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1.
Information on the Arctic sea ice climate indicators is crucial to business strategic planning and climate monitoring. Data on the evolvement of the Arctic sea ice and decadal trends of phenology factors during melt season are necessary for climate prediction under global warming. Previous studies on Arctic sea ice phenology did not involve melt ponds that dramatically lower the ice surface albedo and tremendously affect the process of sea ice surface melt. Temporal means and trends of the Arctic sea ice phenology from 1982 to 2017 were examined based on satellite-derived sea ice concentration and albedo measurements. Moreover, the timing of ice ponding and two periods corresponding to it were newly proposed as key stages in the melt season. Therefore, four timings, i.e., date of snow and ice surface melt onset (MO), date of pond onset (PO), date of sea ice opening (DOO), and date of sea ice retreat (DOR); and three durations, i.e., melt pond formation period (MPFP, i.e., MO–PO), melt pond extension period (MPEP, i.e., PO–DOR), and seasonal loss of ice period (SLIP, i.e., DOO–DOR), were used. PO ranged from late April in the peripheral seas to late June in the central Arctic Ocean in Bootstrap results, whereas the pan-Arctic was observed nearly 4 days later in NASA Team results. Significant negative trends were presented in the MPEP in the Hudson Bay, the Baffin Bay, the Greenland Sea, the Kara and Barents seas in both results, indicating that the Arctic sea ice undergoes a quick transition from ice to open water, thereby extending the melt season year to year. The high correlation coefficient between MO and PO, MPFP illustrated that MO predominates the process of pond formation. 相似文献
2.
Sea ice concentration (SIC) is one of the most important indicators when monitoring climate changes in the polar region. With the development of the Chinese satellite technology, the FengYun (FY) series has been applied to retrieve the sea ice parameters in the polar region. In this paper, to improve the SIC retrieval accuracy from the passive microwave (PM) data of the Microwave Radiation Imager (MWRI) aboard on the FengYun-3B (FY-3B) Satellite, the dynamic tie-point (DT) Arctic Radiation and Turbulence Interaction Study (ARTIST) Sea Ice (ASI) (DT-ASI) SIC retrieval algorithm is applied and obtained Arctic SIC data for nearly 10 a (from November 18, 2010 to August 19, 2019). Also, by applying a land spillover correction scheme, the erroneous sea ice along coastlines in melt season is removed. The results of FY-3B/DT-ASI are obviously improved compared to that of FY-3B/NT2 (NASA-Team2) in both SIC and sea ice extent (SIE), and are highly consistent with the results of similar products of AMSR2 (Advanced Microwave Scanning Radiometer 2)/ASI and AMSR2/DT-ASI. Compared with the annual average SIC of FY-3B/NT2, our result is reduced by 2.31％. The annual average SIE difference between the two FY- 3Bs is 1.65×106 km2, of which the DT-ASI algorithm contributes 87.9％ and the land spillover method contributes 12.1％. We further select 58 MODIS (Moderate-resolution Imaging Spectroradiometer) cloud-free samples in the Arctic region and use the tie-point method to retrieve SIC to verify the accuracy of these SIC products. The root mean square difference (RMSD) and mean absolute difference (MAD) of the FY-3B/DT-ASI and MODIS results are 17.2％ and 12.7％, which is close to those of two AMSR2 products with 6.25 km resolution and decreased 8％ and 7.2％ compared with FY-3B/NT2. Further, FY-3B/DT-ASI has the most significant improvement where the SIC is lower than 60％. A high-quality SIC product can be obtained by using the DT-ASI algorithm and our work will be beneficial to promote the application of FengYun Satellite. 相似文献
3.
南极麦肯齐湾冰间湖的时空变化及主要影响因素分析 总被引：1，自引：0，他引：1
利用2003—2009年AMSR-E日平均海冰密集度数据,对南极普里兹湾埃默里冰架前缘中西部的麦肯齐湾冰间湖进行了分析。针对冰架前缘冰间湖的特点,本文在阈值法和连通域法的基础上,提出了生长点法作为识别此类冰间湖的方法。研究发现,该冰间湖的开始时间为每年的3月中下旬,结束时间为每年的10月末到11月初,平均出现天数为226d。冰间湖的面积每天都发生变化,表现出天气尺度的变化特征。全年累计的冰间湖面积平均为(8.33±1.55)×105 km2。冰间湖最大面积为1.69×104 km2,出现在2004年。结合NCEP再分析数据中的日平均风速资料的分析发现,在6～8月,冰间湖的天气尺度变化主要是受风场的影响,冰间湖面积与离岸风速有很好的相关性。 相似文献
4.
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. 相似文献
5.
The effects of resolving the Canadian Arctic Archipelago in a finite element sea ice model 总被引：1，自引：0，他引：1
Though narrow straits may have a strong influence on the large-scale sea ice mass balance, they are often crudely represented in coarse resolution sea ice models. Unstructured meshes, with their natural ability to fit boundaries and locally increase the mesh resolution, propose an alternative framework to capture the complex oceanic areas formed by coasts and islands. In this paper, we develop a finite element sea ice model to investigate the sensitivity of the Arctic sea ice cover features to the resolution of the narrow straits constituting the Canadian Arctic Archipelago. The model is a two-level dynamic-thermodynamic sea ice model, including a viscous-plastic rheology. It is run over 1979–2005, forced by daily NCEP/NCAR reanalysis data. Confronting qualitatively numerical experiments with observations shows a good agreement with satellite and buoys measurements. Due to its simple representation of the oceanic interactions, the model overestimates the sea ice extent during winter in the southernmost parts of the Arctic, while the Baffin Bay and Kara Sea remain ice-covered during summer. In order to isolate the benefits from resolving the Canadian Arctic Archipelago, a numerical experiment is performed where we artificially close the archipelago. Focusing on the large-scale sea ice thickness pattern, no significant change is found in our model, except in the close surroundings of the archipelago. However, the local and short-term influences of the ice exchanges are nonnegligible. In particular, we show that the ice volume associated to the Canadian Arctic Archipelago represents 10% of the Northern Hemisphere sea ice volume and that the annual mean ice export towards Baffin Bay amounts to 125 km3 yr−1, which may play an important role on the convective overturning in the Labrador Sea. 相似文献
6.
利用欧洲中心气候再分析资料和美国国家冰雪数据中心北极海冰面积资料，分析了夏季北极海冰面积与前期大气经向热量输送年际变化的联系。结果表明：6月北半球中高纬大气的经向热量输送以瞬变热量形式为主，其中巴芬湾西部（B区）和格陵兰岛东部（G区）是瞬变热量向极区传输的两个通道，二者之间存在反位相的协同变化，且这种协同变化与夏季北极海冰面积变化密切相关。可能的机制为：6月，AD、AO和NAO三种北极大气环流型能够引起巴芬湾西部和格陵兰岛东部瞬变热量输送的协同变化，这种协同变化通过涡旋动力作用激发夏季极区大气表现为AD异常，同时影响途经区域的气温，从而通过热动力作用影响夏季北极海冰。将向极区输送的热量称为暖输送，从极区输出的热量为冷输送，则上述两个区域的瞬变热量协同输送可分为三种情况：B暖G冷、B冷G暖、B和G均冷，而B和G均暖的情况十分罕见。当B区向极区输入、G区输出热量时，有利于太平洋扇区和喀拉海的海冰偏少；当G区输入、B区输出热量时，利于喀拉海和拉普捷夫海海冰偏少；当B区和G区均输出热量时，利于波佛特海南部、喀拉海和拉普捷夫海海冰偏多，反之则相反。 相似文献
7.
Quantifying the contribution of natural variability to September Arctic sea ice decline 总被引：1，自引：0，他引：1
Arctic sea ice extent has been declining in recent decades. There is ongoing debate on the contribution of natural internal variability to recent and future Arctic sea ice changes. In this study, we contrast the trends in the forced and unforced simulations of carefully selected global climate models with the extended observed Arctic sea ice records. The results suggest that the natural variability explains no more than 42.3% of the observed September sea ice extent trend during 35 a(1979–2013) satellite observations, which is comparable to the results of the observed sea ice record extended back to 1953(61 a, less than 48.5% natural variability). This reinforces the evidence that anthropogenic forcing plays a substantial role in the observed decline of September Arctic sea ice in recent decades. The magnitude of both positive and negative trends induced by the natural variability in the unforced simulations is slightly enlarged in the context of increasing greenhouse gases in the 21st century.However, the ratio between the realizations of positive and negative trends change has remained steady, which enforces the standpoint that external forcing will remain the principal determiner of the decreasing Arctic sea ice extent trend in the future. 相似文献
8.
9.
Sea ice characteristics between the middle Weddell Sea and the Prydz Bay, Antarctica during the austral summer of 2003 总被引：1，自引：0，他引：1
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. 相似文献
10.
协同主动学习和半监督方法的海冰图像分类 总被引：1，自引：0，他引：1
海冰遥感光谱影像分类中标签样本难以获取,导致海冰分类精度难以提高,但是大量包含丰富信息的未标签样本却没有得到充分利用,针对这种情况,提出一种协同主动学习和半监督学习方法用于海冰遥感图像分类。在主动学习部分,结合最优标号和次优标号、自组织映射神经网络以及增强的聚类多样性算法来选择兼具不确定性和差异性的样本参与训练;在半监督学习部分,利用直推式支持向量机,并且融合主动学习思想从大量未标签样本中选取相对可靠且包含一定信息量的样本进行迭代训练;然后协同主动学习分类结果和半监督分类结果,通过一致性验证保证所加入伪标签样本的正确性。为了验证方法的有效性,分别采用巴芬湾地区30 m分辨率的Hyperion高光谱数据(验证数据为15 m分辨率的Landsat-8数据)和辽东湾地区15 m分辨率的Landsat-8数据(验证数据为4.77 m分辨率的Google Earth数据)进行海冰分类实验。实验结果表明,相对其他传统方法,该协同分类方法可以在只有少量标签样本的情况下,充分利用大量未标签样本中包含的信息,实现快速收敛,并获得较高的分类精度(两个实验的总体精度分别为90.003%和93.288%),适用于海冰遥感图像分类。 相似文献
11.
利用美国冰雪数据中心发布的2003—2008年高分辨率海冰密集度数据,分6个阶段对普里兹湾区域海冰季节性变化的空间分布特征进行了研究,并根据普里兹湾海区的地形和环流对这些特征的成因进行了分析。结果表明,普里兹湾海冰冻结过程和融化过程分别经历7个月和5个月,海冰融化速度最快月份是10月和11月,主要表现形式为海冰密集度的减少;海冰冻结速度4月和6月最快,海冰外缘线向北扩展。由于普里兹湾近岸达恩利角冰间湖、普里兹湾冰间湖和Barrier湾冰间湖的存在,海冰的融化呈现大洋区由北向南、近岸区由南向北的双向融化特征;而在普里兹湾口、弗拉姆浅滩和四女士浅滩均存在不易融化的冰舌,两者之间的低密集度海冰区,则对应于暖水侵入普里兹湾的通道。南极绕极流在流经凯尔盖朗海台中部时向北偏转,造成此处在盛冰期较其它经度的海冰外缘更靠北,可达57°S。南极辐散带的表层流场和上升暖流抑制海冰冻结和聚集,形成了低海冰密集度区域。 相似文献
12.
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
13.
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
14.
辽东湾JZ20—2海域海冰参数的概率分布 总被引：11，自引：1，他引：11
基于辽东湾JZ2 0 2海域 1996 2 0 0 0年 4个冬季的海冰定点观测资料和海冰数值模拟结果 ,对该海域的平整冰厚、冰速、冰向和压缩强度等海冰参数进行了概率分析 ,确定了各自的分布参数 ,并对冰速和冰向进行了联合概率分析。结果表明 :冰厚和冰速分别服从对数正态分布和瑞利分布 ,海冰压缩强度服从正态分布。计算结果可用于JZ2 0 2海域海洋结构可靠性设计和疲劳累积损伤分析的海冰参数 ,也可作为其邻近海域的参考资料 相似文献
15.
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
16.
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
17.
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... 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.
BCC_CSM对北极海冰的模拟:CMIP5和CMIP6历史试验比较 总被引：1，自引：1，他引：0
本文利用北京气候中心气候系统模式(BCC_CSM)在最近两个耦合模式比较计划(CMIP5和CMIP6)的历史试验模拟结果，对北极海冰范围和冰厚的模拟性能进行了比较，结果表明:(1) CMIP6改善了CMIP5模拟海冰范围季节变化过大的问题，总体上更接近观测结果；(2)两个CMIP试验阶段中BCC_CSM模拟的海冰厚度都偏小，但CMIP6试验对夏季海冰厚度过薄问题有所改进。通过对影响海冰生消过程的冰面和冰底热收支的分析，我们探讨了上述模拟偏差以及CMIP6模拟结果改善的成因。分析表明，8?9月海洋热通量、向下短波辐射和反照率对模拟结果的误差影响较大，CMIP6试验在这些方面有较大改善；而12月至翌年2月，CMIP5模拟的北极海冰范围偏大主要是海洋热通量偏低所导致，CMIP6模拟的海洋热通量较CMIP5大，但北大西洋表层海流的改善才是巴芬湾附近海冰外缘线位置改善的主要原因。CMIP试验模拟的夏季海冰厚度偏薄主要是因为6?8月海洋热通量和冰面热收支都偏大，而CMIP6试验模拟的夏季海冰厚度有所改善主要是由于海洋热通量和净短波辐射的改善。海冰模拟结果的改善与CMIP6海冰模块和大气模块参数化的改进有直接和间接的关系，通过改变短波辐射、冰面反照率和海洋热通量，使BCC_CSM模式对北极海冰的模拟性能也得到有效提高。 相似文献