基于遥感观测的2010～2017年秋季北极东北航道通航能力时空变化
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摘要
北极地区海冰的快速消融使得连接亚洲和欧洲的重要海上通道——北极东北航道的通航潜力不断增加.准确掌握海冰快速消融背景下东北航道通航能力的变化特征对航道开发具有重要价值.由于高质量海冰观测数据缺乏,现有研究多基于模式模拟数据对不同升温情景下航道未来的潜在变化进行分析,针对北极航道通航能力现状的研究并未有效开展.本研究利用美国冰雪数据中心发布的逐日海冰密集度数据和经过重建处理、质量得到有效提升的逐日SMOS海冰厚度数据,基于加拿大北极冰区航行系统(AIRSS)的通航可行性模型(ATAM)对北极地区2010～2017年通航季后期普通商船的航行风险进行了逐日量化制图,并对东北航道8年间通航能力的时空变化进行了分析.研究发现,虽然北极东北航道的通航能力已经显著提升,普通商船的通航结束期自2010年以来(除去2013年)平均已达第297±4天(10月24日左右),但过去8年间东北航道上普通商船的通航能力并没有显著增加趋势,且存在很大不确定性.北极东北航道通航结束期主要受东西伯利亚海、新西伯利亚群岛附近海域和北地群岛附近海域3个区域冰情的影响较大, 3个海域附近较早冻结的薄冰往往是导致整个航道通航结束的关键.鉴于北极东北航道通航能力变化的复杂性,高质量、准实时的海冰观测数据对于航道通航风险的准确量化十分必要.
Recently, the Arctic Northeast Route(ANR), an important shipping route connecting Asia and Europe, has become more and more navigable because of the accelerating melt of sea ice in the Arctic. The exploitation of the ANR could shorten the navigational distances from North Asia to Northwestern Europe by 40%(about 2500 nautical miles) and reduce one-third(about 10 days) of the time required for maritime transport by the Royal Road, which can help to save lots of transport costs and bring large environmental benefits. It is very important and urgent to accurately understand the changes in the navigability of the ANR for the development of the route. However, due to the lack of high-quality observed dataset, there are very few observation-based studies. Some model-based studies cannot effectively reflect the real changes in the navigability of the ANR, due to the varying degrees of the data quality issues. In this study, we applied the daily sea ice concentration product provided by the NSIDC and the reconstructed daily SMOS sea ice thickness product into the Arctic Transportation Accessibility Model(ATAM) from the Arctic Ice Regime Shipping System(AIRSS) to assess and map the daily navigation risks for open water vessels in the ANR from 2010–2017 and further analyzed the spatial and temporal changes of the Autumn navigability of the route during the eight years. Assuming the navigability of a specific ice regime is affected by the sea ice conditions and the ice-breaking ability of the vessels, the ATAM model can quantify the navigability of a specific ice regime for seven different vessel types. Since the SMOS ice thickness product is more reliable in thin ice area(lower than 0.5 m), we only focus on the navigability changes of the ANR for open water vessels. We found that,although the end of shipping season for open water vessels across the ANR has extended to the day of 297±4(October 24 th)since 2010(excluding 2013), there is no significant trend in the navigability of the ANR for open water vessels in the last8 years. Further analysis of the spatial distribution map of the navigation risk for open water vessels at the end of shipping season in different years shows that the navigability of the ANR mainly affected by the ice regime around the Eastern Siberian Sea, the Novosibirsk Islands and the Severnaya Zemlya Islands. In most years, it is the earlier frozen ice over the three regions leading to the end of the entire ANR, in the case that most other areas are still safely navigable. We also performed detailed analysis and found that there are significant differences in the navigability changes in the three key straits for open water vessels over the ANR. The Long strait shows great inter-annual variations in the navigability, but has limit impact on the navigability of the entire route because of its late ending of the shipping season. The Sannikov Strait shows the fastest decline, but small inter-annual variations in the navigability. It is also the earliest one that ending the shipping season, and therefore has a great impact on the long-term trend of the navigability of the ANR. The intra-and inter-annual changes in the navigability of the Vlikitsky Strait are very complex. It always fluctuates dramatically, and thus has great impact on the short-term navigability of the entire ANR. In view of the complexity of the change in the ANR navigability, observed high-quality, near real-time sea ice datasets are very important for the quantification of navigation risk in the ASR.
Recently, the Arctic Northeast Route(ANR), an important shipping route connecting Asia and Europe, has become more and more navigable because of the accelerating melt of sea ice in the Arctic. The exploitation of the ANR could shorten the navigational distances from North Asia to Northwestern Europe by 40%(about 2500 nautical miles) and reduce one-third(about 10 days) of the time required for maritime transport by the Royal Road, which can help to save lots of transport costs and bring large environmental benefits. It is very important and urgent to accurately understand the changes in the navigability of the ANR for the development of the route. However, due to the lack of high-quality observed dataset, there are very few observation-based studies. Some model-based studies cannot effectively reflect the real changes in the navigability of the ANR, due to the varying degrees of the data quality issues. In this study, we applied the daily sea ice concentration product provided by the NSIDC and the reconstructed daily SMOS sea ice thickness product into the Arctic Transportation Accessibility Model(ATAM) from the Arctic Ice Regime Shipping System(AIRSS) to assess and map the daily navigation risks for open water vessels in the ANR from 2010–2017 and further analyzed the spatial and temporal changes of the Autumn navigability of the route during the eight years. Assuming the navigability of a specific ice regime is affected by the sea ice conditions and the ice-breaking ability of the vessels, the ATAM model can quantify the navigability of a specific ice regime for seven different vessel types. Since the SMOS ice thickness product is more reliable in thin ice area(lower than 0.5 m), we only focus on the navigability changes of the ANR for open water vessels. We found that,although the end of shipping season for open water vessels across the ANR has extended to the day of 297±4(October 24 th)since 2010(excluding 2013), there is no significant trend in the navigability of the ANR for open water vessels in the last8 years. Further analysis of the spatial distribution map of the navigation risk for open water vessels at the end of shipping season in different years shows that the navigability of the ANR mainly affected by the ice regime around the Eastern Siberian Sea, the Novosibirsk Islands and the Severnaya Zemlya Islands. In most years, it is the earlier frozen ice over the three regions leading to the end of the entire ANR, in the case that most other areas are still safely navigable. We also performed detailed analysis and found that there are significant differences in the navigability changes in the three key straits for open water vessels over the ANR. The Long strait shows great inter-annual variations in the navigability, but has limit impact on the navigability of the entire route because of its late ending of the shipping season. The Sannikov Strait shows the fastest decline, but small inter-annual variations in the navigability. It is also the earliest one that ending the shipping season, and therefore has a great impact on the long-term trend of the navigability of the ANR. The intra-and inter-annual changes in the navigability of the Vlikitsky Strait are very complex. It always fluctuates dramatically, and thus has great impact on the short-term navigability of the entire ANR. In view of the complexity of the change in the ANR navigability, observed high-quality, near real-time sea ice datasets are very important for the quantification of navigation risk in the ASR.
引文
1 Cao Y, Liang S, Chen X, et al. Enhanced wintertime greenhouse effect reinforcing arctic amplification and initial sea-ice melting. Sci Rep, 2017, 7:8462
2 Comiso J C, Parkinson C L, Gersten R, et al. Accelerated decline in the arctic sea ice cover. Geophys Res Lett, 2008, 35:L01703
3 Lang A, Yang S, Kaas E. Sea ice thickness and recent arctic warming. Geophys Res Lett, 2017, 44:409–418
4 Stroeve J, Holland M M, Meier W, et al. Arctic sea ice decline:Faster than forecast. Geophys Res Lett, 2007, 34:L09501
5 Cavalieri D J, Parkinson C L. Arctic sea ice variability and trends, 1979–2010. Cryosphere, 1979, 6:881–889
6 Stroeve J C, Serreze M C, Holland M M, et al. The arctic’s rapidly shrinking sea ice cover:A research synthesis. Clim Change, 2012, 110:1005–1027
7 Kerr R A. Global warming:Arctic summer sea ice could vanish soon but not suddenly. Science, 2009, 323:1655
8 BuixadéFarréA, Stephenson S R, Chen L, et al. Commercial arctic shipping through the northeast passage:Routes, resources, governance,technology, and infrastructure. Polar Geogr, 2014, 37:298-324
9 Smith L C, Stephenson S R. New trans-arctic shipping routes navigable by midcentury. Proc Natl Acad Sci USA, 2013, 110:E1191–E1195
10 Stephenson S R, Brigham L W, Smith L C. Marine accessibility along Russia’s northern sea route. Polar Geogr, 2013, 37:111–133
11 Li X Q, Ci T Y, Luo S Y, et al. Spatio-temporal variations of sea ice and navigability in the Arctic Vilkitsky strait(in Chinese). Chin J Polar Res,2015, 27:282-288[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.极地研究, 2015, 27:282-288]
12 Flanner M G, Shell K M, Barlage M, et al. Radiative forcing and albedo feedback from the northern hemisphere cryosphere between 1979 and2008. Nat Geosci, 2011, 4:151-155
13 Cavalieri D, Parkinson C, Gloersen P, et al. Sea Ice Concentrations From Nimbus-7 SMMR and DMSP SSMI-SSMIS Passive Microwave Data.Boulder, Colorado USA:NASA DAAC at the National Snow and Ice Data Center, 1996
14 Cavalieri D J, Gloersen P, Campbell W J. Determination of sea ice parameters with the Nimbus 7 SMMR. J Geophys Res, 1984, 89:5355–5369
15 Parkinson C L. Spatially mapped reductions in the length of the arctic sea ice season. Geophys Res Lett, 2014, 41:4316–4322
16 Cao Y F, Liang S L. Recent advances in driving mechanisms of the arctic amplification:A review(in Chinese). Chin Sci Bull, 2018, 63:2757–2774[曹云锋,梁顺林.北极地区快速升温的驱动机制研究进展.科学通报, 2018, 63:2757-2774]
17 Ding Q, Schweiger A, L’Heureux M, et al. Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat Clim Change, 2017, 7:289–295
18 Tian-Kunze X, Kaleschke L, Maa?N, et al. Smos-derived thin sea ice thickness:Algorithm baseline, product specifications and initial verification.Cryosphere, 2014, 8:997–1018
19 Huntemann M, Heygster G, Kaleschke L, et al. Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations. Cryosphere, 2014, 8:439–451
20 Ricker R, Hendricks S, Kaleschke L, et al. A weekly arctic sea-ice thickness data record from merged cryosat-2 and smos satellite data. Cryosphere,2017, 11:1607–1623
21 Wang X, Key J, Kwok R, et al. Comparison of arctic sea ice thickness from satellites, aircraft, and piomas data. Remote Sens, 2016, 8:713
22 Seo S. A review and comparison of methods for detecting outliers in univariate data sets. Doctoral Dissertation. Pittsburgh:University of Pittsburgh, 2006
23 Zhang H X, Cao X, Li Q Z, et al. Research on crop identification using mul-temporal NDVI HJ images(in Chinese). Remote Sens Technol Appl,2015, 30:304-311[张焕雪,曹新,李强子,等.基于多时相环境星NDVI时间序列的农作物分类研究.遥感技术与应用, 2015, 30:304-311]
24 Cao Y F, Wang Z X, Deng F P. Fidelity performance of three filters for high quality NDVI time-series analysis(in Chinese). Remote Sens Technol Appl, 2010, 25:118-125[曹云锋,王正兴,邓芳萍. 3种滤波算法对NDVI高质量数据保真性研究.遥感技术与应用, 2010, 25:118-125]
25 Transport Canada. Arctic ice regime shipping system(AIRSS)standards. 1998, Ottawa
26 Stephenson S R, Smith L C, Brigham L W, et al. Projected 21st-century changes to arctic marine access. Clim Change, 2013, 118:885–899
27 Stephenson S R, Smith L C, Agnew J A. Divergent long-term trajectories of human access to the arctic. Nat Clim Change, 2011, 1:156–160
28 Aksenov Y, Popova E E, Yool A, et al. On the future navigability of Arctic sea routes:High-resolution projections of the Arctic Ocean and sea ice.Mar Policy, 2017, 75:300–317
29 Peng G, Meier W N, Scott D J, et al. A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring. Earth Syst Sci Data, 2013, 5:311–318
30 Ahuja R K, Mehlhorn K, Orlin J, et al. Faster algorithms for the shortest path problem. J Assoc Comp Mach, 1990, 37:213–223
31 Coachman L K, Rankin D A. Currents in long strait, arctic ocean. Arctic, 1968, 21:27-38
32 Kucheiko A A, Ivanov A Y, Davydov A A, et al. Iceberg drifting and distribution in the vilkitsky strait studied by detailed satellite radar and optical images. Izv Atmos Ocean Phys, 2017, 52:1031–1040
33 Kwok R. Arctic sea ice thickness, volume, and multiyear ice coverage:Losses and coupled variability(1958–2018). Environ Res Lett, 2018, 13:105005
2 Comiso J C, Parkinson C L, Gersten R, et al. Accelerated decline in the arctic sea ice cover. Geophys Res Lett, 2008, 35:L01703
3 Lang A, Yang S, Kaas E. Sea ice thickness and recent arctic warming. Geophys Res Lett, 2017, 44:409–418
4 Stroeve J, Holland M M, Meier W, et al. Arctic sea ice decline:Faster than forecast. Geophys Res Lett, 2007, 34:L09501
5 Cavalieri D J, Parkinson C L. Arctic sea ice variability and trends, 1979–2010. Cryosphere, 1979, 6:881–889
6 Stroeve J C, Serreze M C, Holland M M, et al. The arctic’s rapidly shrinking sea ice cover:A research synthesis. Clim Change, 2012, 110:1005–1027
7 Kerr R A. Global warming:Arctic summer sea ice could vanish soon but not suddenly. Science, 2009, 323:1655
8 BuixadéFarréA, Stephenson S R, Chen L, et al. Commercial arctic shipping through the northeast passage:Routes, resources, governance,technology, and infrastructure. Polar Geogr, 2014, 37:298-324
9 Smith L C, Stephenson S R. New trans-arctic shipping routes navigable by midcentury. Proc Natl Acad Sci USA, 2013, 110:E1191–E1195
10 Stephenson S R, Brigham L W, Smith L C. Marine accessibility along Russia’s northern sea route. Polar Geogr, 2013, 37:111–133
11 Li X Q, Ci T Y, Luo S Y, et al. Spatio-temporal variations of sea ice and navigability in the Arctic Vilkitsky strait(in Chinese). Chin J Polar Res,2015, 27:282-288[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.极地研究, 2015, 27:282-288]
12 Flanner M G, Shell K M, Barlage M, et al. Radiative forcing and albedo feedback from the northern hemisphere cryosphere between 1979 and2008. Nat Geosci, 2011, 4:151-155
13 Cavalieri D, Parkinson C, Gloersen P, et al. Sea Ice Concentrations From Nimbus-7 SMMR and DMSP SSMI-SSMIS Passive Microwave Data.Boulder, Colorado USA:NASA DAAC at the National Snow and Ice Data Center, 1996
14 Cavalieri D J, Gloersen P, Campbell W J. Determination of sea ice parameters with the Nimbus 7 SMMR. J Geophys Res, 1984, 89:5355–5369
15 Parkinson C L. Spatially mapped reductions in the length of the arctic sea ice season. Geophys Res Lett, 2014, 41:4316–4322
16 Cao Y F, Liang S L. Recent advances in driving mechanisms of the arctic amplification:A review(in Chinese). Chin Sci Bull, 2018, 63:2757–2774[曹云锋,梁顺林.北极地区快速升温的驱动机制研究进展.科学通报, 2018, 63:2757-2774]
17 Ding Q, Schweiger A, L’Heureux M, et al. Influence of high-latitude atmospheric circulation changes on summertime Arctic sea ice. Nat Clim Change, 2017, 7:289–295
18 Tian-Kunze X, Kaleschke L, Maa?N, et al. Smos-derived thin sea ice thickness:Algorithm baseline, product specifications and initial verification.Cryosphere, 2014, 8:997–1018
19 Huntemann M, Heygster G, Kaleschke L, et al. Empirical sea ice thickness retrieval during the freeze-up period from SMOS high incident angle observations. Cryosphere, 2014, 8:439–451
20 Ricker R, Hendricks S, Kaleschke L, et al. A weekly arctic sea-ice thickness data record from merged cryosat-2 and smos satellite data. Cryosphere,2017, 11:1607–1623
21 Wang X, Key J, Kwok R, et al. Comparison of arctic sea ice thickness from satellites, aircraft, and piomas data. Remote Sens, 2016, 8:713
22 Seo S. A review and comparison of methods for detecting outliers in univariate data sets. Doctoral Dissertation. Pittsburgh:University of Pittsburgh, 2006
23 Zhang H X, Cao X, Li Q Z, et al. Research on crop identification using mul-temporal NDVI HJ images(in Chinese). Remote Sens Technol Appl,2015, 30:304-311[张焕雪,曹新,李强子,等.基于多时相环境星NDVI时间序列的农作物分类研究.遥感技术与应用, 2015, 30:304-311]
24 Cao Y F, Wang Z X, Deng F P. Fidelity performance of three filters for high quality NDVI time-series analysis(in Chinese). Remote Sens Technol Appl, 2010, 25:118-125[曹云锋,王正兴,邓芳萍. 3种滤波算法对NDVI高质量数据保真性研究.遥感技术与应用, 2010, 25:118-125]
25 Transport Canada. Arctic ice regime shipping system(AIRSS)standards. 1998, Ottawa
26 Stephenson S R, Smith L C, Brigham L W, et al. Projected 21st-century changes to arctic marine access. Clim Change, 2013, 118:885–899
27 Stephenson S R, Smith L C, Agnew J A. Divergent long-term trajectories of human access to the arctic. Nat Clim Change, 2011, 1:156–160
28 Aksenov Y, Popova E E, Yool A, et al. On the future navigability of Arctic sea routes:High-resolution projections of the Arctic Ocean and sea ice.Mar Policy, 2017, 75:300–317
29 Peng G, Meier W N, Scott D J, et al. A long-term and reproducible passive microwave sea ice concentration data record for climate studies and monitoring. Earth Syst Sci Data, 2013, 5:311–318
30 Ahuja R K, Mehlhorn K, Orlin J, et al. Faster algorithms for the shortest path problem. J Assoc Comp Mach, 1990, 37:213–223
31 Coachman L K, Rankin D A. Currents in long strait, arctic ocean. Arctic, 1968, 21:27-38
32 Kucheiko A A, Ivanov A Y, Davydov A A, et al. Iceberg drifting and distribution in the vilkitsky strait studied by detailed satellite radar and optical images. Izv Atmos Ocean Phys, 2017, 52:1031–1040
33 Kwok R. Arctic sea ice thickness, volume, and multiyear ice coverage:Losses and coupled variability(1958–2018). Environ Res Lett, 2018, 13:105005