September-October Arctic Pacific Sectior Sea-ice Dipole Index Time Series, 1980-2016
收藏Mendeley Data2024-03-27 更新2024-06-30 收录
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https://arcticdata.io/catalog/view/doi:10.18739/A2W669948
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These are the September-October Arctic Pacific Sector Sea-ice Dipole Index and April-May Barents Sea average Sea-ice Index used in Liang et al. (2021). This study used observational and reanalysis datasets in 1980–2016 to show a close connection between a boreal autumn sea ice dipole in the Arctic Pacific sector and sea ice anomalies in the Barents Sea during the following spring. The autumn sea ice dipole index is calculated based on the difference between the sea-ice concentration anomalies averaged over as the Beaufort-Chukchi Seas and the East Siberian-Laptev Seas. The Barents Sea sea ice index is the sea-ice concentration averaged over the Barents Sea region. The September–October Arctic Pacific sea ice dipole variations are highly correlated with the subsequent April–May Barents Sea sea ice variations (r=0.71). The strong connection between the regional sea ice variabilities across the Arctic uncovers a new source of predictability for spring Barents Sea sea ice prediction at 7-month lead time. Please find the detail definition of the indices from Liang et al. (2021). Liang, Y.-C., Y.-O. Kwon, and C. Frankignoul, 2021: Autumn Arctic Pacific Sea-ice Dipole as a Source of Predictability for Subsequent Spring Barents-Kara Sea-ice Condition. J. Climate., 34, 787-804. https://doi.org/10.1175/JCLI-D-20-0172.1.
本数据集包含Liang等人(2021年)研究中使用的9-10月北极太平洋扇区海冰偶极子指数(Arctic Pacific Sector Sea-ice Dipole Index)与4-5月巴伦支海平均海冰指数(Barents Sea average Sea-ice Index)。该研究基于1980–2016年的观测与再分析数据集,揭示了北极太平洋扇区秋季海冰偶极子与次年春季巴伦支海海冰异常之间的显著关联。秋季海冰偶极子指数以波弗特-楚科奇海(Beaufort-Chukchi Seas)与东西伯利亚-拉普捷夫海(East Siberian-Laptev Seas)的海冰密集度异常区域平均值之差计算得到。巴伦支海海冰指数为巴伦支海区域的平均海冰密集度。9-10月北极太平洋扇区海冰偶极子的变化与次年4-5月巴伦支海海冰变化高度相关(相关系数r=0.71)。北极区域海冰变率间的这种强关联,为提前7个月的春季巴伦支海海冰预报提供了全新的可预报性来源。海冰指数的详细定义请参见Liang等人(2021年)的研究:Liang, Y.-C.、Y.-O. Kwon与C. Frankignoul,2021年:《秋季北极太平洋海冰偶极子作为后续春季巴伦支-喀拉海海冰状况的可预报性来源》,《气候学报》(J. Climate),第34卷,787-804页,https://doi.org/10.1175/JCLI-D-20-0172.1。
创建时间:
2023-06-28



