Publications

​Submitted/ In Review​​

• Thapa, Laura, Marybeth C. Arcodia, Elizabeth A. Barnes, 2025. "Digestible Pieces: comparing three options for partitioning the Northeast Pacific Coast for S2S sea surface height prediction", submitted to Artificial Intelligence for the Earth Systems.  

• Furtado, Jason*, Maria Molina*, Marybeth C. Arcodia*, and co-authors (* denotes equal contribution). 2025. "Taking the Garbage Out of Data-Driven Prediction Across Climate Timescales". Submitted to BAMS. Preprint available here. 

• Arcodia, Marybeth C., Richard Karp, and Elizabeth A. Barnes, 2025. "An explainable machine learning prediction system for early-warning of heat stress on Florida’s Coral Reef", submitted to Environmental Research Communications. Preprint available here.  

• Arcodia, Marybeth C. and Elizabeth A. Barnes, 2025. "Identifying the Timing of Regional Summertime Minimum Temperature Threshold Crossings and the Potential Subsequent Climate Evolutions", submitted to Earth's Future. Preprint available here. 

• Ennis, Kelsey E., Elizabeth A. Barnes, Marybeth C. Arcodia, Martin A. Fernandez, Eric D. Maloney, 2025. "Turning Up the Heat: Assessing 2-m Temperature Forecast Errors in AI Weather Prediction Models During Heat Waves", submitted. Preprint available here. 

• Richter, J. Everette Joseph, Marybeth Arcodia, and coauthors. "Earth System Predictability Across Time Scales for a Resilient Society: A Research Community Perspective". 2025. Submitted to BAMS. 

 

2025

• Weston Anderson; Marybeth Arcodia; Dillon Amaya; Emily Becker; John Callahan; Jason Furtado; Benjamin Kirtman; Sanjiv Kumar; Michelle L’Heureux; Sarah Larson; Dan Li; Maria Molina; Matt Newman; Kathleen Pegion; Andrew Robertson; Erin Towler; Baoqiang Xiang. "The Critical Need for Hindcast Infrastructure in Climate Science and Sectoral Applications" (2025). Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-24-0311.1 

• +Kirsten J. Mayer, Sebastian Lerch, Catherine de Burgh-Day, Marybeth C. Arcodia, Chapter 15 - Machine learning for subseasonal-to-seasonal prediction, Editor(s): Andrew W. Robertson, Frédéric Vitart, Sub-seasonal to Seasonal Prediction (Second Edition), Elsevier, 2026, Pages 539-589, ISBN 9780443315381, https://doi.org/10.1016/B978-0-443-31538-1.00003-8.

• Kirtman, B. P., Arcodia, M. C., Becker, E. J., Besong, K., Boyd, J. S., Daher, H., Gifford, I., Infanti, J., Kaiser, J., Kramer, S., 
  Larson, S. M., Laurindo, L. C., Lopez, H., Malloy, K., Martinez, C., Papazian, K., Pegion, K., Perlin, N., Schuler, C.
  Schoenwald, V., Siqueira, L. S. P., Zavadoff, B., & Zhang, W. (2025). A simplified-physics atmosphere general circulation 
  model for idealized climate dynamics studies. Bulletin of the American Meteorological Society, 106(10), E2073–E2086. https://doi.org/10.1175/BAMS-D-24-0196.1

• Arcodia, Marybeth C., Elizabeth A. Barnes, Paul J. Durack, Patrick W. Keys, and Juliette Rocha. "Sea surface salinity provides subseasonal predictability for forecasts of opportunity of US summertime precipitation." Journal of Geophysical Research: Atmospheres 130, no. 6 (2025): e2024JD042402. 

​​

​2024​​

• +Laís G. Fernandes, Matthew C. Wheeler, Alice M. Grimm, Marybeth C. Arcodia, Chapter 17 - Ocean–atmosphere interactions: Madden–Julian Oscillation and El Niño–Southern Oscillation, Editor(s): Bin Guan, Atmospheric Oscillations, Elsevier, 2025, Pages 335-362, ISBN 9780443156380, https://doi.org/10.1016/B978-0-443-15638-0.00017-4.

• Arcodia, M., Becker, E., & Kirtman, B.P. (2024). "Subseasonal Variability of U.S. Coastal Sea Level from MJO and ENSO Teleconnection Interference", Weather and Forecasting. https://doi.org/10.1175/WAF-D-23-0002.1

​

​2023​

• Arcodia, M., E. Barnes, K.J. Mayer, J. Lee, A. Ordonez, M. Ahn. (2023) "Assessing decadal variability of subseasonal  forecasts of opportunity using explainable AI." Environmental Research: Climate,  2(4), 045002. 

• Arcodia, M., Kirtman, B.P. (2023). “Using simplified linear and nonlinear models to assess ENSO-modulated MJO    teleconnections”. Climate Dynamics, 1-21. https://doi.org/10.1007/s00382-023-06864-x

• ​​​​Clement, A., Troxler, T., Keefe, O., Arcodia, M., Cruz, M., Hernandez, A., Moanga, D., Adefris, Z., Brown, N., Jacobson, S., (2023) “Hyperlocal Observations Reveal Persistent Extreme Urban Heat in Southeast Florida”,  Journal of Applied Meteorology and Climatology. https://doi.org/10.1175/JAMC-D-22-0165.1

  • ​​See media coverage: Miami Herald article, News at the U, Phys.org

​

2022

• *Arcodia, M., 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​​​​​​​

• ​Arcodia, M. (2022). Subseasonal Variability of ENSO-Modulated MJO Teleconnections and the Regionalized North American Impacts (Doctoral dissertation, University of Miami).​​​

​

2021

• *Arcodia, M., Becker, E., & Kirtman, B.P. (2021). "Florida Coastal Flooding Today, Tomorrow, and the Coming Decades: A Flooding Report funded by the Miami Herald".  https://github.com/mbarcodia/FL_coastalflooding_report​

​

2020

• Arcodia, M., Kirtman, B. P., & Siqueira, L. S. P. (2020). “How MJO Teleconnections and ENSO Interference Impacts U.S. Precipitation”, Journal of Climate, 33(11), 4621-4640. https://doi.org/10.1175/JCLI-D-19-0448.1

  • See media coverage: News at the U​; Phys.org; Science Daily

  • Recipient of the University of Miami Rosenstiel School Best Student Publication Millero Prize (2020)

​

* denotes tutorials, newsletters, reports

+ denotes textbook chapter