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1.
渤海冬季海冰反照率变化 总被引：1，自引：1，他引：0
渤海海冰对于大尺度气候变化比较敏感,基于CLARA-A1-SAL数据分析了1992~2008年冬季（12、1、2月）渤海海冰区域反照率的时空变化,同时分析了海冰密集度、海冰外延线面积和海水表面温度的变化与海冰反照率的相互关系。渤海海冰区域反照率随时间波动变化且变化趋势不明显,趋势线斜率仅为0.0388%。年际变化在9.93%~14.5%之间,平均值为11.79%。海冰反照率在1999,2000和2005等重冰年的值明显高于其他年份,在1994,1998,2001和2006等轻冰年的值较低。从单个月份反照率来看,12月海冰反照率的增加趋势（趋势线斜率0.0988%）明显高于1月和2月,1月的海冰反照率平均值（12.9%）高于另外两个月份。海冰反照率和海冰密集度呈明显的正相关关系;和海表面温度呈负相关关系（显著性水平90%）。 相似文献
2.
An Antarctic sea ice identification algorithm on the HY-2A scatterometer(HSCAT) employs backscattering coefficient(σ0) and active polarization ratio(APR) for a preliminary sea ice identification.Then standard deviation(STD) filtering and space filtering are carried out.Finally,it is used to identify sea ice.A process uses a σ0,STD threshold and an APR as sea ice indicators.The sea ice identification results are verified using the sea ice distribution data of the ASMR2 released by the National Snow and Ice Data Center as a reference.The results show very good consistence of sea ice development trends,seasonal changes,area distribution,and sea ice edge distribution of the sea ice identification results obtained by this algorithm relative to the ASMR2 sea ice results.The accuracy of a sea ice coverage is 90.8% versus the ASMR2 sea ice results.This indicates that this algorithm is reliable. 相似文献
3.
海冰边缘线是南极海冰监测的重要内容之一。本文基于ENVI RA-2(ENVISAT Radar Altimeter 2)高度计数据开展了南极海冰边缘线提取方法研究。首先根据海冰和海水后向散射系数的不同,利用其各自方差对两者进行区分,获得了冰水分界线;其次通过ENVISAT-ASAR(ENVISAT-Advanced Synthetic Aperture Radar)数据和冰况图对提取的海冰边缘线的正确性进行了验证;最后简要分析了误差存在的原因。研究结果表明,高度计数据在提取大范围海冰边缘线方面具有优势。 相似文献
4.
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. 相似文献
5.
The estimate of sea ice resources quantity in the Bohai Sea based on NOAA/AVHRR data 总被引：5，自引：4，他引：1
YUAN Shuai GU Wei XU Yingjun WANG Ping HUANG Shuqing LE Zhangyan CONG Jianou 《海洋学报(英文版)》2012,31(1):33-40
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). 相似文献
6.
C波段紧缩极化SAR海冰探测能力评估 总被引：1，自引：0，他引：1
The C-band synthetic aperture radar(SAR) data from the Bohai Sea of China, the Labrador Sea in the Arctic and the Weddell Sea in the Antarctic are used to analyze and discuss the sea ice full polarimetric information reconstruction ability under compact polarimetric modes. The type of compact polarimetric mode which has the highest reconstructed accuracy is analyzed, along with the performance impact of the reconstructed pseudo quad-pol SAR data on the sea ice detection and sea ice classification. According to the assessment and analysis, it is recommended to adopt the CTLR mode for reconstructing the polarimetric parameters σ_(HH)~0,σ_(VV)~0, H and α,while for reconstructing the polarimetric parameters σ_(HV)~0, ρ_(H-V), λ_1 and λ_2, it is recommended to use the π/4 mode.Moreover, it is recommended to use the π/4 mode in studying the action effects between the electromagnetic waves and sea ice, but it is recommended to use the CTLR mode for studying the sea ice classification. 相似文献
7.
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。 相似文献
8.
对地球系统模式FIO-ESM同化实验中北极海冰模拟的评估 总被引：3，自引：0，他引：3
本文评估了地球系统模式FIO-ESM(First Institute of Oceanography-Earth System Model)基于集合调整Kalman滤波同化实验对1992-2013年北极海冰的模拟能力。结果显示:尽管同化资料只包括了全球海表温度和全球海面高度异常两类数据,而并没有对海冰进行同化,但实验结果能很好地模拟出与观测相符的北极海冰基本态和长期变化趋势,卫星观测和FIO-ESM同化实验所得的北极海冰覆盖范围在1992-2013年间的线性变化趋势分别为-7.06×105和-6.44×105 km2/(10a),同化所得的逐月海冰覆盖范围异常和卫星观测之间的相关系数为0.78。与FIO-ESM参加CMIP5(Coupled Model Intercomparison Project Phase 5)实验结果相比,该同化结果所模拟的北极海冰覆盖范围的长期变化趋势和海冰密集度的空间变化趋势均与卫星观测更加吻合,这说明该同化可为利用FIO-ESM开展北极短期气候预测提供较好的预测初始场。 相似文献
9.
10.
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。 相似文献
11.
北极中央区海冰低密集度现象研究 总被引：3，自引：3，他引：0
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。 相似文献
12.
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。 相似文献
13.
1982-2016年北极开阔水域变化 总被引：1，自引：0，他引：1
近30年来,北极海冰覆盖范围大幅缩减,开阔水域也相应地发生显著变化。本文利用美国雪冰中心的海冰密集度产品以及美国海洋和大气科学管理局的海水表面温度数据产品,分析了1982-2016年北极开阔水域面积以及开阔水域季节长度的年际变化,并进一步探讨了海水表面温度对开阔水域时空变化的影响。结果表明北极开阔水域面积平均每年增加55.89×103 km2,海冰消退时间以平均0.77 d/a的速度在提前,海冰出现时间以平均0.82 d/a的速度在延迟,导致开阔水域季节长度以平均1.59 d/a的速度在增加。2016年达到了有遥感观测资料以来开阔水域面积和开阔水域季节长度的最大值,分别为13.52×106 km2和182 d。9个海区的开阔水域变化特征有一定的差异,对开阔水域变化贡献最大的有北冰洋核心区、喀拉海和巴伦支海。海水表面温度对开阔水域的变化有着重要影响,且影响的程度与纬度相关,即高纬度地区的海水表面温度对开阔水域的影响高于低纬度地区。 相似文献
14.
一次新的厄尔尼诺事件即将形成 总被引：1，自引：0，他引：1
一次新的厄尔尼诺事件即将形成宋家喜（国家海洋环境预报中心，北京）在１９９１～１９９５年的五年中，赤道东太平洋共发生了三大厄尔尼诺事件，即１９９１年５月至１９９２年６月，１９９３年３月至１０月，１９９４年１０月至１９９５年２月。也有人认为是一次长厄尔尼... 相似文献
15.
卫星记录以来,南极海冰范围发生5次快速下降事件,研究这5次事件的时空特征,对进一步认识海冰快速下降事件的物理机制具有重要意义。基于海冰范围和海冰密集度的卫星数据,从时间和空间两个维度总结5次南极海冰快速下降事件的特征,再结合大气和海洋各项环境因素的再分析数据,探讨海冰快速下降的影响因素及其驱动过程。结果显示:南极海冰快速下降的空间分布存在季节性差异, 2021年8～12月以及2016年8～12月的春季南极海冰快速下降由别林斯高晋海、威德尔海、印度洋和西太平洋区域的海冰减少所主导; 2010年12月至2011年4月以及1985年12月至1986年4月的夏季南极海冰快速下降由威德尔海、罗斯海沿岸和西太平洋区域的海冰减少所主导;2008年4～8月的冬季南极海冰快速下降则由别林斯高晋海和西太平洋的部分区域的海冰减少所主导。探究影响海冰的环境因素发现,海表面温度和海表面净热通量对海冰减少的热力效应影响具有区域性差异。此外,南极海冰快速下降受阿蒙森低压的影响,相应的海表面风异常既通过经向热输运的热力效应导致海冰减少,也通过风的动力效应驱动海冰漂移使得海冰密集度降低。 相似文献
16.
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. 相似文献
17.
基于MODIS热红外数据的渤海海冰厚度反演 总被引：3，自引：1，他引：2
Level ice thickness distribution pattern in the Bohai Sea in the winter of 2009–2010 was investigated in this paper using MODIS night-time thermal infrared imagery.The cloud cover in the imagery was masked out manually.Level ice thickness was calculated using MODIS ice surface temperature and an ice surface heat balance equation.Weather forcing data was from the European Centre for Medium-Range Weather Forecasts(ECMWF) analyses.The retrieved ice thickness agreed reasonable well with in situ observations from two off-shore oil platforms.The overall bias and the root mean square error of the MODIS ice thickness are –1.4 cm and 3.9 cm,respectively.The MODIS results under cold conditions(air temperature –10°C) also agree with the estimated ice growth from Lebedev and Zubov models.The MODIS ice thickness is sensitive to the changes of the sea ice and air temperature,in particular when the sea ice is relatively thin.It is less sensitive to the wind speed.Our method is feasible for the Bohai Sea operational ice thickness analyses during cold freezing seasons. 相似文献
18.
Qi Shu Fangli Qiao Jiping Liu Zhenya Song Zhiqiang Chen Jiechen Zhao Xunqiang Yin Yajuan Song 《海洋学报(英文版)》2021,40(10):65-75
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. 相似文献
19.
利用美国冰雪中心(NSIDC)高分辨率海冰密集度等多种数据,定义了北极高密集度冰区(High concentration ice region:HCIR)海冰变化指数,在此基础上研究了1989—2017年HCIR海冰多尺度变化特征及其极端低值事件的可能形成原因。结果表明:北极HCIR海冰密集度具有显著的单峰型季节变化特征,4月密集度最高,9月密集度最低,年较差达17.70%,兼有夏季融冰期短、冬季结冰期长且持续稳定的特点。HCIR海冰存在显著的年际年代际变化,在2007年发生了年代际转折以后,海冰变化指数的年际变化幅度和频次明显加强,且在2016、2012、2007、2011、2008和2010年依次出现海冰密集度极端降低事件;2016年9月初HCIR海冰密集度达到历史最低值,接近50%。对HCIR海冰密集度极端低值事件的统计研究表明,29年间共出现874天(次)极端低值事件,约占总频次的8%;空间上海冰密集度的降低主要出现在沿HCIR边界线一带,存在巴伦支海-喀拉海北缘的斯瓦尔巴群岛-北地群岛和东西伯利亚-波弗特海两个中心区域,该空间分布与气旋式大气环流引起的北冰洋Ekman漂流的辐散分布相一致。这表明HCIR海冰密集度的极端降低与极涡的动力作用有关,同时风场对海冰的动力辐散作用还会引起HCIR开阔水域的扩大,进一步加强海冰反照率的正反馈机制,使得热力和动力作用耦合起来共同影响HCIR海冰的加速融化。 相似文献
20.
基于CryoSat-2卫星测高数据的北极海冰体积估算方法 总被引：1，自引：1，他引：0
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。 相似文献