BCC_CSM北极海冰模拟性能的改进对东亚冬季气候模拟的影响

国家气候中心气候系统模式(BCC_CSM)将美国Los Alamos国家实验室发展的海冰模式CICE5.0替代原有的海冰模式SIS,形成一个新版本耦合模式,很好地提高了模式对北极海冰和北极气候的模拟能力。在此基础上,本文评估新耦合模式对1985—2014年东亚冬季气候的模拟性能,检验北极海冰模拟性能的改进对东亚冬季气候模拟性能的影响。结果表明,引入CICE5.0后,新耦合模式能较好地模拟出东亚冬季海平面气压、850 hPa风场以及辐射通量,进而改善东亚气温以及降水的气候态空间分布模拟效果。进一步分析发现,与原有耦合模式相比,新耦合模式更好地抓住了东亚冬季海平面气压、总降水量和气温异常对同期巴伦支海-喀拉海海冰密集度异常的响应,进而提高了模式对东亚冬季中高纬度地区气温以及降水变率的模拟能力。     

北极海冰数值预报的初步研究——基于海冰—海洋耦合模式MITgcm的模拟试验

利用最近发展的MITgcm(麻省理工学院通用环流模式)海冰-海洋耦合模式,以NCEP(美国国家环境预测中心)再分析资料为大气强迫场进行了1992年1月至2009年12月北极海冰数值模拟.结果表明,此模式能很好地模拟卫星观测的北极海冰季节和年际变化,具备很好的北极海冰数值模拟能力.以此为基础,对2009年7月和10月北极...     

气候模式对海冰反照率参数化的敏感性

在NCAR的通用气候模式(CCM)中,用三种海冰反照率参数化方案,各作一年的模拟试验,与控制试验结果作比较,以检验这些参数化对极地表面温度、行星反照率和云的影响.试验I中所用的1977年春、夏季北极海盆的海冰反照率是从国防气象卫星图像(DMSP)推演得到的.试验Ⅱ将冰沟和融水池部分预先指定并采用反照率加权方案.试验Ⅲ含有相互作用的海冰/雪反照率参数化的耦合,它为表面状态的函数. 结果表明,与控制试验中所用的标准CCM海冰反照率方案相比,预先指定的或假定的“真实”卫星海冰反照率会产生更高的行星反照率.结果在北极地区温度更低(低0.5K),云量增多.标准CCM海冰反照率方案用来调节,以维持极地海洋“正常”温度.冰沟和融水池增暖海冰地区的辐射作用只是短时间的.与控制值相比,第三种方案明显地得出更低的行星反照率(减少0.07到0.17),及较高的表面温度(高2.0K). CCM摸拟出春、夏北极云量逐渐减少,而观测表明,春季云量陡增.因此有必要检验CCM的程序,特别是云的参数化.     

BCC_CSM对全球海冰面积和厚度模拟及其误差成因分析

本文评估了国家气候中心发展的BCC_CSM模式对全球海冰的模拟能力,结果表明:该气候系统模式能够较好地模拟出全球海冰面积和厚度的时空分布特征,且南半球海冰模拟能力优于北半球。通过对比分析发现:年平均海冰面积模拟误差最大的区域位于鄂霍次克海、白令海和巴伦支海等海区,年平均海冰厚度分布与观测相近,在北半球冬季模拟的厚度偏薄;从海冰季节变化来看,模拟的夏季海冰面积偏低,冬季偏高;从海冰年际变化来看,近60年南北半球海冰面积模拟都比观测偏多,但南半球偏多幅度较小,然而北半球海冰年际变化趋势的模拟却好于南半球。另外,通过对海冰模拟误差成因分析,发现模拟的净辐射能量收入偏低使得海温异常偏冷,是导致北半球冬季海冰模拟偏多的主要原因。     

一个热动力海冰模式的改进与实验

影响海冰变化的物理因素中热力和动力部分是同等重要的，但多数热动力海冰模式的热力部分考虑得较为简单。针对Hibler热动力海冰模式的不足，以1个3层热力模式为基础改进了其热力部分。比较了原模式中的零层热力模式和用于改进的3层热力模式；并应用改进前后的两种热动力模式对1983年的北极海冰进行了模拟。模拟结果表明，海冰厚度比原模式厚，季节变化减弱，海冰密集度与观测资料更为符合。     

BCC-CSM2-MR模式对北极海冰和气候的模拟及预估

基于第六次耦合模式比较计划（CMIP6）,使用新一代全球模式BCC-CSM2-MR的历史试验和未来共享社会经济路径（SSPs）数据,依据Hadley中心的海表面温度和海冰密集度数据及NCEP/NCAR I再分析资料,评估了BCC-CSM2-MR模式对北极海冰及北极气候的模拟能力,并对未来变化进行了预估。结果表明：BCC-CSM2-MR模式可以较好再现北极海冰密集度、近地层大气平均温度和海表温度的多年平均空间分布特征。但模式对北极局地大气平均温度模拟存在一定偏差,可能在一定程度上导致相应地区海冰的模拟存在差异。21世纪,北极海冰范围持续减少,9月减少趋势显著,3月减少趋势相对较弱。3月北极大部地区表现为一致的增温,仅在北大西洋局部出现一定程度的降温,9月北极大气增温幅度弱于3月。与地表平均温度不同,3月和9月的北极大部地区海表温度均出现增加,且9月海表温度的增幅大于3月,仅拉布拉多海海温出现下降。     

Polar WRF模式海冰密集度方案对北极海雾模拟效果的个例研究

全球变暖的背景下,北极航线的常规通航甚至商业运营有望实现,而海雾会严重影响航道上船只的航行安全。海冰的存在使海气之间相互作用变得更为复杂,是研究北极海雾不可忽略的因素。船载观测发现,与中纬度常见平流冷却雾形成时气温下降速度往往超过海水降温速度不同,北极海雾发生时海冰的存在还会使海水降温速度超过空气降温速度。然而目前海冰分布是否会影响模式模拟海雾的准确性还不得而知,因此本文利用Polar WRF（Polar Weather Research and Forecasting）模式模拟了中国第七次北极考察中观测到的一次海雾过程,并进行海冰密集度敏感性试验。通过与船载观测和欧洲中期天气预报中心再分析数据比对发现,在低浮冰区内（海冰密集度小于50%）考虑海冰分布时可以更加准确地刻画潜热通量与水汽通量,模拟出与观测事实相符的表层空气降温与增湿过程以及相对湿度的变化,因此能够更好地刻画海雾的三维结构及其生消演变。     

最新版海冰模式CICE的优势:基于北极海冰时空特征的评估

【研究目的】海冰模式CICE(Los Alamos sea ice model)作为当前国际上的主流海冰模式之一,已被耦合进了大部分地球系统模式,对该模式模拟能力的评估工作是发展地球系统模式的重要参考依据。【创新点】通过观测数据与不同版本CICE模式对北极海冰数值模拟结果进行对比分析,研究了最新版本CICE6.0模拟能力及优势。【重要结论】CICE6.0模拟结果的年际误差最小,且季节变化与观测值最为吻合。相较而言,CICE4.0严重高估了冬季海冰总面积及低估了夏季海冰总面积,而CICE5.0在冬季的误差明显大于其他版本。此外,我们也关注了三个模式对多年冰和季节冰的模拟效果,从其均方根误差空间分布看出:模拟误差主要出现在中央海区及其周边海域。CICE4.0和CICE5.0在这些区域模拟的多年冰偏少、季节冰偏多,均低估了多年冰的变化趋势,且高估了季节冰的变化趋势;CICE6.0很好地解决了这些问题,其模拟的多年冰和季节冰的趋势最接近观测值,特别在北冰洋中部。总的来说,CICE6.0模拟的北极海冰在各方面都优于其他版本。     

最新版海冰模式CICE的优势：基于北极海冰时空特征的评估（英文）

【研究目的】海冰模式CICE (Los Alamos sea ice model)作为当前国际上的主流海冰模式之一,已被耦合进了大部分地球系统模式,对该模式模拟能力的评估工作是发展地球系统模式的重要参考依据。【创新点】通过观测数据与不同版本CICE模式对北极海冰数值模拟结果进行对比分析,研究了最新版本CICE6.0模拟能力及优势。【重要结论】CICE6.0模拟结果的年际误差最小,且季节变化与观测值最为吻合。相较而言,CICE4.0严重高估了冬季海冰总面积及低估了夏季海冰总面积,而CICE5.0在冬季的误差明显大于其他版本。此外,我们也关注了三个模式对多年冰和季节冰的模拟效果,从其均方根误差空间分布看出:模拟误差主要出现在中央海区及其周边海域。CICE4.0和CICE5.0在这些区域模拟的多年冰偏少、季节冰偏多,均低估了多年冰的变化趋势,且高估了季节冰的变化趋势;CICE6.0很好地解决了这些问题,其模拟的多年冰和季节冰的趋势最接近观测值,特别在北冰洋中部。总的来说,CICE6.0模拟的北极海冰在各方面都优于其他版本。     

1966～1991年北极海冰模拟结果与观测的对比

利用宇如聪等1995年建立的北极区域冰-洋耦合模式,以1966～1991年期间逐月的月平均实测海平面气温和气压场为强迫场,模拟了上述26年间北极海冰的时间演变和空间分布,着重分析了大西洋及欧洲沿岸一侧的巴伦支海和格陵兰海的海冰状况,并与目前能够得到的北极海冰密集度观测资料做了对比,结果表明:（1）模式对巴伦支海海冰年际变化的模拟是比较成功的,表现在不仅模拟的1969～1979和1979～1987这两个时段的主要变化趋势和观测事实比较一致,而且模拟出了1979和1984这两个多冰和少冰的极端年份。模拟的主要     

NUMERICAL SIMULATIONS ON INFLUENCES OF VARIATION OF SEA ICE THICKNESS AND EXTENT ON ATMOSPHERIC CIRCULATION*

By using a 2-layer AGCM designed by Institute of Atmospheric Physics,Chinese Academy of Sciences.this paper investigates influences of thickness and extent variations in Arctic sea ice on the atmosphere circulation,particularly on climate variations in East Asia.The simulation results have indicated that sea ice thickness variation in the Arctic exhibits significant influences on simulation results,particularly on East Asian monsoon.A nearly reasonable distribution of sea ice thickness in the model leads directly to stronger winter and summer monsoon over East Asia.and improves the model's simulation results for Siberia high and Icelandic low in winter.On the other hand,sea ice thickness variation can excite a teleconnection wave train across Asian Continent,and in low latitudes,the wave propagates from the western Pacific across the equator to the eastern Pacific.In addition,the variation of sea ice thickness also influences summer convective activitiesover the low latitudes including South China Sea and around the Philippines.Effects of winter sea ice extents in the Barents Sea on atmospheric circulation in the following spring and summer are also significant.The simulation result shows that when winter sea ice extent in the target region is larger (smaller) than normal.(1)in the following spring (averaged from April to June).positive (negative) SLP anomalies occupy the northern central Pacific.which leads directly to weakened (deepened)Aleutian low.and further favors the light (heavy) sea ice condition in the Bering Sea:(2)in the following summer,thermal depression in Asian Continent is deepened (weakened).and the subtropical high in the northwestern Pacific shifts northward(southward) from its normal position and to be strengthened (weakened).     

NUMERICAL SIMULATIONS ON INFLUENCES OF VARIATION OF SEA ICE THICKNESS AND EXTENT ON ATMOSPHERIC CIRCULATION

By using a 2-layer AGCM designed by Institute of Atmospheric Physics,Chinese Academy ofSciences.this paper investigates influences of thickness and extent variations in Arctic sea ice onthe atmosphere circulation,particularly on climate variations in East Asia.The simulation resuhshave indicated that sea ice thickness variation in the Arctic exhibits significant influences onsimulation results,particularly on East Asian monsoon.A nearly reasonable distribution of sea icethickness in the model leads directly to stronger winter and summer monsoon over East Asia.andimproves the model's simulation results for Siberia high and Icelandic low in winter.On the otherhand,sea ice thickness variation can excite a teleconnection wave train across Asian Continent,andin low latitudes,the wave propagates from the western Pacific across the equator to the easternPacific.In addition,the variation of sea ice thickness also influences summer convective activitiesover the low latitudes including South China Sea and around the Philippines.Effects of winter sea ice extents in the Barents Sea on atmospheric circulation in the followingspring and summer are also significant.The simulation result shows that when winter sea iceextent in the target region is larger (smaller) than normal.(1)in the following spring (averagedfrom April to June).positive (negative) SLP anomalies occupy the northern central Pacific.whichleads directly to weakened (deepened)Aleutian low.and further favors the light (heavy) sea icecondition in the Bering Sea:(2)in the following summer,thermal depression in Asian Continent isdeepened (weakened).and the subtropical high in the northwestern Pacific shifts northward(southward) from its normal position and to be strengthened (weakened).     

Inherent sea ice predictability in the rapidly changing Arctic environment of the Community Climate System Model, version 3

Seasonal predictions of Arctic sea ice have typically been based on statistical regression models or on results from ensemble ice model forecasts driven by historical atmospheric forcing. However, in the rapidly changing Arctic environment, the predictability characteristics of summer ice cover could undergo important transformations. Here global coupled climate model simulations are used to assess the inherent predictability of Arctic sea ice conditions on seasonal to interannual timescales within the Community Climate System Model, version 3. The role of preconditioning of the ice cover versus intrinsic variations in determining sea ice conditions is examined using ensemble experiments initialized in January with identical ice?Cocean?Cterrestrial conditions. Assessing the divergence among the ensemble members reveals that sea ice area exhibits potential predictability during the first summer and for winter conditions after a year. The ice area exhibits little potential predictability during the spring transition season. Comparing experiments initialized with different mean ice conditions indicates that ice area in a thicker sea ice regime generally exhibits higher potential predictability for a longer period of time. In a thinner sea ice regime, winter ice conditions provide little ice area predictive capability after approximately 1?year. In all regimes, ice thickness has high potential predictability for at least 2?years.     

北极海冰的厚度和面积变化对大气环流影响的数值模拟

文中利用中国科学院大气物理研究所设计的两层大气环流模式 ,模拟研究了北极海冰厚度和面积变化对大气环流的影响 ,尤其是对东亚区域气候变化的影响。模式中海冰厚度处理趋于合理分布 ,导致东亚冬、夏季风偏强 ,使冬季西伯利亚高压和冰岛低压的模拟结果更趋合理 ;另一方面 ,海冰厚度变化可以激发出跨越欧亚大陆的行星波传播 ,在低纬度地区 ,该行星波由西太平洋向东太平洋地区传播 ;海冰厚度变化对低纬度地区的对流活动也有影响。冬季北极巴伦支海海冰变化对后期大气环流也有显著的影响。数值模拟结果表明 :冬季巴伦支海海冰偏多 (少 )时 ,春季 (4～ 6月 )北太平洋中部海平面气压升高 (降低 ) ,阿留申低压减弱 (加深 ) ,有利于春季白令海海冰偏少 (多 ) ;而夏季 ,亚洲大陆热低压加深 (减弱 ) ,5 0 0 h Pa西太平洋副热带高压位置偏北 (南 )、强度偏强 (弱 ) ,东亚夏季风易偏强 (弱 )。     

Quantifying Arctic contributions to climate predictability in a regional coupled ocean-ice-atmosphere model

The relative importance of regional processes inside the Arctic climate system and the large scale atmospheric circulation for Arctic interannual climate variability has been estimated with the help of a regional Arctic coupled ocean-ice-atmosphere model. The study focuses on sea ice and surface climate during the 1980s and 1990s. Simulations agree reasonably well with observations. Correlations between the winter North Atlantic Oscillation index and the summer Arctic sea ice thickness and summer sea ice extent are found. Spread of sea ice extent within an ensemble of model runs can be associated with a surface pressure gradient between the Nordic Seas and the Kara Sea. Trends in the sea ice thickness field are widely significant and can formally be attributed to large scale forcing outside the Arctic model domain. Concerning predictability, results indicate that the variability generated by the external forcing is more important in most regions than the internally generated variability. However, both are in the same order of magnitude. Local areas such as the Northern Greenland coast together with Fram Straits and parts of the Greenland Sea show a strong importance of internally generated variability, which is associated with wind direction variability due to interaction with atmospheric dynamics on the Greenland ice sheet. High predictability of sea ice extent is supported by north-easterly winds from the Arctic Ocean to Scandinavia.     

一个海洋-海冰耦合模式的北极气候模拟

This paper evaluates the simulation of Arctic sea ice states using an ocean-ice coupled model that employs LASG/IAP(the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics/the Institute of Atmospheric Physics) Climate Ocean Model(LICOM) and the sea-ice model from the Bergen Climate Model(BCM).It is shown that the coupled model can reasonably reproduce the major characteristics of the mean state,annual cycle,and interannual variability of the Arctic sea ice concentration.The coupled model also shows biases that were generally presented in other models,such as the underestimation of summer sea ice concentration and thickness as well as the unsatisfactory sea ice velocity.Sensitivity experiments indicate that the insufficient performance of the ocean model at high latitudes may be the main reason for the biases in the coupled model.The smoother and the fake "island",which had to be used due to the model’s grid in the North Pole region,likely caused the ocean model’s weak performance.Sea ice model thermodynamics are also responsible for the sea ice simulation biases.Therefore,both the thermodynamic module of the sea ice component and the model grid of the ocean component need to be further improved.     

FGOALS_gg1.1极地气候模拟

对中国科学院大气物理研究所大气科学和地球流体力学数值模拟国家重点实验室发展的气候系统模式FGOALS_g1.1的极地气候模拟现状进行了较为全面的评估.结果表明,FGOALS_g1.1对南北极海冰的主要分布特征、季节变化和年代际变化趋势具有一定的模拟能力.但也注意到,与观测相比,模式存在以下几方面的问题:(1)模拟的海冰总面积北极偏多,而南极偏少.北极,北大西洋海冰全年明显偏多;夏季,西伯利亚沿海海冰偏多,而波弗特海海冰偏少.南极,威德尔海和罗斯海冬季海冰偏少.南北极海冰边缘都存在异常的较大范围密集度很小的碎冰区,夏季尤为显著.(2)海冰流速在南北极海冰边缘和南极大陆沿岸附近较大.北极,模式没能模拟出波弗特涡流,并且由于模式网格中北极点的处理问题,造成其附近错误的海冰流场及厚度分布.这些海冰偏差与模式模拟的大气和海洋状况有着密切的联系.进一步分析表明,FGOALS_g1.1模拟的冰岛低压和南极绕极西风带明显偏弱,其通过大气环流和海表面风应力影响向极地的热量输送,在很大程度上导致上述的海冰偏差.此外,耦合模式中大气-海冰-海洋的相互作用可以放大子模式中的偏差.     

Declining summer snowfall in the Arctic: causes, impacts and feedbacks

Recent changes in the Arctic hydrological cycle are explored using in situ observations and an improved atmospheric reanalysis data set, ERA-Interim. We document a pronounced decline in summer snowfall over the Arctic Ocean and Canadian Archipelago. The snowfall decline is diagnosed as being almost entirely caused by changes in precipitation form (snow turning to rain) with very little influence of decreases in total precipitation. The proportion of precipitation falling as snow has decreased as a result of lower-atmospheric warming. Statistically, over 99% of the summer snowfall decline is linked to Arctic warming over the past two decades. Based on the reanalysis snowfall data over the ice-covered Arctic Ocean, we derive an estimate for the amount of snow-covered ice. It is estimated that the area of snow-covered ice, and the proportion of sea ice covered by snow, have decreased significantly. We perform a series of sensitivity experiments in which inter-annual changes in snow-covered ice are either unaccounted for, or are parameterized. In the parameterized case, the loss of snow-on-ice results in a substantial decrease in the surface albedo over the Arctic Ocean, that is of comparable magnitude to the decrease in albedo due to the decline in sea ice cover. Accordingly, the solar input to the Arctic Ocean is increased, causing additional surface ice melt. We conclude that the decline in summer snowfall has likely contributed to the thinning of sea ice over recent decades. The results presented provide support for the existence of a positive feedback in association with warming-induced reductions in summer snowfall.