• Great Lakes ice is simulated with FVCOM-CICE framework for 1940-2022. • Spatial and temporal ice cover, thickness, and velocity are reported and validated. • Ice momentum severity, packed ice probability, and icing risks are analyzed. • Ice intensity index is introduced to guide siting and design of offshore wind turbines. • Erie and Huron show highest, while Ontario and Michigan show lowest ice risks. The Laurentian Great Lakes represent a vast and largely untapped potential for offshore wind energy that is crucial for regional decarbonization, energy security, and economic development. However, seasonal freshwater ice introduces unique engineering and operational risks, especially since limited availability of ice data poses challenges to safe and cost-effective design. To address this gap, we apply a fully coupled three-dimensional hydrodynamic-ice model (FVCOM–CICE) to simulate ice conditions across all five Great Lakes over an 83-year period (1940–2022). The model, validated against satellite and in-situ measurements, yields detailed spatial and temporal distributions of ice concentration, thickness, and velocity. To provide an integrated assessment for wind energy development, a composite ice intensity index is introduced by synthesizing ice momentum, packed ice risk, and icing severity. This index enables site-specific quantification of overall ice hazards to support more robust wind turbine siting, design, and risk mitigation. A case study is also presented to demonstrate practical applications of simulated ice data in evaluating ISO/IEC design ice load limits on offshore wind platforms. Results reveal that extreme ice hazards are prevalent in Lake Erie, Lake Huron, and parts of Lake Superior, while Lake Ontario and central and southern regions of Lake Michigan exhibit consistently lower ice risks. This paper offers a foundation for resilient offshore wind infrastructure and supports multi-criteria decision frameworks in this region.
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Javaherian et al. (Tue,) studied this question.
www.synapsesocial.com/papers/69e1cdc45cdc762e9d85709b — DOI: https://doi.org/10.1016/j.oceaneng.2026.125327
M. Javad Javaherian
Hazem Abdelhady
David Cannon
Ocean Engineering
University of Michigan
Texas A&M University
NOAA Great Lakes Environmental Research Laboratory
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