Sea ice thickness measurement in spring season in Bothnian Bay using an electromagnetic induction instrument

As an important component of the cryosphere,sea ice is very sensitive to the climate change.The study of the sea ice physics needs accurate sea ice thickness.This paper presents an electromagnetic-induction(EM) technique which can be used to measure the sea ice thickness distribution efficiently,and the successful application in Bothnian Bay.Based on the electromagnetic field theory and the electrical properties of sea ice and seawater,EM technique can detect the distance between the instrument and the ice/water interface accurately,than the sea ice thickness is obtained.Contrastive analysis of the apparent conductivity data obtained by EM and the value of drill-hole at same positions allows a construction of a transformable formula of the apparent conductivity to sea ice thickness.The verification of the sea ice thickness calculated by this formula indicates that EM technique is able to get reliable sea ice thickness with average relative error of only 12%.The statistic of all ice thickness profiles shows that the level ice distribution in Bothnian Bay was 0.4-0.6 m.     

Summer sea ice characteristics of the Chukchi Sea

During August 1999, we investigated sea ice characteristics; its distribution, surface feature, thickness, ice floe movement, and the temperature field around inter-borders of air/ice/seawater in the Chukchi Sea. Thirteen ice cores were drilled at 11 floe stations in the area of 72°24′ 77°18′N, 153°34′ 163°28′W and the ice core structure was observed. From field observation, three melting processes of ice were observed; surface layer melting, surface and bottom layers melting, and all of ice melting. The observation of temperature fields around sea ice floes showed that the bottom melting under the ice floes were important process. As ice floes and open water areas were alternately distributed in summer Arctic Ocean; the water under ice was colder than the open water by 0.4 2.8℃. The sun radiation heated seawater in open sea areas so that the warmer water went to the bottom when the ice floes move to those areas. This causes ice melting to start at the bottom of the ice floes. This process can balance effectively the temperature fluctuating in the sea in summer. From the crystalline structure of sea ice observed from the cores, it was concluded that the ice was composed of ice crystals and brine-ice films. During the sea ice melting, the brine-ice films between ice crystals melted firstly; then the ice crystals were encircled by brine films; the sea ice became the mixture of ice and liquid brine. At the end of melting, the ice crystals would be separated each other, the bond between ice crystals weakens and this leads to the collapse of the ice sheet.     

Spatial variation of stable isotopes in different waters during melt season in the Laohugou Glacial Catchment,Shule River basin

To evaluate isotopic tracers at natural abundances by providing basic isotope data of the hydrological investigations and assessing the impacts of different factors on the water cycle, a total of 197 water samples were collected from the Laohugou Glacial catchment in the Shule River basin northwestern China during the 2013 ablation seasons and analyzed their H- and O-isotope composition. The results showed that the isotopic composition of precipitation in the Qilianshan Station in the Laohugou Glacial catchment was remarkable variability. Correspondingly, a higher slope of δ~(18)O-δD diagram, with an average of 8.74, is obtained based on the precipitation samples collected on the Glacier No.12, mainly attributed to the lower temperature on the glacier surface. Because of percolation and elution, the isotopic composition at the bottom of the firn is nearly steady. The δ~(18)O /altitude gradients for precipitation and melt water were -0.37‰/100 m and -0.34‰/100 m, respectively. Exposed to the air and influenced by strong ablation and evaporation, the isotopic values and the δ~(18)O vs δD diagram of the glacial surface ice show no altitudinal effect, indicating that glacier ice has the similar origins with the firn. The variation of isotopic composition in the melt water, varying from -10.7‰ to -16.9‰(δ~(18)O) and from -61.1‰ to -122.1‰(δD) indicates the recharging of snowmelt and glacial ice melt water produced at different altitudes. With a mean value of -13.3‰ for δ~(18)O and -89.7‰ for δD, the isotopic composition of the stream water is much closer to the melt water, indicating that stream water is mainly recharged by the ablation water. Our results of the stable isotopic compositions in natural water in the Laohugou Glacial catchment indicate the fractionations and the smoothing fluctuations of the stable isotopes during evaporation, infiltration and mixture.     

北极海冰范围时空变化及其与海温气温间的数值分析

本文利用美国国家冰雪中心提供的1989-2014年海冰范围资料,分析了北极海冰范围的年际变化和季节变化规律。分析发现,北极海冰范围呈减少趋势,每年减小\begin{array}{c}5.91\times {10}^4\mathit{k}{m}^2\end{array},夏季减少趋势显著,冬季减少趋势弱。北极海冰范围显现相对稳定的季节变化规律,海冰的结冰和融化主要发生在各个边缘海,夏季期间的海冰具有融化快、冻结快的特征。结合海温、气温数据,进行北极海冰范围与海温、气温间的数值分析,结果表明北极海冰范围变化通过影响北极海温变化进而影响北极气温变化。海冰范围的季节变化滞后于海温和气温的季节变化。基于北极考察走航海温气温数据,进行楚科奇海海冰范围线与海温气温间的数值分析,发现楚科奇海海冰范围线所在区域的海温、气温与纬度高低、离陆地远近有关。     

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness (PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer (MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index (DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.     

Seasonal change of ice algal and phytoplankton assemblages in the Nella Fjord near Zhongshan Station, East Antarctica

The ice algal and phytoplankton assemblages were studied from Nella Fjord near Zhongshan Station, East Antarctica from April 12 to December 30, 1992. Algal blooms occurred about 3 cm thick on the bottom of sea ice in late April and mid November to early December respectively, and a phytoplankton bloom appeared in the underlying surface water in mid December following the spring ice algal bloom. The biomass in ice bottom was 1 to 3 orders of magnitude higher than that of surface water. Amphiprora kjellmanii, Berkeleya sp., Navicula glaciei, Nitzschia barkelyi, N. cylindrus /N. curta, N. lecointei and Nitzschia sp. were common in the sea ice temporarily or throughout the study period. The biomass in a certain ice segment was decreased gradually and the dominant species were usually succeeded as the season went on. Nitzschia sublineata and Dactyliosolen antarctica were two seasonal dominant species only observed in underlying water column. The assemblages between bottom of ice and underlying surface water were different except when spring ice algae bloomed. The evidence shows that the ice algal blooms occurred mainly by in situ growth of ice algae, and the phytoplankton bloom was mostly caused by the release of ice algae.     

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.     

Spatial distribution of Ice Shelf Water in front of the Amery Ice Shelf,Antarctica in summer

As a unique low-temperature water mass in Antarctic coastal region, the Ice Shelf Water (ISW) is an important component for the formation of the Antarctic Bottom Water (AABW). In this paper, we present a criterion for ISW identification based on freezing point at the sea surface, and we study spatial distribution of ISW in front of the Amery Ice Shelf (AIS) and its flow path in Prydz Bay by analyzing hydrographic data from Australian cruises in 2001 and 2002, as well as Chinese cruises in 2003, 2005, 2006, and 2008, all being made in the austral summer. The relatively cold and fresh ISW occurred as several discrete water blocks with cold cores in front of the AIS, within the depth range of 100?600 m, under the seasonal thermocline. ISW had obvious temporal and spatial variations and the spatial distribution pattern changed greatly after 2005. Most of ISW was concentrated west of 73°E during 2001 to 2003 and 2006, but it was widespread to east in 2005 and 2008. In all observation years, a small amount of cold ISW always occurs at the west end of the AIS front section, where the coldest ISW in the whole section also occurred in 2001, 2003 and 2006. Considering general cyclonic circulation pattern under the AIS, the ISW flowing out from west end of the AIS front might have experienced the longest cooling period under ice shelf, so it would have the lowest temperature. Analysis of data from meridian sections in Prydz Bay in 2003 implied that ISW in the west could spread north to the continental break along the east flank of the Fram Bank near 70.5°E, mix with the upwelling Circumpolar Deep Water and possibly contribute to the formation of AABW.     

基于CryoSat-2数据反演2011～2017年波弗特海海冰厚度变化

利用CryoSat-2卫星测高数据反演波弗特海的海冰厚度，并利用2010~2013年10月份仰视声呐(ULS)和2011年冰桥计划（IceBridge）数据对结果进行精度评估。结果表明，测高反演的海冰吃水深度与ULS吃水深度差值的最大值和标准差分别为14 cm和4 cm；测高反演的海冰厚度与冰桥计划海冰厚度差值的平均值和标准差分别为2.7 cm和65.7 cm，优于Laxon(2013)研究结果（分别优化2.1 cm和6.6 cm）。在此基础上，研究2011~2017年波弗特海夏冬两季的海冰厚度变化，发现二者具有类似的分布特征，且冬季3月海冰覆盖范围更广，厚度更大；进一步分析2011~2017年3月份冬季海冰厚度年际变化，发现其呈整体下降趋势，且2012年最小，2014年最大。     

Snow and sea ice thermodynamics in the Arctic： Model validation and sensitivity study against SHEBA data

Evolution of the Arctic sea ice and its snow cover during the SHEBA year were simulated by applying a high-resolution thermodynamic snow/ice model （HIGHTSI）. Attention was paid to the impact of albedo on snow and sea ice mass balance, effect of snow on total ice mass balance, and the model vertical resolution. The SHEBA annual simulation was made applying the best possible external forcing data set created by the Sea Ice Model Intercomparison Project. The HIGHTSI control run reasonably reproduced the observed snow and ice thickness. A number of albedo schemes were incorporated into HIGHTSI to study the feedback processes between the albedo and snow and ice thickness. The snow thickness turned out to be an essential variable in the albedo parameterization. Albedo schemes dependent on the surface temperature were liable to excessive positive feedback effects generated by errors in the modelled surface temperature. The superimposed ice formation should be taken into account for the annual Arctic sea ice mass balance.     

Increasing Winter Conductive Heat Transfer in the Arctic Sea-ice-covered Areas:1979–2014

Sea ice is a quite sensitive indicator in response to regional and global climate changes. Based on monthly mean PanArctic Ice Ocean Modeling and Assimilation System(PIOMAS) sea ice thickness fields, we computed the conductive heat flux(CHF) in the Arctic Ocean in the four winter months(November–February) for a long period of 36 years(1979–2014). The calculated results for each month manifest the increasing extension of the domain with high CHF values since 1979 till 2014. In 2014, regions of roughly 90% of the central Arctic Ocean have been dominated by the CHF values larger than 18 Wm~(-2)(November–December) and 12 Wm~(-2)(January–February), especially significant in the shelf seas around the Arctic Ocean. Moreover, the population distribution frequency(PDF) patterns of the CHF with time show gradually peak shifting toward increased CHF values. The spatiotemporal patterns in terms of the trends in sea ice thickness and other three geophysical parameters, surface air temperature(SAT), sea ice thickness(SIT), and CHF, are well coupled. This suggests that the thinner sea ice cover preconditions for the more oceanic heat loss into atmosphere(as suggested by increased CHF values), which probably contributes to warmer atmosphere which in turn in the long run will cause thinner ice cover. This represents a positive feedback mechanism of which the overall effects would amplify the Arctic climate changes.     

Spatio-temporal Variation of Arctic Sea Ice in Summer from 2003 to 2013

The variation in Arctic sea ice has significant implications for climate change due to its huge influence on the global heat balance. In this study, we quantified the spatio-temporal variation of Arctic sea ice distribution using Advanced Microwave Scanning Radiometer(AMSR-E) sea-ice concentration data from 2003 to 2013. The results found that, over this period, the extent of sea ice reached a maximum in 2004, whereas in 2007 and 2012, the extent of summer sea ice was at a minimum. It declined continuously from 2010 to 2012, falling to its lowest level since 2003. Sea-ice extent fell continuously each summer between July and mid-September before increasing again. It decreased most rapidly in September, and the summer reduction rate was 1.35 × 10~5 km~2/yr, twice as fast as the rate between 1979 and 2006, and slightly slower than from 2002 to 2011. Area with 90% sea-ice concentration decreased by 1.32 × 10~7 km~2/yr, while locations with 50% sea-ice concentration, which were mainly covered by perennial ice, were near the North Pole, the Beaufort Sea, and the Queen Elizabeth Islands. Perennial Arctic ice decreased at a rate of 1.54 × 10~5 km~2 annually over the past 11 years.     

Analysis of multi-dimensional SAR for determining the thickness of thin sea ice in the Bohai Sea

Flat thin ice (<30 cm thick) is a common ice type in the Bohai Sea, China. Ice thickness detection is important to offshore exploration and marine transport in winter. Synthetic aperture radar (SAR) can be used to acquire sea ice data in all weather conditions, and it is a useful tool for monitoring sea ice conditions. In this paper, we combine a multi-layered sea ice electromagnetic (EM) scattering model with a sea ice thermodynamic model to assess the determination of the thickness of flat thin ice in the Bohai Sea using SAR at different frequencies, polarization, and incidence angles. Our modeling studies suggest that co-polarization backscattering coefficients and the co-polarized ratio can be used to retrieve the thickness of flat thin ice from C- and X-band SAR, while the co-polarized correlation coefficient can be used to retrieve flat thin ice thickness from L-, C-, and X-band SAR. Importantly, small or moderate incidence angles should be chosen to avoid the effect of speckle noise.     

Role of sea ice in air-sea exchange and its relation to sea fog

Synchronous or quasi-synchronous stereoscopic sea-ice-air comprehensive observation was conducted during the First China Arctic Expedition in summer of 1999. Based on these data, the role of sea ice in sea-air exchange was studied. The study shows that the kinds, distribution and thickness of sea ice and their variation significantly influence the air-sea heat exchange. In floating ice area, the heat momentum transferred from ocean to atmosphere is in form of latent heat; latent heat flux is closely related to floating ice concentration; if floating ice is less, the heat flux would be larger. Latent heat flux is about 21 23.6 W*m-2, which is greater than sensible heat flux. On ice field or giant floating ice, heat momentum transferred from atmosphere to sea ice or snow surface is in form of sensible heat. In the floating ice area or polynya, sea-air exchange is the most active, and also the most sensible for climate. Also this area is the most important condition for the creation of Arctic vapor fog. The heat exchange of a large-scale vapor fog process of about 500000 km2 on Aug. 21 22,1999 was calculated; the heat momentum transferred from ocean to air was about 14.8×109 kW. There are various kinds of sea fog, radiation fog, vapor fog and advection fog, forming in the Arctic Ocean in summer. One important cause is the existence of sea ice and its resultant complexity of both underlying surface and sea-air exchange.     

Arctic multiyear sea ice variability observed from satellites: a review

In comparison with seasonal sea ice(first-year ice,FY ice),multiyear(MY) sea ice is thicker and has more opportunity to survive through the summer melting seasons.Therefore,the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer.As a response,the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover.Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice.During the recent decades,many active and passive sensors onboard a variety of satellites(QuikSCAT,ASCAT,SSMIS,ICESat,CryoSat-2,etc.) have been used to monitor the dramatic loss of Arctic MY ice.The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations.We summarize the primary satellite data sources that are used to identify MY ice.The methodology to classify MY ice and derive MY ice concentration is reviewed.The interannual variability and trends in the MY ice time series in terms of coverage,thickness,volume,and age composition are evaluated.The potential causes associated with the observed Arctic MY ice loss are outlined,which are primarily related to the export and melting mechanisms.In addition,the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion,as well as the roles of synoptic weather,are analyzed.The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed.Moreover,some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed.     

北极海冰的基本特征

本文应用1953～1984年的北极海冰资料,分析各区海冰的季节变化、年际变化、自相关特性及互相关特性。认为Ⅰ区海冰占有最大权重,又具有较大的方差,在全区海冰中起着重耍作用。冬季,各区海冰相互关联,其余季节,基本上相互独立。各区海冰均提供了气候“贮存”机制,一个季节的冰能影响下一个季节冰的特性;冬季的贮存能力大于夏季,春秋次之;Ⅱ区和Ⅳ区冰的持续性优于Ⅰ区 。     

Crystals and fabrics analysis of an Arctic thermal growth multi-year ice sample

One of sea ice core samples was taken from Arctic by the First Chinese National Arctic Research Expedition Team in 1999. 20 vertical and 2 horizontal ice sections were cut out of the ice core sample 2.22 m in length, which covered the ice sheet from surface to bottom except losses for during sampling and section cutting. From the observation and analysis of the fabrics and crystals along the depth of the ice core sample, followings were found. Whole ice sheet consists of columnar, refrozen clastic pieces, granular, columnar, refrozen clastic pieces, granular, columnar and refrozen clastic pieces. This indicates that the ice core sample was 3-year old, and the ice sheet surface thawed and the melt water flowed into ice sheet during summer. Hence, the annual energy balance in Arctic can be determined by the ice sheet surface thawing in summer, and bottom growth in winter. The thickness of the ice sheet is kept constantly at a certain position based on the corresponding climate and ocean conditions; A new     

Effect of compressive strength on the performance of the NEMO-LIM model in Arctic Sea ice simulation

Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s. The prediction of sea ice is highly important, but accurate simulation of sea ice variations remains highly challenging. For improving model performance, sensitivity experiments were conducted using the coupled ocean and sea ice model (NEMO-LIM), and the simulation results were compared against satellite observations. Moreover, the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed. The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant (C^rhg). By reducing the C^rhg constant, the sea ice compressive strength increases, leading to improved simulated sea ice states. The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength. Meanwhile, dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration, reducing the simulation bias in the central Arctic Ocean in summer. The root mean square error (RMSE) between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution. The ice thickness, especially of multiyear thick ice, was also reduced and matched with the satellite observation better in the freezing season. These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.

     

Mechanism of sea ice formation based on comprehensive observation data in Liaodong Bay,China

Sea ice disaster is one of the principal natural hazards that affect some coastal areas of China, and the formation of ice cover in a wave field has important characteristics. However, analysis of the mechanism in which waves affect the thermodynamic process of sea ice is lacking, and the influence of waves is not taken into consideration in numerical models of sea ice, largely because of a lack of simultaneous observations of waves and sea ice. Using observational data of the sea ice cycle in the coastal waters of Liaodong Bay(China), we analyzed the characteristics of hydrology, meteorology, and sea ice thickness during the formation of sea ice, and explored the changes in the interrelationships among heat fluxes, waves, and sea ice under actual sea conditions. The results could provide a decision-making support as a reference to the establishment and improvement of China's early warning system to sea ice disasters, and the protection of ice drilling operations and production platform safety.