Arctic sea ice volume export through the Fram Strait from combined satellite and model data: 1979–2012

By combing satellite-derived ice motion and concentration with ice thickness fields from a popular model PIOMAS we obtain the estimates of ice volume flux passing the Fram Strait over the 1979–2012 period. Since current satellite and field observations for sea ice thickness are limited in time and space, the use of PIOMAS is expected to fill the gap by providing temporally continued ice thickness fields. Calculated monthly volume flux exhibits a prominent annual cycle with the peak record in March(roughly 145 km3/month) and the trough in August(10 km~3/month). Annual ice volume flux(1 132 km~3) is primarily attributable to winter(October through May) outflow(approximately 92%). Uncertainty in annual ice volume export is estimated to be 55 km~3(or 5.7%). Our results also verified the extremely large volume flux appearing between late 1980 s and mid-1990 s. Nevertheless, no clear trend was found in our volume flux results. Ice motion is the primary factor in the determination of behavior of volume flux. Ice thickness presented a general decline trend may partly enhance or weaken the volume flux trend. Ice concentration exerted the least influences on modulating trends and variability in volume flux. Moreover, the linkage between winter ice volume flux and three established Arctic atmospheric schemes were examined. Compared to NAO, the DA and EOF3 mechanism explains a larger part of variations of ice volume flux across the strait.     

Physical structure and vertical distribution of chlorophyll a in winter sea ice from the northwestern Weddell Sea, Antarctica

The investigation on sea-ice biology in combination with physics, chemistry and ecology was carried out in the northwestern Weddell Sea, Antarctica, during the cruise ANT/XX III-7 on board POLARSTERN in the austral winter (August-October) in 2006. The distribution of chlorophyll a was measured and related to sea ice texture. The mean concentrations of chlorophyll a in the sea ice varied considerably with ice texture. The concentration of chlorophyll a per core ranged from 2.10– 84.40 μg/dm 3 with a mean of 16.56 μg/dm 3 . And the value of R (chlorophyll a / gross chlorophyll) ranged from 0.79–0.83. These high winter chlorophyll values indicate that primary production is considerable and confirms that there is significant primary production in Antarctic sea ice during winter. Thus this constitutes a major proportion of southern ocean primary production and carbon flux before the sea ice retreats.     

Spatiotemporal characteristics of sea ice transport in the Baffin Bay and its association with atmospheric variability

Sea ice export through the Baffin Bay plays a vital role in modulating the sea ice cover variability in the Labrador Sea.In this study,satellite-derived sea ice products are used to obtain the sea ice area flux (SIAF) through the three passages in the Baffin Bay (referred to as A,B,and C for the north,middle,and south passages,respectively).The spatial variability of the monthly sea ice drift in the Baffin Bay is presented.The interannual variability and trends in SIAF via the three passages are outlined.The connection to several large-scale atmospheric circulation modes is assessed.Over the period of 1988–2015,the average annual (October to the following September) SIAF amounts to 555×10~3 km~2,642×10~3 km~2,and 551×10~3 km~2 through Passages A,B,and C,respectively.These quantities are less than that observed through the Fram Strait (FS,707×10~3 km~2) of the corresponding period.The positive trends in annual SIAF,on the order of 53.1×10~3 km~2/(10 a) and 43.2×10~3 km~2/(10 a)(significant at the 95%confidence level),are identified at Passages A and B,respectively.The trend of the south passage (C),however,is slightly negative (–13.3×10~3 km~2/(10 a),not statistically significant).The positive trends in annual SIAF through the Passages A and B are primarily attributable to the significant increases after 2000.The connection between the Baffin Bay sea ice export and the North Atlantic Oscillation is not significant over the studied period.By contrast,the association with the cross-gate sea level pressure difference is robust in the Baffin Bay (R equals 0.69 to 0.71,depending on the passages considered),but relatively weaker than that over FS (R=0.74).     

Rapid decrease in Antarctic sea ice in recent years

A 41-year Antarctic sea ice concentration(SIC) dataset derived from satellite passive microwave radiometers during the period of 1979–2019 has been used to analyze sea ice changes in recent decades. The trends of SIC and sea ice extent(SIE) are calculated during the periods of 1979–2019, 1979–2013, and 2014–2019. The trends show regionally dependent features. The SIC shows an increasing trend in most of the regions except the Bellingshausen Sea and Amundsen Sea(BA) during 1979–2019 and 1979–2013. The SIE trend shows a decreasing or decelerating trend in the period of 1979–2019((6 835±2 210) km2/a) compared with the 1979–2013 period((18 600±2 203) km~2/a). In recent years(2014–2019), the SIC and SIE have exhibited decreasing trends(–(34 567±3 521) km~2/month), especially in the Weddell Sea(WS) and Ross Sea(RS) during summer and autumn. The trends are related to regionally dependent causes. The analyses show that the SIC and SIE decreased in response to the warming trend of 2 m air temperature(T_(a-2m)) and have exhibited a good relationship with T_(a-2m) in summer and autumn in recent years. The sea ice decrease in the Antarctic is mainly caused by increases in absorbed energy and southward energy transportation in recent years, such as the increase in gained solar radiation and moist static energy from the south, which demonstrate notable regional characteristics. In the WS region, the local positive feedback from the additional absorbed solar radiation, resulting in warmer air and reduced sea ice, is the main reason for the sea ice decrease in recent years. The increase in southward energy transport has also favored a decrease in sea ice. In the RS region, the increase in southward-transported moist static energy has contributed to the decrease in sea ice, and the increases in cloud cover and longwave radiation have prevented sea ice growth.     

The long-term trend of Bohai Sea ice in different emission scenarios

Based on a coupled ocean-sea ice model, this study investigates how changes in the mean state of the atmosphere in different CO_2 emission scenarios(RCP 8.5, 6.0, 4.5 and 2.6) may affect the sea ice in the Bohai Sea, China,especially in the Liaodong Bay, the largest bay in the Bohai Sea. In the RCP 8.5 scenario, an abrupt change of the atmospheric state happens around 2070. Due to the abrupt change, wintertime sea ice of the Liaodong Bay can be divided into 3 periods: a mild decreasing period(2021–2060), in which the sea ice severity weakens at a nearconstant rate; a rapid decreasing period(2061–2080), in which the sea ice severity drops dramatically; and a stabilized period(2081–2100). During 2021–2060, the dates of first ice are approximately unchanged, suggesting that the onset of sea ice is probably determined by a cold-air event and is not sensitive to the mean state of the atmosphere. The mean and maximum sea ice thickness in the Liaodong Bay is relatively stable before 2060, and then drops rapidly in the following decade. Different from the RCP 8.5 scenario, atmospheric state changes smoothly in the RCP 6.0, 4.5 and 2.6 scenarios. In the RCP 6.0 scenario, the sea ice severity in the Bohai Sea weakens with time to the end of the twenty-first century. In the RCP 4.5 scenario, the sea ice severity weakens with time until reaching a stable state around the 2070 s. In the RCP 2.6 scenario, the sea ice severity weakens until the2040 s, stabilizes from then, and starts intensifying after the 2080 s. The sea ice condition in the other bays of the Bohai Sea is also discussed under the four CO_2 emissions scenarios. Among atmospheric factors, air temperature is the leading one for the decline of the sea ice extent. Specific humidity also plays an important role in the four scenarios. The surface downward shortwave/longwave radiation and meridional wind only matter in certain scenarios, while effects from the zonal wind and precipitation are negligible.     

渤海模型冰运动特征的现场观测研究

Sea ice drift is mainly controlled by ocean currents, local wind, and internal ice stress. Information on sea ice motion, especially in situ synchronous observation of an ice velocity, a current velocity, and a wind speed, is of great significance to identify ice drift characteristics. A sea ice substitute, the so-called "modelled ice", which is made by polypropylene material with a density similar to Bohai Sea ice, is used to complete a free drift experiment in the open sea. The trajectories of isolated modelled ice, currents and wind in the Bohai Sea during non-frozen and frozen periods are obtained. The results show that the currents play a major role while the wind plays a minor role in the free drift of isolated modelled ice when the wind is mild in the Bohai Sea. The modelled ice drift is significantly affected by the ocean current and wind based on the ice–current–wind relationship established by a multiple linear regression. The modelled ice velocity calculated by the multiple linear regression is close to that of the in situ observation, the magnitude of the error between the calculated and observed ice velocities is less than12.05%, and the velocity direction error is less than 6.21°. Thus, the ice velocity can be estimated based on the observed current velocity and wind speed when the in situ observed ice velocity is missing. And the modelled ice of same thickness with a smaller density is more sensitive to the current velocity and the wind speed changes. In addition, the modelled ice drift characteristics are shown to be close to those of the real sea ice, which indicates that the modelled ice can be used as a good substitute of real ice for in situ observation of the free ice drift in the open sea, which helps solve time availability, safety and logistics problems related to in situ observation on real ice.     

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.     

北极各海域海冰覆盖范围的变化特征

Sea ice in the Arctic has been reducing rapidly in the past half century due to global warming.This study analyzes the variations of sea ice extent in the entire Arctic Ocean and its sub regions.The results indicate that sea ice extent reduction during 1979–2013 is most significant in summer,following by that in autumn,winter and spring.In years with rich sea ice,sea ice extent anomaly with seasonal cycle removed changes with a period of 4–6 years.The year of 2003–2006 is the ice-rich period with diverse regional difference in this century.In years with poor sea ice,sea ice margin retreats further north in the Arctic.Sea ice in the Fram Strait changes in an opposite way to that in the entire Arctic.Sea ice coverage index in melting-freezing period is an critical indicator for sea ice changes,which shows an coincident change in the Arctic and sub regions.Since 2002,Region C2 in north of the Pacific sector contributes most to sea ice changes in the central Aarctic,followed by C1 and C3.Sea ice changes in different regions show three relationships.The correlation coefficient between sea ice coverage index of the Chukchi Sea and that of the East Siberian Sea is high,suggesting good consistency of ice variation.In the Atlantic sector,sea ice changes are coincided with each other between the Kara Sea and the Barents Sea as a result of warm inflow into the Kara Sea from the Barents Sea.Sea ice changes in the central Arctic are affected by surrounding seas.     

2010年马卡罗夫海盆冰站观测期间垂向热通量时间变化研究

Based on hydrographic data obtained at an ice camp deployed in the Makarov Basin by the 4th Chinese Arctic Research Expedition in August of 2010, temporal variability of vertical heat flux in the upper ocean of the Makarov Basin is investigated together with its impacts on sea ice melt and evolution of heat content in the remnant of winter mixed layer(r WML). The upper ocean of the Makarov Basin under sea ice is vertically stratified. Oceanic heat flux from mixed layer(ML) to ice evolves in three stages as a response to air temperature changes, fluctuating from 12.4 W/m2 to the maximum 43.6 W/m2. The heat transferred upward from ML can support(0.7±0.3) cm/d ice melt rate on average, and daily variability of melt rate agrees well with the observed results. Downward heat flux from ML across the base of ML is much less, only 0.87 W/m2, due to enhanced stratification in the seasonal halocline under ML caused by sea ice melt, indicating that increasing solar heat entering summer ML is mainly used to melt sea ice, with a small proportion transferred downward and stored in the r WML. Heat flux from ML into r WML changes in two phases caused by abrupt air cooling with a day lag. Meanwhile, upward heat flux from Atlantic water(AW) across the base of r WML, even though obstructed by the cold halocline layer(CHL), reaches0.18 W/m2 on average with no obvious changing pattern and is also trapped by the r WML. Upward heat flux from deep AW is higher than generally supposed value near 0, as the existence of r WML enlarges the temperature gradient between surface water and CHL. Acting as a reservoir of heat transferred from both ML and AW, the increasing heat content of r WML can delay the onset of sea ice freezing.     

基于HY-2A散射计数据的极区海冰范围反演方法及其评估

A sea ice extent retrieval algorithm over the polar area based on scatterometer data of HY-2A satellite has been established.Four parameters are used for distinguishing between sea ice and ocean with Fisher's linear discriminant analysis method.The method is used to generate polar sea ice extent maps of the Arctic and Antarctic regions of the full 2013–2014 from the scatterometer aboard HY-2A(HY-2A-SCAT) backscatter data.The time series of the ice mapped imagery shows ice edge evolution and indicates a similar seasonal change trend with total ice area from DMSP-F17 Special Sensor Microwave Imager/Sounder(SSMIS) sea ice concentration data.For both hemispheres,the HY-2A-SCAT extent correlates very well with SSMIS 15% extent for the whole year period.Compared with Synthetic Aperture Radar(SAR) imagery,the HY-2A-SCAT ice extent shows good correlation with the Sentinel-1 SAR ice edge.Over some ice edge area,the difference is very evident because sea ice edges can be very dynamic and move several kilometers in a single day.     

基于卫星观测的2003-2013年北极海冰体积变化估算

北极海冰正处于快速减退时期，北极海冰体积变化是全球气候变化的重要指示因子。本文利用两种卫星高度计数据（ICESat和CryoSat-2）反演得到的海冰厚度数据，结合星载辐射计提取的海冰密集度数据以及海冰年龄数据，估算了近期的北极海冰体积以及一年冰和多年冰体积变化。CryoSat-2观测时段（2011-2013年）与ICESat观测时段（2003-2008年）相比，北极海冰体积在秋季（10-11月）和冬季（2-3月）分别减少了1 426 km3和412 km3。其中，秋季和冬季的一年冰的体积增加了702 km3和2 975 km3。相反，多年冰分别减少了2 108 km3和3 206 km3。多年冰的大量流失是造成北极海冰净储量下降的主要原因。     

基于遥感数据的近40 a弗雷姆海峡海冰输出研究

北极海冰作为一个巨大的淡水资源库, 每年向全球输送大量淡水资源, 从北极输出的海冰在向南输送的过程中融化, 对海洋水循环与水环境产生影响, 进而影响全球气候变化, 弗雷姆海峡作为北极海冰输出的主要通道, 对其研究显得尤为重要。为了解弗雷姆海峡海冰长期输出量, 利用美国冰雪数据中心（NSIDC）发布的海冰密集度、海冰厚度与海冰漂移速度数据, 计算得到 1979 年至 2019 年弗雷姆海峡海冰输出面积通量与 2010 至 2019 年弗雷姆海峡海冰输出体积通量, 并在此基础上分析弗雷姆海峡近 40 a 海冰输出量的变化状况以及弗雷姆海峡海冰输出的年际变化、季节变化, 并分析了影响弗雷姆海峡海冰输出量的可能原因。结果表明: 近 40 a 弗雷姆海峡年均海冰输出面积通量为 7.83×10⁵ km²,近 10 a 弗雷姆海峡海冰年均输出体积通量为 1.34×10⁶ km³, 从长期来看, 弗雷姆海峡海冰输出面积通量呈略微增加趋势, 弗雷姆海峡海冰输出体积通量在 2010—20...     

长序列北极海冰覆盖数据集对比分析

国家卫星气象中心使用2011年至今的风云三号卫星数据开发了一套基于Nasa Team2（NT2）算法的北极海冰密集度数据集，并可实时业务更新。将该数据集与其他国家不同机构业务运行并实时更新多种同类型数据集进行横向对比分析，其中包括:（1）美国冰雪中心基于Nasa Team（NT）算法以及SSM/I、SSMIS数据制作的1978年至今25 km分辨率全球极区海冰覆盖数据集；（2）美国冰雪中心基于Boot Strap（BS）算法以及SSM/I、SSMIS数据制作的1978年至今25 km分辨率全球极区海冰覆盖数据集；（3）美国NOAA基于多种卫星资料、地面观测数据以及海冰模型制作的2004年至今4 km分辨率北半球海冰覆盖数据集（IMS）。对比表明，上述数据集在北极地区不同的时空范围内存在一定的偏差。以分辨率较高的IMS数据集为基准，对其他3种长序列数据集进行初步评价，总体最大偏差超过100×104 km2，其中，NT2数据集过估较明显。经过与IMS数据集多年各月监测最大值的对比订正，NT2数据集过估情况得到改善。在此基础上的分析结果表明，NT、BS、NT2等3种数据集与IMS数据集相比，过估区域主要分布在海岸线附近，夏季过估比冬季更加明显，少估区域与算法、月份相关性明显，夏季少估面积也较冬季更大。NT、BS、NT2等3种数据集之中，NT2数据集与IMS数据集偏差最小，NT数据集次之，BS数据集与IMS数据集偏差最大。结果表明使用风云三号卫星数据的北极海冰覆盖数据集精度与国外3种同类型数据集相当。     

1982—2001年与2002—2021年北极秋季海冰的时空变化及原因

基于北极海冰密集度、海冰范围、大气环流和海温数据,研究了1982—2001年与2002—2021年两阶段各20 a间北极秋季海冰的时空变化特征及其原因。结果表明,近20 a（2002—2021年）北极海冰密集度的下降中心由过去（1982—2001年）的楚科奇海及白令海峡一带,转移至亚欧大陆海岸的巴伦支海附近,且海冰范围每10 a减少量由0.44×10⁶ km²增长至0.72×10⁶ km²,减少速度加快约64%。秋季北极海冰范围与海水表面温度（Sea Surface Temperature,SST）、表面气温（Surface Air Temperature,SAT）及比湿（Specific Humidity）均呈显著负相关。2002—2021年的相关系数较1982—2001年有所提高,且与温度相关系数最高的月份提前了一个月。通过对海水表面温度、表面气温、比湿、气压场和风场的经验正交分解（Empirical Orthogonal Function,EOF）可知,1982—2001年间,北极地区的温度及比湿的上升中心集中在楚科奇海及白令海峡一带;2002—2021年间,上升中心则转移至巴伦支海一带。气压场和风场在前后两阶段也出现了中心转移的分布变化。北极地区大气与海洋环流各因素的协同变化影响着北极海冰的消融。     

地球系统模式FIO-ESM对北极海冰的模拟和预估

评估了地球系统模式FIO-ESM(First Institute of Oceanography-Earth System Model)基于CMIP5(Coupled Model Intercomparison Project Phase 5)的历史实验对北极海冰的模拟能力,分析了该模式基于CMIP5未来情景实验在不同典型浓度路径(RCPs,Representative Concentration Pathways)下对北极海冰的预估情况。通过与卫星观测的海冰覆盖范围资料相比,该模式能够很好地模拟出多年平均海冰覆盖范围的季节变化特征,模拟的气候态月平均海冰覆盖范围均在卫星观测值±15%范围以内。FIO-ESM能够较好地模拟1979-2005年期间北极海冰的衰减趋势,模拟衰减速度为每年减少2.24×104 km2,但仍小于观测衰减速度(每年减少4.72×104 km2)。特别值得注意的是:不同于其他模式所预估的海冰一直衰减,FIO-ESM对21世纪北极海冰预估在不同情景下呈现不同的变化趋势,在RCP2.6和RCP4.5情景下,北极海冰总体呈增加趋势,在RCP6情景下,北极海冰基本维持不变,而在RCP8.5情景下,北极海冰呈现继续衰减趋势。     

Reproduction of the large-scale state of water and sea ice in the Arctic Ocean from 1948 to 2002: Part II. The state of ice and snow cover

This paper presents the results of reconstructing the state of ice and snow covers on the Arctic Ocean from 1948 to 2002 obtained with a couplod model of ocean circulation and sea-ice evolution. The area of the North Atlantic and Arctic Ocean north of 65° N, excluding Hudson Bay, is considered. The monthly mean ice areas and extents are analyzed. The trends of these areas are calculated separately for the periods of 1970–1979, 1979–1990, and 1990–2002. A systematic slight underestimation by the model is observed for the ice extent. This error is estimated to fit the error of 100 km in determining the position of the ice edge (i.e., close to the model resolution). In summer the model fails to reproduce many observed polynias: by observational data, the ice concentration in the central part of the Arctic Ocean constitutes around 0.8, while the model yields around 0.99. The average trend for the area of ice propagation in 1960–2002 is 13931 km²/year (or approximately 2% per decade); the trend of the ice area is 17643 km²/year (or approximately 3% per decade). This is almost three times lower than satellite data. The calculated data for ice thickness in the late winter varies from 3.5 to 4.8 m, with a clear indication of periods of thick ice (the 1960s–1970s) and relatively thin ice (the 1980s); 1995 is the starting point for quick ice-area reduction. The maximum ice accumulation is in 1977 and 1988; here, the average trend is negative: −121 km³/year (or approximately 5.5% per decade). In 1996–2002, the average change in the ice thickness constituted +1.7 cm/year. This speaks to the relatively fast disappearance of thin-ice fractions. This model also slightly underestimates the snow mass with a trend of −2.5 km³/year (almost 0.35 mm of snow per year or 0.1 mm of liquid water per year). An analysis of the monthly mean ice-drift velocity indicates the good quality of the model. Data on the average drift velocity and the results of comparisons between the calculated and satellite data for individual months are presented. A comparison with observational data from 1990–1996 in the Fram Strait shows that the model yields 3.28 m for the average ice thickness against the observed value of approximately 3.26 m. For the same period, the model yields a monthly mean transport of 291.29 km³ as compared to the observed value of 237.17 km³. A comparison between the measured and calculated drift velocities in the Fram Strait indicates that the model value is around 9.78 cm/s, which is comparable to the measured value of 10.2 cm/s. The existing problems with describing the ice redistribution by thickness gradations are illustrated by comparing data on ice thickness in the Fram Strait.     

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AMSR-E数据的多年冰密集度反演算法研究

In recent years, the rapid decline of Arctic sea ice area(SIA) and sea ice extent(SIE), especially for the multiyear(MY) ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square(rms) difference of MY SIA derived from the algorithm of this study are 0.65×106 km2 and0.69×106 km2 during January to March, –0.06×106 km2 and 0.14×106 km2 during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×106 km2 and 0.84×106 km2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang's, Lomax' and NASA TEAM algorithms.     

Sediment transport by sea ice in the Chukchi and Beaufort Seas: Increasing importance due to changing ice conditions?

Sediment-laden sea ice is widespread over the shallow, wide Siberian Arctic shelves, with off-shelf export from the Laptev and East Siberian Seas contributing substantially to the Arctic Ocean's sediment budget. By contrast, the North American shelves, owing to their narrow width and greater water depths, have not been deemed as important for basin-wide sediment transport by sea ice. Observations over the Chukchi and Beaufort shelves in 2001/02 revealed the widespread occurrence of sediment-laden ice over an area of more than 100,000 km² between 68 and 74°N and 155 and 170°W. Ice stratigraphic studies indicate that sediment inclusions were associated with entrainment of frazil ice into deformed, multiple layers of rafted nilas, indicative of a flaw-lead environment adjacent to the landfast ice of the Chukchi and Beaufort Seas. This is corroborated by buoy trajectories and satellite imagery indicating entrainment in a coastal polynya in the eastern Chukchi Sea in February of 2002 as well as formation of sediment-laden ice along the Beaufort Sea coast as far eastward as the Mackenzie shelf. Moored upward-looking sonar on the Mackenzie shelf provides further insight into the ice growth and deformation regime governing sediment entrainment. Analysis of Radarsat Synthetic Aperture (SAR) imagery in conjunction with bathymetric data help constrain the water depth of sediment resuspension and subsequent ice entrainment (>20 m for the Chukchi Sea). Sediment loads averaged at 128 t km^–2, with sediment occurring in layers of roughly 0.5 m thickness, mostly in the lower ice layers. The total amount of sediment transported by sea ice (mostly out of the narrow zone between the landfast ice edge and waters too deep for resuspension and entrainment) is at minimum 4×10⁶ t in the sampling area and is estimated at 5–8×10⁶ t over the entire Chukchi and Beaufort shelves in 2001/02, representing a significant term in the sediment budget of the western Arctic Ocean. Recent changes in the Chukchi and Beaufort Sea ice regimes (reduced summer minimum ice extent, ice thinning, reduction in multi-year ice extent, altered drift paths and mid-winter landfast ice break-out events) have likely resulted in an increase of sediment-laden ice in the area. Apart from contributing substantially to along- and across-shelf particulate flow, an increase in the amount of dirty ice significantly impacts (sub-)ice algal production and may enhance the dispersal of pollutants.