共查询到20条相似文献，搜索用时 15 毫秒
1.
Effect of compressive strength on the performance of the NEMO-LIM model in Arctic Sea ice simulation
Satellite records show that the extent and thickness of sea ice in the Arctic Ocean have significantly decreased since the early 1970s. The prediction of sea ice is highly important, but accurate simulation of sea ice variations remains highly challenging. For improving model performance, sensitivity experiments were conducted using the coupled ocean and sea ice model (NEMO-LIM), and the simulation results were compared against satellite observations. Moreover, the contribution ratios of dynamic and thermodynamic processes to sea ice variations were analyzed. The results show that the performance of the model in reconstructing the spatial distribution of Arctic sea ice is highly sensitive to ice strength decay constant (Crhg). By reducing the Crhg constant, the sea ice compressive strength increases, leading to improved simulated sea ice states. The contribution of thermodynamic processes to sea ice melting was reduced due to less deformation and fracture of sea ice with increased compressive strength. Meanwhile, dynamic processes constrained more sea ice to the central Arctic Ocean and contributed to the increases in ice concentration, reducing the simulation bias in the central Arctic Ocean in summer. The root mean square error (RMSE) between modeled and the CryoSat-2/SMOS satellite observed ice thickness was reduced in the compressive strength-enhanced model solution. The ice thickness, especially of multiyear thick ice, was also reduced and matched with the satellite observation better in the freezing season. These provide an essential foundation on exploring the response of the marine ecosystem and biogeochemical cycling to sea ice changes.
相似文献2.
Snow and sea ice thermodynamics in the Arctic： Model validation and sensitivity study against SHEBA data 总被引：2，自引：0，他引：2
Timo Vihma 《极地研究(英文版)》2008,19(2):108-122
Evolution of the Arctic sea ice and its snow cover during the SHEBA year were simulated by applying a high-resolution thermodynamic snow/ice model （HIGHTSI）. Attention was paid to the impact of albedo on snow and sea ice mass balance, effect of snow on total ice mass balance, and the model vertical resolution. The SHEBA annual simulation was made applying the best possible external forcing data set created by the Sea Ice Model Intercomparison Project. The HIGHTSI control run reasonably reproduced the observed snow and ice thickness. A number of albedo schemes were incorporated into HIGHTSI to study the feedback processes between the albedo and snow and ice thickness. The snow thickness turned out to be an essential variable in the albedo parameterization. Albedo schemes dependent on the surface temperature were liable to excessive positive feedback effects generated by errors in the modelled surface temperature. The superimposed ice formation should be taken into account for the annual Arctic sea ice mass balance. 相似文献
3.
INTRODUCTIONAnimportantachievementofoceanographysincethe 1960swasthediscoveryofmesoscaleed dieswithspatialscaleofhundredsofmeters,andtimescaleofhours;andaverageflowvelocityofabout 10cm s.Theenormousenergyofthemesoscaleeddyiscomparabletothatofacycloneoran ticycloneintheatmosphere .Themesoscaleeddyisoneoftheimportantfactorsthatdecidethechangeoftheocean .Intherecentdecades,ChineseandforeignscientistshavedonelotsofworkontheEastChinaSeasmesoscaleeddies,theformationmechanismofwhicharethefocuso… 相似文献
4.
A regional sea ice-ocean coupled model for the Arctic Ocean was developed, based on the MITgcm ocean circulation model and classical Hibler79 type two categorythermodynamics-dynamics sea ice model. The sea ice dynamics and thermodynamicswere considered based on Viscous-Plastic (VP) and Winton three-layer models, respectively. A detailed configuration of coupled model has been introduced. Special attention has been paid to the model grid setup, subgrid paramerization, ice-ocean coupling and open boundary treatment. The coupled model was then applied and two test run examples were presented. The first model run was a climatology simulation with 10 years (1992?002) averaged NCAR/NCEP reanalysis data as atmospheric forcing. The second model run was a seasonal simulation for the period of 1992?007. The atmospheric forcing was daily NCAR/NCEP reanalysis. The climatology simulation captured the general pattern of the sea ice thickness distribution of the Arctic, i.e., the thickest sea ice is situated around the CanadaArchipelago and the north coast of the Greenland. For the second model run, themodeled September Sea ice extent anomaly from 1992?007 was highly correlated with the observations, with a linear correlation coefficient of 0.88. Theminimum of the Arctic sea ice area in the September of 2007 was unprecedented. The modeled sea ice area and extent for this minimum was overestimated relative to the observations. However, it captured the general pattern of the sea ice retreat. 相似文献
5.
This study revisits the Arctic sea ice extent(SIE) for the extended period of 1979-2015 based on satellite measurements and finds that the Arctic SIE experienced three different periods: a moderate sea ice decline period for 1979-1996, an accelerated sea ice decline period from 1997 to 2006, and large interannual variation period after 2007, when Arctic sea ice reached its tipping point reported by Livina and Lenton(2013). To address the response of atmospheric circulation to the lowest sea ice conditions with a large interannual variation, we investigated the dominant modes for large atmospheric circulation responses to the projected 2007 Arctic sea ice loss using an atmospheric general circulation model(ECHAM5). The response was obtained from two 50-yr simulations: one with a repeating seasonal cycle of specified sea ice concentration for the period of 1979-1996 and one with that of sea ice conditions in 2007. The results suggest more occurrences of a negative Arctic Oscillation(AO) response to the 2007 Arctic sea ice conditions, accompanied by an North Atlantic Oscillation(NAO)-type atmospheric circulation response under the largest sea ice loss, and more occurrences of the positive Arctic Dipole(AD) mode under the 2007 sea ice conditions, with an across-Arctic wave train pattern response to the largest sea ice loss in the Arctic. This study offers a new perspective for addressing the response of atmospheric circulation to sea ice changes after the Arctic reached the tipping point in 2007. 相似文献
6.
Bi Haibo Liang Yu Wang Yunhe Liang Xi Zhang Zehua Du Tingqin Yu Qinglong Huang Jue Kong Mei Huang Haijun 《中国海洋湖沼学报》2020,38(4):962-984
In comparison with seasonal sea ice(first-year ice,FY ice),multiyear(MY) sea ice is thicker and has more opportunity to survive through the summer melting seasons.Therefore,the variability of wintertime MY ice plays a vital role in modulating the variations in the Arctic sea ice minimum extent during the following summer.As a response,the ice-ocean-atmosphere interactions may be significantly affected by the variations in the MY ice cover.Satellite observations are characterized by their capability to capture the spatiotemporal changes of Arctic sea ice.During the recent decades,many active and passive sensors onboard a variety of satellites(QuikSCAT,ASCAT,SSMIS,ICESat,CryoSat-2,etc.) have been used to monitor the dramatic loss of Arctic MY ice.The main objective of this study is to outline the advances and remaining challenges in monitoring the MY ice changes through the utilization of multiple satellite observations.We summarize the primary satellite data sources that are used to identify MY ice.The methodology to classify MY ice and derive MY ice concentration is reviewed.The interannual variability and trends in the MY ice time series in terms of coverage,thickness,volume,and age composition are evaluated.The potential causes associated with the observed Arctic MY ice loss are outlined,which are primarily related to the export and melting mechanisms.In addition,the causes to the MY ice depletion from the perspective of the oceanic water inflow from Pacific and Atlantic Oceans and the water vapor intrusion,as well as the roles of synoptic weather,are analyzed.The remaining challenges and possible upcoming research subjects in detecting the rapidly changing Arctic MY ice using the combined application of multisource remote sensing techniques are discussed.Moreover,some suggestions for the future application of satellite observations on the investigations of MY ice cover changes are proposed. 相似文献
7.
CryoSat-2卫星海冰区域波形识别及海冰干舷高确定 总被引：1，自引：0，他引：1
利用40%阈值法对CryoSat-2卫星波形数据进行重跟踪,将波形特征参数和海冰浓度相结合,对海冰和Lead(浮冰之间的开阔水域)进行有效识别。利用沿轨前后搜索算法计算海冰干舷高,并引用AWI结果,绘制2011~2013年北冰洋多年冰区域和一年冰区域平均海冰干舷高变化趋势图。比较本文结果与AWI结果的各年同期数据,验证本文结果的可靠性。 相似文献
8.
One of sea ice core samples was taken from Arctic by the First Chinese National Arctic Research Expedition Team in 1999. 20 vertical and 2 horizontal ice sections were cut out of the ice core sample 2.22 m in length, which covered the ice sheet from surface to bottom except losses for during sampling and section cutting. From the observation and analysis of the fabrics and crystals along the depth of the ice core sample, followings were found. Whole ice sheet consists of columnar, refrozen clastic pieces, granular, columnar, refrozen clastic pieces, granular, columnar and refrozen clastic pieces. This indicates that the ice core sample was 3-year old, and the ice sheet surface thawed and the melt water flowed into ice sheet during summer. Hence, the annual energy balance in Arctic can be determined by the ice sheet surface thawing in summer, and bottom growth in winter. The thickness of the ice sheet is kept constantly at a certain position based on the corresponding climate and ocean conditions; A new 相似文献
9.
Motoyoshi Ikeda 《极地研究(英文版)》2008,19(2):212-217
The sea ice cover in the Arctic Ocean has been reducing and hit the low record in the summer of 2007. The anomaly was extremely large in the Pacific sector. The sea level height in the Bering Sea vs. the Greenland Sea has been analyzed and compared with the current meter data through the Bering Strait. A recent peak existed as a consequence of atmospheric circulation and is considered to contribute to inflow of the Pacific Water into the Arctic Basin. The timing of the Pacific Water inflow matched with the sea ice reduction in the Pacific sector and suggests a significant increase in heat flux. This component should be included in the model prediction for answering the question when the Arctic sea ice becomes a seasonal ice cover. 相似文献
10.
Potential links between the Arctic sea-ice concentration anomalies and extreme precipitation in China are explored. Associations behind these links can be explained by physical interpretations aided by... 相似文献
11.
1 Introduction Itiswellknownthatseaiceinthepolarregionplaysanimportantroleintheglobal climatechangesasapartofclimatesystem(Carleton1989;YuanandMartinson2000, 2001;ChengandBian2002;LiuandMartinson2002;LiuandZhang2004;Gigorand Wallace2002etal).Infact,numerousmodelingstudiessuggestanimportantinfluence throughtheseaicefieldsalone(Grumbine1994,Meehl1990,Rindetal.1995).Inor dertounderstandthevariabilityofArcticandAntarcticseaicealongwiththepossiblecon nectionswithclimaticanomaliesindetail… 相似文献
12.
Sea ice is a sensitive indicator of climate change and an important component of climate system models. The Los Alamos Sea Ice Model 5.0(CICE5.0) was introduced to the Beijing Climate Center Climate System Model(BCC_CSM) as a new alternative to the Sea Ice Simulator(SIS). The principal purpose of this paper is to analyze the impacts of these two sea ice components on simulations of basic Arctic sea ice, atmosphere, and ocean states. Two sets of experiments were conducted with the same configurations except for the sea ice component used, i.e., SIS and CICE. The distributions of sea ice concentration and thickness reproduced by the CICE simulations in both March and September were closer to actual observations than those reproduced by SIS simulations, which presented a very thin sea ice cover in September. Changes in sea ice conditions also brought about corresponding modifications to the atmosphere and ocean circulation. CICE simulations showed higher agreement with the reference datasets than did SIS simulations for surface air temperature, sea level pressure, and sea surface temperature in most parts of the Arctic Ocean. More importantly, compared with simulations with SIS, BCC_CSM with CICE revealed stronger Atlantic meridional overturning circulation(AMOC), which is more consistent with actual observations. Thus, CICE shows better performance than SIS in BCC_ CSM. However, both components demonstrate a number of common weaknesses, such as overestimation of the sea ice cover in winter, especially in the Nordic Sea and the Sea of Okhotsk. Additional studies and improvements are necessary to develop these components further. 相似文献
13.
Results of numerical simulation of currents in the western North Tropical Pacific Ocean by using a barotropic primitive equation model with fine horizontal resolution agreed well with observations and showed that the Mindanao Cyclonic Eddy located north of the equator and east of Mindanao Island exists during most of the year with monthly (and large seasonal) variations in scope . strength and central location . In June , an anticyclonic eddy occurs northeast of Halmahera Island, strengthens to maximum in August , exists until October and then disappears . The observed large-scale circulation systems such as the North Equatorial Current . the Mindanao Current and the North Equatorial Countercurrent are all very well reproduced in the simulations. 相似文献
14.
Satellite-observed trends in the Arctic sea ice concentration for the period 1979–2016 总被引：1，自引：0，他引：1
Wang Yunhe Bi Haibo Huang Haijun Liu Yanxia Liu Yilin Liang Xi Fu Min Zhang Zehua 《中国海洋湖沼学报》2019,37(1):18-37
Arctic sea ice cover has decreased dramatically over the last three decades. This study quanti?es the sea ice concentration(SIC) trends in the Arctic Ocean over the period of 1979–2016 and analyzes their spatial and temporal variations. During each month the SIC trends are negative over the Arctic Ocean, wherein the largest(smallest) rate of decline found in September(March) is-0.48%/a(-0.10%/a).The summer(-0.42%/a) and autumn(-0.31%/a) seasons show faster decrease rates than those of winter(-0.12%/a) and spring(-0.20%/a) seasons. Regional variability is large in the annual SIC trend. The largest SIC trends are observed for the Kara(-0.60%/a) and Barents Seas(-0.54%/a), followed by the Chukchi Sea(-0.48%/a), East Siberian Sea(-0.43%/a), Laptev Sea(-0.38%/a), and Beaufort Sea(-0.36%/a). The annual SIC trend for the whole Arctic Ocean is-0.26%/a over the same period. Furthermore, the in?uences and feedbacks between the SIC and three climate indexes and three climatic parameters, including the Arctic Oscillation(AO), North Atlantic Oscillation(NAO), Dipole anomaly(DA), sea surface temperature(SST), surface air temperature(SAT), and surface wind(SW), are investigated. Statistically, sea ice provides memory for the Arctic climate system so that changes in SIC driven by the climate indices(AO, NAO and DA) can be felt during the ensuing seasons. Positive SST trends can cause greater SIC reductions, which is observed in the Greenland and Barents Seas during the autumn and winter. In contrast, the removal of sea ice(i.e., loss of the insulating layer) likely contributes to a colder sea surface(i.e., decreased SST), as is observed in northern Barents Sea. Decreasing SIC trends can lead to an in-phase enhancement of SAT, while SAT variations seem to have a lagged in?uence on SIC trends. SW plays an important role in the modulating SIC trends in two ways: by transporting moist and warm air that melts sea ice in peripheral seas(typically evident inthe Barents Sea) and by exporting sea ice out of the Arctic Ocean via passages into the Greenland and Barents Seas, including the Fram Strait, the passage between Svalbard and Franz Josef Land(S-FJL),and the passage between Franz Josef Land and Severnaya Zemlya(FJL-SZ). 相似文献
15.
北极海冰对全球气候起着非常重要的调制作用,海冰范围是海冰监测的基本参数。近40年,北极地区持续变暖,北极海冰显著减少,进而引发北极自然环境恶化、北半球极端天气频发、全球海平面上升等一系列环境和气候问题。准确获取北极海冰范围及其演变趋势,确定海冰变化对全球气候系统的响应,是研究和预测全球气候变化趋势的关键之一。HasISST和OISST海冰数据集在海冰监测中应用最为广泛,可为北极地区长时间序列海冰变化研究提供基础数据,但这2套数据集空间分辨率相对较低,应用于北极关键区对中国气候响应研究方面存在很大的局限,为解决这一问题和弥补国内海冰监测微波遥感数据的空白,2011年6月27日,国家卫星气象中心（National Satellite Meteorological Center, NSMC）发布了FY（Fengyun, FY）北极海冰数据集,该数据集利用搭载在FY卫星上的微波成像仪（Microwave Radiation Imager, MWRI）数据,使用Enhance NASA Team算法制作,该算法利用前向辐射传输模型模拟北极地区4种海表类型（海水、新生冰、一年冰和多年冰）在不同大气条件下MWRI辐射亮温,进而得到每种大气条件下0~100%的海冰覆盖度查找表（海冰覆盖度每次增加1%）,通过观测值与模拟值的比对得到海冰覆盖度,由该数据集计算得到的北极海冰范围在大部分区域与实际情况相符。该产品虽已进行通道间匹配误差修正和定位精度偏差订正,但由于其搭载的微波成像仪（Microwave Radiation Imager, MWRI）天线长度有限,造成传感器探测到的地物回波信号相对较弱,难以区分海冰和近岸附近的陆地,影响了该数据集的精度和应用。为解决这一问题,本文基于美国冰雪中心（National Snow and Ice Data Center, NSIDC）发布的海冰产品对FY海冰数据集进行优化,NSIDC产品利用判断矩阵对海岸线附近的像元进行识别,并对误差像元进行不同程度的修正,由NSIDC产品计算得到的北极海冰范围与实际情况更为符合。数据集优化大大提高了FY海冰数据集的精度,研究结果表明,优化后FY海冰数据集与NSIDC产品相关系数高达0.9997,且二者日、月、年平均最大海冰范围偏差仅为3.5%、1.9%、0.9%,且FY海冰数据集优化过程对其较好的空间分异特征无明显影响。该数据集可正确地反映北极海冰范围及其变化情况,且海岸线附近海冰的分布情况更准确,可为北极海冰变化研究提供可靠的基础数据。 相似文献
16.
本文应用统计方法分析陆雪和海冰与东亚夏季风的关系。分析结果表明:前期海冰和陆雪,对夏季风强度有影响,而与夏季风同时的海冰和陆雪的异常,却与夏季风相关甚小,这是由于大气状况的变化与下垫面的能量储放有关。本文初步探讨北极海冰对东亚夏季风影响的可能途径,认为海冰通过大西洋海温、大西洋副热带高压及青藏高压,由夏季对流层上层的东西热力环流圈和季风环流圈,对东亚夏季风起一定影响。 相似文献
17.
Remote sensing data from passive microwave and satellite-based altimeters, associated with the data measured underway, were used to characterize seasonal and spatial changes in sea ice conditions along... 相似文献
18.
In this paper,a Bayesian sea ice detection algorithm is first used based on the HY-2A/SCAT data,and a backpropagation(BP)neural network is used to classify the Arctic sea ice type.During the implementation of the Bayesian sea ice detection algorithm,linear sea ice model parameters and the backscatter variance suitable for HY-2A/SCAT were proposed.The sea ice extent obtained by the Bayesian sea ice detection algorithm was projected on a 12.5 km grid sea ice map and validated by the Advanced Microwave Scanning Radiometer 2(AMSR2)15%sea ice concentration data.The sea ice extent obtained by the Bayesian sea ice detection al-gorithm was found to be in good agreement with that of the AMSR2 during the ice growth season.Meanwhile,the Bayesian sea ice detection algorithm gave a wider ice edge than the AMSR2 during the ice melting season.For the sea ice type classification,the BP neural network was used to classify the Arctic sea ice type(multi-year and first-year ice)from January to May and October to De-cember in 2014.Comparison results between the HY-2A/SCAT sea ice type and Equal-Area Scalable Earth Grid(EASE-Grid)sea ice age data showed that the HY-2A/SCAT multi-year ice extent variation had the same trend as the EASE-Grid data.Classification errors,defined as the ratio of the mismatched sea ice type points between HY-2A/SCAT and EASE-Grid to the total sea ice points,were less than 12%,and the average classification error was 8.6%for the study period,which indicated that the BP neural network classification was a feasible algorithm for HY-2A/SCAT sea ice type classification. 相似文献
19.
The sea ice community plays an important role in the Arctic marine ecosystem. Because of the predicted environmental changes in the Arctic environment and specifically related to sea ice, the Arctic pack ice biota has received more attention in recent years using modem ice-breaking research vessels. Studies show that the Arctic pack ice contains a diverse biota and besides ice algae, the bacterial and protozoan biomasses can be high. Surprisingly high primary production values were observed in the pack ice of the central Arctic Ocean. Occasionally biomass maximum were discovered in the interior of the ice floes, a habitat that had been ignored in most Arctic studies. Many scientific questions, which deserve special attention, remained unsolved due to logistic limitations and the sea ice characteristics. Little is know about the pack ice community in the central Arctic Ocean. Almost no data exists from the pack ice zone for the winter season. Concerning the abundance of bacteria and protozoa, more studies are needed to understand the microbial network within the ice and its role in material and energy flows. The response of the sea ice biota to global change will impact the entire Arctic marine ecosystem and a long-term monitoring program is needed. The techniques, that are applied to study the sea ice biota and the sea ice ecology, should be improved. 相似文献
20.
北极熊是北极最重要的哺乳动物之一,近年来数量却在减少。海冰作为北极熊狩猎、活动和繁殖的平台,是其栖息地的重要组成部分。因此其种群栖息地变化主要依赖于海冰变化。本文基于美国雪冰中心的海冰密集度和NOAA提供的ETOPO1基岩数据,分析了北极海冰密集度、开阔水域面积、海冰消退时间、海冰出现时间、开阔水域季节长度的年际变化,进而评价北极熊栖息地的稳定性。结果表明,海冰密集度呈现降低的趋势,开阔水域面积增大,多年冰数量减少,大多变为一年冰。海冰消退时间提前,海冰出现时间延后,开阔水域季节长度大幅增加,与1992年相比增加了72 d。19个栖息地中,巴伦支海是开阔水域面积和季节长度变化贡献最大的海域,增加速度分别为9.71×103 km2/a和71.69 d/10a。以开阔水域季节长度变化率为依据,将北极熊栖息地划分为稳定、次稳定和不稳定3个等级。总共有3个稳定栖息地,包括分布在相对其他栖息地而言纬度较低的楚科奇海、西哈得孙湾和南哈得孙湾。13个次稳定栖息地,包括拉普捷夫海、喀拉海、东格陵兰、巴芬湾、戴维斯海峡、福克斯湾、布西亚湾、麦克林托克海峡、梅尔维尔子爵海峡、挪威湾、北波弗特、南波弗特和兰开斯特海峡。3个不稳定栖息地,均位于70°N以北,包括北极盆地、巴伦支海和凯恩盆地。稳定区主要位于低纬度,不稳定区全部位于高纬度。该分级结果表明高纬度地区虽然海冰覆盖多,但是年际变化十分显著,不稳定的3个区域内北极熊对海冰变化适应时间更少,年际迁移变化大,对北极熊的生存发展更为不利。 相似文献