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Publications

Submitted/In Review

• Gordon, Emily M. and N. S. Diffenbaugh “The emergence of regional record-breaking summer heat predictability” in review NPJ Climate and Atmospheric Science, preprint: https://www.researchsquare.com/article/rs-10796829/v1

• Alessi, M. J., E. Plesiat, L. D. Merner, R. J. H. Dunn, D. A. Herrera, P. Ayabagabo, D. Degbey, D. Muheki, W. Thiery, C. Martinez, J. Kimutai, E. M. Gordon. “Using Artificial Intelligence to Create a Gridded Climate Extremes Dataset for Global South Regions” submitted

Accepted

• Trok, J. T., E. A. Barnes, E. M. Gordon, F. V. Davenport and N. S. Diffenbaugh, (2026). “Global emissions since the Paris agreement have strengthened Europe’s recent heatwaves”, accepted GRL, preprint available https://essopenarchive.org/doi/full/10.22541/essoar.15005315/v1

Published

• Wills R. C. J., C. Deser, K. A. McKinnon, A. Phillips, S. Po-Chedley, S. Sippel, A. L. Merrifield, C. Bone, C. Bonfils, ́G. Camps-Valls, S. Cropper, C. Connolly, S. Duan, H. Durand, A. Feigin, M. A. Fernandez, G. Gastineau, A. Gavrilov, E. M. Gordon, M. Gunther, M. Hover, S. Kravtsov, Y-N Kuo, J. Lien, G. D. Madakumbura, N. Mankovich, M. Newman, J. Rader., J-R Shi, S-I Shin, G. Varando (2026) “Forced Component Estimation Statistical Method Intercomparison Project (ForceSMIP)”, Journal of Climate, https://doi.org/10.1175/JCLI-D-25-0326.1

• Gordon, E. M. and N. S. Diffenbaugh (2026) “Machine learning predictions of summertime warming jumps on decadal timescales”, Environmental Research: Climate, https://iopscience.iop.org/article/10.1088/2752-5295/ae488c

• Rader, J. K., C. J. Connolly, M. A. Fernandez, and E. M. Gordon (2025), “Attribution of the record-high 2023 SST using a deep-learning framework”, Environmental Research Communications, https://iopscience.iop.org/article/10.1088/2515-7620/add322.

• Gordon, E. M. and N. S. Diffenbaugh (2025) “Identifying a pattern of predictable decadal North Pacific SST variability in historical observations”, Geophysical Research Letters, https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2024GL112729

• Davenport, F. V., E. A. Barnes, and E. M. Gordon. (2024) “Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability”. Geophysical Research Letters, https://doi.org/10.1029/2023GL108099

• Gordon, E. M., E. A. Barnes and F. V. Davenport. (2023). “Separating Internal and Forced Contributions to Near Term SST Predictability in the CESM2-LE”. Environmental Research Letters, https://doi.org/10.1088/1748-9326/acfdbc

• Gordon, E. M. and E. A. Barnes (2022). Incorporating Uncertainty into a Regression Neural Network Enables Identification of Decadal State-Dependent Predictability, Geophysical Research Letters, https://doi.org/10.1029/2022GL098635

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

• Barnes, E. A., K. J. Mayer, B. Toms, Z. K. Martin and E. M. Gordon (2020). Identifying Opportunities for Skillful Weather Prediction with Interpretable Neural Networks. NeurIPS, https://arxiv.org/abs/2012.07830.

• Gordon, E. M., Seppälä, A., Funke, B., Tamminen, J., and Walker, K. A. (2021). Observational evidence of energetic particle precipitation NOx (EPP-NOx) interaction with chlorine curbing Antarctic ozone loss, Atmos. Chem. Phys., 21, https://doi.org/10.5194/acp-21-2819-2021

• Gordon, E. M., Seppälä, A., and Tamminen, J. (2020)*: Evidence for energetic particle precipitation and quasi-biennial oscillation modulations of the Antarctic NO2 springtime stratospheric column from OMI observations, Atmos. Chem. Phys., https://doi.org/10.5194/acp-20-6259-2020. *Most downloaded article from Atmos. Chem. Phys. in 2020

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Software:

• Arcodia, M, E. A. Barnes, C. Connolly, F. V. Davenport, Z. Carlo Frontera, E. M. Gordon, D. Hueholt, A. Mamalakis and E. Valkonen: Applied Machine Learning Tutorial for Earth Scientists, 2022, Zenodo: https://doi.org/10.5281/zenodo.6686879. Github: https://github.com/eabarnes1010/ml_tutorial_csu