共查询到17条相似文献，搜索用时 109 毫秒
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.
《海洋预报》2016,(5)
基于耦合模式比较计划第五阶段(CMIP5)的全球气候预估数据,分析了黄、渤海区域内海表面2m气温的增量,并将该增量叠加在1978—2008年的再分析气象场上,驱动海冰-海洋耦合模式,对2015—2045年黄、渤海的海冰变化特征进行了预估。结果显示:在RCP2.6、RCP4.5、RCP6.0和RCP8.5 4种排放情景下,辽东湾、渤海湾、莱州湾和黄海北部4个海湾的海冰均呈现显著减少的趋势。但随着排放增多,4个海湾的海冰并非单调的减少,而在RCP4.5下减少最多,RCP6.0和RCP8.5次之,RCP2.6最少。对4种情景下的海冰冰情进行平均,可以发现4个海湾结冰面积依次减少438、121、23和84 km2;结冰范围依次减少9、7、2和7 nmi(海里,1 nmi=1.852 km)。就整个黄、渤海而言,未来31a内结冰面积减少24%,结冰范围减少19%,持续天数缩短10%。 相似文献
3.
CMIP5模式对南海SST的模拟和预估 总被引：4，自引：1，他引：3
分析了32个CMIP5模式对南海历史海表温度(SST)的模拟能力和不同排放情景下未来SST变化的预估。通过检验各气候模式对南海历史SST增温趋势和均方差的模拟,发现大部分模式都能较好地模拟出南海20世纪历史SST的基本特征和变化规律,但也有部分模式的模拟存在较大偏差。尽管这些模拟偏差较大的模式对SST多模式集合平均的影响不大,但会增加未来情景预估的不确定性。剔除15个模式后,分析了南海SST在RCP26、RCP45和RCP85三种排放情景下的变化趋势,发现在未来百年呈明显的增温趋势,多模式集合平均的增温趋势分别为0.42、1.50和3.30℃/(100a)。这些增温趋势在空间上变化不大,但随时间并不是均匀变化的。在前两种排放情景下,21世纪前期的增温趋势明显强于后期,而在RCP85情景下,21世纪后期的增温趋势强于前期。 相似文献
4.
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
5.
《海洋预报》2016,(6)
基于中国气象局热带气旋最佳路径数据集,结合7个全球耦合模式在4个气候情景(Historical、RCP2.6、RCP4.5、RCP8.5)下的模拟场,对比分析了模式模拟1986—2005年登陆我国热带气旋(LTC)活动的气候特征,并评估了未来(2026—2045年)不同气候情景下LTC活动的频数和强度变化特征。结果表明:在Historical情景下虽然各模式模拟的1986—2005年LTC均少于观测值,但仍然较好的再现LTC的季节分布、地理位置分布和强度分布特征。未来气候情景下不同强度LTC的频数预估则显示,相对于Historical情景,RCP2.6和RCP8.5情景下较弱的LTC有减少的趋势,而较强的LTC则表现为略微的增加。另外,对比不同模式的结果可以发现,模式中若中国大陆近海区域平均垂直风切变和海平面气压较大,则其对应的LTC活动较少;若模式中海表温度较高,则LTC的平均登陆强度较大。 相似文献
6.
观测显示过去几十年间北极入海径流呈现增加趋势,CMIP5耦合模式预测表明21世纪北极入海径流仍会增加,在RCP8.5路径下,21世纪末北极入海径流量将会是1950年的1.4倍。本文利用冰-海耦合数值模式研究了北极径流增加对大西洋经向翻转环流的影响。基于两个数值实验的结果表明,如果北极入海径流按每年0.22%的速度（与RCP8.5路径下的速度相当）增加,大西洋经向翻转环流的强度在100、150和200年后将会分别减弱0.6（3%）、1.2（7%）和1.8（11%） Sv。北极入海径流增加导致大西洋经向翻转环流减弱的主要原因是,北极入海径流增加的淡水被输运到北大西洋后,会抑制北大西洋深层水的生成,这也会导致北大西洋深层水海水年龄的增加。 相似文献
7.
基于参与第六次耦合模式比较计划（CMIP6）的8个地球系统耦合模式所输出的历史模拟结果,本文通过与观测对比,评估了CMIP6模式对东南印度洋亚南极模态水的模拟能力,并预估了在中等强迫情景和高强迫情景下,该模态水潜沉率、体积及性质的变化趋势。结果表明:与Argo观测相比,CMIP6模式中南印度洋混合层偏深且上层海洋的位势密度偏小,因此其模拟的东南印度洋亚南极模态水潜沉率偏大而位势密度偏小。不同CMIP6模式之间模拟的东南印度洋亚南极模态水潜沉区存在差异,混合层侧向输入是导致这一差异的主要原因。此外,在历史模拟和两种情景试验中,东南印度洋亚南极模态水均呈现出潜沉率和体积减小、温度升高、盐度和密度降低的趋势。其中,在高强迫情景下,变化趋势最大,中等强迫情景次之,历史模拟中的变化趋势最小。这表明,辐射强迫越强,东南印度洋海表温度升高和淡水输入增加的趋势越大,导致混合层变浅及其南北梯度减小的趋势越快,东南印度洋亚南极模态水潜沉率、体积和性质变化的趋势也随之增大。 相似文献
8.
北极海冰冰盖自20世纪以来经历了前所未有的缩减，这使得北极海冰异常对大气环流的反馈作用日益显现。尽管目前的气候模式模拟北极海冰均为减少的趋势，但各模式间仍然存在较大的分散性。为了评估模式对于北极海冰变化及其气候效应的模拟能力，我们将海冰线性趋势和年际异常两者结合起来构造了一种合理的衡量指标。我们还强调巴伦支与卡拉海的重要性，因为前人研究证明此区域海冰异常是近年来影响大尺度大气环流变异的关键因子。根据我们设定的标准，CMIP5模式对海冰的模拟可被归为三种类型。这三组多模式集合平均之间存在巨大的差异，验证了这种分组方法的合理性。此外，我们还进一步探讨了造成模式海冰模拟能力差别的潜在物理因子。结果表明模式所采用的臭氧资料集对海冰模拟能力有显著的影响。 相似文献
9.
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
10.
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
11.
This paper is focused on the seasonality change of Arctic sea ice extent(SIE) from 1979 to 2100 using newly available simulations from the Coupled Model Intercomparison Project Phase 5(CMIP5).A new approach to compare the simulation metric of Arctic SIE between observation and 31 CMIP5 models was established.The approach is based on four factors including the climatological average,linear trend of SIE,span of melting season and annual range of SIE.It is more objective and can be popularized to other comparison of models.Six good models(GFDL-CM3,CESM1-BGC,MPI-ESM-LR,ACCESS-1.0,Had GEM2-CC,and Had GEM2-AO in turn) are found which meet the criterion closely based on above approach.Based on ensemble mean of the six models,we found that the Arctic sea ice will continue declining in each season and firstly drop below 1 million km~2(defined as the ice-free state) in September 2065 under RCP4.5 scenario and in September 2053 under RCP8.5 scenario.We also study the seasonal cycle of the Arctic SIE and find out the duration of Arctic summer(melting season) will increase by about 100 days under RCP4.5 scenario and about 200 days under RCP8.5 scenario relative to current circumstance by the end of the 21 st century.Asymmetry of the Arctic SIE seasonal cycle with later freezing in fall and early melting in spring,would be more apparent in the future when the Arctic climate approaches to "tipping point",or when the ice-free Arctic Ocean appears.Annual range of SIE(seasonal melting ice extent) will increase almost linearly in the near future 30–40 years before the Arctic appears ice-free ocean,indicating the more ice melting in summer,the more ice freezing in winter,which may cause more extreme weather events in both winter and summer in the future years. 相似文献
12.
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
13.
基于AMSR-E数据的多年冰密集度反演算法研究 总被引：2，自引：1，他引：1
In recent years, the rapid decline of Arctic sea ice area(SIA) and sea ice extent(SIE), especially for the multiyear(MY) ice, has led to significant effect on climate change. The accurate retrieval of MY ice concentration retrieval is very important and challenging to understand the ongoing changes. Three MY ice concentration retrieval algorithms were systematically evaluated. A similar total ice concentration was yielded by these algorithms, while the retrieved MY sea ice concentrations differs from each other. The MY SIA derived from NASA TEAM algorithm is relatively stable. Other two algorithms created seasonal fluctuations of MY SIA, particularly in autumn and winter. In this paper, we proposed an ice concentration retrieval algorithm, which developed the NASA TEAM algorithm by adding to use AMSR-E 6.9 GHz brightness temperature data and sea ice concentration using 89.0GHz data. Comparison with the reference MY SIA from reference MY ice, indicates that the mean difference and root mean square(rms) difference of MY SIA derived from the algorithm of this study are 0.65×106 km2 and0.69×106 km2 during January to March, –0.06×106 km2 and 0.14×106 km2 during September to December respectively. Comparison with MY SIE obtained from weekly ice age data provided by University of Colorado show that, the mean difference and rms difference are 0.69×106 km2 and 0.84×106 km2, respectively. The developed algorithm proposed in this study has smaller difference compared with the reference MY ice and MY SIE from ice age data than the Wang's, Lomax' and NASA TEAM algorithms. 相似文献
14.
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
15.
A coupled ice-ocean model is configured for the pan-Arctic and northern North Atlantic Ocean with a 27.5 km resolution. The model is driven by the daily atmospheric climatology averaged from the 40-year NCEP reanalysis (1958–1997). The ocean model is the Princeton Ocean Model (POM), while the sea ice model is based on a full thermodynamical and dynamical model with plastic-viscous rheology. A sea ice model with multiple categories of thickness is utilized. A systematic model-data comparison was conducted. This model reasonably reproduces seasonal cycles of both the sea ice and the ocean. Climatological sea ice areas derived from historical data are used to validate the ice model performance. The simulated sea ice cover reaches a maximum of 14 × 106 km2 in winter and a minimum of 6.7 × 106 km2 in summer. This is close to the 95-year climatology with a maximum of 13.3 × 106 km2 in winter and a minimum of 7 × 106 km2 in summer. The simulated general circulation in the Arctic Ocean, the GIN (Greenland, Iceland, and Norwegian) seas, and northern North Atlantic Ocean are qualitatively consistent with historical mapping. It is found that the low winter salinity or freshwater in the Canada Basin tends to converge due to the strong anticyclonic atmospheric circulation that drives the anticyclonic ocean surface current, while low summer salinity or freshwater tends to spread inside the Arctic and exports out of the Arctic due to the relaxing wind field. It is also found that the warm, saline Atlantic Water has little seasonal variation, based on both simulation and observations. Seasonal cycles of temperature and salinity at several representative locations reveals regional features that characterize different water mass properties. 相似文献
16.
夏江江 严中伟 周文 FONG Soi Kun LEONG Ka Cheng TANG Iu Man CHANG S W LEONG W K 靳少非 《海洋学报(英文版)》2015,34(9):78-84
Projections of potential submerged area due to sea level rise are helpful for improving understanding of the influence of ongoing global warming on coastal areas. The Ensemble Empirical Mode Decomposition method is used to adaptively decompose the sea level time series in order to extract the secular trend component. Then the linear relationship between the global mean sea level(GMSL) change and the Zhujiang(Pearl) River Delta(PRD)sea level change is calculated: an increase of 1.0 m in the GMSL corresponds to a 1.3 m(uncertainty interval from1.25 to 1.46 m) increase in the PRD. Based on this relationship and the GMSL rise projected by the Coupled Model Intercomparison Project Phase 5 under three greenhouse gas emission scenarios(representative concentration pathways, or RCPs, from low to high emission scenarios RCP2.6, RCP4.5, and RCP8.5), the PRD sea level is calculated and projected for the period 2006–2100. By around the year 2050, the PRD sea level will rise 0.29(0.21 to 0.40) m under RCP2.6, 0.31(0.22 to 0.42) m under RCP4.5, and 0.34(0.25 to 0.46) m under RCP8.5, respectively.By 2100, it will rise 0.59(0.36 to 0.88) m, 0.71(0.47 to 1.02) m, and 1.0(0.68 to 1.41) m, respectively. In addition,considering the extreme value of relative sea level due to land subsidence(i.e., 0.20 m) and that obtained from intermonthly variability(i.e., 0.33 m), the PRD sea level will rise 1.94 m by the year 2100 under the RCP8.5scenario with the upper uncertainty level(i.e., 1.41 m). Accordingly, the potential submerged area is 8.57×103 km2 for the PRD, about 1.3 times its present area. 相似文献
17.
N. G. Yakovlev 《Izvestiya Atmospheric and Oceanic Physics》2009,45(4):478-494
This paper presents the results of reconstructing the state of ice and snow covers on the Arctic Ocean from 1948 to 2002 obtained
with a couplod model of ocean circulation and sea-ice evolution. The area of the North Atlantic and Arctic Ocean north of
65° N, excluding Hudson Bay, is considered. The monthly mean ice areas and extents are analyzed. The trends of these areas
are calculated separately for the periods of 1970–1979, 1979–1990, and 1990–2002. A systematic slight underestimation by the
model is observed for the ice extent. This error is estimated to fit the error of 100 km in determining the position of the
ice edge (i.e., close to the model resolution). In summer the model fails to reproduce many observed polynias: by observational
data, the ice concentration in the central part of the Arctic Ocean constitutes around 0.8, while the model yields around
0.99. The average trend for the area of ice propagation in 1960–2002 is 13931 km2/year (or approximately 2% per decade); the trend of the ice area is 17643 km2/year (or approximately 3% per decade). This is almost three times lower than satellite data. The calculated data for ice
thickness in the late winter varies from 3.5 to 4.8 m, with a clear indication of periods of thick ice (the 1960s–1970s) and
relatively thin ice (the 1980s); 1995 is the starting point for quick ice-area reduction. The maximum ice accumulation is
in 1977 and 1988; here, the average trend is negative: −121 km3/year (or approximately 5.5% per decade). In 1996–2002, the average change in the ice thickness constituted +1.7 cm/year.
This speaks to the relatively fast disappearance of thin-ice fractions. This model also slightly underestimates the snow mass
with a trend of −2.5 km3/year (almost 0.35 mm of snow per year or 0.1 mm of liquid water per year). An analysis of the monthly mean ice-drift velocity
indicates the good quality of the model. Data on the average drift velocity and the results of comparisons between the calculated
and satellite data for individual months are presented. A comparison with observational data from 1990–1996 in the Fram Strait
shows that the model yields 3.28 m for the average ice thickness against the observed value of approximately 3.26 m. For the
same period, the model yields a monthly mean transport of 291.29 km3 as compared to the observed value of 237.17 km3. A comparison between the measured and calculated drift velocities in the Fram Strait indicates that the model value is around
9.78 cm/s, which is comparable to the measured value of 10.2 cm/s. The existing problems with describing the ice redistribution
by thickness gradations are illustrated by comparing data on ice thickness in the Fram Strait. 相似文献