Seasonal and inter-annual variations of the primary types of the Arctic sea-ice drifting patterns

Abstract Monthly mean sea ice motion vectors and monthly mean sea level pressure （SLP） for the period of 1979-2006 are investigated to understand the spatial and temporal changes of Arctic sea-ice drift. According to the distinct differences in monthly mean ice velocity field as well as in the distribution of SLP, there are four primary types in the Arctic Ocean： Beaufort Gyre＋Transpolar Drift, Anticyclonic Drift, Cyclonic Drift and Double Gyre Drift. These four types account for 81% of the total, and reveal distinct seasonal variations. The Cyclonic Drift with a large-scale anticlockwise ice motion pattern trends to prevail in summer while the Anticyclonic Drift with an opposite pattern trends to prevail in winter and spring. The prevailing seasons for the Beaufort Gyre＋Transpolar Drift are spring and autumn, while the Double Gyre Drift trends to prevail in winter, especially in Feb- ruary. The annual occurring times of the Anticyclonic Drift and the Cyclonic Drift are closely correlated with the yearly mean Arc- tic Oscillation （AO） index, with a correlation coefficient of -0.54 and 0.54 （both significant with the confident level of 99%）, re- spectively. When the AO index stays in a high positive （negative） condition, the sea-ice motion in the Arctic Ocean demonstrates a more anticlockwise （clockwise） drifting pattern as a whole. When the AO index stays in a neutral condition, the sea-ice motion becomes much more complicated and more transitional types trend to take place.     

Ice concentration assimilation in a regional ice-ocean cou- pled model and its application in sea ice forecasting

A reasonable initial state of ice concentration is essential for accurate short-term forecasts of sea ice using ice-ocean coupled models. In this study, sea ice concentration data are assimilated into an operational ice forecast system based on a com- bined optimal interpolation and nudging scheme. The scheme produces a modeled sea ice concentration at every time step, based on the difference between observational and forecast data and on the ratio of observational error to modeled error. The impact and the effectiveness of data assimilation are investigated. Significant improvements to predictions of sea ice extent were obtained through the assimilation of ice concentration, and minor improvements through the adjustment of the upper ocean properties. The assimilation of ice thickness data did not significantly improve predictions. Forecast experiments show that the forecast accuracy is higher in summer, and that the errors on five-day forecasts occur mainly around the marginal ice zone.     

Russian researchers reach subglacial Lake Vostok in Antarctica

Opening a new scientific frontier lying under the Antarctic ice, Russian researchers have drilled down and finally reached the surface of the gigantic freshwater lake, Lake Vostok. The mission chief likened the achievement to placing a man on the moon. Drilling in the area of the lake began 22 years ago in 1990, but progressed slowly as a result of funding shortages, equipment breakdowns, difficulties of drilling in the ＂warm＂ ice, and environmental concerns. In 1996, six years after drilling was started, a group of Russian and British scientists discovered the lake believed to be one of the largest fresh water reservoirs on the planet. This lake is among the last unexplored places on Earth. Sealed from the Earth＇s atmosphere for millions of years, it may provide vital information about microbial evolution, the past climate of the Earth, and the formation of the Antarctic ice sheet. Russian experts waited several years for international approval of their drilling technology before proceeding. As anticipated, lake water under pressure rushed up the borehole, pushing the drilling fluid up and away, then froze, forming a protective plug that prevented contamination of the lake. In December of the next Antarctic season, 2012--2013, researchers plan to re-drill the frozen sample of subglacial water for analysis.     

Dome Argus： Ideal site for deep ice drilling

Located on the centre of ice drainage range, the highest Dome Argus （Dome A） of East Antarctic Ice Sheet （EAIS）, could be represented as an ideal site for deep ice cores drilling containing oldest paleo-climate records. To select a suitable drilling site for deep ice core, it needs gather all information pertaining to the local meteorology, ice sheet landforms, ice thickness, subgla- cial topography of bed rocks, ice velocity, internal structures of ice sheet, etc. Based on the International Partnerships in Ice Core Sciences （IPICS）, we present recent achievement of glaciological research and its perspective at Dome A in this paper. We system- atically discussed the merits and possible ventures of potential drilling sites around Dome A. Among all the candidates, we find that the Chinese Antarctic Kunlun Station is the best site for and assess further the possibility to obtain a replicate core for carrying out the first deep ice core drilling campaign. We emphasize studying dynamics and evolution of climate change.     

Thickness distribution and extent of sea ice and snow in Prydz Bay and surrounding waters observed in 2012/2013 austral summer

Three ship-based observational campaigns were conducted to survey sea ice and snow in Prydz Bay and the surrounding waters(64.40°S–69.40°S, 76.11°E–81.29°E) from 28 November 2012 to 3 February 2013. In this paper, we present the sea ice extent and its variation, and the ice and snow thickness distributions and their variations with time in the observed zone. In the pack ice zone, the southern edge of the pack ice changed little, whereas the northern edge retreated significantly during the two earlier observation periods. Compared with the pack ice, the fast ice exhibited a significantly slower variation in extent with its northernmost edge retreating southwards by 6.7 km at a rate of 0.37 km?d-1. Generally, ice showed an increment in thickness with increasing latitude from the end of November to the middle of December. Ice and snow thickness followed an approximate normal distribution during the two earlier observations(79.7±28.9 cm, 79.1±19.1 cm for ice thickness, and 11.6±6.1 cm, 9.6±3.4 cm for snow thickness, respectively), and the distribution tended to be more concentrated in mid-December than in late November. The expected value of ice thickness decreased by 0.6 cm, whereas that of snow thickness decreased by 2 cm from 28 November to 18 December 2012. Ice thickness distribution showed no obvious regularity between 31 January and 3 February, 2013.     

Influence of sea ice and sea surface temperature on the abundance of Antarctic krill in Area 48.2

In this paper we examine the relationship between Antarctic krill catch, sea ice concentration, and sea surface tempera- ture （SST）. Data on the Antarctic krill catch from 2003 to 2010 in CCAMLR Area 48.2 were combined with sea ice and SST data. Results showed that krill fishing in Area 48.2 took place from February to August each year but the catch was concentrated from March to July, with production during this period accounting for about 99.3% of the annual catch. Regression analysis showed that the catch per unit effort （CPUE） was clearly related to sea ice concentration and SST intervals. CPUE was negatively correlated with the area of sea ice among years （R2=0.64）, and the correlation was strongest （R2=0.71） when sea ice concentration was greater than 90%. Over the months the CPUE initially increased, then decreased as the area of sea ice increased. The relationship was strongest （R2=0.88） when the concentration of sea ice was 60%--70%. There was no negative correlation among years between CPUE and the ice-free area when S ST was between -2 ℃ and 3 ℃ （R2=0.21）, but there was a significant negative correlation when SST was between 1 ℃ and 2℃ （R2=0.82）. Over the months, CPUE initially increased then decreased with increasing sea ice-free area, and the relationship was strongest （R2=0.94） when SST was between 0℃and 1 ℃. This study shows that sea ice concentra- tion and SST have significant effects on the abundance of krill in Area 48.2, and the findings have practical significance for the use and conservation of Antarctic krill resources.     

Population distribution,structure and growth condition of Antarctic krill （Euphausia superba Dana） during the austral summer in the Southern Ocean

Antarctic krill （Euphausia superba Dana） was collected using a High Speed Collector and an Isaac-Kidd midwater trawl （IKMT） net during the austral summer of 2007/2008 and 2008/2009 in the circumpolar and Prydz Bay regions of the Southern Ocean, respectively. Combined with the simultaneous recording of environmental factors, spatial distribution, population structure and growth condition of E. superba were studied. The abundance of E. superba in the Weddell Sea was higher than in Prydz Bay. However, the abundance of E. superba in both the Weddell Sea and Prydz Bay was lower than figures reported in previous krill surveys for the same time period. With respect to the total study area, E. superba displayed a normal growing state during the two expeditions. E. superba grew relatively poorly in some stations, which may be due to the late retreat of sea ice or lower chlorophyll a concentrations. The number of juvenile E. superba collected using the High Speed Collector was proportionally greater in stations located at the edge of the sea ice, while adults dominated in long-term non-ice regions. This phenomenon reflects the different distribution pattern between juvenile and adult krill. The population structure of E. superba differed between sea regions, which may affect recruitment.     

Analysis of recent climate change over the Arctic using ERA-Interim reanalysis data

This study investigates recent climate change over the Arctic and its link to the mid-latitudes using the ERA-Interim global atmospheric reanalysis data from the European Center for Medium-Range Weather Forecast （ECMWF）. Since 1979, sub- stantial surface warming, associated with the increase in anthropogenic greenhouse gases, has occurred over the Arctic. The great- est warming in winter has taken place offshore in the Kara-Barents Sea, and is associated with the increase in turbulent heat fluxes from the marginal ice zone. In contrast to the marked warming over the Arctic Ocean in winter, substantial cooling appears over Siberia and eastern Asia, linked to the reduction of Arctic sea ice during the freezing season （September-March）. However, in summer, very little change is observed in surface air temperature over the Arctic because increased radiative heat melts the sea ice and the amount of turbulent heat gain from the ocean is relatively small. The heat stored in the upper ocean mixed layer in summer with the opening of the Arctic Ocean is released back to the atmosphere as turbulent heat fluxes during the autumn and through to the following spring. This warming of the Arctic and the reduced sea ice amplifies surface cooling over Siberia and eastern Asia in winter.     

Temperature biases in modeled polar climate and adoption of physical parameterization schemes

An annual cycle of atmospheric variations for 1989 in the Arctic has been simulated with the Weather Research and Forecasting （WRF） model. A severe cold bias was found around a cold center in surface air temperature over the Arctic Ocean, compared with results from ERA-Interim reanalysis. Four successive numerical experiments have been carried out to find out the reasons for this. The results show that the sea ice albedo scheme has the biggest influence in summer, and the effect of the cloud microphysics scheme is significant in both summer and winter. The effect of phase transition between ice and water has the biggest influence over the region near the sea ice edge in summer, and contributes little to improvement of the severe cold bias. The origi- nal crude albedo parameterization in the surface process scheme is the main reason for the large simulated cold bias of the cold center in summer. With a different land surface scheme than in the control run, cold biases of simulated surface air temperature over the Arctic Ocean are greatly reduced, by as much as 10 K, implying that the land surface scheme is critical for polar climate simulation.     

The research of Polar sea ice and its role in climate change

As an important part of global climate system, the Polar sea ice is effccting on global climate changes through ocean surface radiation balance, mass balance, energy balance as well as the circulating of sea water temperature and salinity. Sea ice research has a centuries - old history. The many correlative sea ice projects were established through the extensive international cooperation during the period from the primary research of intensity and the boaring capacity of sea ice to the development of sea/ice/air coupled model. Based on these reseamhes, the sea ice variety was combined with the global climate change. All research about sea ice includes： the physical properties and processes of sea ice and its snow cover, the ecosystem of sea ice regions, sea ice and upper snow albedo, mass balance of sea ice regions, sea ice and climate coupled model. The simulation suggests that the both of the area and volume of polar sea ice would be reduced in next century. With the developing of the sea ice research, more scientific issues are mentioned. Such as the interaction between sea ice and the other factors of global climate system, the seasonal and regional distribution of polar sea ice thickness, polar sea ice boundary and area variety trends, the growth and melt as well as their influencing factors, the role of the polynya and the sea/air interactions. We should give the best solutions to all of the issues in future sea ice studying.     

Polar Sea Ice Identification and Classification Based on HY-2A/SCAT Data

In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification.     

松辽冰盖与松辽分水岭翘板迁移间的动力关系探讨

广布于松辽平原科尔沁沙地的巨型弧形沙垄的成因一直未能得到很好解释,该项研究采用罗根冰碛变形机理对该区的巨型弧形沙垄、密布的湖泡等特征地貌进行分析,发现其与斯堪的纳维亚冰盖、劳伦泰德冰盖的冰下软基变形地貌特征具有同一性,从而确认巨型弧形沙垄为冰盖冰流形成的巨型流线、罗根冰碛与锅穴构造的遗留,从而佐证了“松辽冰盖”的存在。研究发现松辽平原的巨型弧形流线显示出具有从盆地周围向沉降中心延伸、汇聚的特征,这表明流线的走向受制于地势的控制,而非所谓的风力所为。此外根据巨型冰川流线的展布形态与松辽分水岭之间存在的反常关系,发现在冰盖消融后松辽分水岭发生了北移现象,这一现象的产生可能与冰盖消融后导致的松辽盆地的失压反弹隆升有关。根据松辽分水岭的迁移时间、风沙层序年龄、荷载构造扰动地层年龄的综合约束,初步判断松辽冰盖的最近一次的发生时间在晚更新世早期(MIS4阶段)的东山冰期。     

基于Bedmap2与冰雷达数据的南极局部冰盖三维建模

利用Bedmap2数据与中国第29次南极科学考察期间获取的冰雷达数据,在中山站至Dome A断面的Gamburtsev山脉地区首次构建11.3 km×11.5 km南极局部冰盖三维模型。着重介绍三维模型建立过程中的数据处理,其中冰雷达数据采集首次采用中国自主研发的冰雷达系统。详细阐述了冰雷达数据的处理流程,包括数据预处理、常规图像修正技术以及冰下地形获取,得到冰下基岩埋深和冰盖内部等时层埋深,插值得到100 m分辨率的冰下基岩DEM（海拔1 729 m~2 718 m）和等时层DEM（海拔2 601 m~2 950 m）,利用南极Bedmap2冰表面栅格影像得到100 m分辨率的冰盖表面DEM（海拔3 679 m~3 745 m）。结合冰盖内外部数据处理结果,构建包含冰盖表面、冰盖内部等时层和冰下基岩地形特征的三维模型,并对模型进行检验,对冰盖内外部地形特征进行初步分析。     

The progress in the study of Arctic pack ice ecology

The sea ice community plays an important role in the Arctic marine ecosystem. Because of the predicted environmental changes in the Arctic environment and specifically related to sea ice, the Arctic pack ice biota has received more attention in recent years using modem ice-breaking research vessels. Studies show that the Arctic pack ice contains a diverse biota and besides ice algae, the bacterial and protozoan biomasses can be high. Surprisingly high primary production values were observed in the pack ice of the central Arctic Ocean. Occasionally biomass maximum were discovered in the interior of the ice floes, a habitat that had been ignored in most Arctic studies. Many scientific questions, which deserve special attention, remained unsolved due to logistic limitations and the sea ice characteristics. Little is know about the pack ice community in the central Arctic Ocean. Almost no data exists from the pack ice zone for the winter season. Concerning the abundance of bacteria and protozoa, more studies are needed to understand the microbial network within the ice and its role in material and energy flows. The response of the sea ice biota to global change will impact the entire Arctic marine ecosystem and a long-term monitoring program is needed. The techniques, that are applied to study the sea ice biota and the sea ice ecology, should be improved.     

基于NSIDC海冰产品的FY北极海冰数据集优化

北极海冰对全球气候起着非常重要的调制作用,海冰范围是海冰监测的基本参数。近40年,北极地区持续变暖,北极海冰显著减少,进而引发北极自然环境恶化、北半球极端天气频发、全球海平面上升等一系列环境和气候问题。准确获取北极海冰范围及其演变趋势,确定海冰变化对全球气候系统的响应,是研究和预测全球气候变化趋势的关键之一。HasISST和OISST海冰数据集在海冰监测中应用最为广泛,可为北极地区长时间序列海冰变化研究提供基础数据,但这2套数据集空间分辨率相对较低,应用于北极关键区对中国气候响应研究方面存在很大的局限,为解决这一问题和弥补国内海冰监测微波遥感数据的空白,2011年6月27日,国家卫星气象中心（National Satellite Meteorological Center, NSMC）发布了FY（Fengyun, FY）北极海冰数据集,该数据集利用搭载在FY卫星上的微波成像仪（Microwave Radiation Imager, MWRI）数据,使用Enhance NASA Team算法制作,该算法利用前向辐射传输模型模拟北极地区4种海表类型（海水、新生冰、一年冰和多年冰）在不同大气条件下MWRI辐射亮温,进而得到每种大气条件下0~100%的海冰覆盖度查找表（海冰覆盖度每次增加1%）,通过观测值与模拟值的比对得到海冰覆盖度,由该数据集计算得到的北极海冰范围在大部分区域与实际情况相符。该产品虽已进行通道间匹配误差修正和定位精度偏差订正,但由于其搭载的微波成像仪（Microwave Radiation Imager, MWRI）天线长度有限,造成传感器探测到的地物回波信号相对较弱,难以区分海冰和近岸附近的陆地,影响了该数据集的精度和应用。为解决这一问题,本文基于美国冰雪中心（National Snow and Ice Data Center, NSIDC）发布的海冰产品对FY海冰数据集进行优化,NSIDC产品利用判断矩阵对海岸线附近的像元进行识别,并对误差像元进行不同程度的修正,由NSIDC产品计算得到的北极海冰范围与实际情况更为符合。数据集优化大大提高了FY海冰数据集的精度,研究结果表明,优化后FY海冰数据集与NSIDC产品相关系数高达0.9997,且二者日、月、年平均最大海冰范围偏差仅为3.5%、1.9%、0.9%,且FY海冰数据集优化过程对其较好的空间分异特征无明显影响。该数据集可正确地反映北极海冰范围及其变化情况,且海岸线附近海冰的分布情况更准确,可为北极海冰变化研究提供可靠的基础数据。     

Status of the Recent Declining of Arctic Sea Ice Studies

In the past 30 years, a large-scale change occurred in the Arctic climatic system, which had never been observed before 1980s. At the same time, the Arctic sea ice experienced a special evolution with more and more rapidly dramatic declining. In this circumstance, the Arctic sea ice became a new focus of the Arctic research. The recent advancements about abrupt change of the Arctic sea ice are reviewed in this paper .The previous analyses have demonstrated the accelerated declining trend of Arctic sea ice extent in the past 30 years, based on in-situ and satellite-based observations of atmosphere, as well as the results of global and regional climate simulations. Especially in summer, the rate of decrease for the ice extents was above 10% per decade. In present paper, the evolution characteristics of the arctic sea ice and its possible cause are discussed in three aspects, i.e. the sea ice physical properties, the interaction process of sea ice, ocean and atmosphere and its response and feedback mechanism to global and arctic climate system.     

2015—2020年辽东湾海冰冰情时空特征及其影响因素

受冬季强寒潮侵袭,辽东湾会出现大范围结冰现象。为了分析2015—2020年辽东湾海冰冰情的变化规律与影响因素,本文选取Sentinel-1A/B数据开展辽东湾海冰监测。首先,采用巴氏距离选择最优纹理特征组合,再利用最大似然方法实现海冰分类;然后,根据上述海冰分类结果,分析海冰冰情等级、海冰外缘线、海冰面积、海冰类型和海冰结冰概率等冰情特征的变化规律;最后,研究海水深度、海温、气温和风速与海冰冰情的关系。主要结论如下：① 采用不同纹理特征组合方法和本文方法对2020年2月1日Sentinel-1B影像进行实验,结果表明本文方法的总体分类精度和Kappa系数分别为93.16%和0.85,分类精度最高。② 11月末到12月海冰类型以初生冰为主,间有灰冰;1月到2月中上旬以灰冰为主,间有初生冰和白冰;2月下旬到3月上旬的海冰类型以灰冰和初生冰为主。辽东湾内部结冰概率存在差异,北部沿岸结冰概率高于南部,东部结冰概率高于西部。辽东湾海冰冰情受海水深度、海温和气温影响明显,受风速影响较小。     

Mechanical and numerical models for sea ice dynamics on small-meso scale

On small-meso scale, the sea ice dynamic characteristics are quite different from that on large scale. To model the sea ice dynamics on small-meso scale, a new elastic-viscous-plastic （EVP） constitutive model and a hybrid Lagrangian- Eulerian （HLE） numerical method are developed based on continuum theory. While a modified discrete element model （DEM） is introduced to model the ice cover at discrete state. With the EVP constitutive model, the numerical simulation for ice ridging in an idealized rectangular basin is carried out and the results are comparable with the analytical solution of jam theory. Adopting the HLE numerical model, the sea ice dynamic process is simulated in a vortex wind field. The furthering application of DEM is discussed in details for modeling the discrete distribution of sea ice. With this study, the mechanical and numerical models for sea ice dynamics can be improved with high precision and computational efficiency.     

A coupled ice-ocean ecosystem model for 1-D and 3-D applications in the Bering and Chukchi Seas

Primary production in the Bering and Chukchi Seas is strongly influenced by the annual cycle of sea ice. Here pelagic and sea ice algal ecosystems coexist and interact with each other. Ecosystem modeling of sea ice associated phytoplankton blooms has been understudied compared to open water ecosystem model applications. This study introduces a general coupled ice-ocean ecosystem model with equations and parameters for 1-D and 3-D applications that is based on 1-D coupled ice-ocean ecosystem model development in the landfast ice in the Chukchi Sea and marginal ice zone of Bering Sea. The biological model includes both pelagic and sea ice algal habitats with 10 compartments： three phytoplankton （pelagic diatom, flagellates and ice algae： D, F, and Ai） , three zooplankton （copepods, large zooplankton, and microzooplankton ： ZS, ZL, ZP） , three nutrients （ nitrate ＋ nitrite, ammonium, silicon ： NO3 , NH4, Si） and detritus （Det）. The coupling of the biological models with physical ocean models is straightforward with just the addition of the advection and diffusion terms to the ecosystem model. The coupling with a multi-category sea ice model requires the same calculation of the sea ice ecosystem model in each ice thickness category and the redistribution between categories caused by both dynamic and thermodynamic forcing as in the physical model. Phytoplankton and ice algal self-shading effect is the sole feedback from the ecosystem model to the physical model.     

Numerical simulation of the impact of underlying surface changes on Arctic climate

Using a regional atmospheric model for Arctic climate simulation, two groups of numerical experiments were carried out to study the inlfuence of changes in the underlying surface (land surface, sea sur...