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1.
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
2.
地球系统模式FIO-ESM对北极海冰的模拟和预估 总被引：5，自引：3，他引：2
评估了地球系统模式FIO-ESM(First Institute of Oceanography-Earth System Model)基于CMIP5(Coupled Model Intercomparison Project Phase 5)的历史实验对北极海冰的模拟能力,分析了该模式基于CMIP5未来情景实验在不同典型浓度路径(RCPs,Representative Concentration Pathways)下对北极海冰的预估情况。通过与卫星观测的海冰覆盖范围资料相比,该模式能够很好地模拟出多年平均海冰覆盖范围的季节变化特征,模拟的气候态月平均海冰覆盖范围均在卫星观测值±15%范围以内。FIO-ESM能够较好地模拟1979-2005年期间北极海冰的衰减趋势,模拟衰减速度为每年减少2.24×104 km2,但仍小于观测衰减速度(每年减少4.72×104 km2)。特别值得注意的是:不同于其他模式所预估的海冰一直衰减,FIO-ESM对21世纪北极海冰预估在不同情景下呈现不同的变化趋势,在RCP2.6和RCP4.5情景下,北极海冰总体呈增加趋势,在RCP6情景下,北极海冰基本维持不变,而在RCP8.5情景下,北极海冰呈现继续衰减趋势。 相似文献
3.
北极夏季海冰的快速减少使得北极航道提前开通成为可能。为了给北极冰区船运活动提供及时可靠有效的海冰预报保障,急需提高海冰预报水平。本文基于麻省理工大学通用环流模式(MITgcm),使用牛顿松弛逼近(Nudging)资料同化方法将德国不莱梅大学的第二代先进微波辐射成像仪(AMSR2)海冰密集度资料同化到模式中,建立了北极海冰数值预报系统。设计试验对比3种不同Nudging系数计算方案的改进效果,结果表明选择合适参数后,不同方案均能显著改进海冰密集度初始场。通过设计有无Nudging同化的两组预报试验,结合卫星遥感海冰密集度及中国第五次北极科学考察期间"雪龙"船的走航海冰密集度观测数据,定量分析了Nudging同化方案对北极海冰密集度的24~120 h预报结果的改进效果。结果表明,Nudging同化对120 h内全北极海冰密集度的空间分布和移动单点目标的海冰密集度预报结果均有显著改善;但在海冰变化很小的情况下,Nudging同化试验的24~120 h预报结果均劣于惯性预报结果,说明基于Nudging同化的数值预报系统还需进一步提高预报技巧。 相似文献
4.
本文系统地评估了国家海洋环境预报中心于我国第七次北极科学考察期间开展的北极海冰密集度数值预报结果。该预报系统基于麻省理工大学通用环流模式，并采用牛顿松弛逼近（Nudging）资料同化方法，计算输出未来1~5 d的北极海冰密集度预报产品。本文将数值预报结果同卫星观测的海冰密集度、再分析资料和"雪龙"号第七次北极考察期间观测的海冰密集度数据进行了对比分析。结果表明，预报的北极海冰密集度小于卫星观测值，24 h、72 h和120 h预报结果的偏差分别为-2.7%、-3.1%和-3.2%；数值产品的预报技巧好于气候态结果和惯性预报，但是在海冰出现快速融化或冻结时，基于Nudging同化的数值预报技巧仍有不足。另外，相比船测数据，数值预报结果在海冰边缘区的偏差相对较大，24 h、72 h和120 h预报结果的偏差分别为8.8%、12.0%和14.5%。 相似文献
5.
误差订正对2018年夏季次季节尺度海冰预测的作用 总被引：1，自引：1，他引：0
北极海冰次季节尺度预测在针对破冰船和商船的实际服务中十分重要，但常常受制于气候模拟的模拟能力。本研究提出了一种误差订正方法并分别应用到两个气候模式：海洋一所地球系统模式（FIOESM）和美国国家环境预报中心（NCEP）的气候预报系统（CFS），来改善北极海冰60天尺度的预测。本研究的预测工作是中国第9次北极科学考察和2018年夏季中远集团北极商业航行的业务化海冰服务保障的重要部分。模式起报时间分别是2018年7月1日、8月1日和9月1日，预报时效均是60天。结果显示，FIOESM整体上低估了海冰密集度的数值，平均偏差可达30%。误差订正对海冰密集度（SIC）的均方根偏差（RMSE）的改进比例可达27%，对海冰外缘线（SIE）的整体偏差（IIEE）的改进比例为10%。而对于CFS，SIE在边缘区域的过高估计是其主要特点。误差订正导致了SIC的RMSE改进了7%，而对SIE的IIEE改进了17%。在海冰范围预测方面，FIOESM预测的最小范围数值和时间点都和观测接近，而CFS的预测结果偏差较大。另外和其他S2S模式的结果比较发现，本研究提出的误差订正方法对存在较大偏差的预测结果改进更为有效。 相似文献
6.
北极地区不同冰龄的海冰厚度变化研究 总被引：1，自引：0，他引：1
In this study, changes in Arctic sea ice thickness for each ice age category were examined based on satellite observations and modelled results. Interannual changes obtained from Ice, Cloud, and Land Elevation Satellite(ICESat)-based results show a thickness reduction over perennial sea ice(ice that survives at least one melt season with an age of no less than 2 year) up to approximately 0.5–1.0 m and 0.6–0.8 m(depending on ice age) during the investigated winter and autumn ICESat periods, respectively. Pan-Arctic Ice Ocean Modeling and Assimilation System(PIOMAS)-based results provide a view of a continued thickness reduction over the past four decades. Compared to 1980 s, there is a clear thickness drop of roughly 0.50 m in 2010 s for perennial ice. This overall decrease in sea ice thickness can be in part attributed to the amplified warming climate in north latitudes. Besides, we figure out that strongly anomalous southerly summer surface winds may play an important role in prompting the thickness decline in perennial ice zone through transporting heat deposited in open water(primarily via albedo feedback) in Eurasian sector deep into a broader sea ice regime in central Arctic Ocean. This heat source is responsible for enhanced ice bottom melting, leading to further reduction in ice thickness. 相似文献
7.
The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the midlatitudes but also brings more opportunities for Arctic shipping routes and polar resource exploration, both of which motivate us to further understand causes of sea-ice variations and to obtain more accurate estimates of seaice cover in the future. Here, a novel data-driven method, the causal effect networks algorithm, is applied to identify the direct precursors of September sea-ice extent covering the Northern Sea Route and Transpolar Sea Route at different lead times so that statistical models can be constructed for sea-ice prediction. The whole study area was also divided into two parts: the northern region covered by multiyear ice and the southern region covered by seasonal ice. The forecast models of September sea-ice extent in the whole study area(TSIE) and southern region(SSIE) at lead times of 1–4 months can explain over 65% and 79% of the variances, respectively,but the forecast skill of sea-ice extent in the northern region(NSIE) is limited at a lead time of 1 month. At lead times of 1–4 months, local sea-ice concentration and sea-ice thickness have a larger influence on September TSIE and SSIE than other teleconnection factors. When the lead time is more than 4 months, the surface meridional wind anomaly from northern Europe in the preceding autumn or early winter is dominant for September TSIE variations but is comparable to thermodynamic factors for NSIE and SSIE. We suggest that this study provides a complementary approach for predicting regional sea ice and is helpful in evaluating and improving climate models. 相似文献
8.
北极海冰密集度预报对大气强迫敏感性的个例研究 总被引：3，自引：0，他引：3
YANG Qinghu LIU Jiping ZHANG Zhanhai SUI Cuijuan XING Jianyong LI Ming LI Chunhu ZHAO Jiechen ZHANG Lin 《海洋学报(英文版)》2014,33(12):15-23
A regional Arctic configuration of the Massachusetts Institute of Technology general circulation model （MIT-gcm） is used as the coupled ice-ocean model for forecasting sea ice conditions in the Arctic Ocean at the Na-tional Marine Environmental Forecasting Center of China （NMEFC）, and the numerical weather prediction from the National Center for Environmental Prediction Global Forecast System （NCEP GFS） is used as the atmospheric forcing. To improve the sea ice forecasting, a recently developed Polar Weather Research and Forecasting model （Polar WRF） model prediction is also tested as the atmospheric forcing. Their forecasting performances are evaluated with two different satellite-derived sea ice concentration products as initializa-tions： （1） the Special Sensor Microwave Imager/Sounder （SSMIS） and （2） the Advanced Microwave Scanning Radiometer for EOS （AMSR-E）. Three synoptic cases, which represent the typical atmospheric circulations over the Arctic Ocean in summer 2010, are selected to carry out the Arctic sea ice numerical forecasting experiments. The evaluations suggest that the forecasts of sea ice concentrations using the Polar WRF atmo-spheric forcing show some improvements as compared with that of the NCEP GFS. 相似文献
9.
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
10.
Arctic sea ice volume export through the Fram Strait from combined satellite and model data: 1979–2012 总被引：1，自引：0，他引：1
By combing satellite-derived ice motion and concentration with ice thickness fields from a popular model PIOMAS we obtain the estimates of ice volume flux passing the Fram Strait over the 1979–2012 period. Since current satellite and field observations for sea ice thickness are limited in time and space, the use of PIOMAS is expected to fill the gap by providing temporally continued ice thickness fields. Calculated monthly volume flux exhibits a prominent annual cycle with the peak record in March(roughly 145 km3/month) and the trough in August(10 km~3/month). Annual ice volume flux(1 132 km~3) is primarily attributable to winter(October through May) outflow(approximately 92%). Uncertainty in annual ice volume export is estimated to be 55 km~3(or 5.7%). Our results also verified the extremely large volume flux appearing between late 1980 s and mid-1990 s. Nevertheless, no clear trend was found in our volume flux results. Ice motion is the primary factor in the determination of behavior of volume flux. Ice thickness presented a general decline trend may partly enhance or weaken the volume flux trend. Ice concentration exerted the least influences on modulating trends and variability in volume flux. Moreover, the linkage between winter ice volume flux and three established Arctic atmospheric schemes were examined. Compared to NAO, the DA and EOF3 mechanism explains a larger part of variations of ice volume flux across the strait. 相似文献
11.
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
12.
The seasonal prediction of sea surface temperature(SST) and precipitation in the North Pacific based on the hindcast results of The First Institute of Oceanography Earth System Model(FIO-ESM) is assessed in this study.The Ensemble Adjusted Kalman Filter assimilation scheme is used to generate initial conditions, which are shown to be reliable by comparison with the observations. Based on this comparison, we analyze the FIO-ESM 6-month hindcast results starting from each month of 1993–2013. The model exhibits high SST prediction skills over most of the North Pacific for two seasons in advance. Furthermore, it remains skillful at long lead times for midlatitudes. The reliable prediction of SST can transfer fairly well to precipitation prediction via air-sea interactions.The average skill of the North Pacific variability(NPV) index from 1 to 6 months lead is as high as 0.72(0.55) when El Ni?o-Southern Oscillation and NPV are in phase(out of phase) at initial conditions. The prediction skill of the NPV index of FIO-ESM is improved by 11.6%(23.6%) over the Climate Forecast System, Version 2. For seasonal dependence, the skill of FIO-ESM is higher than the skill of persistence prediction in the later period of prediction. 相似文献
13.
Numerical sea ice prediction in China 总被引：5，自引：2，他引：3
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14.
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
15.
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 总被引：2，自引：2，他引：0
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。 相似文献
16.
北极海冰输出研究综述 总被引：1，自引：1，他引：0
北极海冰对全球气候变化起重要的指示作用。除了海水冻结和融化过程以外,通过弗拉姆海峡(Fram Strait)的海冰输出也是影响北极海冰质量变化的重要动力机制。观测数据中的多源卫星遥感数据(尤其是辐射计观测数据)在获取大尺度连续观测方面具有独特的优势,在研究北极海冰输出面积通量变化方面有着广泛应用。本文总结了北极弗拉姆海峡、其他通道(S-FJL、FJL-SZ、加拿大群岛、Nares海峡通道)海冰输出面积或体积通量,着重介绍了弗拉姆海峡不同年龄海冰输出情况,并总结和分析了影响北极海冰输运的大尺度大气活动模态。最后,本文阐明北极海冰输出方面现有研究的不足之处以及未来的突破方向。 相似文献
17.
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
18.
海冰管理是抵御寒区海洋资源开发海冰威胁的有效手段,海冰风险的准确、快速预测是海冰管理系统的关键组成部分。文中面向海冰管理中的冰情短时预测需求,明确了基于现场监测的海冰风险预测模式,开展了应用机械学习理论的海冰风险短时预测方法研究,并以渤海辽东湾海冰管理为例,讨论了神经网络与小波分解等非线性预测方法在冰情短时预测中的适用性。结果表明,时间序列小波神经网络在短时(6 h)冰厚预测中的预测精度与Elman神经网络相仿,而在24~48 h预测中的精度偏差较大;Elman神经网络在6 h、24 h与48 h的冰厚预测中均能保持较好的预测精度,在冰流速与来冰方向预测中,模型预测精度达到80%左右。 相似文献
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The HY-1A satellite is the first oceanic satellite of China. During the winter of 2002～2003, the data of the HY-1A were applied to the sea ice monitoring and forecasting for the Bohai Sea of China for the first time. The sea ice retrieval system of the HY-1A has been constructed. It receives 1B data from the satellite, outputs sea ice images and provides digital products of ice concentration, ice thickness and ice edge, which can be used as important information for sea ice monitoring and the initial fields of the numeric sea ice forecast and as one of the reference data for the sea ice forecasting verification. The sea ice retrieval system of the satellite is described, including its processes, methods and parameters. The retrieving results and their application to the sea ice monitoring and forecasting for the Bohai Sea are also discussed. 相似文献
20.
The HY-1A satellite is the first oceanic satellite of China. During the winter of 2002-2003, the data of the HY-1A were applied to the sea ice monitoring and forecasting for the Bohai Sea of China for the fhst time. The sea ice retrieval system of the HY-1A has been constructed. It receives 1B data from the satellite, outputs sea ice images and provides digital products of ice concentration, ice thickness and ice edge, which can be used as important information for sea ice monitoring and the initial fields of the numeric sea ice forecast and as one of the reference data for the sea ice forecasting verification. The sea ice retrieval system of the satellite is described, including its processes, methods and parameters. The retrieving results and their application to the sea ice monitoring and forecasting for the Bohai Sea are also discussed. 相似文献