Working with Daily Climate Model Output Data in R and the futureheatwaves Package. - G. Brooke Anderson; Colin Eason; Elizabeth A. Barnes (R J., 2017)

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability. - Benjamin A. Toms; Elizabeth A. Barnes; Imme Ebert-Uphoff (CoRR, 2019)

Explainable Artificial Intelligence in Meteorology and Climate Science: Model Fine-Tuning, Calibrating Trust and Learning New Science. - Antonios Mamalakis; Imme Ebert-Uphoff; Elizabeth A. Barnes (xxAI@ICML, 2020)

Will Artificial Intelligence supersede Earth System and Climate Models? - Christopher Irrgang; Niklas Boers; Maike Sonnewald; Elizabeth A. Barnes; Christopher Kadow; Joanna Staneva; Jan Saynisch-Wagner (CoRR, 2021)

Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset. - Antonios Mamalakis; Imme Ebert-Uphoff; Elizabeth A. Barnes (CoRR, 2021)

Controlled abstention neural networks for identifying skillful predictions for regression problems. - Elizabeth A. Barnes; Randal J. Barnes (CoRR, 2021)

Controlled abstention neural networks for identifying skillful predictions for classification problems. - Elizabeth A. Barnes; Randal J. Barnes (CoRR, 2021)

Towards neural Earth system modelling by integrating artificial intelligence in Earth system science. - Christopher Irrgang; Niklas Boers; Maike Sonnewald; Elizabeth A. Barnes; Christopher Kadow; Joanna Staneva; Jan Saynisch-Wagner (Nat. Mach. Intell., 2021)

Investigating the fidelity of explainable artificial intelligence methods for applications of convolutional neural networks in geoscience. - Antonios Mamalakis; Elizabeth A. Barnes; Imme Ebert-Uphoff (CoRR, 2022)

Carefully choose the baseline: Lessons learned from applying XAI attribution methods for regression tasks in geoscience. - Antonios Mamalakis; Elizabeth A. Barnes; Imme Ebert-Uphoff (CoRR, 2022)

ClimSim: A large multi-scale dataset for hybrid physics-ML climate emulation. - Sungduk Yu; Walter M. Hannah; Liran Peng; Jerry Lin; Mohamed Aziz Bhouri; Ritwik Gupta; Björn Lütjens; Justus C. Will; Gunnar Behrens; Julius Busecke; Nora Loose; Charles Stern; et al. (NeurIPS, 2023)

ClimSim: An open large-scale dataset for training high-resolution physics emulators in hybrid multi-scale climate simulators. - Sungduk Yu; Walter M. Hannah; Liran Peng; Mohamed Aziz Bhouri; Ritwik Gupta; Jerry Lin; Björn Lütjens; Justus C. Will; Tom Beucler; Bryce E. Harrop; Benjamin R. Hillman; Andrea M. Jenney; et al. (CoRR, 2023)

AI2ES: The NSF AI Institute for Research on Trustworthy AI for Weather, Climate, and Coastal Oceanography. - Amy McGovern; Imme Ebert-Uphoff; Elizabeth A. Barnes; Ann Bostrom; Mariana G. Cains; Phillip Davis; Julie L. Demuth; Dimitrios I. Diochnos; Andrew H. Fagg; Philippe Tissot; John K. Williams; Christopher D. Wirz (AI Mag., 2024)

Recommendations for Comprehensive and Independent Evaluation of Machine Learning-Based Earth System Models. - Paul A. Ullrich; Elizabeth A. Barnes; William D. Collins; Katherine Dagon; Shiheng Duan; Joshua Elms; Jiwoo Lee; L. Ruby Leung; Dan Lu; Maria J. Molina; Travis A. O'Brien (CoRR, 2024)

Multi-Year-to-Decadal Temperature Prediction using a Machine Learning Model-Analog Framework. - M. A. Fernandez; Elizabeth A. Barnes (CoRR, 2025)

Predicting Tropical Cyclone Track Forecast Errors using a Probabilistic Neural Network. - M. A. Fernandez; Elizabeth A. Barnes; Randal J. Barnes; Mark DeMaria; Marie McGraw; Galina Chirokova; Lixin Lu (CoRR, 2025)

Turning Up the Heat: Assessing 2-m Temperature Forecast Errors in AI Weather Prediction Models During Heat Waves. - Kelsey E. Ennis; Elizabeth A. Barnes; Marybeth C. Arcodia; Martin A. Fernandez; Eric D. Maloney (CoRR, 2025)

AI-Informed Model Analogs for Subseasonal-to-Seasonal Prediction. - Jacob B. Landsberg; Elizabeth A. Barnes; Matthew Newman (CoRR, 2025)