Study of Short-Term Sea Ice Prediction Along the Northern Sea Route
- Liyanarachchi Waruna Arampath De Silva (The University of Tokyo) | Hajime Yamaguchi (The University of Tokyo)
- Document ID
- International Society of Offshore and Polar Engineers
- The 28th International Ocean and Polar Engineering Conference, 10-15 June, Sapporo, Japan
- Publication Date
- Document Type
- Conference Paper
- 2018. International Society of Offshore and Polar Engineers
- Numerical modeling, Sea ice predictions, Northern sea route, Arctic sea ice
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- 16 since 2007
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Precise prediction of sea ice distribution along the Northern sea route is crucial for safe and efficient navigation in the Arctic Ocean. A high-resolution (about 2.5 km) ice-ocean coupled model is developed for forecasting the short-term (5days) sea ice distribution along the Northern sea route. The experiment was run from 05 May 2015 to 20 November 2015. The atmospheric forcing data used for the model was European Center for Medium-Range Weather Forecast Interim reanalysis data. The correlation score of ice-edge error and sea ice concentration distribution quantifies forecast skill. Freezing season iceedge error is higher than the melting season. The average forecast skill of ice-edge error in the ice-ocean coupled model is about 10 km with the 15% thresholds of ice concentration for the ice edge. That is in good agreement with the ship crew requirement of 10 km.
With the recent rapid decrease in summer sea ice in the Arctic Ocean extending the navigation period in the Northern sea routes (NSR), the precise short-term (5days) prediction of ice distribution is crucial for safe and efficient navigation in the Arctic Ocean. However, the sea ice distribution varies with hourly time scales due to the atmospheric and oceanic conditions. Therefore, sea ice predictions and observations are important to protect the ships and offshore and coastal structures in order to utilize the Northern sea route (NSR). Goerlandt et al., (2017); Kujala et al., (2009); Kuuliala et al., (2017) have developed the transit simulation models and guidelines to safe navigation in the ice covered ocean. Global climate models and regional models have been employed to assess the predictability of Arctic sea ice such as (Kubat, Sayed, Savage, & Carrieres, 2010; Turnbull & Taylor, 2017).
However, most of the available numerical models ((Preller & Posey, 1989; Schweiger & Zhang, 2015)) have shown high uncertainties in the short-term (about 5days) and narrow-area predictions, especially marginal ice zones such as the NSR (Hebert et al. 2015). Successful sea ice predictions are relying on comprehensive initial conditions of sea ice variables and ocean variables and an ability of forecast systems to capture high-frequency atmospheric variability and associated feedbacks.
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