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。     

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.     

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.     

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.     

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.     

利用2012年中国北极考察现场观测数据和卫星遥感资料评估海洋2号卫星扫描辐射计数据反演北极海冰密集度的研究

A retrieval algorithm of arctic sea ice concentration(SIC) based on the brightness temperature data of "HY-2" scanning microwave radiometer has been constructed. The tie points of the brightness temperature were selected based on the statistical analysis of a polarization gradient ratio and a spectral gradient ratio over open water(OW), first-year ice(FYI), and multiyear ice(MYI) in arctic. The thresholds from two weather filters were used to reduce atmospheric effects over the open ocean. SIC retrievals from the "HY-2" radiometer data for idealized OW, FYI, and MYI agreed well with theoretical values. The 2012 annual SIC was calculated and compared with two reference operational products from the National Snow and Ice Data Center(NSIDC) and the University of Bremen. The total ice-covered area yielded by the "HY-2" SIC was consistent with the results from the reference products. The assessment of SIC with the aerial photography from the fifth Chinese national arctic research expedition(CHINARE) and six synthetic aperture radar(SAR) images from the National Ice Service was carried out. The "HY-2" SIC product was 16% higher than the values derived from the aerial photography in the central arctic. The root-mean-square(RMS) values of SIC between "HY-2" and SAR were comparable with those between the reference products and SAR, varying from 8.57% to 12.34%. The "HY-2" SIC is a promising product that can be used for operational services.     

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.     

波弗特海2019年夏季海冰极端低值成因研究

波弗特海海冰的剧烈变化对区域内生态系统以及经济活动具有重要影响。基于美国国家冰雪数据中心发布的海冰密集度数据,本文对2019年波弗特海夏季海冰面积出现极端低值的机制进行了探讨。2019年融冰季（5–9月）海冰覆盖面积为1.38×105 km2,远低于1998–2020年平均面积2.28×105 km2,统计2019年前秋（2018年10–12月）和前冬季节（2019年1–4月）海冰覆盖面积,发现其与1998–2019年多年平均结果无显著差异;先前季节的海冰冰况不是造成极端低值事件的主要原因。综合海冰漂移场、海冰厚度、10 m风场以及海表面净热通量数据发现,2019年5月份海冰面积减小2.33×105 km2,是1998年以来5月海冰损失量最大的年份,占融冰季节海冰面积减小量的62%。与1998年、2008年、2012年以及2016年波弗特海夏季发生海冰覆盖面积极端低值现象的机制不同,不断减小的海冰厚度以及2019年5月异常强的风场,促使海冰快速向外输出,波弗特海南部5月16日就形成开阔水域;伴随着异常高的海表面净热通量使得海冰更多地融化,造成了2019年夏季海冰的异常现象。随着海冰厚度的不断变薄,海冰对风场的响应越来越强,海冰消退时间不断提前,波弗特海夏季海冰的极端低值现象可能更为频繁地出现。     

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 Effect of Seasonal Variability of Atlantic Water on the Arctic Sea Ice Cover

Under the influence of global warming, the sea ice in the Arctic Ocean (AO) is expected to reduce with a transition toward a seasonal ice cover by the end of this century. A comparison of climate-model predictions with measurements shows that the actual rate of ice cover decay in the AO is higher than the predicted one. This paper argues that the rapid shrinking of the Arctic summer ice cover is due to its increased seasonality, while seasonal oscillations of the Atlantic origin water temperature create favorable conditions for the formation of negative anomalies in the ice-cover area in winter. The basis for this hypothesis is the fundamental possibility of the activation of positive feedback provided by a specific feature of the seasonal cycle of the inflowing Atlantic origin water and the peaking of temperature in the Nansen Basin in midwinter. The recently accelerated reduction in the summer ice cover in the AO leads to an increased accumulation of heat in the upper ocean layer during the summer season. The extra heat content of the upper ocean layer favors prerequisite conditions for winter thermohaline convection and the transfer of heat from the Atlantic water (AW) layer to the ice cover. This, in turn, contributes to further ice thinning and a decrease in ice concentration, accelerated melting in summer, and a greater accumulation of heat in the ocean by the end of the following summer. An important role is played by the seasonal variability of the temperature of AW, which forms on the border between the North European and Arctic basins. The phase of seasonal oscillation changes while the AW is moving through the Nansen Basin. As a result, the timing of temperature peak shifts from summer to winter, additionally contributing to enhanced ice melting in winter. The formulated theoretical concept is substantiated by a simplified mathematical model and comparison with observations.     

北极夏季海冰反照率的观测和数值模拟试验

在中国第3次北极科学考察浮冰站开展了积雪/海冰反照率观测.本文对观测结果进行了分析,并结合一维高分辨雪/冰模式(HIGHTSI)对3个常用的反照率参数化方案在天气尺度的表现进行了评估.观测期间测站反照率变化范围0.75～0.85,其天气尺度变化同天气和表面冰、雪状况紧密相关,降雪和吹雪过程可改变表面积雪厚度及水平分布,...     

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.     

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。     

北极楚科奇海海冰面积多年变化的研究

北极气候系统正在发生显著变化,其中,海冰面积和厚度的减小是其最主要的特征.楚科奇海是海冰面积变化最有代表性的区域.文章利用积累了9a的高分辨率海冰分布数据研究海冰面积的多年变化特征.结果表明,各年的冰情有显著的季节内变化,海冰面积距平曲线体现了不同时期海冰面积变化的动态过程.在1997~2005年间,楚科奇海海冰面积经历了轻(1997年)-重(2000~2001年)-轻(2002~2005年)的变化过程.9a的数据总体上体现了海冰面积减小的趋势,2005年的冰情呈现了历史新低.每年融冰期的长短与冰情轻重有密切的关系,冰轻年份融冰开始时间早,冻结结束时间晚.各年海冰面积最小值发生在9月下旬至10月初,各个年份海冰最小面积差别很大.有的年份只有4%,而重冰年可以大于50%.文章采用4个重要参数表达海冰多年变化.其中海冰面积指数反映了当年总体平均的海冰面积距平;海冰最小面积反映了融冰期海冰的极限情况;上一个冬季的气温积温也与翌年海冰面积有良好的关联;分析了风场对海冰的影响,表明风场在融冰期能够在短时间内改变海冰的覆盖面积.     

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.     

Heat Transfer Coefficient between Ice Cover and Water in the Bohai Sea

The calculative method of heat transfer coefficient between ice cover and water is analyzed considering the heat balance at ice cover bottom firstly. The heat transfer coefficient is calculated with the meteorological, oceanographic data and sea ice conditions measured on the JZ20-2 Oil/Gas Platform in the Bohai Sea during the winter of 1997/1998. From the results, it is shown that the heat transfer coefficient is smaller in the freezing and melting periods, which is about 0.16× 10-3 and 0.04× 10-3 respectively. In the middle of ice season, the heat transfer coefficient has a larger value, which is about 0.5 × 10-3. Lastly, the influences of ice thickness and ice type on the heat transfer coefficient are discussed. With the heat transfer coefficient determined above, the oceanic heat flux in the winter of 1997～1998 is calculated, and its trend in the winter is analyzed. This study can be referenced in the sea ice numerical simulation and prediction in the Bohai Sea.     

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.     

Dimethylsulphide and dimethylsulphoniopropionate in Antarctic sea ice and their release during sea ice melting

This study presents concentrations of dimethylsulphide (DMS) and its precursor compound dimethylsulphoniopropionate (DMSP) in a variety of sea ice and seawater habitats in the Antarctic Sea Ice Zone (ASIZ) during spring and summer. Sixty-two sea ice cores of pack and fast ice were collected from twenty-seven sites across an area of the eastern ASIZ (64°E to 110°E; and the Antarctic coastline north to 62°S). Concentrations of DMS in 81 sections of sea ice ranged from < 0.3 to 75 nM, with an average of 12 nM. DMSP in 60 whole sea ice cores ranged from 25 to 796 nM and showed a negative relationship with ice thickness (y = 125x^− 0.8). Extremely high DMSP concentrations were found in 2 cores of rafted sea ice (2910 and 1110 nM). The relationship of DMSP with ice thickness (excluding rafted ice) suggests that the release of large amounts of DMSP during sea ice melting may occur in discrete areas defined by ice thickness distribution, and may produce ‘hot spots’ of elevated seawater DMS concentration of the order of 100 nM. During early summer across a 500 km transect through melting pack ice, elevated DMS concentrations (range 21–37 nM, mean 31 nM, n = 15) were found in surface seawater. This band of elevated DMS concentration appeared to have been associated with the release of sea ice DMS and DMSP rather than in situ production by an ice edge algal bloom, as chlorophyll a concentrations were relatively low (0.09–0.42 μg l^− 1). During fast ice melting in the area of Davis station, Prydz Bay, sea ice DMSP was released mostly as extracellular DMSP, since intracellular DMSP was negligible in both hyposaline brine (5 ppt) and in a melt water lens (4–5 ppt), while extracellular DMSP concentrations were as high as 149 and 54 nM, respectively in these habitats. DMS in a melt water lens was relatively high at 11 nM. During the ice-free summer in the coastal Davis area, DMS concentrations in surface seawater were highest immediately following breakout of the fast ice cover in late December (range 5–14 nM), and then remained at relatively low concentrations through to late February (< 0.3–6 nM). These measurements support the view that the melting of Antarctic sea ice produces elevated seawater DMS due to release of sea ice DMS and DMSP.     

北极地区不同冰龄的海冰厚度变化研究

In this study, changes in Arctic sea ice thickness for each ice age category were examined based on satellite observations and modelled results. Interannual changes obtained from Ice, Cloud, and Land Elevation Satellite(ICESat)-based results show a thickness reduction over perennial sea ice(ice that survives at least one melt season with an age of no less than 2 year) up to approximately 0.5–1.0 m and 0.6–0.8 m(depending on ice age) during the investigated winter and autumn ICESat periods, respectively. Pan-Arctic Ice Ocean Modeling and Assimilation System(PIOMAS)-based results provide a view of a continued thickness reduction over the past four decades. Compared to 1980 s, there is a clear thickness drop of roughly 0.50 m in 2010 s for perennial ice. This overall decrease in sea ice thickness can be in part attributed to the amplified warming climate in north latitudes. Besides, we figure out that strongly anomalous southerly summer surface winds may play an important role in prompting the thickness decline in perennial ice zone through transporting heat deposited in open water(primarily via albedo feedback) in Eurasian sector deep into a broader sea ice regime in central Arctic Ocean. This heat source is responsible for enhanced ice bottom melting, leading to further reduction in ice thickness.