Please wait a minute...
img

官方微信

遥感技术与应用  2019, Vol. 34 Issue (6): 1162-1172    DOI: 10.11873/j.issn.1004-0323.2019.6.1162
冰雪遥感专栏     
北极重要海峡海冰密集度时空变化呈现异质性
张天媛1,2(),黄季夏1,3(),曹云锋1,王利3,孙宇晗1,杨林生3
1.北京林业大学 教育部森林培育与保护重点实验室室,北京 100083
2.北京师范大学 地表过程与资源生态国家重点实验室,北京 100875
3.中国科学院地理科学与资源研究所 陆地表层格局与模拟重点实验室,北京 100101
Spatiotemporal Variation of Sea Ice Concentration in Important Arctic Straits is Heterogeneous
Tianyuan Zhang1,2(),Jixia Huang1,3(),Yunfeng Cao1,Li Wang3,Yuhan Sun1,Linsheng Yang3
1.Beijing key Laboratory of Precision Forestry, Beijing Forestry University, Beijing 100083, China
2.State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China
3.Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
 全文: PDF(4820 KB)   HTML
摘要:

在全球变暖背景下,北极海冰的面积与厚度正逐渐减小,为北极通航提供了可能,而重要海峡的冰情直接影响到北极航道的开通。以东北航道和西北航道上14个重要海峡近35年的海冰密集度为研究对象,利用核K-means方法进行时空聚类,通过经验模态分解模型研究不同聚类模式下的时间序列趋势,探究冰情变化异质性,结果如下:①各海峡海冰密集度呈3种聚类结果,同一聚类结果中海峡的密集度变化具有较强一致性,不同聚类结果之间差异较大,海冰密集度低的海峡全部位于东北航道。②全年尺度中除白令海峡和德米特里拉普捷夫海峡之外,其他海峡海冰密集度呈下降趋势。呈上升趋势的两个海峡均为海冰密集度低的海峡。③夏季融冰期尺度中各海峡海冰密集度变化趋势类型多样,除单纯的上升、下降趋势外,还出现了包括“U”形曲线在内的各种波动型趋势。

关键词: 北极海冰密集度重要海峡经验模态分解核K-means聚类    
Abstract:

In the context of global warming, the area and thickness of Arctic sea ice is gradually decreasing, which provides the possibility for Arctic navigation. As an important transportation hub of sea transportation, the Arctic Strait has a direct impact on the opening of the Arctic Channel. In this study, the sea ice density in the Northeast Passage and the Northwest Passage of the Arctic region in recent 35 years was used as the research object, and the sea ice concentration was clustered by using Kernel K-means clustering. The trend of time series of sea ice intensity under different clustering models is analyzed by Empirical Mode Decomposition (EMD), and the heterogeneity of ice regime changes in important straits is explored. Then taking the summer melting ice age as the study period, the cluster and heterogeneity analysis were carried out, and the following conclusions were drawn: ①The sea ice concentration of 14 straits in the North Pole showed three spatio-temporal clustering models. The variation of sea ice concentration in the same clustering model has strong consistency, and the variation of sea ice concentration is quite different among different models. All the straits with low sea ice concentration are in the Northeast Passage. ②The sea ice density of the other straits except the Bering Strait and the Dimitri Laptev Strait shows a decreasing trend in the whole year. The two straits with a decreasing trend are the straits with low sea ice concentration. ③The variation trends of sea ice concentration in each strait during the summer melting ice period are various. in addition to the simple increasing and decreasing trends, there are also various fluctuating trends, including the U-shape trend.

Key words: Arctic    Sea ice density    Straits    Empirical Mode Decomposition    Kernel K-means clustering
收稿日期: 2018-07-30 出版日期: 2020-03-23
ZTFLH:  P343.6  
基金资助: 中国科学院重点部署项目(ZDRW?ZS?2017?4);中国科学院先导科技专项(XDA19070502)
通讯作者: 黄季夏     E-mail: zty12396@126.com;huangjx@bjfu.edu.cn
作者简介: 张天媛(1997-),女,河北石家庄人,博士研究生,主要从事空间数据分析研究。E?mail:zty12396@126.com
服务  
把本文推荐给朋友
加入引用管理器
E-mail Alert
RSS
作者相关文章  
张天媛
黄季夏
曹云锋
王利
孙宇晗
杨林生

引用本文:

张天媛,黄季夏,曹云锋,王利,孙宇晗,杨林生. 北极重要海峡海冰密集度时空变化呈现异质性[J]. 遥感技术与应用, 2019, 34(6): 1162-1172.

Tianyuan Zhang,Jixia Huang,Yunfeng Cao,Li Wang,Yuhan Sun,Linsheng Yang. Spatiotemporal Variation of Sea Ice Concentration in Important Arctic Straits is Heterogeneous. Remote Sensing Technology and Application, 2019, 34(6): 1162-1172.

链接本文:

http://www.rsta.ac.cn/CN/10.11873/j.issn.1004-0323.2019.6.1162        http://www.rsta.ac.cn/CN/Y2019/V34/I6/1162

所属航道中文名称英文名称经度纬度图1、2海峡代号
东北航道白令海峡Bering Strait169°00′W66°00′N1
桑尼科夫海峡Sannikov Strait140°00′E74°30′N2
德米特里拉普捷夫海峡Dmitry Laptev Strait142°00′E73°00′N3
维利基茨基海峡Vilkitsky Strait103°35′E77°57′N4
尤戈尔斯基沙尔海峡Yugorskiy Shar60°30′E69°45′N5
喀拉海峡Kara Strait58°00′E70°30′N6
西北航道维多利亚海峡Victoria Strait101°00′W69°00′N7
巴罗海峡Barrow Strait97°48′W74°23′N8
兰开斯特海峡Lancaster Sound84°00′W74°13′N9
麦克林托克海峡McClintock102°00′W72°00′N10
皮尔海峡Peel Sound96°30′W73°00′N11
麦克卢尔海峡M'clure Strait117°00′W74°42′N12
威尔士亲王海峡Prince of Wales Strait119°18′W72°15′N13
梅尔维尔子爵海峡Viscount Melville Sound105°00′W74°15′N14
表1  北极地区重要海峡坐标信息
图1  北极重要海峡海冰密集度时空聚类结果
海峡25%分位50%分位75%分位平均值标准差
维利基茨基海峡86.5097.2598.8886.4023.18
巴罗海峡90.0198.1799.4488.7122.27
麦克林托克海峡91.5597.9199.4589.4419.92
皮尔海峡94.1196.6798.4490.1517.60
麦克卢尔海峡95.3698.0099.3691.7418.15
梅尔维尔子爵海峡95.5597.9199.3692.5515.65
桑尼科夫海峡77.2497.9599.5979.4034.64
德米特里拉普捷夫海峡81.4097.0099.1381.3631.84
维多利亚海峡67.8897.4299.2577.3534.83
兰开斯特海峡43.1796.2199.3773.0335.07
威尔士亲王海峡66.7896.3398.5682.0025.87
白令海峡0.0020.9597.3845.2545.82
尤戈尔斯基沙尔海峡0.0076.3997.3657.2042.13
喀拉海峡0.0043.1593.6446.8940.82
表2  各海峡海冰密集度的数学特征量
图2  北极14个海峡1980~2014年海冰密集度分布(横轴表示14个海峡,纵轴表示海冰密集度指数,范围自下而上为0~100)
图3  3种不同聚类结果中海峡EMD时间序列分解趋势
图4  东北航道重要海峡近35 a来海冰密集度变化趋势
图5  西北航道重要海峡近35 a来海冰密集度变化趋势
图6  夏季融冰期东北航道重要海峡近35 a来海冰密集度变化趋势
图7  西北航道重要海峡近35 a来海冰密集度变化趋势
1 NSIDC (2012) National Snow & Ice Data Center Ongoing Data Updates[Z]. Available atwww.nsidc.org. Accessed December 20, 2012.
2 Peng Zhenwu, Wang Yunchuang. Significance and Domestic Impact of Navigable Arctic Channel[J]. Port and Waterway Engineering, 2014(7):86-89, 109.
2 彭振武, 王云闯. 北极航道通航的重要意义及对我国的影响[J]. 水运工程,2014(7):86-89, 109.
3 Fu Qiang, Research on the Variation of Ice Condition in the Key Areas of the Northwest Passage[D]. Dalian: Dalian Maritime University, 2012.
3 付强. 北极西北航道通航关键海区海冰变化规律研究[D]. 大连:大连海事大学, 2012.
4 Yang Chenglin. Strategic Analysis of Navigation Conditions in the Northeast Passage[D]. Ji'nan: Shandong Normal University, 2016.
4 杨成林. 北极东北航道通航条件战略分析[D]. 济南: 山东师范大学, 2016.
5 Su Jie, Xu Dong, Zhao Jinping, et al. Features of Northwest Passage Sea Ice’s Distribution and Variation under Arctic Rapidly Warming Condition[J]. Chinese Journal of Polar Research, 2010, 22 (2): 104-124.
5 苏洁, 徐栋, 赵进平,等. 北极加速变暖条件下西北航道的海冰分布变化特征[J]. 极地研究, 2010, 22(2):104-124.
6 Li Renda. On the Overflight System of Arctic Passage through Relevant Straits [J]. The New Orient, 2016 (6): 18-20.
6 李人达. 论北极航道途经有关海峡的航行飞越制度[J]. 新东方, 2016(6):18-20.
7 Meng Debin. Study on the Influence of Arctic Passage on Global Trade Pattern[D]. Shanghai: Shanghai Academy of Social Sciences, 2015.
7 孟德宾. 北极航道对全球贸易格局的影响研究[D]. 上海:上海社会科学院, 2015.
8 Sun Yimeng. Study on the Influence of the Potential of China's Foreign Trade by the Arctic Route[D]. Dalian: Dalian Maritime University, 2014.
8 孙艺萌. 北极航线对我国对外贸易潜力的影响研究[D]. 大连: 大连海事大学, 2014.
9 Meng Shang, Li Ming, Tian Zhongxiang, et al. Characteristics of the Sea Ice Variation in the Arctic Northeast Passage [J], Marine Forecasts, 2013,30 (2): 8-13.
9 孟上, 李明, 田忠翔,等. 北极东北航道海冰变化特征分析研究[J]. 海洋预报, 2013, 30(2):8-13.
10 Kong Rui, Zhang Guoxuan, Shi Zesheng, et al. Kernel-based K-means Clustering [J], Computer Engineering, 2004, 30 (11): 12-13.
10 孔锐, 张国宣, 施泽生,等. 基于核的K-均值聚类[J]. 计算机工程, 2004, 30(11):12-13.
11 Caliński T, Harabasz J. A Dendrite Method for Cluster Analysis[J]. Communications in Statistics, 1974, 3(1):1-27.
12 Team R C. R: A Language and Environment for Statistical Computing. 2012.[J]. Computing, 2012, 1:12-21.
13 Karatzoglou A, Smola A, Hornik K, et al. Kernlab - An S4 Package for Kernel Methods in R[J]. Journal of Statistical Software, 2004, 11(i09):721-729.
14 Xu Xiaogang, Xu Guanlei, Wang Xiaotong, et al. Empirical Mode Decomposition and its Application [J]. Acta Electronica Sinica, 2009,37 (3): 581-585.
14 徐晓刚, 徐冠雷, 王孝通,等. 经验模式分解(EMD)及其应用[J]. 电子学报, 2009, 37(3):581-585.
15 Wang limin, Xing Changyu, Liu Xiangdong, et al. Study on Price Discovery Function of Steel Futures based on Empirical Mode Decomposition[J]. Science Technology and Industry, 2012, 12 (8): 78-82.
15 王立民, 兴长宇, 刘祥东,等. 基于EMD分解的螺纹钢期货价格发现的实证研究[J]. 科技和产业, 2012, 12(8):78-82.
16 Li Xinqing, Tianyu Ci, Luo Sihan, et al. Spatio-temporal Variation of Sea Ice and Navigability in the Arctic Vilkitsky Strait[J]. Chinese Journal of Polar Research, 2015 (3): 282-288.
16 李新情, 慈天宇, 罗斯瀚,等. 北极东北航道维利基茨基海峡海冰时空变化及适航性分析[J]. 极地研究, 2015(3):282-288.
17 Wang Dan, Wang Jie, Zhang Hao. Study of Arctic Waterway Transit Policy and Its Development on Circumpolar Nations and Regions[J]. Chinese Journal of Polar Research,2015(1): 74-82.
17 王丹, 王杰, 张浩. 环北极国家与地区的北极航道通行政策及其发展趋势分析[J]. 极地研究, 2015(1):74-82.
18 Bai Jiayu, Sun Yan, Zhang Xia. Cooperation Mechanism of Bering Strait Governance [J]. Chinese Journal of Polar Research, 2017,29 (2): 256-269.
18 白佳玉, 孙妍, 张侠. 白令海峡治理的合作机制研究[J]. 极地研究, 2017, 29(2):256-269.
19 Li Pixue. Dominant Climate Factors Influencing the Arctic Runoff and Association between the Runoff and Arctic Sea Ice [D]. Dalian: Dalian Maritime University, 2009.
19 李丕学. 北极径流变化的关键气候因子及其对北冰洋海冰变化影响的研究[D]. 大连:大连海事大学, 2009.
20 Gu Wei, Gu Songgang, Shi Peijun, et al. The Temporal Change Characteristics of Sea Ice Thickness and Regeneration Period of Sea Ice in Liaodong Gulf [J], Resources Science, 2003, 25 (3): 24-32.
20 顾卫, 顾松刚, 史培军,等. 海冰厚度的时间变化特征与海冰再生周期研究[J]. 资源科学, 2003, 25(3):24-32.
21 Wei Lixin. Study on Variation of Arctic Sea Ice and its Effect on Global Climate[D].Dalian: Dalian Maritime University, 2008.
21 魏立新. 北极海冰变化及其气候效应研究[D]. 大连: 大连海事大学, 2008.
22 Liu Jiang, Mu Dejun. Dynamic Feature Extraction of Sea Ice in SAR Imagery[J]. Remote Sensing Technology an Application,2018,33(1):55-60.
22 刘建歌, 慕德俊. 基于SAR影像海冰动态特征的提取方法[J]. 遥感技术与应用, 2018, 33(1):55-60.
23 Becagli S, Lazzara L, Marchese C, et al. Relationships Linking Primary Production, Sea Ice Melting, and Biogenic Aerosol in the Arctic[J]. Atmospheric Environment, 2016, 136:1-15.
24 Castro-Morales K, Ricker R, Gerdes R. Regional Distribution and Variability of Model-simulated Arctic Snow on Sea Ice[J]. Polar Science, 2017, 13:33-49.
25 Granskog M A, Rösel A, Dodd P A, et al. Snow Contribution to First‐Year and Second‐year Arctic Sea Ice Mass Balance North of Svalbard[J]. Journal of Geophysical Research, 2017, 122(3):2539-2549.
26 Henderson G R, Barrett B S, Lafleur D M. Arctic Sea Ice and the Madden–Julian Oscillation (MJO)[J]. Climate Dynamics, 2014, 43(7-8):1-12.
27 Huang W, Lei R, Han H, et al. Physical Structures and Interior Melt of the Central Arctic Sea Ice/Snow in Summer 2012[J]. Cold Regions Science & Technology, 2016, 124:127-137.
28 Landy J C,Ehn J K,Babb D G, et al. Sea Ice Thickness in the Eastern Canadian Arctic: Hudson Bay Complex & Baffin Bay[J].Remote Sensing of Environment,2017,200(200):281-294.
29 Nam J H, Park I, Lee H J, et al. Simulation of Optimal Arctic Routes Using a Mumerical Sea Ice Model based on an Ice-coupled Ocean Circulation Method[J]. International Journal of Naval Architecture & Ocean Engineering, 2013, 5(2):210-226.
30 Sandø A. B., Gao Y., Langehaug H. R.. Poleward Ocean Heat Transports, Sea Ice Processes, and Arctic Sea Ice Variability in NorESM1‐M Simulations[J]. Journal of Geophysical Research Oceans, 2014, 119(3):2095-2108.
31 Wu S Q, Zeng Q C, Bi X Q. Modeling of Arctic Sea Ice Variability During 1948~2009: Validation of Two Versions of the Los Alamos Sea Ice Model (CICE)[J]. Atmospheric and Oceanic Science Letters, 2015, 8(4):215-219.
[1] 张翔,王振占,谌华. 一种利用HY-2卫星扫描微波辐射计数据反演极地海冰密集度的算法[J]. 遥感技术与应用, 2012, 27(6): 912-918.
[2] 董士伟, 周子勇, 文百红. 基于EMD与神经网络的油膜高光谱数据特征提取[J]. 遥感技术与应用, 2010, 25(2): 221-226.
[3] 曹梅盛, 晋 锐. 遥感技术监测海冰密集度[J]. 遥感技术与应用, 2006, 21(3): 259-264.