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.     

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。     

Some parameters in arctic sea ice dynamics from an expedition in the summer of 2003

1Introduction Seaiceoccupiesthemainpartofthesurfaceof theArcticOcean.ThefocusoftheSecondChineseNa- tionalArcticResearchExpedition(CHINAE-2003) wastounderstandthevariationsofarcticmarineenvi- ronmentsandtheseaiceeffectsontheclimatechanges ofglobalextent,inmiddleandlowerlatitudesareas, especiallyinChina.Therefore,thejointsea-ice-airob- servationforseaicestudieswasoneofthekeypro- jectsinCHINARE-2003.Theinvestigatedareacov- ered3000kmfromsouthtonorthand900kmfrom westtoeast.Seventemporali…     

MOSAiC现场观测期间海冰厚度季节变化模拟误差分析

北极气候研究多学科漂流观测计划（Multidisciplinary drifting Observatory for the Study of Arctic Climate, MOSAiC）于2019年10月至2020年9月开展,期间获得了变量完整的大气、海洋、海冰厚度及积雪厚度观测,为海冰模式的发展提供了新的契机。本研究利用两个完整观测时段（2019年11月1日至2020年5月7日、2020年6月26日至7月27日）的大气和海洋强迫场,驱动一维海冰柱模式ICEPACK,模拟了MOSAiC期间海冰厚度的季节演变,同海冰厚度观测进行了对比,并诊断分析了海冰厚度模拟误差的原因。结果表明,在冬春季节,模式可以再现海冰厚度增长过程,但由于模式在春季高估了积雪向海冰的转化及对海冰物质平衡的贡献,模拟的春季海冰厚度偏厚。在夏季期间,2种热力学方案及3种融池方案的组合都表明模式高估了海冰表层的消融过程,导致模拟结束阶段的海冰厚度偏薄。我们的研究表明,使用变量完整的MOSAiC大气和海洋强迫场可以诊断目前海冰模式中的问题,为海冰模式的改进奠定基础。     

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.     

Arctic sea ice distribution in summer based on aerial photos

1Introduction TheArcticOceanisoneoftheimportantcold sourcesontheearth,whichaffectsglobalclimateand oceancirculationseriously.Itsinteractionwithglobal climatesystemisrepresentedbyseaice,whichisthe mainfeatureonthesurfaceoftheArcticOcean(Aa- gaard,etal.,1989).Firstly,seaiceplaysapivotalrole intheheatandmassbalanceonthesurfaceoftheArc- ticOcean.Seaicenotonlyobstructstheheatexchange betweenatmosphereandocean,butalsoreflectsthe mostofthelocalsolarradiationbacktotheatmo- spherebecauseofitshighalb…     

夏季北极海冰表皮温度实测资料与MODIS产品的对比分析

Ship-borne infrared radiometric measurements conducted during the Chinese National Arctic Research Expedition(CHINARE) in 2008, 2010, 2012, 2014, 2016 and 2017 were used for in situ validation studies of the Moderate Resolution Imaging Spectroradiometer(MODIS) sea ice surface temperature(IST) product.Observations of sea ice were made using a KT19.85 radiometer mounted on the Chinese icebreaker Xuelong between July and September over six years. The MODIS-derived ISTs from the satellites, Terra and Aqua, both show close correspondence with ISTs derived from radiometer spot measurements averaged over areas of 4 km×4 km, spanning the temperature range of 262–280 K with a ±1.7 K(Aqua) and ±1.6 K(Terra) variation. The consistency of the results over each year indicates that MODIS provides a suitable platform for remotely deriving surface temperature data when the sky is clear. Investigation into factors that cause the MODIS IST bias(defined as the difference between MODIS and KT19.85 ISTs) shows that large positive bias is caused by increased coverage of leads and melt ponds, while large negative bias mostly arises from undetected clouds. Thin vapor fog forming over Arctic sea ice may explain the cold bias when cloud cover is below 20%.     

北极海冰密集度预报对大气强迫敏感性的个例研究

A regional Arctic configuration of the Massachusetts Institute of Technology general circulation model （MIT-gcm） is used as the coupled ice-ocean model for forecasting sea ice conditions in the Arctic Ocean at the Na-tional Marine Environmental Forecasting Center of China （NMEFC）, and the numerical weather prediction from the National Center for Environmental Prediction Global Forecast System （NCEP GFS） is used as the atmospheric forcing. To improve the sea ice forecasting, a recently developed Polar Weather Research and Forecasting model （Polar WRF） model prediction is also tested as the atmospheric forcing. Their forecasting performances are evaluated with two different satellite-derived sea ice concentration products as initializa-tions： （1） the Special Sensor Microwave Imager/Sounder （SSMIS） and （2） the Advanced Microwave Scanning Radiometer for EOS （AMSR-E）. Three synoptic cases, which represent the typical atmospheric circulations over the Arctic Ocean in summer 2010, are selected to carry out the Arctic sea ice numerical forecasting experiments. The evaluations suggest that the forecasts of sea ice concentrations using the Polar WRF atmo-spheric forcing show some improvements as compared with that of the NCEP GFS.     

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。     

Spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice in multiple dimensions during 1979 to 2020

Arctic sea ice is broadly regarded as an indicator and amplifier of global climate change. The rapid changes in Arctic sea ice have been widely concerned. However, the spatiotemporal changes in the horizontal and vertical dimensions of Arctic sea ice and its asymmetry during the melt and freeze seasons are rarely quantified simultaneously based on multiple sources of the same long time series. In this study, the spatiotemporal variation and freeze-thaw asymmetry of Arctic sea ice were investigated from both the horizontal and vertical dimensions during 1979–2020 based on remote sensing and assimilation data. The results indicated that Arctic sea ice was declining at a remarkably high rate of –5.4 × 10～4 km²/a in sea ice area（SIA） and –2.2 cm/a in sea ice thickness（SIT）during 1979 to 2020, and the reduction of SIA and SIT was the largest in summer and the smallest in winter.Spatially, compared with other sub-regions, SIA showed a sharper declining trend in the Barents Sea, Kara Sea,and East Siberian Sea, while SIT presented a larger downward trend in the northern Canadian Archipelago,northern Greenland, and the East Siberian Sea. Regarding to the seasonal trend of sea ice on sub-region scale, the reduction rate of SIA exhibited an apparent spatial heterogeneity among seasons, especially in summer and winter, i.e., the sub-regions linked to the open ocean exhibited a higher decline rate in winter; however, the other sub-regions blocked by the coastlines presented a greater decline rate in summer. For SIT, the sub-regions such as the Beaufort Sea, East Siberian Sea, Chukchi Sea, Central Arctic, and Canadian Archipelago always showed a higher downward rate in all seasons. Furthermore, a striking freeze-thaw asymmetry of Arctic sea ice was also detected. Comparing sea ice changes in different dimensions, sea ice over most regions in the Arctic showed an early retreat and rapid advance in the horizontal dimension but late melting and gradual freezing in the vertical dimension. The amount of sea ice melting and freezing was disequilibrium in the Arctic during the considered period, and the rate of sea ice melting was 0.3 × 10～4 km²/a and 0.01 cm/a higher than that of freezing in the horizontal and vertical dimensions, respectively. Moreover, there were notable shifts in the melting and freezing of Arctic sea ice in 1997/2003 and 2000/2004, respectively, in the horizontal/vertical dimension.     

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.     

利用卫星遥感和再分析数据刻画2017-2018年格陵兰北部罕见冰间湖事件

以90%海冰密集度为阈值,基于卫星遥感数据,2017-2018年冰季在格陵兰北部识别了两次冰间湖事件,分别出现在冬季和夏季。冬季的冰间湖事件从2018年2月20日持续至3月3日,夏季的事件从8月2日持续到9月5日。AMSR2被动微波的海冰密集度产品表明,冬季和夏季冰间湖事件对应的最低海冰密集度分别为72%和65%。两次冰间湖事件都与格陵兰北部东西气压梯度异常引起的南风加强有关,而气压梯度的异常则与对流层中部极涡的扰动有关。冬季冰间湖事件期间,相对暖和的气温和频繁出现的冰间湖,导致冬季海冰生长不持续,海冰热力增厚较小,这为夏季海冰发生破碎并形成冰间湖创造了条件。南风减弱和新冰生成是冬季冰间湖消失的主要原因。对于夏季的冰间湖,导致其消失的主要原因则是从北部输入的浮冰增加。Sentinel-1 合成孔径雷达产品相对AMSR2被动微波观测产品更加适合于应用到冰间湖事件伴随的新冰生长,这与前者具有更高的空间分辨率有关。格陵兰北部是北冰洋多年冰的聚集地,该区域被认为是北冰洋海冰的“避难所”。因此区域在2017-2018年出现罕见的冰间湖事件,对于整个北冰洋海冰的快速减少具有重要意义,也助于北冰洋海冰,尤其是多年冰的消退。     

基于温度链浮标获取南极普里兹湾积雪和固定冰厚度的研究

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

融冰季节北极破碎冰区热通量的初步研究

利用航空遥感数字影像的解析结果和实测气象,海洋和海冰资料,定量研究了夏季融冰期北极破碎冰区的热通量,计算了海洋对大气的热贡献,结果表明,在北极夏季海冰融化时,短波辐射远远大于感热和潜热通量,是表面热通量的决定因素,海洋对大气的热贡献主要由长波辐射决定,在观测期间,海洋对大气的热贡献为38～104Wm^-2,这部分热量的大小与海冰的密集度有关,当海冰密集度小于0.8时,海洋对大气的热贡献随海冰密度度的增大而减小,而当海冰密集度超过0.8以后,该热通量将随海冰密集度的增大而增大。     

基于合成孔径雷达遥感的北极海冰总形变率分析

基于RADARSAT地球物理处理器系统(RGPS)的北极海冰运动散度、旋度和剪切产品,本文计算了北极海冰总形变率,给出了所有RGPS产品时空覆盖范围的总形变率空间分布和时间平均总形变率大于0.01d-1的概率分布。结果表明:对整个RGPS数据库而言(时间跨度从1996年11月至2008年4月),平均总形变率为0.020 4d-1,总形变率大于0.01d-1的数据样本为总样本的45.89%。总形变率高值主要分布在近岸海域,靠近北极点附近的总形变率相对较小。北极海冰总形变率随季节变化,夏季平均总形变率及总形变率大于0.01d-1发生概率要比冬季大,其中,夏季总形变率大于0.01d-1发生概率为59%,而冬季要比夏季低18%。其可能机制主要是,夏季北极地区温度升高,形成海冰融化-破碎-更易融化-更易破碎的放大效果,导致北极海冰总形变率变大。     

北极中央区海冰密集度与云量相关性分析

本文使用海冰密集度以及低云、中云、高云的日平均数据,借助滑动相关分析方法,研究了北极中央区海冰密集度与云量之间的相关性,分析了海冰与云的相互作用机制。研究表明,在春季海冰融化季节(4、5月)、秋季海冰冻结季节(10、11月),低云与海冰密集度之间表现为较好的负相关,表明在这段时间内冰区海面蒸发强烈,对低云的形成有重要贡献。在10月和11月,中云与海冰密集度也有很好的负相关,表明秋季低云可以通过抬升形成中云。高云与海冰密集度之间并没有明显的相关性,可能原因:一方面海冰的空间分布对高云无影响,另一方面,高云主要影响到达的短波辐射,从而影响海冰的融化和冻结速度,与海冰厚度有直接显著的关系,而与海冰密集度的关系不明显。此外,在海冰密集度与低云存在较好负相关的情况下会出现某些年份相关性不好的情况,我们的研究发现这是北极中央区与周边海区发生了海冰交换或云交换的结果。