William Pringle

Biography

Dr. William Pringle specializes in modeling the coastal ocean and its interactions with atmosphere, land, and hydrological processes. His particular interest is in coastal hazards including tsunamis, storm surges and waves driven by tropical cyclones and winter storms, as well as tides. He is principal investigator on projects related to probabilistic hurricane surge prediction and hurricane risks to coastal railroads. He also partners with Argonne’s Decision and Infrastructure Science division, the EVS Sociocultural Systems Department, and DOE on projects involving hurricane risks to offshore infrastructure and Great Lakes Region coastal and climate science.

Pringle has a strong background in coastal engineering, physical oceanography, and numerical modeling. He graduated in Civil Engineering from the University of Canterbury in New Zealand and conducted his post-graduate work in tsunami research at the Kyoto University’s Disaster Prevention Research Institute. He completed a 3-year postdoc at the University of Notre Dame, helping to advance modeling capabilities for tides and storm surge all over the world.

Over the course of Pringle’s career, he has led advancements in coastal ocean modeling and risk assessment, with wide-reaching impacts across research, government, and industry. These include:

Developing global storm surge and tide modeling capabilities in the ADCIRC model, leading to the deployment of NOAA’s operational Global Surge and Tide Forecast System.
Co-creating the widely used OceanMesh2D software for coastal ocean mesh generation, which supports accurate modeling and is adopted internationally by researchers and consultants.
Building high-resolution coastal flood risk modeling frameworks to support U.S. utility organizations like AT&T and the New York Power Authority, contributing to national resilience planning.
Quantifying uncertainties in storm tide forecasts and regional climate simulations with advanced machine learning capabilities. His published methods and software are shaping new research directions for NOAA and DOE-sponsored projects.

Recent Publications

Pringle, W. J., Huang, C., Xue, P., Wang, J., et al. (2025). Coupled Lake‐Atmosphere‐Land Physics Uncertainties in a Great Lakes Regional Climate Model. Journal of Advances in Modeling Earth Systems, 17(2), e2024MS004337. https://doi.org/10.1029/2024MS004337
Sohrabi S., Darestani, Y., Pringle, W. J., Dowden D., Dehghanian, P. (2025). Age-Dependent Fragility and Life Cycle Cost Analyses of Prestressed Concrete Poles in Coastal Power Transmission Systems. Journal of Structural Engineering, https://doi.org/10.1061/JSENDH.STENG-13840
Bernier, N. B., Hemer, M., Mori, N., …, Pringle, W. J., et al. (2024). Storm Surges and Extreme Sea Levels: Review, Establishment of Model Intercomparison and Coordination of Surge Climate Projection Efforts (SurgeMIP). Weather and Climate Extremes, 45, 100689. https://doi.org/10.1016/j.wace.2024.100689
Blakely, C. P., Wirasaet, D., Cerrone, A. R., Pringle, W. J., et al. (2024). Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal / External Mode Total Water Level Model. Journal of Advances in Modeling Earth Systems, 16(12), e2024MS004502. https://doi.org/10.1029/2024MS004502
Jones, D., Steinschneider, S., Roebber, P., …, Pringle, W. J., et al. (2024). Mapping out how machine learning and artificial intelligence will change Great Lakes observations, modeling, and forecasting in the coming decade. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-24-0304.1
Kayastha, M. B., Huang, C., Wang, J., …, Pringle, W. J., et al. (2024). How will a Warmer Future Climate Reshape Lake-Effect Snowstorms? Earth’s Future, 12, e2024EF004622. https://doi.org/10.1029/2024EF004622
Yang, Z., Wang, J., Qian, Y., …, Pringle, W. J., et al. (2024). Summer Convective Precipitation Changes over the Great Lakes Region under a Warming Scenario. Journal of Geophysical Research: Atmospheres, 129, e2024JD041011. https://doi.org/10.1029/2024JD041011
Wang, J., Hendricks, E., Rozoff, C. M., …, Pringle, W. J., et al. (2024). Modeling and observations of North Atlantic cyclones: Implications for U.S. Offshore wind energy. Journal of Renewable and Sustainable Energy, 16(052702). https://doi.org/10.1063/5.0214806
Wang, J., Deskos, G., Pringle, W. J., Haupt, S. E., Feng, S., et al. (2024). Impact of Tropical and Extratropical Cyclones on Future U.S. Offshore Wind Energy. Bulletin of the American Meteorological Society. https://doi.org/10.1175/BAMS-D-24-0080.1
Pringle, W. J., Burnett, Z., Sargsyan, K., Moghimi, S., Myers, E. (2023). Efficient Probabilistic Prediction and Uncertainty Quantification of Tropical Cyclone-driven Storm Tides and Inundation. Artificial Intelligence for the Earth Systems, 2. https://doi.org/10.1175/AIES-D-22-0040.1