2007和2012年北极最小海冰范围空间分布不同的原因分析

Satellite records show the minimum Arctic sea ice extents(SIEs) were observed in the Septembers of 2007 and2012, but the spatial distributions of sea ice concentration reduction in these two years were quite different.Atmospheric circulation pattern and the upper-ocean state in summer were investigated to explain the difference.By employing the ice-temperature and ice-specific humidity(SH) positive feedbacks in the Arctic Ocean, this paper shows that in 2007 and 2012 the higher surface air temperature(SAT) and sea level pressure(SLP)accompanied by more surface SH and higher sea surface temperature(SST), as a consequence, the strengthened poleward wind was favorable for melting summer Arctic sea ice in different regions in these two years. SAT was the dominant factor influencing the distribution of Arctic sea ice melting. The correlation coefficient is –0.84 between SAT anomalies in summer and the Arctic SIE anomalies in autumn. The increase SAT in different regions in the summers of 2007 and 2012 corresponded to a quicker melting of sea ice in the Arctic. The SLP and related wind were promoting factors connected with SAT. Strengthening poleward winds brought warm moist air to the Arctic and accelerated the melting of sea ice in different regions in the summers of 2007 and 2012. Associated with the rising air temperature, the higher surface SH and SST also played a positive role in reducing summer Arctic sea ice in different regions in these two years, which form two positive feedbacks mechanism.     

Quantifying the contribution of natural variability to September Arctic sea ice decline

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.     

基于高效统计模型预测9月北极航道沿线海冰范围

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.     

An evaluation of the Arctic clouds and surface radiative fluxes in CMIP6 models

To assess the performances of state-of-the-art global climate models on simulating the Arctic clouds and surface radiation balance, the 2001–2014 Arctic Basin surface radiation budget, clouds, and the cloud radiative effects(CREs) in 22 coupled model intercomparison project 6(CMIP6) models are evaluated against satellite observations. For the results from CMIP6 multi-model mean, cloud fraction(CF) peaks in autumn and is lowest in winter and spring, consistent with that from three satellite observation products(Cloud Sat-CALIPSO, CERESMODIS, and APP-x). Simulated CF also shows consistent spatial patterns with those in observations. However,almost all models overestimate the CF amount throughout the year when compared to CERES-MODIS and APP-x.On average, clouds warm the surface of the Arctic Basin mainly via the longwave(LW) radiation cloud warming effect in winter. Simulated surface energy loss of LW is less than that in CERES-EBAF observation, while the net surface shortwave(SW) flux is underestimated. The biases may result from the stronger cloud LW warming effect and SW cooling effect from the overestimated CF by the models. These two biases compensate each other,yielding similar net surface radiation flux between model output(3.0 W/m~2) and CERES-EBAF observation(6.1 W/m~2). During 2001–2014, significant increasing trend of spring CF is found in the multi-model mean,consistent with previous studies based on surface and satellite observations. Although most of the 22 CMIP6 models show common seasonal cycles of CF and liquid water path/ice water path(LWP/IWP), large inter-model spreads exist in the amounts of CF and LWP/IWP throughout the year, indicating the influences of different cloud parameterization schemes used in different models. Cloud Feedback Model Intercomparison Project(CFMIP)observation simulator package(COSP) is a great tool to accurately assess the performance of climate models on simulating clouds. More intuitive and credible evaluation results can be obtained based on the COSP model output. In the future, with the release of more COSP output of CMIP6 models, it is expected that those inter-model spreads and the model-observation biases can be substantially reduced. Longer term active satellite observations are also necessary to evaluate models' cloud simulations and to further explore the role of clouds in the rapid Arctic climate changes.     

基于卫星高度计的北极海冰厚度变化研究

A modified algorithm taking into account the first year(FY) and multiyear(MY) ice densities is used to derive a sea ice thickness from freeboard measurements acquired by satellite altimetry ICESat(2003–2008). Estimates agree with various independent in situ measurements within 0.21 m. Both the fall and winter campaigns see a dramatic extent retreat of thicker MY ice that survives at least one summer melting season. There were strong seasonal and interannual variabilities with regard to the mean thickness. Seasonal increases of 0.53 m for FY the ice and 0.29 m for the MY ice between the autumn and the winter ICESat campaigns, roughly 4–5 month separation, were found. Interannually, the significant MY ice thickness declines over the consecutive four ICESat winter campaigns(2005–2008) leads to a pronounced thickness drop of 0.8 m in MY sea ice zones. No clear trend was identified from the averaged thickness of thinner, FY ice that emerges in autumn and winter and melts in summer. Uncertainty estimates for our calculated thickness, caused by the standard deviations of multiple input parameters including freeboard, ice density, snow density, snow depth, show large errors more than 0.5 m in thicker MY ice zones and relatively small standard deviations under 0.5 m elsewhere. Moreover, a sensitivity analysis is implemented to determine the separate impact on the thickness estimate in the dependence of an individual input variable as mentioned above. The results show systematic bias of the estimated ice thickness appears to be mainly caused by the variations of freeboard as well as the ice density whereas the snow density and depth brings about relatively insignificant errors.     

2018年北极太平洋区域夏季海冰物理及光学性质的研究

The reduction in Arctic sea ice in summer has been reported to have a significant impact on the global climate. In this study, Arctic sea ice/snow at the end of the melting season in 2018 was investigated during CHINARE-2018, in terms of its temperature, salinity, density and textural structure, the snow density, water content and albedo, as well as morphology and albedo of the refreezing melt pond. The interior melting of sea ice caused a strong stratification of temperature, salinity and density. The temperature of sea ice ranged from –0.8℃ to 0℃, and exhibited linear cooling with depth. The average salinity and density of sea ice were approximately 1.3 psu and 825 kg/m~3, respectively, and increased slightly with depth. The first-year sea ice was dominated by columnar grained ice. Snow cover over all the investigated floes was in the melt phase, and the average water content and density were 0.74% and 241 kg/m~3, respectively. The thickness of the thin ice lid ranged from 2.2 cm to 7.0 cm, and the depth of the pond ranged from 1.8 cm to 26.8 cm. The integrated albedo of the refreezing melt pond was in the range of 0.28–0.57. Because of the thin ice lid, the albedo of the melt pond improved to twice as high as that of the mature melt pond. These results provide a reference for the current state of Arctic sea ice and the mechanism of its reduction.     

Modelling the annual cycle of landfast ice near Zhongshan Station,East Antarctica

A high resolution one-dimensional thermodynamic snow and ice(HIGHTSI) model was used to model the annual cycle of landfast ice mass and heat balance near Zhongshan Station, East Antarctica. The model was forced and initialized by meteorological and sea ice in situ observations from April 2015 to April 2016. HIGHTSI produced a reasonable snow and ice evolution in the validation experiments, with a negligible mean ice thickness bias of(0.003±0.06) m compared to in situ observations. To further examine the impact of different snow conditions on annual evolution of first-year ice(FYI), four sensitivity experiments with different precipitation schemes(0, half, normal, and double) were performed. The results showed that compared to the snow-free case,the insulation effect of snow cover decreased bottom freezing in the winter, leading to 15%–26% reduction of maximum ice thickness. Thick snow cover caused negative freeboard and flooding, and then snow ice formation,which contributed 12%–49% to the maximum ice thickness. In early summer, snow cover delayed the onset of ice melting for about one month, while the melting of snow cover led to the formation of superimposed ice,accounting for 5%–10% of the ice thickness. Internal ice melting was a significant contributor in summer whether snow cover existed or not, accounting for 35%–56% of the total summer ice loss. The multi-year ice(MYI)simulations suggested that when snow-covered ice persisted from FYI to the 10 th MYI, winter congelation ice percentage decreased from 80% to 44%(snow ice and superimposed ice increased), while the contribution of internal ice melting in the summer decreased from 45% to 5%(bottom ice melting dominated).     

Interaction of an anticyclonic eddy with sea ice in the western Arctic Ocean: an eddy-resolving model study

The dramatic decline of summer sea ice extent and thickness has been witnessed in the western Arctic Ocean in recent decades, which hasmotivated scientists to search for possible factors driving the sea ice variability. An eddy-resolving, ice-ocean coupled model covering the entire Arctic Ocean is implemented, with focus on the western Arctic Ocean. Special attention is paid to the summer Alaskan coastal current (ACC), which has a high temperature (up to 5℃ ormore) in the upper layer due to the solar radiation over the open water at the lower latitude. Downstream of the ACC after Barrow Point, a surface-intensified anticyclonic eddy is frequently generated and propagate towards the Canada Basin during the summer season when sea ice has retreated away from the coast. Such an eddy has a warm core, and its source is high-temperature ACC water. A typical warm-core eddy is traced. It is trapped just below summer sea ice melt water and has a thickness about 60 m. Temperature in the eddy core reaches 2-3℃, and most water inside the eddy has a temperature over 1℃. With a definition of the eddy boundary, an eddy heat is calculated, which can melt 1 600 km² of 1mthick sea ice under extreme conditions.     

欧亚大陆冬季温度对北极海冰减少的敏感性及非线性响应研究

The recent decline in the Arctic sea ice has coincided with more cold winters in Eurasia.It has been hypothesized that the Arctic sea ice loss is causing more mid-latitude cold extremes and cold winters,yet there is lack of consensus in modeling studies on the impact of Arctic sea ice loss.Here we conducted modeling experiments with Community Atmosphere Model Version 5(CAM5) to investigate the sensitivity and linearity of Eurasian winter temperature response to the Atlantic sector and Pacific sector of the Arctic sea ice loss.Our experiments indicate that the Arctic sea ice reduction can significantly affect the atmospheric circulation by strengthening the Siberian High,exciting the stationary Rossby wave train,and weakening the polar jet stream,which in turn induce the cooling in Eurasia.The temperature decreases by more than 1°C in response to the ice loss in the Atlantic sector and the cooling is less and more shifts southward in response to the ice loss in the Pacific sector.More interestingly,sea ice loss in the Atlantic and Pacific sectors together barely induces cold temperatures in Eurasia,suggesting the nonlinearity of the atmospheric response to the Arctic sea ice loss.     

北极气旋的季节、年际变化及其与北极海冰、大气遥相关的关系

The seasonal and inter-annual variations of Arctic cyclone are investigated. An automatic cyclone tracking algorithm developed by University of Reading was applied on the basis of European Center for Medium-range Weather Forecasts(ECMWF) ERA-interim mean sea level pressure field with 6 h interval for 34 a period. The maximum number of the Arctic cyclones is counted in winter, and the minimum is in spring not in summer.About 50% of Arctic cyclones in summer generated from south of 70°N, moving into the Arctic. The number of Arctic cyclones has large inter-annual and seasonal variabilities, but no significant linear trend is detected for the period 1979–2012. The spatial distribution and linear trends of the Arctic cyclones track density show that the cyclone activity extent is the widest in summer with significant increasing trend in CRU(central Russia)subregion, and the largest track density is in winter with decreasing trend in the same subregion. The linear regressions between the cyclone track density and large-scale indices for the same period and pre-period sea ice area indices show that Arctic cyclone activities are closely linked to large-scale atmospheric circulations, such as Arctic Oscillation(AO), North Atlantic Oscillation(NAO) and Pacific-North American Pattern(PNA). Moreover,the pre-period sea ice area is significantly associated with the cyclone activities in some regions.     

地球系统模式FIO-ESM对北极海冰的模拟和预估

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。     

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

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。     

On the future navigability of Arctic sea routes: High-resolution projections of the Arctic Ocean and sea ice

The rapid Arctic summer sea ice reduction in the last decade has lead to debates in the maritime industries on the possibility of an increase in cargo transportation in the region. Average sailing times on the North Sea Route along the Siberian Coast have fallen from 20 days in the 1990s to 11 days in 2012–2013, attributed to easing sea ice conditions along the Siberian coast. However, the economic risk of exploiting the Arctic shipping routes is substantial. Here a detailed high-resolution projection of ocean and sea ice to the end of the 21st century forced with the RCP8.5 IPCC emission scenario is used to examine navigability of the Arctic sea routes. In summer, opening of large areas of the Arctic Ocean previously covered by pack ice to the wind and surface waves leads to Arctic pack ice cover evolving into the Marginal Ice Zone. The emerging state of the Arctic Ocean features more fragmented thinner sea ice, stronger winds, ocean currents and waves. By the mid 21st century, summer season sailing times along the route via the North Pole are estimated to be 13–17 days, which could make this route as fast as the North Sea Route.     

误差订正对2018年夏季次季节尺度海冰预测的作用

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。     

CMIP5模式对中国近海海表温度的模拟及预估

基于观测和再分析资料;利用多种指标和方法评估了国际耦合模式比较计划（CMIP5）中21个模式对中国近海海温的月、季节和年际变化模拟能力。多模式集合能够再现气候平均意义下近海海温的空间分布特征;但量值上存在一定的低估。在渤海和黄海;集合平均与观测差别比较明显。在年际尺度上;与观测数据对比;模式模拟海温与Niño3指数相关性较小。中国近海海表面温度在1960-2002年有明显的升高趋势;从2003年开始增温趋缓。评估结果表明;ACCESS1.0、BCC-CSM1.1、HadGEM2-ES、IPSL-CM5A-MR、CMCC-CM、FGOALS-g2、CNRM-CM5-2、INMCM4八个模式对中国近海海温的变化有较好的模拟能力。利用ACCESS1.0、INMCM4、BCC-CSM1.1、IPSL-CM5A-MR、CMCC-CM这5个模式结果对中国近海海温未来的变化进行了预估。在RCP4.5、RCP8.5情景下;未来近100年中国近海海温有明显升高趋势;最优模式多模式集合平均增温分别可达到1.5℃、3.3℃;净热通量变化和平流变化共同促进了东海升温。     

The Effect of Seasonal Variability of Atlantic Water on the Arctic Sea Ice Cover

Under the influence of global warming, the sea ice in the Arctic Ocean (AO) is expected to reduce with a transition toward a seasonal ice cover by the end of this century. A comparison of climate-model predictions with measurements shows that the actual rate of ice cover decay in the AO is higher than the predicted one. This paper argues that the rapid shrinking of the Arctic summer ice cover is due to its increased seasonality, while seasonal oscillations of the Atlantic origin water temperature create favorable conditions for the formation of negative anomalies in the ice-cover area in winter. The basis for this hypothesis is the fundamental possibility of the activation of positive feedback provided by a specific feature of the seasonal cycle of the inflowing Atlantic origin water and the peaking of temperature in the Nansen Basin in midwinter. The recently accelerated reduction in the summer ice cover in the AO leads to an increased accumulation of heat in the upper ocean layer during the summer season. The extra heat content of the upper ocean layer favors prerequisite conditions for winter thermohaline convection and the transfer of heat from the Atlantic water (AW) layer to the ice cover. This, in turn, contributes to further ice thinning and a decrease in ice concentration, accelerated melting in summer, and a greater accumulation of heat in the ocean by the end of the following summer. An important role is played by the seasonal variability of the temperature of AW, which forms on the border between the North European and Arctic basins. The phase of seasonal oscillation changes while the AW is moving through the Nansen Basin. As a result, the timing of temperature peak shifts from summer to winter, additionally contributing to enhanced ice melting in winter. The formulated theoretical concept is substantiated by a simplified mathematical model and comparison with observations.     

北极海冰的年代际转型与中国冻雨年代际变化的关系

基于1961-2013年HadISST海冰密集度资料,定义了北极海冰的季节性融冰指数,结果显示近几十年来北极季节性融冰范围呈显著的上升趋势,并分别在20世纪70年代末和90年代中期存在显著的年代际转型,相应地,中国冻雨发生频数总体上呈现出显著的减少趋势,但也存在显著的年代际转型。在20世纪70年代末之前,北极季节性融冰范围较小但显著增长,中国冻雨频数年际变化振幅较大,且主要受巴伦支海、喀拉海海冰的影响;20世纪70年代末至90年代中期北极季节性融冰范围维持振荡特征,没有显著的线性趋势,中国冻雨频数变化振幅减小,与北极海冰相关较弱,主要相关因子为北大西洋及北太平洋海表温度变化;而90年代中期以后,北极海冰融化加快,特别是2007年以后,季节性融冰范围大大增加,而中国冻雨频数处于低发时段,其变化与太平洋扇区海冰及堪察加半岛附近海温呈显著负相关,季节性融冰的显著区域也从东西伯利亚海逆时针旋转向波弗特海-加拿大群岛北部扩张,同时向北极中央区扩张。不同年代影响冻雨的海温或海冰关键海区不同,产生特定的大气环流异常响应,进而影响到我国冻雨。     

BCC_CSM对北极海冰的模拟:CMIP5和CMIP6历史试验比较

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。     

CMIP6模式对北冰洋海洋热含量的模拟能力评估

本文利用PHC、ECCO2、SODA、GECCO3和CMIP6资料,分析了北冰洋热含量的水平分布特征、季节变化和长期变化趋势等,评估了CMIP6模式对北冰洋海洋热含量的模拟能力。研究发现,北冰洋海洋热含量表现出明显的季节变化：热含量在4月份最低,9月份最高;在历史情形下(1850−2014年),相较观测和再分析资料,CMIP6多模式集合平均(MME)的上层500 m热含量在格陵兰海偏暖,在挪威海、巴伦支海和欧亚海盆偏冷,MME的全水深热含量在北冰洋几乎所有区域均偏暖,在格陵兰海偏差最大;CMIP6模式对北冰洋温度剖面模拟偏差较大,MME平均温度在1 000 m以深均高于观测和再分析资料。在未来情形下(2015−2100年),MME表现出明显的北冰洋增暖情形,但绝大多数中国模式没有表现出明显的增暖情形。中国模式中,BCC-CSM2-MR和BCC-ESM1对北冰洋年平均热含量的模拟较差,CIESM对热含量季节和年代际变化模拟较差,FIO-ESM-2-0对北冰洋上层500 m年平均热含量及热含量季节和年代际变化的模拟都比较好。