基于温度链浮标获取南极普里兹湾积雪和固定冰厚度的研究

极地积雪和海冰厚度是气候变化的重要指标,也是船舶在冰区航行需要掌握的主要参数。2014和2015年在南极普里兹湾中山站附近布放了一种新式的温度链浮标,该浮标每天进行4次常规温度观测和1次加热升温观测,用于实时获取积雪和海冰剖面温度及厚度数据的研究。通过分析剖面温度曲线和升温曲线反映出的大气、积雪、海冰和海水4种介质的热传导特性差异,可利用人工识别的方法（人工经验法）获得大气/积雪、积雪/海冰和海冰/海水界面的位置。根据统计不同介质在升温响应和垂直温度梯度等方面的特性,找到合理阈值,可通过编写程序自动判断各界面的位置（自动程序法）。本文利用这两种方法来判断不同物质界面位置从而计算得到积雪和海冰厚度。与现场人工观测的海冰厚度相比,人工经验法的平均偏差和均方根偏差分别为2.1 cm和6.4 cm（2014年）以及4.3 cm和6.5 cm（2015年）,自动程序法的平均偏差和均方根偏差分别为-6.8 cm和6.4 cm（2014年）以及4.5 cm和 6.6 cm（2015年）;对于积雪,人工经验法与现场人工观测的平均偏差和均方根偏差分别为0.5 cm和 8.5 cm,而自动程序法的平均偏差和均方根偏差分别为4.7 cm和10.8 cm。自动程序法误差较人工经验法偏大,但考虑到整体冰厚和现场观测的误差,两种方法的结果均是可信的,精度是可以接受的。利用新式的温度链浮标实时获取南极普里兹湾积雪和海冰厚度是可行的。     

基于CryoSat-2卫星测高数据的北极海冰体积估算方法

近30年来,北极海冰正发生着剧烈的变化。海冰体积是量化海冰变化的重要指标之一。本文以2015年CryoSat-2卫星测高数据和OSI SAF海冰类型产品为基础。提取了浮冰出水高度、积雪深度、海冰密集度、海冰类型等属性信息,通过数据内插、投影变换、栅格转换、空间重采样等工作将海冰属性信息统一为25 km×25 km分辨率的栅格数据集。根据流体静力学平衡原理,逐个估算栅格像元对应的海冰厚度值,将其与对应的海冰面积相乘,估算了北极海冰密集度大于75%海域的海冰体积,并分析了海冰厚度和体积的月变化和季节变化特征。用NASA IceBridge海冰厚度产品对反演的海冰厚度进行验证。结果表明二者相关系数为0.72,有较高的一致性。北极海冰平均厚度春季最大,夏季最小,分别约为2.99 m和1.77 m,最厚的海冰集中在格陵兰沿岸北部和埃尔斯米尔半岛以北海域。多年冰平均厚度大于一年冰。冬季海冰体积最大,约为23.30×103 km3,经过夏季的融化,减少了近70%。一年冰体积季节波动较大,而多年冰体积相对稳定,季节变化不明显。     

基于中国北极考察布放浮标观测和数值模拟的海冰和积雪厚度变化分析

Sea ice and the snow pack on top of it were investigated using Chinese National Arctic Research Expedition(CHINARE) buoy data.Two polar hydrometeorological drifters,known as Zeno? ice stations,were deployed during CHINARE 2003.A new type of high-resolution Snow and Ice Mass Balance Arrays,known as SIMBA buoys,were deployed during CHINARE 2014.Data from those buoys were applied to investigate the thickness of sea ice and snow in the CHINARE domain.A simple approach was applied to estimate the average snow thickness on the basis of Zeno~ temperature data.Snow and ice thicknesses were also derived from vertical temperature profile data based on the SIMBA buoys.A one-dimensional snow and ice thermodynamic model(HIGHTSI) was applied to calculate the snow and ice thickness along the buoy drift trajectories.The model forcing was based on forecasts and analyses of the European Centre for Medium-Range Weather Forecasts(ECMWF).The Zeno~ buoys drifted in a confined area during 2003–2004.The snow thickness modelled applying HIGHTSI was consistent with results based on Zeno~ buoy data.The SIMBA buoys drifted from 81.1°N,157.4°W to 73.5°N,134.9°W in 15 months during2014–2015.The total ice thickness increased from an initial August 2014 value of 1.97 m to a maximum value of2.45 m before the onset of snow melt in May 2015;the last observation was approximately 1 m in late November2015.The ice thickness based on HIGHTSI agreed with SIMBA measurements,in particular when the seasonal variation of oceanic heat flux was taken into account,but the modelled snow thickness differed from the observed one.Sea ice thickness derived from SIMBA data was reasonably good in cold conditions,but challenges remain in both snow and ice thickness in summer.     

2016年南极中山站固定冰冰厚观测分析

极区海冰是全球气候系统的重要组成部分，南极的固定冰普遍存在于其沿海地区，中山站周边固定冰一般在11月中下旬达到最厚。海冰厚度是海冰的重要参数之一，2016年在南极中山站附近3个站点(S1、S2、S3站点)共布放了4套温度链浮标，包括1套SIMBA (Snow and Ice Mass Balance Array)温度链浮标和3套太原理工大学温度链浮标(TY温度链浮标)，SIMBA温度链浮标每天观测4次，TY温度链浮标每小时观测1次。利用浮标观测的温度剖面以及海冰和海水间不同介质温度差异计算得到海冰厚度。在S3站点，同时布放了SIMBA温度链浮标和TY温度链浮标。温度链浮标计算冰厚和人工钻孔观测冰厚比较结果显示，S1站点TY温度链浮标计算的海冰厚度平均误差和均方根误差分别为3.3 cm和14.7 cm，S2站点和S3站点分别为6.6 cm、6.9 cm以及4.0 cm、4.8 cm。S3站点的SIMBA温度链浮标计算冰厚和人工观测冰厚的平均误差和均方根误差为8.2 cm和9.7 cm。因而S3站点TY温度链浮标计算的海冰厚度更接近人工观测的结果。进一步对Stefan定律海冰生长模型进行对比，模型计算得到的海冰生长率为0.1～0.8 cm/d，生长率快于TY温度链浮标的结果，且受积雪影响明显。相比于卫星遥感反演冰厚的误差和观测时段的限制以及有限的人工观测，2种温度链浮标未来对于中山站附近海冰的长期监测均有重要的应用价值。     

Sea ice characteristics between the middle Weddell Sea and the Prydz Bay, Antarctica during the austral summer of 2003

The antarctic sea ice was investigated upon five occasions between January 4 and February 15, 2003. The investigations included: (1) estimation of sea ice distribution by ship-based observations between the middle Weddell Sea and the Prydz Bay; (2) estimation of sea ice distribution by aerial photography in the Prydz Bay; (3) direct measurements of fast ice thickness and snow cover, as well as ice core sampling in Nella Fjord; (4) estimation of melting sea ice distribution near the Zhongshan Station; and (5) observation of sea ice early freeze near the Zhongshan Station. On average, sea ice covered 14.4% of the study area. The highest sea ice concentration (80%) was observed in the Weddell Sea. First-year ice was dominant (99.7%-99.8%). Sea ice distributions in the Prydz Bay were more variable due to complex inshore topography, proximity of the Larsemann Hills, and/or grounded icebergs. The average thickness of landfast ice in NeUa Fjord was 169.5 cm. Wind-blown snow redistribution plays an important role in affecting the ice thickness in Nella Fjord. Preliminary freezing of sea ice near the Zhongshan Station follows the first two phases of the pancake cycle.     

Modelling the annual cycle of landfast ice near Zhongshan Station,East Antarctica

A high resolution one-dimensional thermodynamic snow and ice(HIGHTSI) model was used to model the annual cycle of landfast ice mass and heat balance near Zhongshan Station, East Antarctica. The model was forced and initialized by meteorological and sea ice in situ observations from April 2015 to April 2016. HIGHTSI produced a reasonable snow and ice evolution in the validation experiments, with a negligible mean ice thickness bias of(0.003±0.06) m compared to in situ observations. To further examine the impact of different snow conditions on annual evolution of first-year ice(FYI), four sensitivity experiments with different precipitation schemes(0, half, normal, and double) were performed. The results showed that compared to the snow-free case,the insulation effect of snow cover decreased bottom freezing in the winter, leading to 15%–26% reduction of maximum ice thickness. Thick snow cover caused negative freeboard and flooding, and then snow ice formation,which contributed 12%–49% to the maximum ice thickness. In early summer, snow cover delayed the onset of ice melting for about one month, while the melting of snow cover led to the formation of superimposed ice,accounting for 5%–10% of the ice thickness. Internal ice melting was a significant contributor in summer whether snow cover existed or not, accounting for 35%–56% of the total summer ice loss. The multi-year ice(MYI)simulations suggested that when snow-covered ice persisted from FYI to the 10 th MYI, winter congelation ice percentage decreased from 80% to 44%(snow ice and superimposed ice increased), while the contribution of internal ice melting in the summer decreased from 45% to 5%(bottom ice melting dominated).     

2018年北极太平洋区域夏季海冰物理及光学性质的研究

The reduction in Arctic sea ice in summer has been reported to have a significant impact on the global climate. In this study, Arctic sea ice/snow at the end of the melting season in 2018 was investigated during CHINARE-2018, in terms of its temperature, salinity, density and textural structure, the snow density, water content and albedo, as well as morphology and albedo of the refreezing melt pond. The interior melting of sea ice caused a strong stratification of temperature, salinity and density. The temperature of sea ice ranged from –0.8℃ to 0℃, and exhibited linear cooling with depth. The average salinity and density of sea ice were approximately 1.3 psu and 825 kg/m~3, respectively, and increased slightly with depth. The first-year sea ice was dominated by columnar grained ice. Snow cover over all the investigated floes was in the melt phase, and the average water content and density were 0.74% and 241 kg/m~3, respectively. The thickness of the thin ice lid ranged from 2.2 cm to 7.0 cm, and the depth of the pond ranged from 1.8 cm to 26.8 cm. The integrated albedo of the refreezing melt pond was in the range of 0.28–0.57. Because of the thin ice lid, the albedo of the melt pond improved to twice as high as that of the mature melt pond. These results provide a reference for the current state of Arctic sea ice and the mechanism of its reduction.     

Characterization of sea ice and its snow cover in the Arctic Pacific sector during the summer of 2016

A comprehensive analysis of sea ice and its snow cover during the summer in the Arctic Pacific sector was conducted using the observations recorded during the 7th Chinese National Arctic Research Expedition(CHIANRE-2016) and the satellite-derived parameters of the melt pond fraction(MPF) and snow grain size(SGS)from MODIS data. The results show that there were many low-concentration ice areas in the south of 78°N, while the ice concentration and thickness increased significantly with the latitude above the north of 78°N during CHIANRE-2016. The average MPF presented a trend of increasing in June and then decreasing in early September for 2016. The average snow depth on sea ice increased with latitude in the Arctic Pacific sector. We found a widely developed depth hoar layer in the snow stratigraphic profiles. The average SGS generally increased from June to early August and then decreased from August to September in 2016, and two valley values appeared during this period due to snowfall incidents.     

Geophysics of sea ice in the Baltic Sea: A review

With improved observation methods, increased winter navigation, and increased awareness of the climate and environmental changes, research on the Baltic Sea ice conditions has become increasingly active. Sea ice has been recognized as a sensitive indicator for changes in climate. Although the inter-annual variability in the ice conditions is large, a change towards milder ice winters has been detected from the time series of the maximum annual extent of sea ice and the length of the ice season. On the basis of the ice extent, the shift towards a warmer climate took place in the latter half of the 19th century. On the other hand, data on the ice thickness, which are mostly limited to the land-fast ice zone, basically do not show clear trends during the 20th century, except that during the last 20 years the thickness of land-fast ice has decreased. Due to difficulties in measuring the pack-ice thickness, the total mass of sea ice in the Baltic Sea is, however, still poorly known. The ice extent and length of the ice season depend on the indices of the Arctic Oscillation and North Atlantic Oscillation. Sea ice dynamics, thermodynamics, structure, and properties strongly interact with each other, as well as with the atmosphere and the sea. The surface conditions over the ice-covered Baltic Sea show high spatial variability, which cannot be described by two surface types (such as ice and open water) only. The variability is strongly reflected to the radiative and turbulent surface fluxes. The Baltic Sea has served as a testbed for several developments in the theory of sea ice dynamics. Experiences with advanced models have increased our understanding on sea ice dynamics, which depends on the ice thickness distribution, and in turn redistributes the ice thickness. During the latest decade, advance has been made in studies on sea ice structure, surface albedo, penetration of solar radiation, sub-surface melting, and formation of superimposed ice and snow ice. A high vertical resolution has been found as a prerequisite to successfully model thermodynamic processes during the spring melt period. A few observations have demonstrated how the river discharge and ice melt affect the stratification of the oceanic boundary layer below the ice and the oceanic heat flux to the ice bottom. In general, process studies on ice–ocean interaction have been rare. In the future, increasingly multidisciplinary studies are needed with close links between sea ice physics, geochemistry and biology.     

The physical structures of snow and sea ice in the Arctic section of 150°-180°W during the summer of 2010

The physical structures of snow and sea ice in the Arctic section of 150°-180°W were observed on the basis of snow-pit, ice-core, and drill-hole measurements from late July to late August 2010. Almost all the investigated floes were first-year ice, except for one located north of Alaska, which was probably multi-year ice transported from north of the Canadian Arctic Archipelago during early summer. The snow covers over all the investigated floes were in the melting phase, with temperatures approaching 0℃ and densities of 295-398 kg/m3 . The snow covers can be divided into two to five layers of different textures, with most cases having a top layer of fresh snow, a round-grain layer in the middle, and slush and/or thin icing layers at the bottom. The first-year sea ice contained about 7%-17% granular ice at the top. There was no granular ice in the lower layers. The interior melting and desalination of sea ice introduced strong stratifications of temperature, salinity, density, and gas and brine volume fractions. The sea ice temperature exhibited linear cooling with depth, while the salinity and the density increased linearly with normalized depth from 0.2 to 0.9 and from 0 to 0.65, respectively. The top layer, especially the freeboard layer, had the lowest salinity and density, and consequently the largest gas content and the smallest brine content. Both the salinity and density in the ice basal layer were highly scattered due to large differences in ice porosity among the samples. The bulk average sea ice temperature, salinity, density, and gas and brine volume fractions were-0.8℃, 1.8, 837 kg/m3 , 9.3% and 10.4%, respectively. The snow cover, sea ice bottom, and sea ice interior show evidences of melting during mid-August in the investigated floe located at about 87°N, 175°W.     

雪热传导系数与穿过海冰的热通量研究

雪热传导系数是海冰质量平衡过程中的重要物理参数，决定了穿透海冰的热传导通量。北冰洋海冰质量平衡浮标观测获得多年冰上冬季温度链剖面可以明显地区分冰雪界面。本文考虑到冰雪界面处温度随时间变化，再根据冰雪界面热传导通量连续假定，提出了新的雪热传导系数计算方法。受不同环境因素影响，多年冰上各个浮标的雪热传导系数在0.23~0.41 W/（m·K）之间，均值为（0.32±0.08） W/（m·K）。北冰洋多年冰上冬季穿过海冰的热传导通量最大发生在11月至翌年3月，约14~16 W/m2。结冰季节，来自海冰自身降温的热量对穿过海冰向大气传输的热量贡献逐月减少，从9月100%减小到12月的35%，翌年的1月至3月稳定在10%左右。夏季，短波辐射通能量通过热传导自上而下加热海冰，海冰上层温度高于下层，热量传播方向与冬季反向，往海冰内部传递。直到9月短波辐射完全消失，气温下降，热量再次转变为自下往上传递。从冰底热传导来看，夏季出现海冰向冰水界面传递热量现象。由于雪较好的绝热性，冰上覆雪极大地削弱了海冰上层热传导通量，从而减缓了秋冬季节的结冰速度。尽管受雪厚影响，多年冰上层热传导通量与气温依旧具有很好的线性关系，气温每降低1℃，热传导通量增加约0.59 W/m2。     

Estimation of Sea Ice Thickness from SARAL/AltiKa in Drifting Orbit Phase

Abstract

Intra and inter-annual variations in the sea ice thickness are highly sensitive indicators of climatic variations undergoing in the earth’s atmosphere and oceans. This paper describes the method of estimating sea ice thickness using radar waveforms data acquired by SARAL/Altika mission during its drifting orbit phase from July 2016 onwards yielding spatially dense data coverage. Based on statistical analysis of return echoes, classification of the surface has been carried out in three different types, viz. floe, lead and mixed. Time delay correction methods were suitably selected and implemented to make corrections in altimetric range measurements and thereby freeboard. By assuming hydrostatic equilibrium, freeboard data were converted into sea ice thickness. Results show that sea ice thickness varies from 4 to 5?m near ice shelves and 1 to 2.5?m in the marginal sea ice regions. Freeboard and sea ice thickness estimates were also validated using NASA’s Operation Ice Bridge (OIB) datasets. Freeboard measurements show very high correlation (0.97) having RMSE of 0.13. Overestimation of approximately 1–2?m observed in the sea ice thickness, which could be attributed to distance between AltiKa footprint and OIB locations. Moreover, sensitivity analysis shows that snow depth and snow density over sea ice play crucial role in the estimation of sea ice thickness.     

渤海和北极海冰组构及晶体结构特征分析

渤海一年冰的组构和晶体结构表现出的冰的非连续生长速率使得冰层底部具有气泡含量不同的许多分层;静水生长的灰冰具有典型的粒状冰和柱状冰;融化-重冻结冰和重叠冰具有特定的晶体结构.位于北冰洋72°24.037'N,153°33.994'W,长2.2m的冰心样为三年冰,并且发现其内有一种新的晶体类型,定义为碎屑凝聚冰.由位于北冰洋74°58.614'N,160°31.830'W,长4.86m的冰心样的晶体结构分析发现它过去为冰脊,后经融化-冻结改造形成冰丘的证据.     

南极威德尔海春季海冰物理结构对叶绿素上限含量的影响

2006年冬末春初,在德国POLARSTERN科学考察船执行南极威德尔海西北海域考察期间,调查了考察区海冰物理和海洋生物。本文观测了航线上钻取的27支海冰冰芯的组构和71个冰晶体薄片;分析得到393组冰温数据;348组盐度、密度数据和311组叶绿素a和脱镁叶绿素含量数据;通过302组冰内相同深度孔隙率和叶绿素a含量数据分析,发现海冰物理参数影响冰内叶绿素a含量的新证据;利用收集的雪、冰厚度数据以及环境容量制约生态平衡的规律,建立了雪、冰厚度对冰底叶绿素繁荣的影响以及;确立了南极粒状冰和柱状冰内叶绿素a上限含量同卤水体积的关系。从而表达了冰晶体对卤水排泄的效应和冰物理性质对南极春季冰底和冰-水界面叶绿素a增长的贡献。此外,还得出海冰物理性质影响冰藻,并且是南极冰区水体浮游植物繁荣的关键控制因素。     

基于MODIS热红外数据的渤海海冰厚度反演

Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009–2010 was investigated in this paper using MODIS night-time thermal infrared imagery.The cloud cover in the imagery was masked out manually.Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation.Weather forcing data was from the European Centre for Medium-Range Weather Forecasts(ECMWF) analyses.The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms.The overall bias and the root mean square error of the MODIS ice thickness are –1.4 cm and 3.9 cm,respectively.The MODIS results under cold conditions(air temperature –10°C) also agree with the estimated ice growth from Lebedev and Zubov models.The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature,in particular when the sea ice is relatively thin.It is less sensitive to the wind speed.Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons.     

Sea ice volume and age: Sensitivity to physical parameterizations and thickness resolution in the CICE sea ice model

New dynamics parameterizations in Version 5 of the Los Alamos Sea Ice Model, CICE, feature an anisotropic rheology and variable drag coefficients. This study investigates their effect on Arctic sea ice volume and age simulations, along with the effects of several pre-existing model options: a parameter that represents the mean cumulative area of ice participating in ridging, the resolution of the ice thickness distribution, and the resolution of the vertical temperature and salinity profiles.By increasing shear stress between floes, the anisotropic rheology slows the ice motion, producing a thicker, older ice pack. The inclusion of variable drag coefficients, which depend on modeled roughness elements such as deformed ice and melt pond edges, leads to thinner ice and a more realistic simulation of sea ice age. Several feedback processes act to enhance differences among the runs. Notably, if less open water is produced mechanically through ice deformational processes, the simulated ice thins relative to runs with more mechanically produced open water. Thermodynamic processes can have opposing effects on ice age and volume; for instance, growth of new ice increases the volume while decreasing the age of the pack. Therefore, age data provides additional information useful for differentiating among process parameterization effects and sensitivities to other model parameters.Resolution of thicker ice types is crucial for proper modeling of sea ice volume, because the volume of ice in the thicker ice categories determines the total ice volume. Model thickness categories tend to focus resolution for thinner ice; this paper demonstrates that 5 ice thickness categories are not enough to accurately resolve the ice thickness distribution for simulations of ice volume.     

Discrimination of different sea ice types from CryoSat-2 satellite data using an Object-based Random Forest (ORF)

Abstract

Sea ice type is one of the most sensitive variables in Arctic sea ice monitoring, and it is important for the retrieval of ice thickness. In this study, we analyzed various waveform features that characterize the echo waveform shape and Sigma0 (i.e., backscatter coefficient) of CryoSat-2 synthetic aperture radar altimeter data over different sea ice types. Arctic and Antarctic Research Institute operational ice charts were input as reference. An object-based random forest (ORF) classification method is proposed with overall classification accuracy of 90.1%. Accuracy of 92.7% was achieved for first-year ice (FYI), which is the domain ice type in the Arctic. Accuracy of 76.7% was achieved at the border of FYI and multiyear ice (MYI), which is better than current state-of-the-art methods. Accuracy of 83.8% was achieved for MYI. Results showed the overall accuracy of the ORF method was increased by ～8% in comparison with other methods, and the classification accuracy at the border of FYI and MYI was increased by ～10.5%. Nevertheless, ORF classification performance might be influenced by the selected waveform features, snow loading, and the ability to distinguish sea ice from leads.     

Modeling the evolution of snow, snow ice and ice in the Baltic Sea

A numerical 1‐dimensional fine grid sea ice thermodynamic model is constructed accounting specially for: (1) slush formation via flooding and percolation of rain‐ and snow meltwater, (2) the consequent snow ice formation via slush freezing, and (3) the effects of snow compaction on heat diffusion in snow cover. The model simulations from ice winter period 1979–90 are viewed against corresponding observations at the Kemi fast ice station (65 °39.8' N, 24° 31.4' E). The 11‐year averaged model results show good overall consistency with corresponding total ice thickness observations. The model slightly overestimates the snow ice thickness and underestimates the snow thickness in February and March, which is mainly addressed to the model assumption of isostatic balance (i.e., slush formation via flooding), which was probably not fully satisfied at the coastal Kemi fast ice station. Supposing that this assumption is nevertheless generally valid away from the very coastal fast ice zone, an estimate for sea ice sensitivity to changes in winter precipitation rate is produced. Increased precipitation leads to an increase only in snow ice thickness with little change in total ice thickness, while a reduction in precipitation of more than {213}50% causes a significant increase in total ice thickness. The difference in modeled total ice thickness for the case of artificially neglecting snow ice physics is about 25%, which indicates the importance of including snow ice physics in a sea ice model dealing with the seasonal sea ice zone.     

基于卫星高度计的北极海冰厚度变化研究

A modified algorithm taking into account the first year(FY) and multiyear(MY) ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat(2003–2008). Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns(2005–2008) leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.     

东南极中山站陆缘固定冰2011/2012年度的时空变化

Annual observations of first-year ice(FYI) and second-year ice(SYI) near Zhongshan Station, East Antarctica,were conducted for the first time from December 2011 to December 2012. Melt ponds appeared from early December 2011. Landfast ice partly broke in late January, 2012 after a strong cyclone. Open water was refrozen to form new ice cover in mid-February, and then FYI and SYI co-existed in March with a growth rate of 0.8 cm/d for FYI and a melting rate of 2.7 cm/d for SYI. This difference was due to the oceanic heat flux and the thickness of ice,with weaker heat flux through thicker ice. From May onward, FYI and SYI showed a similar growth by 0.5 cm/d.Their maximum thickness reached 160.5 cm and 167.0 cm, respectively, in late October. Drillings showed variations of FYI thickness to be generally less than 1.0 cm, but variations were up to 33.0 cm for SYI in March,suggesting that the SYI bottom was particularly uneven. Snow distribution was strongly affected by wind and surface roughness, leading to large thickness differences in the different sites. Snow and ice thickness in Nella Fjord had a similar "east thicker, west thinner" spatial distribution. Easterly prevailing wind and local topography led to this snow pattern. Superimposed ice induced by snow cover melting in summer thickened multi-year ice,causing it to be thicker than the snow-free SYI. The estimated monthly oceanic heat flux was ～30.0 W/m2 in March–May, reducing to ～10.0 W/m2 during July–October, and increasing to ～15.0 W/m2 in November. The seasonal change and mean value of 15.6 W/m2 was similar to the findings of previous research. The results can be used to further our understanding of landfast ice for climate change study and Chinese Antarctic Expedition services.