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.     

融池热力学模式以及它在2010年夏季北极中央区的应用研究

A one-dimensional thermodynamic model of melt pond is established in this paper.The observation data measured in the summer of 2010 by the Chinese National Arctic Research Expedition(CHINARE-2010) are used to partially parameterize equations and to validate results of the model.About 85% of the incident solar radiation passed through the melt pond surface,and some of it was released in the form of sensible and latent heat.However,the released energy was very little(about 15%),compared to the incident solar radiation.More than 58.6% of the incident energy was absorbed by melt pond water,which caused pond-covered ice melting and variation of pond water temperature.The simulated temperature of melt pond had a diurnal variation and its value ranged between 0.0°C and 0.3°C.The melting rate of upper pond-covered ice is estimated to be around two times faster than snow-covered ice.At same time,the change of melting rate was relatively quick for pond depth less than 0.4 m,while the melting rate kept relatively constant(about 1.0 cm/d) for pond depth greater than 0.4 m.     

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 latitud...     

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.     

2007和2012年北极最小海冰范围空间分布不同的原因分析

Satellite records show the minimum Arctic sea ice extents(SIEs) were observed in the Septembers of 2007 and2012, but the spatial distributions of sea ice concentration reduction in these two years were quite different.Atmospheric circulation pattern and the upper-ocean state in summer were investigated to explain the difference.By employing the ice-temperature and ice-specific humidity(SH) positive feedbacks in the Arctic Ocean, this paper shows that in 2007 and 2012 the higher surface air temperature(SAT) and sea level pressure(SLP)accompanied by more surface SH and higher sea surface temperature(SST), as a consequence, the strengthened poleward wind was favorable for melting summer Arctic sea ice in different regions in these two years. SAT was the dominant factor influencing the distribution of Arctic sea ice melting. The correlation coefficient is –0.84 between SAT anomalies in summer and the Arctic SIE anomalies in autumn. The increase SAT in different regions in the summers of 2007 and 2012 corresponded to a quicker melting of sea ice in the Arctic. The SLP and related wind were promoting factors connected with SAT. Strengthening poleward winds brought warm moist air to the Arctic and accelerated the melting of sea ice in different regions in the summers of 2007 and 2012. Associated with the rising air temperature, the higher surface SH and SST also played a positive role in reducing summer Arctic sea ice in different regions in these two years, which form two positive feedbacks mechanism.     

Crucial physical characteristics of sea ice in the Arctic section of 143°–180°W during August and early September 2008

Sea-ice physical characteristics were investigated in the Arctic section of 143°-180°W during August and early September 2008. Ship-based observations show that both the sea-ice thickness and concentration recorded during southward navigation from 30 August to 6 September were remarkably less than those recorded during northward navigation from 3 to 30 August, especially at low latitudes. Accordingly, the marginal ice zone moved from about 74.0°N to about 79.5°N from mid-August to early September. Melt-pond coverage increased with increasing latitude, peaking at 84.4°N, where about 27% of ice was covered by melt ponds. Above this latitude, melt-pond coverage decreased evidently as the ice at high latitudes experienced a relatively short melt season and commenced its growth stage by the end of August. Regional mean ice thickness increased from 0.8 (±0.5) m at 75.0°N to 1.5 (±0.4) m at 85.0°N along the northward navigation while it decreased rapidly to 0.6 (±0.3) m at 78.0°N along the southward navigation. Because of relatively low ice concentration and thin ice in the investigated Arctic sector, both the short-term ice stations and ice camp could only be set up over multiyear sea ice. Observations of ice properties based on ice cores collected at the short-term ice stations and the ice camp show that all investigated floes were essentially isothermal with high temperature and porosity, and low density and salinity. Most ices had salinity below 2 and mean density of 800-860 kg/m~3 . Significant ice loss in the investigated Arctic sector during the last 15 a can be identified by comparison with the previous observations.     

北极夏季海冰单轴抗压强度研究

The results on the uniaxial compressive strength of Arctic summer sea ice are presented based on the samples collected during the fifth Chinese National Arctic Research Expedition in 2012(CHINARE-2012). Experimental studies were carried out at different testing temperatures(-3,-6 and-9°C), and vertical samples were loaded at stress rates ranging from 0.001 to 1 MPa/s. The temperature, density, and salinity of the ice were measured to calculate the total porosity of the ice. In order to study the effects of the total porosity and the density on the uniaxial compressive strength, the measured strengths for a narrow range of stress rates from 0.01 to 0.03 MPa/s were analyzed. The results show that the uniaxial compressive strength decreases linearly with increasing total porosity, and when the density was lower than 0.86 g/cm3, the uniaxial compressive strength increases in a power-law manner with density. The uniaxial compressive behavior of the Arctic summer sea ice is sensitive to the loading rate, and the peak uniaxial compressive strength is reached in the brittle-ductile transition range. The dependence of the strength on the temperature shows that the calculated average strength in the brittle-ductile transition range, which was considered as the peak uniaxial compressive strength, increases steadily in the temperature range from-3 to-9°C.     

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.     

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.     

中国第四次北极科学考察期间北极点附近海冰和融池的航空遥感观测

An aerial photography has been used to provide validation data on sea ice near the North Pole where most polar orbiting satellites cannot cover. This kind of data can also be used as a supplement for missing data and for reducing the uncertainty of data interpolation. The aerial photos are analyzed near the North Pole collected during the Chinese national arctic research expedition in the summer of 2010(CHINARE2010). The result shows that the average fraction of open water increases from the ice camp at approximately 87°N to the North Pole, resulting in the decrease in the sea ice. The average sea ice concentration is only 62.0% for the two flights(16 and 19 August 2010). The average albedo(0.42) estimated from the area ratios among snow-covered ice,melt pond and water is slightly lower than the 0.49 of HOTRAX 2005. The data on 19 August 2010 shows that the albedo decreases from the ice camp at approximately 87°N to the North Pole, primarily due to the decrease in the fraction of snow-covered ice and the increase in fractions of melt-pond and open-water. The ice concentration from the aerial photos and AMSR-E(The Advanced Microwave Scanning Radiometer-Earth Observing System) images at 87.0°–87.5°N exhibits similar spatial patterns, although the AMSR-E concentration is approximately 18.0%(on average) higher than aerial photos. This can be attributed to the 6.25 km resolution of AMSR-E, which cannot separate melt ponds/submerged ice from ice and cannot detect the small leads between floes. Thus, the aerial photos would play an important role in providing high-resolution independent estimates of the ice concentration and the fraction of melt pond cover to validate and/or supplement space-borne remote sensing products near the North Pole.     

基于Icepack海冰柱模式的融池反照率模拟研究

基于Icepack一维海冰柱模式,以2014年中国第6次北极科学考察长期冰站ICE06的3个融池的辐射参量和气象参量的连续观测作为大气强迫数据,对融池反照率及相关参量进行了模拟。本文引入观测的融池深度及海冰厚度作为初始条件,通过考虑融池覆盖率的作用,改进了平整冰融池参数化方案中海冰干舷的计算,修正了冰上可允许的最大融池深度,成功实现了对融池参数变化的模拟;同时,还修正了入射辐射分量比例系数与对应反照率分量权重系数不一致的问题。标准试验中,模拟的3个融池的反照率与观测结果之间的平均误差分别为0.01、0.05和0.13;入射辐射比例的敏感性试验结果表明,当可见光辐射比例增大8%时,融池反照率的模拟结果增大了6%～8%;融池表面再冻结试验的结果显示,当再冻结冰层厚度小于2 cm时,模拟冰面反照率的增加不足0.006,由此引起的表面能量收支减少了约1.1 W/m²。本文研究指出,准确的入射辐射比例对于改善北极海冰反照率模拟是必要的;并指出目前模式仍存在融池表面再冻结参数化、热收支计算、表面吹雪效应等有待解决的问题。     

Arctic summer sea ice phenology including ponding from 1982 to 2017

Information on the Arctic sea ice climate indicators is crucial to business strategic planning and climate monitoring. Data on the evolvement of the Arctic sea ice and decadal trends of phenology factors during melt season are necessary for climate prediction under global warming. Previous studies on Arctic sea ice phenology did not involve melt ponds that dramatically lower the ice surface albedo and tremendously affect the process of sea ice surface melt. Temporal means and trends of the Arctic sea ice phenology from 1982 to 2017 were examined based on satellite-derived sea ice concentration and albedo measurements. Moreover, the timing of ice ponding and two periods corresponding to it were newly proposed as key stages in the melt season. Therefore, four timings, i.e., date of snow and ice surface melt onset (MO), date of pond onset (PO), date of sea ice opening (DOO), and date of sea ice retreat (DOR); and three durations, i.e., melt pond formation period (MPFP, i.e., MO–PO), melt pond extension period (MPEP, i.e., PO–DOR), and seasonal loss of ice period (SLIP, i.e., DOO–DOR), were used. PO ranged from late April in the peripheral seas to late June in the central Arctic Ocean in Bootstrap results, whereas the pan-Arctic was observed nearly 4 days later in NASA Team results. Significant negative trends were presented in the MPEP in the Hudson Bay, the Baffin Bay, the Greenland Sea, the Kara and Barents seas in both results, indicating that the Arctic sea ice undergoes a quick transition from ice to open water, thereby extending the melt season year to year. The high correlation coefficient between MO and PO, MPFP illustrated that MO predominates the process of pond formation.     

北极阿蒙森湾一年冰物理和光学性质的观测研究

利用加拿大环极冰间水道系统研究项目,作者对2007年11月24日至2008年1月26日北极群岛阿蒙森湾海域秋冬季节一年冰的物理和光学性质进行了观测研究。结果显示,观测期间的海冰厚度整体在27～108 cm范围内变化,积雪厚度仅为0～6 cm。海冰温度、盐度和密度在冰内的分布特征为：海冰表层最低温度为–22.4℃,底层最高温度为–2.2℃,冰内温度随深度单调增大;盐度变化范围为3.30～11.70,冰内盐度剖面呈现“C”形,即表层和底层盐度较大,而中间层盐度较小;海冰的平均密度略大,为（0.91±0.03）g/cm³。通过观测人造光源在海冰中的透射辐射谱分布,发现一年冰的光谱透射辐射在490 nm和589 nm处呈明显的双峰结构,但随着海冰厚度的增加,双峰结构逐渐减弱,体现了海冰对于不同谱段辐射能衰减作用的差异。在可见光范围内,裸冰和雪覆冰的吸收率最小值出现在490 nm,在443～490 nm范围内二者的吸收率随波长增大而降低,在490～683 nm范围内二者的吸收率随波长增大而升高,但雪覆冰的吸收率在可见光范围内基本保持不变,体现了雪覆冰吸收率的光谱独立性。一年冰的谱衰减系数随波长呈“U”字形分布,紫光和红光谱段的衰减系数较大,中间谱段的衰减系数较小,589 nm波长的衰减系数最小,为1.7 m^–1。将谱衰减系数在可见光范围内积分,得到一年冰的积分漫射衰减系数约为2.3 m^–1,略高于多年浮冰的漫射衰减系数1.5 m^–1。阿蒙森湾一年冰与加拿大海盆北部多年浮冰辐射光学性质的差异,主要源于陆源物质输入引起的海冰内含物组分的改变,而不同组分对光谱的吸收和散射性质不同,进一步导致了光学性质的整体变化。     

CICE海冰模式中融池参数化方案的比较研究

冰面融池的反照率介于海水和海冰之间,获得较准确的融池覆盖率对认识极区气冰海耦合系统的热量收支有重要意义。在数值模式中,融池覆盖率的模拟结果直接影响到冰面反照率计算的准确性,本文对CICE5.0中的3种融池参数化方案进行了较系统的比较分析,结果显示3种方案各有优缺点,模拟结果都存在一些问题。cesm方案中判断融池冻结的条件更为合理。比较而言,融池冻结条件更改后的topo方案模拟的北冰洋区域平均融池覆盖率的年际变化幅度、融池覆盖范围、融池发展盛期持续时间与MODIS数据最接近。通过修改CICE5.0中的代码漏洞,研究了融池水的垂向渗透效应,这一效应会带来一些负面影响,如lvl方案中多年冰上几乎没有融池,说明目前的CICE模式中对于海冰渗透性演化或其他物理机制的处理仍有待改进。最后,着重讨论了topo方案的改进思路。     

积雪密度演变对北极积雪深度模拟的影响

积雪具有复杂的时空分布,在高纬度地区的气−冰−海耦合系统中扮演了重要的角色。准确的积雪质量平衡计算可以帮助我们更好地理解海冰演变过程以及极区冰雪与大气之间的相互作用。雪密度是影响积雪质量平衡众多因素中的重要因子。现有的一维高分辨率冰雪热力学模型（如HIGHTSI）中,使用常数块体雪密度均值将降雪雪水当量转化为积雪深度。本文参考拉格朗日冰上积雪模型（SnowModel-LG）模式对积雪分层压实的处理,简化为新、旧两个雪层,并在质量守恒条件下同时考虑新、旧雪层深度对压实增密的响应,将该物理过程加入HIGHTSI模式中。利用ERA-Interim再分析数据作为大气强迫,针对北极15个冰质量平衡浮标沿其漂移轨迹模拟了降雪积累期海冰表面雪密度变化对积雪深度变化的影响,在原HIGHTSI设置下分别采用定常块体雪密度均值330 kg/m³（T1试验）、接近实际的常数新雪密度200 kg/m³（T2试验）以及改进后框架下新、旧雪层随时间压实增密的雪密度（T3试验）计算积雪深度,并将模拟结果与浮标观测进行对比。结果表明,本文改进的算法对雪密度变化的处理更为合理,且能较好地再现积雪深度的变化;考虑新、旧雪层深度对压实增密的响应能较好地避免以较低的降雪密度持续过度积累,以浮标观测为标准,分层积雪密度压实计算得到的平均绝对误差相对T2减小了5 cm。     

两种盐度参数化方案在海冰模式中的应用比较

本文详细介绍了SIS海冰模式中引进两种盐度参数化方案即等盐度方案和盐度廓线方案对海冰模拟所存在的差异。利用盐度廓线方案导出的表征盐度与海冰温度间关系的方程比等盐度方案多出一项,将定义为盐度差异项。盐度差异项对海冰厚度的热力作用表现为：在海冰厚度增长季节（11月到次年5月）,盐度差异项通过升高海冰内部温度,抑制海冰增长;在消融的第一阶段（6．8月）,盐度差异项通过升高海冰内部温度加快海冰消融;在消融的第二阶段（9．10月）,盐度差异项通过降低海冰内部的温度抑制海冰消融。但尺度分析表明,盐度差异项要比方程中队海冰温度作用最大项小1．2个量级,如果采用一级近似,可以略去盐度差异项,因此盐度差异项对海冰增长和消融影响很小。同时利用冰洋耦合模式（ModularOceanModel,MOM4）,分别采用两种盐度参数化方案模拟北极海冰厚度和海冰密集度的季节性变化,模拟结果也表明两种方案模拟得到的海冰厚度和海冰密集度的季节性变化相差甚小。