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我国的渤海和黄海北部在每年冬季都出现不同程度的冰情,它直接影响结冰海区的石油平台、船舶以及港口等设施的正常作业,对海冰的观测与预报随着上述海区的开发利用越发显得重要,利用航海雷达连续准确地跟踪海冰漂移运动轨迹,是当今观测、调查、研究冰漂流移动规律的有效方法之一,我国的科研人员利用雷达成像技术对渤海的冰情进行了长期的雷达海冰观测、研究,在由雷达海冰图像对海冰的物理特征的识别、分类以及冰漂流场测量方面取得了一些进展[1-4]。 相似文献
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北极海冰数值模拟研究述评 总被引：7，自引：0，他引：7
根据国内外近年发表的主要文献,详细介绍北极海冰数值模拟工作的最新进展。综合评述各种主要动力学模式的特点和不足,指出与数值模式有关的主要物理问题,重点介绍海冰模式所特有的问题以及海冰数值模拟工作的发展方向。对以往的数值工作和海冰数值模拟的主要问题进行了总结,并在理论和实践方面进行了深入探讨,有助于我国相关工作的开展。 相似文献
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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
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对海冰的运动规律进行精确、连续和长周期的实时监测有助于海冰热力学和动力学的研究,也可保障冰区生产活动的安全进行。针对辽东湾海冰的运动特点和工程需求,在JZ20-2油气平台上建立了海冰雷达监测系统。采用数字图像处理技术对海冰雷达监测图像进行了分析和软件开发,可对海冰密集度、速度和冰块面积等海冰参数进行提取。采用该海冰雷达监测系统和数字图像处理软件,在2011-2012年冬季对该海域的海冰运动规律进行了全冰期的连续监测,在此基础上重点对海冰速度的雷达图像监测结果进行了分析,讨论了海冰速度场分布以及连续48 h的变化过程。以上结果为海冰的生消运移规律研究和油气作业区的海冰管理工作提供了可靠的现场监测数据。对海冰雷达现场监测及数字图像处理中的问题及改进方法进行了讨论。 相似文献
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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。 相似文献
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《Ocean Modelling》2002,4(2):137-172
A new sea ice model, GELATO, was developed at Centre National de Recherches Météorologiques (CNRM) and coupled with OPA global ocean model. The sea ice model includes elastic–viscous–plastic rheology, redistribution of ice floes of different thicknesses, and it also takes into account leads, snow cover and snow ice formation. Climatologies of atmospheric surface parameters are used to perform a 20-year global ocean–sea ice simulation, in order to compute surface heat fluxes from diagnosed sea ice or ocean surface temperature. A surface salinity restoring term is applied only to ocean grid cells with no sea ice to avoid significant surface salinity drifts, but no correction of sea surface temperature is introduced. In the Arctic the use of an ocean model substantially improves the representation of sea ice, and particularly of the ice edge in all seasons, as advection of heat and salt can be more accurately accounted for than in the case of, for example, a sea ice–ocean mixed layer model. In contrast, in the Antarctic, a region where ocean convective processes bear a much stronger influence in shaping sea ice characteristics, a better representation of convection and probably of sea ice (for example, of frazil sea ice, brine rejection) would be needed to improve the simulation of the annual cycle of the sea ice cover. The effect of the inclusion of several ice categories in the sea ice model is assessed by running a sensitivity experiment in which only one category of sea ice is considered, along with leads. In the Arctic, such an experiment clearly shows that a multicategory sea ice model better captures the position of the sea ice edge and yields much more realistic sea ice concentrations in most of the region, which is in agreement with results from Bitz et al. [J. Geophys. Res. 106 (C2) (2001) 2441–2463]. 相似文献
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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
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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