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Snow-depth observations by adventurers traveling on Arctic sea ice

机译:在北极海冰上旅行的冒险家的雪深观测

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摘要

Snow depth is a key parameter for assessing the sea-ice mass budget in the Arctic and for the surface energy balance at the atmosphere—snow—ice—ocean interfaces. However, scientific expeditions to the high Arctic Ocean are rare, and for large parts of the year no snow and ice data are collected in situ in most regions. Therefore any additional in situ observations of snow depth are of interest to the scientific community. Arctic adventurers and tourists are among the most frequent visitors to the Arctic Ocean and North Pole. If properly trained and carefully adhering to standard protocols, they could collect valuable snow-depth data from large regions. Here we analyse such data from four Arctic basin ski traverses carried out between 1994 and 2007. Individual datasets show characteristic regional differences of snow thickness, which provide invaluable information for the validation of models and satellite data. The observations were made applying a continuously upgraded observation guideline scheme. Earlier observations were based on a relatively broad view of easily observable snow and ice parameters. Improvements included requirements of more detailed snow-thickness surveys in order to observe the spatial variability over varying sea-ice surfaces in a better way. Possibilities for, and limitations of, ice-thickness estimates and measurements are also discussed. Often, assessment of ice thickness is more problematic since measurements are either time-consuming or biased, meaning that possibilities for collecting large datasets are limited.
机译:雪深是评估北极海冰质量预算以及大气-雪-冰-海洋界面的表面能平衡的关键参数。但是,极少有人进行北冰洋高原的科学考察,而且在一年中的大部分时间里,大部分地区都没有现场收集冰雪数据。因此,科学界对降雪深度的任何其他原位观测都感兴趣。北极冒险家和游客是北冰洋和北极最常来的游客。如果经过适当培训并认真遵守标准协议,他们可以从大范围收集有价值的积雪深度数据。在这里,我们分析了1994年至2007年间进行的四次北极盆地滑雪遍历的数据。单个数据集显示了典型的积雪厚度差异,这为模型验证和卫星数据验证提供了宝贵信息。观察是使用不断升级的观察指南方案进行的。较早的观察是基于相对容易观察到的冰雪参数的较广泛观点。改进措施包括需要更详细的积雪厚度调查,以便更好地观察变化的海冰表面的空间变异性。还讨论了冰厚估计和测量的可能性和局限性。通常,由于测量是费时的或有偏差的,因此对冰厚度的评估通常会带来更多问题,这意味着收集大型数据集的可能性受到限制。

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