Publications

Peer-Reviewed Publications

In Preparation

  • McGraw, M.C., K. Haynes, K. Musgrave, C. Slocum, J. Knaff, and I. Ebert-Uphoff: Tropical Cyclone Structure in Synthetic Microwave Satellite Imgaery, in preparation for submission to Artificial Intelligence in Earth Systems, winter 2026.

    • Preprints and Under Review

  • Gomez, M.S., S. Ganesh S., M. McGraw, Frederick I.-H. Tam, I. Azizi, S. Darmon, M. Feldmann, S. Bourdin, L. Poulain--Auzéau, S.J. Camargo, J. Lin, D. Chavas, C.-Y. Lee, R. Gupta, A. Jenney, and T. Beucler: TCBench: A Benchmark for Tropical Cyclone Track and Intensity Forecasting at the Global Scale. submitted to arXiv 20 January, 2026. [preprint available at arXiv]
  • Ganesh S., Saranya, F. I.-H. Tam, M.S. Gomez, M. McGraw, M. DeMaria, K. Musgrave, J. Runge, and T. Beucler: Multidata Causal Discovery for Statistical Hurricane Intensity Forecasting, accepted at Weather and Forecasting, spring 2026. [preprint available at arXiv]

    • 2025

  • Fernandez, M.A., E.A. Barnes, R.J. Barnes, M. DeMaria, M. McGraw, G. Chirokova, and L. Lu (2025): Predicting tropical cyclone track forecast errors using a probabilistic neural network. Artificial Intelligence in the Earth Systems, 4, doi:10.1175/AIES-D-24-0066.1. [link])

    • 2024

  • V. Eyring, W.D. Collins, and coauthors (inc. M. McGraw): Pushing the frontiers in climate modeling and analysis with machine learning. Nature Climate Change, 14, 916-928, doi:s41558-024-02095-y. [link]
  • McGovern, A., A. Bostrom, M. McGraw, R.J. Chase, D.J. Gagne II, I. Ebert-Uphoff, K. Musgrave, and A. Schumacher: Identifying and categorizing bias in AI/ML for earth sciences. Bull. Amer. Meteorol. Soc., 105, doi:10.117/BAMs-D-23-0196.1 [link]

    • 2023

  • McGovern, A., and coauthors (inc. M. McGraw) (2023): Trustworthy artificial intelligence for environmental sciences: An innovative approach for summer school. Bull. Amer. Meteorol. Soc., 104, doi:10.1175/BAMS-D-22-0225.1. [PDF]
  • Haynes, K., R. Lagerquist, M. McGraw, K. Musgrave, I. Ebert-Uphoff (2023): Creating and evaluating uncertainty estimates with neural networks for environmental-science applications, Artificial Intelligence for the Earth Systems, 1, doi:10.1175/AIES-D-22-0061.1. [PDF]

    • 2022

  • McGraw, M.C., Blanchard-Wrigglesworth, E., Clancy, R.P., and Bitz, C.M. (2022): Understanding the Predictability of Arctic Sea Ice Loss on Subseasonal Timescales. Journal of Climate, 35, doi:10.1175/JCLI-D-21-0301.1. [PDF]
  • Gonzalez, A.O., I. Ganguly, M.C. McGraw, and J. Larson (2022): Rapid dynamical evolution of ITCZ events over the east Pacific. Journal of Climate, 35,doi:10.1175/JCLI-D-21-0216.1.[PDF]

    • 2021 and Earlier

  • Clancy, R.P., C.M. Bitz, E. Blanchard-Wrigglesworth, M.C. McGraw, and S. M. Cavallo (2021): Drivers of asymmetric patterns in the atmosphere and sea ice during Arctic cyclones. Journal of Climate, 34, doi:10.1175/JCLI-D-21-0093.1. [PDF]
  • McGraw, M.C. and E.A. Barnes (2020): New insights on subseasonal Arctic-midlatitude causal connections from a regularized regression model. Journal of Climate, 33, doi:10.1175/JCLI-D-19-0142.1. [PDF; supplemental]
  • McGraw, M.C., C.F. Baggett, C. Liu, and B.D. Mundhenk (2019): Changes in Arctic moisture transport over the North Pacific associated with sea ice loss. Climate Dynamics, 54, doi:10.1007/s00382-019-05011-9. [PDF]
  • Samarasinghe, S., M.C. McGraw, E.A. Barnes, and I. Ebert-Uphoff (2019): A study of links between the Arctic and the midlatitude jet-streams using Granger and Pearl causality. Environmentrics, 30, doi:10.1002/env.2540. [PDF]
  • McGraw, M.C., and E.A. Barnes (2018): Memory matters: A case for Granger causality in climate variability studies. Journal of Climate, 31, doi:10.1175/JCLI-D-17-0334.1. [PDF]
  • Woollings, T., E. Barnes, B. Hoskins, Y.-O. Kwon, R.W. Lee, C. Li, E. Madonna, M. McGraw, T. Parker, R. Rodrigues, C. Spensberger, K. Williams (2018): Daily to decadal modulation of jet variability. Journal of Climate, 31, doi:10.1175/JCLI-D-17-0286.1. [PDF]
  • McGraw, M.C., E.A. Barnes, and C. Deser (2016): Reconciling the observed and modeled Southern Hemisphere circulation response to volcanic eruptions. Geophys. Res. Lett.,, L069835, doi:10.1002/2016GL069835. [PDF]
  • McGraw, M.C., and E.A. Barnes (2016): Seasonal sensitivity of the eddy-driven jet to tropospheric heating in an idealized AGCM. J. Climate, 29, doi:10.1175/JCLI-D-15-0723.1. [PDF]

    • Technical Reports and White Papers

  • Pricing the tipping point: The economic impact of an AMOC collapse. Jupiter Intelligence. April 2026
  • Climate risk in Ethiopia. Jupiter Intelligence and the United Nations Development Programme. March 2026
  • Climate risk in the Middle East and North Africa. Jupiter Intelligence and the First National Bank of Abu Dhabi. February 2026
  • Katrina After 20 Years — What We’ve Learned and How it Could Happen Again . Jupiter Intelligence. August 2025
  • Sospreda-Alfonso, R., Exenberger, J., Dang, K., and M.C. McGraw: Statistical adjustment of decadal climate predictions using deep learning. Tackling Climate Change with Machine Learning Workshop, NeurIPS 2022.
  • Samarasinghe, S., M. McGraw, E. Barnes, and I. Ebert-Uphoff (2017): A study of causal links between the Arctic and the midlatitude jet-streams. Proceedings of the Seventh International Workshop on Climate Informatics (CI 2017), NCAR Technical Note NCAR/TN-536+PROC.