earth Systems, https://doi.org/10.1029/2021MS002464.

  • Toms, Benjamin, Elizabeth A. Barnes and James Hurrell: Assessing Decadal Predictability in an Earth-System Model Using Explainable Neural Networks,Geophysical Research Letters, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021GL093842.

  • Mayer, Kirsten and Elizabeth Barnes: Subseasonal Forecasts of Opportunity Identified by an Explainable Neural Network, Geophysical Research Letters, https://doi.org/10.1029/2020GL092092.

  • Tseng, Kai-Chih, Nathaniel Johnson, Eric Maloney, Elizabeth A. Barnes and Sarah B. Kapnick: Mapping Large-scale Climate Variability to Hydrological Extremes: An Application of the Linear Inverse Model to Subseasonal-to-Seasonal prediction, Journal of Climate, https://doi.org/10.1175/JCLI-D-20-0502.1

  • Samarasinghe, Savini, Elizabeth Barnes, Charlotte Connolly, Imme Ebert-Uphoff and Lantao Sun: Strengthened causal connections between the MJO and the North Atlantic with climate warming, Geophysical Research Letters, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2020GL091168. Also, a Research Highlight in Nature Climate Change.

  • Keys, Patrick, Elizabeth A. Barnes and Neil Carter: A machine-learning approach to human footprint index estimation with applications to sustainable development. Environmental Research Letters, https://iopscience.iop.org/article/10.1088/1748-9326/abe00a

  • **Klein et al.: Advancing the Predictability of Water Cycle Phenomena via the Application of AI to Model Ensemble Simulations and Observations, AI4ESP White Paper on Earth System Predictability, doi: 10.2172/1769656

  • **Dagon et al.: 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

  • **Ma et al.: Facilitating better and faster simulations of aerosol-cloud interactions in Earth system models, AI4ESP White Paper on Earth System Predictability, doi: 10.2172/1769709

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