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Nimbus-7和DMSP卫星资料的北极海冰变化特征分析

利用1979—2012年Nimbus-7和DMSP海冰密集度资料对北极海冰进行研究。EOF分析结果表明整个北极海域海冰密集度变化具有非常强的季节变化特征,海冰最多的月份在1—4月、最少的在7—10月,其中鄂霍次克海和日本海、白令海等海域夏季无冰。北极海冰变化的总体趋势是减少,喀拉海和巴伦支海的减少速度最快,只有白令海海冰密集度呈增加趋势。北极区域海冰面积异常变化的主要周期一般在1 a左右,喀拉海和巴伦支海的主周期较长,为18.5 a。     

夏季北极密集冰区范围确定及其时空变化研究

研究夏季北极密集冰区的范围变化是了解北极海冰融化过程的重要手段。密集冰区与海冰边缘区之间没有明确的分界线, 海冰密集度在两者之间平滑过渡, 确定密集冰区范围就需确定一个密集度阈值。文中依据分辨率为6.25 km的AMSR-E遥感数据, 发现不同密集度阈值所围范围在密集冰区边缘处的减小存在由快变慢的过程, 同时与周围格点的密集度差异变化在该处最为显著, 对这两个特征进行统计分析, 获得的阈值同为89%, 具有明确的物理意义和合理性。以此为基础, 运用腐蚀算法剔除海冰边缘区, 同时结合连通域法排除小范围密集冰的影响, 进而确定密集冰区的范围。结果表明, 2002-2006年密集冰区覆盖范围较大, 年际变化较小, 2007年以后明显减小, 2010年与2011年相继出现最小值, 其中2011年的范围最小值仅为2006年的64%。密集冰区范围的变化不同于海冰覆盖范围, 是具有独立特性的海冰变化参数, 反映出高密集度海冰区域的变化特征。海冰的融化与海冰边缘区的变化是导致密集冰区范围发生变化的两个主要因素, 受动力学因素的影响, 海冰边缘区发生伸展或收缩, 发生密集冰区与海冰边缘区互相转化。本文提出了一种研究北极海冰变化的新思路, 密集冰区覆盖范围的减小表明北极中央区域高密集度海冰正持续减少。     

基于CryoSat-2数据的2010-2017年北极海冰厚度和体积的季节与年际变化特征

北极海冰变化影响着全球物质平衡、能量交换和气候变化。本文基于CryoSat-2测高数据和OSI SAF海冰密集度及海冰类型产品，分析了2010-2017年北极海冰面积、厚度和体积的季节和年际变化特征，结合NCEP再分析资料探讨了融冰期北极气温异常和夏季风异常对海冰变化的影响。结果表明，结冰期海冰面积的增加量波动较大，海冰厚度的增加量呈明显下降趋势。融冰期海冰厚度的减小量波动较大，2013年以后融冰期海冰面积的减小量逐年增加。海冰体积的变化趋势和面积变化更相似，融冰期的减小速率大于结冰期的增加速率。融冰期北极海表面大气温度异常与海冰融化量正相关。夏季风影响海冰的辐合和辐散，在弗拉姆海峡海冰的输运过程中起关键作用，促进了北冰洋表层水向大洋深层的传输。     

夏季加拿大海盆海冰边缘区声体积后向散射强度研究

声信号的体积后向散射强度是声传播过程中一个关键的参数。海冰边缘区的声体积后向散射强度研究对深入认识北极声场环境有着十分重要的意义。本文利用中国第六次北极科学考察获取的数据资料研究了海冰边缘区声体积后向散射强度特性。结果表明:加拿大海盆海冰边缘区是声体积后向散射强度的明显过渡区。无冰海面(海冰密集度小于15%)海洋深层水的声体积后向散射强度明显大于密集海冰区域的海水(海冰密集度大于50%)。讨论了声体积后向散射强度与海冰融化之间的关系,造成融冰区声体积后向散射强度增大的原因是水下悬浮泥沙、浮游生物等悬浮物质增加。根据海冰密集海域的海水后向散射强度弱的特点,对北极下放式声学多普勒测流仪(LADCP)观测的设置提出建议。     

北极海冰的变化特征

本文使用了北极海冰１９７２～１９８９年的资料，分析了北极海冰的区域特征、季节变化和长期变化的特征。发现由于北极海冰南侧被殴亚和北美大陆所包围，处于基本封闭状态，只有通过白令海峡与太平洋相连和格陵兰海和娜威海与大西洋相连的两个通道，其环境条件与南极海冰绝然不同，因此其特征也明显不同。１、季节变化小。净冰面积冬季是夏季的２倍左右，而南极海有６倍之差。２、海冰寿命较长。以多年冰为主，平均寿命为１．３年。     

北极中央区海冰低密集度现象研究

近年，北极中央密集冰区出现海冰低密集度的异常现象。为了探讨这一现象的成因，本文使用ERA-Interim再分析资料，定义了北极中央区海冰低密集度（LCCA）指数，研究了2009-2016年的6-9月北极中央区发生的海冰低密集度现象。分析表明，研究时段内在北极中央区发生了6次明显的海冰低密集度（LCCA峰值）过程。在这些过程中，局地气温异常并不是导致海冰低密集度现象发生最主要的因素；海冰低密集度区域的形态及冰速场分布均与大气环流场相对应；在LCCA指数峰值发生前均有气旋中心出现在北冰洋70°N以北并伴随向北移动，气旋引起海冰辐散，同时所携带的较低纬度的热量导致海冰迅速融化。在6次过程中，有3次为气旋影响配合北极偶极子（DA）型环流。LCCA指数与84°N平均向北温度平流和北极中央区海冰速度散度呈正相关。在LCCA指数峰值前，温度平流对海冰低密集度区域形成的影响大于海冰辐散的影响。     

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...     

基于冰基浮标数据的2018-2019年北极海冰运动特性时空变化分析

海冰运动是影响北极海冰平流输运和物质平衡空间重新分布的重要因素。本研究基于2018年9月至2019年8月期间北冰洋66个冰基浮标位置记录数据,结合大气再分析数据,计算得到了海冰运动速度、冰速与风速的比值和海冰运动惯性强度,以刻画北极海冰运动学特征参数在一个冰季的时空变化,并讨论了不同区域冰速与风速比与海冰密集度的关联性。海冰漂移速度在波弗特–楚科奇海、东北极中央区和西北极中央区呈秋冬降低春夏升高的季节变化特征。格陵兰海月均海冰漂移速度（（0.32±0.06）m/s）最大,其次是弗拉姆海峡（（0.17±0.07）m/s）和波弗特–楚科奇海（（0.14±0.05）m/s）,而东北极中央区（（0.09±0.02）m/s）和西北极中央区（（0.07±0.03）m/s）较低。在月尺度上,冰漂移速度与风速的比值主要受海冰漂移速度支配。弗拉姆海峡和格陵兰海受较强的表层海流影响,冰速与风速比值较大,西北极中央区、东北极中央区和波弗特–楚科奇海的冰速与风速比值随着海冰密集度的增加趋近,并分布在0～0.02之间。所有浮标的月平均惯性运动指数为0.158±0.144,秋冬季过渡期间,海冰对风的响应以及海冰运...     

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.     

Arctic sea ice concentration and thickness data assimilation in the FIO-ESM climate forecast system

To improve the Arctic sea ice forecast skill of the First Institute of Oceanography-Earth System Model (FIO-ESM) climate forecast system, satellite-derived sea ice concentration and sea ice thickness from the Pan-Arctic Ice-Ocean Modeling and Assimilation System (PIOMAS) are assimilated into this system, using the method of localized error subspace transform ensemble Kalman ?lter (LESTKF). Five-year (2014–2018) Arctic sea ice assimilation experiments and a 2-month near-real-time forecast in August 2018 were conducted to study the roles of ice data assimilation. Assimilation experiment results show that ice concentration assimilation can help to get better modeled ice concentration and ice extent. All the biases of ice concentration, ice cover, ice volume, and ice thickness can be reduced dramatically through ice concentration and thickness assimilation. The near-real-time forecast results indicate that ice data assimilation can improve the forecast skill significantly in the FIO-ESM climate forecast system. The forecasted Arctic integrated ice edge error is reduced by around 1/3 by sea ice data assimilation. Compared with the six near-real-time Arctic sea ice forecast results from the subseasonal-to-seasonal (S2S) Prediction Project, FIO-ESM climate forecast system with LESTKF ice data assimilation has relatively high Arctic sea ice forecast skill in 2018 summer sea ice forecast. Since sea ice thickness in the PIOMAS is updated in time, it is a good choice for data assimilation to improve sea ice prediction skills in the near-real-time Arctic sea ice seasonal prediction.     

Recent satellite-derived sea ice volume flux through the Fram Strait: 2011–2015

The Fram Strait(FS) is the primary region of sea ice export from the Arctic Ocean and thus plays an important role in regulating the amount of sea ice and fresh water entering the North Atlantic seas. A 5 a(2011–2015) sea ice thickness record retrieved from Cryo Sat-2 observations is used to derive a sea ice volume flux via the FS. Over this period, a mean winter accumulative volume flux(WAVF) based on sea ice drift data derived from passivemicrowave measurements, which are provided by the National Snow and Ice Data Center(NSIDC) and the Institut Francais de Recherche pour d'Exploitation de la Mer(IFREMER), amounts to 1 029 km~3(NSIDC) and1 463 km~3(IFREMER), respectively. For this period, a mean monthly volume flux(area flux) difference between the estimates derived from the NSIDC and IFREMER drift data is –62 km~3 per month(–18×10~6 km~2 per month).Analysis reveals that this negative bias is mainly attributable to faster IFREMER drift speeds in comparison with slower NSIDC drift data. NSIDC-based sea ice volume flux estimates are compared with the results from the University of Bremen(UB), and the two products agree relatively well with a mean monthly bias of(5.7±45.9) km~3 per month for the period from January 2011 to August 2013. IFREMER-based volume flux is also in good agreement with previous results of the 1990 s. Compared with P1(1990/1991–1993/1994) and P2(2003/2004–2007/2008), the WAVF estimates indicate a decline of more than 600 km~3 in P3(2011/2012–2014/2015). Over the three periods, the variability and the decline in the sea ice volume flux are mainly attributable to sea ice motion changes, and second to sea ice thickness changes, and the least to sea ice concentration variations.     

北极冬季季节性海冰双模态特征分析

近年来北极海冰快速变化,北极中央区边缘正由以多年冰为主转为季节性海冰为主。通过对北极冬季季节性海冰的EOF分解发现,2002-2012年期间北极季节性海冰变化的前两模态主要体现为2005年和2007年的季节性海冰距平。其中第二模态主要体现了北极海冰在2005年的一种极端变化,而第一模态不仅体现了北极海冰在2007年的变化,还体现了北极季节性海冰的从负位相到正位相的转变。通过比较发现,在研究时段北极季节性海冰最主要的变化发生在北极太平洋扇区,在2007年,冬季季节性海冰距平发生位相转变,2007-2010年一直维持正位相,北极太平洋扇区冬季季节性海冰保持显著正距平。太平洋扇区表面温度最大异常也发生在2007年,从大气环流来看,2007年之后波弗特海区异常高压有利于夏季太平洋扇区海冰的减少,而西风急流的减弱有利于夏季波弗特海区异常高压的维持,结合夏季海冰速度,顺时针的冰速分布有利于海冰离开太平洋扇区,因而会导致冬季太平洋扇区季节性海冰转为正距平并且从2007年一直维持到2010年。     

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.     

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。     

The estimate of sea ice resources quantity in the Bohai Sea based on NOAA/AVHRR data

The research on sea ice resources is the academic base of sea ice exploitation in the Bohai Sea. According to the ice-water spectrum differences and the correlation between ice thickness and albedo, this paper comes up with a sea ice thickness inversion model based on the NOAA/AVHRR data. And then a sea ice resources quantity (SIQ) time series of Bohai Sea is established from 1987 to 2009. The results indicate that the average error of inversion sea ice thickness is below 30%. The maximum sea ice resources quantity is about 6 × 10 9 m 3 and the minimum is 1.3 × 10 9 m 3 . And a preliminary analysis has been made on the errors of the estimate of sea ice resources quantity (SIQ).