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Real‐time forecasted ice information for large lakes, such as the Great Lakes, is critical for essential operations, such as ice breaking, commercial navigation, search and rescue, and oil spill response. Existing forecast products for large lake ice conditions are not available for medium‐range time horizons (5–16 days out), yet they could provide important information for decision making, particularly for ice breaking and spill responses. In addition, ice forecasts for Earth's largest lakes at these timescales could be important for Medium‐Range Weather (MRW) forecasting. However, the skill of existing operational products in predicting ice conditions at MRW timescales has not been studied. This work aims to determine how well ice forecasts from a coupled large lake hydrodynamic‐ice model perform for MRW forecast horizons. Simulations were carried out for the 2022 Great Lakes ice season, using 8 different 16‐day forecast periods. Forecast results were compared to observations of meteorology and ice conditions from the U.S. National Ice Center. Results show the MRW ice forecasts in the Great Lakes outperform persistence‐based forecasts. These findings could inform the development or extension of lake operational ice forecasting and the potential of coupling between atmospheric and large lake models at medium‐range forecast time scales. Plain Language Summary: Ice information for large lakes is critical for essential operations, such as ice breaking, commercial navigation, search and rescue, and oil spill response. Ice forecasts are currently not available in the medium‐range (5–16 days out) time scale, yet they could provide valuable information for decision making. This information could also be important for medium‐range weather forecasts. The skill of existing Great Lakes ice forecast products at medium‐range time scales is currently unknown. Our work aims to determine how well ice forecasts from an atmosphere‐lake model perform for medium‐range time scales. Simulations were carried out for the 2022 Great Lakes ice season, using 8 different 16‐day forecast periods. The modeled forecasts were compared to observations from the National Ice Center and observed meteorological conditions. Results show that the modeled forecasts outperform persistence forecasts. These findings could inform the development or extension of ice forecasting and the potential of an atmosphere‐lake model at medium‐range time scales. Key Points: Medium‐range (5‐ to 16‐day) ice forecasts show utility in the Great LakesModel ice forecasts outperform ice persistence forecastsAir temperature bias in medium‐range weather forecasts is the likely greatest contributor to ice error [ABSTRACT FROM AUTHOR]

The rise in oil trade and transportation has led to a continuous increase in the risk of oil spills, posing a serious worldwide concern. However, there is a lack of numerical models for predicting oil spill transport in freshwater, especially under icy conditions. To tackle this challenge, we developed a prediction system for oil with ice modeling by coupling the General NOAA Operational Modeling Environment (GNOME) model with the Great Lakes Operational Forecast System (GLOFS) model. Taking Lake Erie as a pilot study, we used observed drifter data to evaluate the performance of the coupled model. Additionally, we developed six hypothetical oil spill cases in Lake Erie, considering both with and without ice conditions during the freezing, stable, and melting seasons spanning from 2018 to 2022, to investigate the impacts of ice cover on oil spill processes. The results showed the effective performance of the coupled model system in capturing the movements of a deployed drifter. Through ensemble simulations, it was observed that the stable season with high-concentration ice had the most significant impact on limiting oil transport compared to the freezing and melting seasons, resulting in an oil-affected open water area of 49 km 2 on day 5 with ice cover, while without ice cover it reached 183 km 2 . The stable season with high-concentration ice showed a notable reduction in the probability of oil presence in the risk map, whereas this reduction effect was less prominent during the freezing and melting seasons. Moreover, negative correlations between initial ice concentration and oil-affected open water area were consistent, especially on day 1 with a linear regression R-squared value of 0.94, potentially enabling rapid prediction. Overall, the coupled model system serves as a useful tool for simulating oil spills in the world's largest freshwater system, particularly under icy conditions, thus enhancing the formulation of effective emergency response strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)