| 基于温度链浮标获取南极普里兹湾积雪和固定冰厚度的研究 |
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| 引用本文： | 赵杰臣,杨清华,程斌,汪宁,惠凤鸣,沈辉,韩晓鹏,张林,TimoVihma.基于温度链浮标获取南极普里兹湾积雪和固定冰厚度的研究[J].海洋学报,2017,39(11):115-127. |
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| 作者姓名： | 赵杰臣 杨清华 程斌 汪宁 惠凤鸣 沈辉 韩晓鹏 张林 TimoVihma |
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| 作者单位： | 1.国家海洋环境预报中心 国家海洋局海洋灾害预报技术研究重点实验室, 北京 100081;中国海洋大学 海洋与大气学院, 山东 青岛 266100;芬兰气象研究所, 芬兰 赫尔辛基 00101 |
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| 基金项目： | 国家自然科学基金（41406218，41428603，41376005）；国家外专局出国培训项目（2016-51688）；南北极环境综合考察与评估专项（CHINARE-01-01）。 |
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| 摘 要： | 极地积雪和海冰厚度是气候变化的重要指标，也是船舶在冰区航行需要掌握的主要参数。2014和2015年在南极普里兹湾中山站附近布放了一种新式的温度链浮标，该浮标每天进行4次常规温度观测和1次加热升温观测，用于实时获取积雪和海冰剖面温度及厚度数据的研究。通过分析剖面温度曲线和升温曲线反映出的大气、积雪、海冰和海水4种介质的热传导特性差异，可利用人工识别的方法（人工经验法）获得大气/积雪、积雪/海冰和海冰/海水界面的位置。根据统计不同介质在升温响应和垂直温度梯度等方面的特性，找到合理阈值，可通过编写程序自动判断各界面的位置（自动程序法）。本文利用这两种方法来判断不同物质界面位置从而计算得到积雪和海冰厚度。与现场人工观测的海冰厚度相比，人工经验法的平均偏差和均方根偏差分别为2.1 cm和6.4 cm（2014年）以及4.3 cm和6.5 cm（2015年），自动程序法的平均偏差和均方根偏差分别为-6.8 cm和6.4 cm（2014年）以及4.5 cm和 6.6 cm（2015年）；对于积雪，人工经验法与现场人工观测的平均偏差和均方根偏差分别为0.5 cm和 8.5 cm，而自动程序法的平均偏差和均方根偏差分别为4.7 cm和10.8 cm。自动程序法误差较人工经验法偏大，但考虑到整体冰厚和现场观测的误差，两种方法的结果均是可信的，精度是可以接受的。利用新式的温度链浮标实时获取南极普里兹湾积雪和海冰厚度是可行的。
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| 关 键 词： | 海冰质量浮标 积雪 海冰 温度 厚度 南极 普里兹湾 |
| 收稿时间： | 2016/11/26 0:00:00 |
| 修稿时间： | 2017/6/27 0:00:00 |
Snow and land-fast sea ice thickness derived from thermistor chain buoy in the Prydz Bay, Antarctic |
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| Zhao Jiechen,Yang Qinghu,Cheng Bin,Wang Ning,Hui Fengming,Shen Hui,Han Xiaopeng,Zhang Lin and Timo Vihma.Snow and land-fast sea ice thickness derived from thermistor chain buoy in the Prydz Bay, Antarctic[J].Acta Oceanologica Sinica (in Chinese),2017,39(11):115-127. |
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| Authors: | Zhao Jiechen Yang Qinghu Cheng Bin Wang Ning Hui Fengming Shen Hui Han Xiaopeng Zhang Lin and Timo Vihma |
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| Institution: | Key Laboratory of Research on Marine Hazards Forecasting of State Oceanic Administration, National Marine Environmental Forecasting Center, Beijing 100081, China;College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China;Finnish Meteorological Institute, Helsinki 00101, Finland,Key Laboratory of Research on Marine Hazards Forecasting of State Oceanic Administration, National Marine Environmental Forecasting Center, Beijing 100081, China,Finnish Meteorological Institute, Helsinki 00101, Finland,Marine Hydrometeorological Center of the North China Sea Fleat, Qingdao 266003, China,College of Global Change and Earth System Sciences, Beijing Normal University, Beijing 100875, China,Key Laboratory of Research on Marine Hazards Forecasting of State Oceanic Administration, National Marine Environmental Forecasting Center, Beijing 100081, China,Key Laboratory of Research on Marine Hazards Forecasting of State Oceanic Administration, National Marine Environmental Forecasting Center, Beijing 100081, China,Key Laboratory of Research on Marine Hazards Forecasting of State Oceanic Administration, National Marine Environmental Forecasting Center, Beijing 100081, China and Finnish Meteorological Institute, Helsinki 00101, Finland |
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| Abstract: | Snow and sea ice in the polar regions react strongly to the climate change. Sea ice thickness is also a critical parameter for navigation in the polar oceans. In this paper, we present measurements taken using a high-resolution thermistor chain (SIMBA) to monitor snow and ice thickness in the land-fast ice zone in winters 2014 and 2015 in the Prydz Bay outside Zhongshan Station, Antarctic. SIMBA measures vertical temperature profiles 4 times a day as well as two daily sensor heating temperature profiles in 60 s and 120 s. Snow and ice thickness were derived (a) manually on the basis of different linear temperature gradients in air, snow, ice, and water, and (b) applying an automatic algorithm based on temporal variation of the temperature gradients associated with analyses of heating temperature response statistics. Compared with borehole in situ measurements, the manually estimated ice thickness had a mean bias and RMSE of 2.1 cm and 6.4 cm in 2014, 4.3 cm and 6.5 cm in 2015. The mean bias and RMSE of algorithm-based ice thickness was-6.8 cm and 6.4 cm in 2014, 4.5 cm and 6.6 cm in 2015. The snow thickness was estimated only for winter 2015, and the mean bias and RMSE of manual and algorithm methods were 0.5 cm and 8.5 cm, 4.7 cm and 10.8 cm, respectively. The manual estimation, in general, yielded better results. Our results reveal that SIMBA is capable to monitor snow and ice thickness in the Prydz Bay, Antarctic. |
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| Keywords: | sea ice mass balance buoy snow ice temperature thickness Antarctica Prydz Bay |
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