Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes
Regional September Sea Ice Forecasting with Complex Networks and Gaussian Processes
复制标题
利用复杂网络和高斯过程进行区域九月海冰预报
DOI：
10.1175/waf-d-19-0107.1
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发表时间：
2020
影响因子：
2.9
通讯作者：
Peter Sollich
中科院分区：
文献类型：
--
作者：
W. Gregory;M. Tsamados;J. Stroeve;Peter Sollich
对北极社区和行业利益攸关方来说，对北冰洋海冰覆盖的可靠预测正变得至关重要。在这项研究中，使用复杂网络统计方法预测了泛北极和区域9月份的平均海冰范围，提前时间长达3个月。该方法通过构建时空同质性区域(即节点)来利用气候时间序列数据中的关系，并随后推导出它们之间的遥相关联系。网络的节点和链路是根据6月、7月和8月的月平均海冰密集场生成的；因此，每个月都构建了单独的网络。然后，在线性高斯过程回归预测模型中利用网络信息，这是一种贝叶斯推理技术，以产生海冰范围的预测。泛北极预报捕捉到了9月份海冰范围卫星观测的大量可变性，在3个月、2个月和1个月的提前期，去势预测技能分别为0.53、0.62和0.81。还对九个北极地区进行了区域预报。平均而言，在加拿大群岛、波弗特、楚科奇、东西伯利亚、拉普特夫和喀拉海取得了最高的预测技能，尽管这些地区中的许多地区的准确预测能力似乎正在随着时间的推移而变化。
Reliable predictions of the Arctic sea ice cover are becoming of paramount importance for Arctic communities and industry stakeholders. In this study pan-Arctic and regional September mean sea ice extents are forecast with lead times of up to 3 months using a complex network statistical approach. This method exploits relationships within climate time series data by constructing regions of spatiotemporal homogeneity (i.e., nodes), and subsequently deriving teleconnection links between them. Here the nodes and links of the networks are generated from monthly mean sea ice concentration fields in June, July, and August; hence, individual networks are constructed for each respective month. Network information is then utilized within a linear Gaussian process regression forecast model, a Bayesian inference technique, in order to generate predictions of sea ice extent. Pan-Arctic forecasts capture a significant amount of the variability in the satellite observations of September sea ice extent, with detrended predictive skills of 0.53, 0.62, and 0.81 at 3-, 2-, and 1-month lead times, respectively. Regional forecasts are also performed for nine Arctic regions. On average, the highest predictive skill is achieved in the Canadian Archipelago, Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas, although the ability to accurately predict many of these regions appears to be changing over time.
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影响因子：
4.6
作者：
Donges, Jonathan F.;Petrova, Irina;Kurths, Juergen
通讯作者：
Kurths, Juergen
DOI：
10.1029/2018jc014471
发表时间：
2019-04
期刊：
Journal of Geophysical Research: Oceans
影响因子：
--
作者：
Dripta Sarkar;Michael A. Osborne;T. Adcock
通讯作者：
Dripta Sarkar;Michael A. Osborne;T. Adcock
利用被动微波海冰观测对九月北极海冰范围进行熟练的春季预报
DOI：
10.1002/2016ef000495
发表时间：
2017
期刊：
Earth's Future
影响因子：
--
作者：
Petty A
通讯作者：
Petty A
影响因子：
16.6
作者：
Boers, N.;Bookhagen, B.;Marengo, J. A.
通讯作者：
Marengo, J. A.