Seasonal forecasting of landfast ice in Foggy Island Bay, Alaska in support of ice road operations.

Landfast ice along the Arctic coasts plays an important role in supporting ecosystem services, local communities and offshore activities by industry. Seasonal predictions of landfast ice conditions are generally lacking in current seasonal forecasting products but are needed especially for planning of on-ice activities such as the construction of ice roads. This study focuses on the planned offshore development of Liberty Island where there is a need to generate seasonal forecasts for the construction of ice roads in Foggy Island Bay along the Beaufort Sea coast in northern Alaska. Due to the lack of prior in-situ observations of ice thickness in the region, a combination of newly obtained field measurements, remote sensing data analysis and modeling were employed to produce prototype seasonal forecasts of ice thickness in the landfast ice around Liberty Island. Seasonal forecasts initialized in September and March were built using Climate Forecast System seasonal forecast model data coupled with a single column ice model to forecast ice thickness that capture the start and end of the operational season, respectively. The results showed that the model forecasts had modest skill in capturing the timing of key ice thickness thresholds needed to support vehicle traffic during the start of the season in November–December but very limited skill with end of season and ice breakup forecasts. Much of the forecast skill was improved through bias correction but such improvement was hampered by the lack of long-term observational data in the region. Integration of the observational data and modeling is necessary to begin development of seasonal forecasts in this data sparse region. • Seasonal sea ice thickness forecasts developed for Foggy Island Bay, Alaska. • Forecasts needed to support ice road operations for Liberty Island development. • Required integration of field observations, forecast model data, and an ice model. • Forecast skill was improved through bias correction. [ABSTRACT FROM AUTHOR]