Seasonal forecasting of landfast ice in Foggy Island Bay, Alaska in support of ice road operations
Seasonal forecasting of landfast ice in Foggy Island Bay, Alaska in support of ice road operations
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阿拉斯加雾岛湾陆地冰的季节性预测，支持冰路运营
DOI：
10.1016/j.coldregions.2022.103618
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发表时间：
2022
影响因子：
4.1
通讯作者：
Bhatt, Uma S.
中科院分区：
文献类型：
--
作者：
Bieniek, Peter A.;Eicken, Hajo;Jin, Meibing;Mahoney, Andrew R.;Jones, Josh;Bhatt, Uma S.
北极沿岸沿着的陆地坚冰在支持生态系统服务、当地社区和工业的近海活动方面发挥着重要作用。在目前的季节性预报产品中，通常缺乏对陆地坚冰状况的季节性预报，但对于规划冰上活动（如建造冰路）来说，这是特别需要的。这项研究的重点是自由岛的计划海上开发，有必要产生季节性预测的冰路在雾岛湾沿着博福特海岸在阿拉斯加北方。由于缺乏在该地区的冰厚度事先在现场观测，结合新获得的现场测量，遥感数据分析和建模，产生原型的季节性预测的冰厚度在自由岛周围的陆固冰。在9月和3月初始化的季节性预报建立了气候预报系统季节性预报模型数据，再加上一个单柱冰模型，以预测冰厚度，捕捉业务季节的开始和结束，分别。结果表明，模型预测在捕捉关键冰厚度阈值的时间需要支持车辆交通在11月至12月的季节开始，但非常有限的技能与季节结束和冰破裂的预测。通过偏差校正，大部分预测技能得到了改善，但由于该区域缺乏长期观测数据，这种改善受到了阻碍。观测数据和建模的整合是必要的，开始在这个数据稀疏的地区的季节预报的发展。
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.
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