北太平洋夏季的两个典型层积云区与海温和大气低层热力过程的关系

比较ISCCP D2层积云云量的季节平均图后发现:除了大洋东部常年有层积云外,北太平洋夏季也存在一片大值区.在副热带东北太平洋和中纬度西北太平洋各取一个10°×10°的区域,分别记为NEP和NWP.利用OI-SST、NCEP/NCAR再分析资料和ERBE资料,通过相关和气候分析,提出了新的物理意义明确的稳定度判据,比较了NEP和NWP两个区的夏季层积云云量与海温、大气热力过程的异同.结果表明:夏季,NEP和NWP都有正的稳定度,有利于层积云的形成和维持.NEP区和NWP区夏季的海气温差、感热通量、潜热通量有明显的差异.从海温和夏季层积云云量的相关分析发现:在NEP区,海温滞后于层积云云量,其相关于滞后3个月时有最大负值,这可能是由于海洋有较大的热惯性,对层积云遮蔽太阳辐射而导致洋面降温的响应需要3个月;而在NWP区,则是海温超前于层积云云量,其相关于超前1个月时有最大负值,这可能是由于从5月份开始海温低于气温,且有暖空气平流,有利于随后层积云、层云和雾的形成.     

基于CloudSat–CALIPSO资料的全球不同类型云的水平和垂直分布特征

基于Cloud Sat-CALIPSO(Cloud Sat–Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observations)卫星观测资料,分析了全球总云量和8类云的云量、云底高、云顶高、云厚度的水平和垂直分布。分析结果表明,全球平均总云量为66.7%,其中卷云(Ci)和层积云(Sc)云量之和与其他6类云量总和相当,是全球云量最多的两类云。积状云云量呈现从赤道向极地递减的特征,层状云则相反,反映了二者不同的生成环境,同时下垫面地形和天气系统也严重影响云的分布。8类云的高度及厚度特征有显著差异。Ci的云底高度和云顶高度都较高,厚度则较薄;高层云(As)和高积云(Ac)的云底高度和云顶高度都位于大气中层,但As比Ac出现的高度高且厚度大;层云(St)、层积云和积云(Cu)的云底高度和云顶高度都很低,属于薄的低云;雨层云(Ns)和深对流云(DC)云底较低但云顶伸展很高,归属于厚云类。总体而言,海洋上云底高度较陆地低;赤道等大气不稳定地区,云底较高,云厚度较大;高原地区则表现出"高云不高,低云不低,云厚较薄"的特征。     

1960-2005年京津冀地区地表太阳辐射变化及成因分析

利用1960—2005年京津冀地区的地面太阳辐射资料,综合分析了该地区45年太阳辐射的分布状况和变化趋势,并结合云量、降水量、气溶胶光学厚度和大气含水量,分析了该地区太阳辐射的变化原因。结果表明:(1)京津冀地区的太阳辐射并没有出现20世纪80年代末到90年代中期的"变亮"现象;同期冬、春季总辐射下降,夏、秋季上升;(2)在1985—1997年间,依据总辐射变化情况,京津冀地区被分为截然相反的两个区域:东部地区总辐射增加,倾向率为1.016 MJ.m-2.mon-1.(10a)-1;西部地区总辐射减少,倾向率为10.092MJ.m-2.mon-1.(10a)-1;(3)总辐射增加的区域,主要是由于云量减少、降水量减少所伴随的日照时数增加以及气溶胶光学厚度降低所造成的;(4)总辐射减少的区域,云量、气溶胶光学厚度和降水量变化并不显著,总辐射持续减少。     

基于ISCCP观测的云量全球分布及其在NCEP再分析场中的指示

国际卫星云气候计划(ISCCP)已经积累了20多年的持续云观测资料,提供了迄今为止最具权威的全球尺度云量信息,为全面认识全球尺度云气候特征提供了有利条件.利用长期稳定的ISCCP D2云量资料,文中系统地分析了全球尺度总云量以及高、中、低云云量的空间分布特征.结果表明,全球总云量均值为66.5(单位:%),其中洋面71.6.陆面55.9.全球云量分布极不均衡,且海陆差异显著,洋面局部云量最高可达90,而包括南极大陆在内的所有陆面区域多为云量低值中心.高云和低云全球分布形式存在明显差异,其中陆面以高云为主,洋面低云相对较多.低云集中分布于太平洋东南部和东北部的近海岸地区以及南半球洋面,热带辐合带、南太平洋辐合带等大尺度强对流活动区内高云数量占优势.特别,在气候平均态上分离低云和高云区,并结合对NCEP再分析资料所提供环流背景场的分析,研究发现两类云所对应的垂直和水平风场具有明显的差异.高云区从低空到对流层顶为一致的强下降运动,低云区的中高层被上升气流所控制但近地面一般存在弱的上升运动.反映在水平辐散场上,两类云对应的辐散度在垂直方向上变化趋势相反,其中低云对应的典型背景场为低层辐散高空辐合.进一步考虑水汽因素,600与850 hPa水汽通量散度差对低云(负差异)和高云(正差异)的云量空间分布有较好的指示意义.     

西北典型地域条件下云量的对比分析

采用NASA地球观测系统（EOS）“云与地球辐射能量系统(CERES)”2002年7月至2004年6月CERES SSF Aqua MODIS Edition 1B云资料,选取我国西北地区不同气候环境条件下的4个典型地域,研究了总云量、低层云和高层云云量的空间分布特征以及季节和年变化特征。结果表明,低层云量的高值区不仅分布在山脉地区,而且也分布在非山脉地区。但高层云的云量高值区只分布在山脉地区;总体来说,云量大小随地域的不同相差相当大,高层云云量年平均值的最大差异发生在祁连山区和塔克拉玛干沙漠之间,两者相差16.4%。而总云量和低层云量年平均值在季风区和塔克拉玛干沙漠地区相差最大,分别可达27.6%和19.5%。季风区和祁连山区云量最大值一般都出现在夏季,天山和塔克拉玛干沙漠地区云量最大值一般都出现在春季,最小值则均出现在秋冬季。总的来说,3个云量参数值在3～9月较高,最低值出现在10～12月。     

基于CloudSat资料的中国及周边地区各类云的宏观特征分析

利用2006年7月—2009年4月的CloudSat2B-CLDCLASS云分类资料,针对中国及周边地区(0°—60°N,70°—140°E)各类云量和垂直结构参数的地理分布及季节变化进行了统计分析,并根据气候特征的地域差异从该区域选出8个子区域,逐区统计了各类云的垂直结构特征。结果表明,各类云量的分布存在较明显的区域差异和季节变化;青藏高原和帕米尔高原地区卷云、高层云和高积云等中高云的高度和厚度相对较小,陆上深对流云的云底高度大于海上,而热带、副热带地区云顶高度大于中纬度地区;除积云、层积云和雨层云外,中国南方地区其他各类云的云层厚度均大于北方地区;除了层积云外,其他各类云的云顶高度在各区域都存在比较明显的季节变化,低云云底高度的季节变化和区域差异都很小,而中高云的云底高度除了在印度洋季风区、南海和西太平洋地区季节差异较小外,其他地区季节差异较明显,各个地区在任何季节内,深对流云厚度最大,层积云最小;各类云出现频率随高度的分布具有较明显的区域差异;卷云与高积云的相关性比较强,经常相伴出现,夏季更加明显,而雨层云和深对流云之间相互排斥,两者几乎不可能同时出现。此外,统计中国及周边地区各类云的水平均一性发现,中...     

基于FY-2F资料的新疆区域云系特征研究

利用2014年1—12月FY-2F/CTA、FY-2F/CLC小时产品,采用平均值合成法,监测分析了新疆各区域的云系特征。结果表明:(1)新疆区域总云量年平均值为37.7%,其中,阿尔泰山山区云资源最为充沛,其次是昆仑山山区、准噶尔盆地、天山山区,而塔里木盆地最匮乏,各区域总云量年平均值依次为45.7%,40.0%,38.2%,37.9%及26.1%;天山以北区域(含天山山区)的云资源冬春季较为丰富,天山以南区域则春季较为丰富;新疆各区域总云量均白天较少,夜间相对较多,但塔里木盆地与之相反。(2)天山以北区域总云量呈北高南低、西高东低分布,天山以南区域则呈南高北低、西高东低分布。(3)三大山区主要以高层云或雨层云、卷层云为主,其中昆仑山山区的密卷云也较多,两大盆地则以层积云或高积云为主。新疆区域尤其是三大山区云的时空分布具有一定的地域性和稳定性,有利于开展人工增雨。     

青藏高原的层积云

一、云态 层积云(Sc)是最普遍最常见的云状之一。青藏地区以积云性层积云最为常见。云态一般与东部地区类似。这里只介绍几幅与局地地形和逆温层,以及强风有关的层积云态。 1.堡状层积云(Sc cast) 通常情况下,青藏高原上出现的Sc cast会较快地演变成积雨云(Cb)。但照片1、2(本文照片见     

内蒙古地区云量时空分布及变化趋势分析

利用1983年7月至2001年9月ISCCP云气候资料集D2资料集内蒙古区域云资料和内蒙古地区117个地面观测站的多年月平均气温、降水资料,采用趋势分析方法分析了内蒙古地区总云量和高、中、低云量的时空分布和多年变化趋势,以及温度和降水的多年变化趋势。结果表明：内蒙古地区总云量、低云量、中云量呈白西向东逐渐增多,高云量自西南向东北逐渐减少的空间分布特征;19年来东部地区总云量呈增加趋势,中、西部地区总云量呈减少趋势,温度的升高可能是云量变化的原因之一,内蒙古东北部云量和降水量变化趋势一致。     

福建沿海区域大气稳定度分类方法对比研究

利用福建沿海某个厂址所建的100 m气象铁塔观测数据,采用2018年1月1日至2018年12月31日塔层所获取的风向、风速、温度的观测资料以及地面站太阳辐射表观测的总辐射值和净辐射值,用太阳辐射法、温度梯度法、温度梯度—风速法、理查逊数法计算大气的稳定度。从观测区域各类稳定度结果四季的分布、各类稳定度频率的日变化和不同来流情况下各类稳定度的分布三个方面,分析了这四种方法在该地区稳定度分类结果的不同。鉴于该区域日间辐射强、低层风速值较小的特点,四种分类方法中以温度梯度—风速法和理查逊数法对本区域大气稳定度的划分适用性比较好。     

Arctic climate change in 21st century CMIP5 simulations with EC-Earth

The Arctic climate change is analyzed in an ensemble of future projection simulations performed with the global coupled climate model EC-Earth2.3. EC-Earth simulates the twentieth century Arctic climate relatively well but the Arctic is about 2 K too cold and the sea ice thickness and extent are overestimated. In the twenty-first century, the results show a continuation and strengthening of the Arctic trends observed over the recent decades, which leads to a dramatically changed Arctic climate, especially in the high emission scenario RCP8.5. The annually averaged Arctic mean near-surface temperature increases by 12 K in RCP8.5, with largest warming in the Barents Sea region. The warming is most pronounced in winter and autumn and in the lower atmosphere. The Arctic winter temperature inversion is reduced in all scenarios and disappears in RCP8.5. The Arctic becomes ice free in September in all RCP8.5 simulations after a rapid reduction event without recovery around year 2060. Taking into account the overestimation of ice in the twentieth century, our model results indicate a likely ice-free Arctic in September around 2040. Sea ice reductions are most pronounced in the Barents Sea in all RCPs, which lead to the most dramatic changes in this region. Here, surface heat fluxes are strongly enhanced and the cloudiness is substantially decreased. The meridional heat flux into the Arctic is reduced in the atmosphere but increases in the ocean. This oceanic increase is dominated by an enhanced heat flux into the Barents Sea, which strongly contributes to the large sea ice reduction and surface-air warming in this region. Increased precipitation and river runoff lead to more freshwater input into the Arctic Ocean. However, most of the additional freshwater is stored in the Arctic Ocean while the total Arctic freshwater export only slightly increases.     

一个冰气相互作用数值模拟结果的可能统计解释

基于一个全球海-冰-气耦合模式的数值模拟结果,选取冬季格陵兰海海表面温度(SST)、海冰密集度、海表面感热通量等物理量以及3个相关区域海平面气压分别作经验正交函数展开,取第一模时间系数作相关分析。结果表明,上一年海冰密集度偏大(小)与来年的SST偏低(高)相联系,但二者同期相关性最大。当海气热通量交换变化超前一年时,其与SST相关性最大。模式最低层大气温度与海洋表面热通量之间的同时相关性最大,冬季模式最低层气温偏高(低)与海洋表面失去的感热、潜热通量偏少(多)相联系。气温、比湿都和冰岛低压区及格陵兰海的海平面气压相关性最强,冰岛低压气压偏低(高)与模式最低层气温和比湿偏高(低)相联系。所以,在海-冰-气年际尺度的相互作用中,主要关系是大气环流调整造成大气中云量和低层气温、湿度变化,进而影响海气界面上的通量交换,造成SST的变化。SST变化决定着海冰范围及海冰密集度的变化,但海冰变化时通过相变潜热的释放或吸收反过来对SST变化有较明显影响。     

Impacts of the Autumn Arctic Sea Ice on the Intraseasonal Reversal of the Winter Siberian High

During 1979–2015, the intensity of the Siberian high(SH) in November and December–January(DJ) is frequently shown to have an out-of-phase relationship, which is accompanied by opposite surface air temperature and circulation anomalies.Further analyses indicate that the autumn Arctic sea ice is important for the phase reversal of the SH. There is a significantly positive(negative) correlation between the November(DJ) SH and the September sea ice area(SIA) anomalies. It is suggested that the reduction of autumn SIA induces anomalous upward surface turbulent heat flux(SHF), which can persist into November, especially over the Barents Sea. Consequently, the enhanced eddy energy and wave activity flux are transported to mid and high latitudes. This will then benefit the development of the storm track in northeastern Europe. Conversely, when downward SHF anomalies prevail in DJ, the decreased heat flux and suppressed eddy energy hinder the growth of the storm track during DJ over the Barents Sea and Europe. Through the eddy–mean flow interaction, the strengthened(weakened)storm track activities induce decreased(increased) Ural blockings and accelerated(decelerated) westerlies, which makes the cold air from the Arctic inhibited(transported) over the Siberian area. Therefore, a weaker(stronger) SH in November(DJ) occurs downstream. Moreover, anomalously large snowfall may intensify the SH in DJ rather than in November. The ensemble-mean results from the CMIP5 historical simulations further confirm these connections. The different responses to Arctic sea ice anomalies in early and middle winter set this study apart from earlier ones.     

南海及周边地区云量分布及低云量与南海海温的关系

利用国际卫星云气候计划提供的月平均云气候资料集,分析了南海及周边地区云量的分布特征,并进一步研究了低云量与南海海温的关系。结果表明:(1)南海及周边地区总云量分布存在显著的季节性差异特征。(2)低云主要分布在南海海区,中云为华南地区,而高云则主要位于靠近赤道区域。(3)低云受海表温度影响较大,而中高云则主要与强对流相对应。低云主要分布于南海海表冷水中心南侧的暖水区内的温度梯度区,其高值区分布与海表温度梯度分布基本一致,海表温度梯度的大小与高值中心的低云量成正比。(4)低云量高值中心位置与水平海温梯度区两侧基本一致,高温暖水受西边界强迫上升在海表层辐合,有利于低云的生成。     

Atmospheric Precursors of and Response to Anomalous Arctic Sea Ice in CMIP5 Models

This study examines pre-industrial control simulations from CMIP5 climate models in an effort to better understand the complex relationships between Arctic sea ice and the stratosphere, and between Arctic sea ice and cold winter temperatures over Eurasia. We present normalized regressions of Arctic sea-ice area against several atmospheric variables at extended lead and lag times. Statistically significant regressions are found at leads and lags, suggesting both atmospheric precursors of, and responses to, low sea ice; but generally, the regressions are stronger when the atmosphere leads sea ice, including a weaker polar stratospheric vortex indicated by positive polar cap height anomalies. Significant positive midlatitude eddy heat flux anomalies are also found to precede low sea ice. We argue that low sea ice and raised polar cap height are both a response to this enhanced midlatitude eddy heat flux. The so-called "warm Arctic, cold continents" anomaly pattern is present one to two months before low sea ice, but is absent in the months following low sea ice, suggesting that the Eurasian cooling and low sea ice are driven by similar processes. Lastly, our results suggest a dependence on the geographic region of low sea ice, with low Barents–Kara Sea ice correlated with a weakened polar stratospheric vortex, whilst low Sea of Okhotsk ice is correlated with a strengthened polar vortex. Overall, the results support a notion that the sea ice, polar stratospheric vortex and Eurasian surface temperatures collectively respond to large-scale changes in tropospheric circulation.     

A dissection of the surface temperature biases in the Community Earth System Model

Based upon the climate feedback-responses analysis method, a quantitative attribution analysis is conducted for the annual-mean surface temperature biases in the Community Earth System Model version 1 (CESM1). Surface temperature biases are decomposed into partial temperature biases associated with model biases in albedo, water vapor, cloud, sensible/latent heat flux, surface dynamics, and atmospheric dynamics. A globally-averaged cold bias of ?1.22 K in CESM1 is largely attributable to albedo bias that accounts for approximately ?0.80 K. Over land, albedo bias contributes ?1.20 K to the averaged cold bias of ?1.45 K. The cold bias over ocean, on the other hand, results from multiple factors including albedo, cloud, oceanic dynamics, and atmospheric dynamics. Bias in the model representation of oceanic dynamics is the primary cause of cold (warm) biases in the Northern (Southern) Hemisphere oceans while surface latent heat flux over oceans always acts to compensate for the overall temperature biases. Albedo bias resulted from the model’s simulation of snow cover and sea ice is the main contributor to temperature biases over high-latitude lands and the Arctic and Antarctic region. Longwave effect of water vapor is responsible for an overall warm (cold) bias in the subtropics (tropics) due to an overestimate (underestimate) of specific humidity in the region. Cloud forcing of temperature biases exhibits large regional variations and the model bias in the simulated ocean mixed layer depth is a key contributor to the partial sea surface temperature biases associated with oceanic dynamics. On a global scale, biases in the model representation of radiative processes account more for surface temperature biases compared to non-radiative, dynamical processes.     

北半球高纬地区年际尺度循环过程中的气-海-冰相互作用关系

基于一个全球气-海-冰耦合模式数值模拟结果,对北半球高纬度地区年际尺度的气-海-冰相互作用进行了分析。在所使用的全球气-海-冰耦合模式中,大气环流模式和陆面过程模式来自国家气候中心,海洋环流模式和海冰模式来自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。采用一种逐日通量距平耦合方案实现次网格尺度海冰非均匀条件下大气环流模式和海洋环流模式在高纬地区的耦合。只对50 a模拟结果中的后30 a结果进行了分析。在分析中,首先对滤波后的北半球高纬度地区海平面气压、表面大气温度、海表面温度、海冰密集度及海表面感热通量的标准化距平做联合复经验正交函数分解,取第一模进行重建,然后讨论了在一个循环周期(约4 a)中北半球高纬度地区气-海-冰的作用关系。结果表明:(1)当北大西洋涛动处于正位相时,格陵兰海出现南风异常,使表面大气温度升高,海洋失去感热通量减少,海洋表面温度升高,海冰密集度减小;当北大西洋涛动处于负位相时,格陵兰海出现北风异常,使表面大气温度降低,海洋失去感热通量增多,海洋表面温度降低,海冰密集度增加。巴伦支海变化特点与格陵兰海相似,但在时间上并不完全一致。(2)多年平均而言,北冰洋内部靠近极点区域为冷中心。当北冰洋内部为低压异常时,因异常中心偏向太平洋一侧,使北冰洋内部靠近太平洋部分为暖平流异常,靠近大西洋一侧为冷平流异常。伴随着暖、冷平流异常,这两侧分别出现暖异常和冷异常,海表面给大气的感热通量分别偏少和偏多,上述海区海表面温度分别偏高和偏低,海冰密集度分别偏小和偏大。当北冰洋内部为高压异常时特点正好与上述相反。由上述分析结果可知,在海洋、大气年际循环中,大尺度大气环流变率起主导作用,海洋表面温度和海冰密集度变化主要是对大气环流变化的响应。     

西太平洋-南海地区潜热通量长期变化趋势的南北差异及成因分析

利用1958~2014年美国伍兹霍尔海洋研究所客观分析海气通量项目(OAFlux)的月平均潜热通量和相关气象要素数据,以及NCEP/NCAR再分析表面气压数据,通过Trend-EOF分析方法,本文研究了西太平洋—南海地区潜热通量的长期变化趋势。发现西太平洋—南海地区潜热通量整体呈上升的趋势,其中冬季上升趋势最强。冬季潜热通量趋势存在明显的南北差异,特别是在南海地区,南海北部为上升趋势而南部为下降趋势。南海北部以及菲律宾海地区冬季潜热通量上升的主要原因是海气比湿差的增大,而南海南部潜热通量呈下降趋势,在东侧主要原因是风速减小,在西侧主要原因是海气比湿差减小。南海潜热通量长期趋势的南北差异是风速和海气比湿差的共同作用造成的。另外,研究发现风速变化趋势受到局地环流变化的影响,在表面气压下降中心线以北地区为上升趋势,在其以南为下降趋势,而海气比湿差的变化趋势则主要取决于海表温度的变化趋势。     

Sensitivity of Arctic warming to sea surface temperature distribution over melted sea-ice region in atmospheric general circulation model experiments

Substantial reduction in Arctic sea ice in recent decades has intensified air-sea interaction over the Arctic Ocean and has altered atmospheric states in the Arctic and surrounding high-latitude regions. This study has found that the atmospheric responses related to Arctic sea-ice melt in the cold season (October–March) depend on sea-ice fraction and are very sensitive to in situ sea surface temperature (SST) from a series of atmospheric general circulation model (AGCM) simulations in which multiple combinations of SSTs and sea-ice concentrations are prescribed in the Arctic Ocean. It has been found that the amplitude of surface warming over the melted sea-ice region is controlled by concurrent in situ SST even if these simulations are forced by the same sea-ice concentration. Much of the sensitivity of surface warming to in situ SST are related with large changes in surface heat fluxes such as the outgoing long-wave flux in early winter (October–December) and the sensible and latent heat fluxes for the entire cold season. Vertical extension of surface warming and moistening is sensitive to these changes as well; the associated condensational heating modulates a static stability in the lower troposphere. This study also indicates that changes in SST fields in AGCM simulations must be implemented with extra care, especially in the melted sea-ice region in the Arctic. The statistical method introduced in this study for adjusting SSTs in conjunction with a given sea-ice change can help to model the atmospheric response to sea-ice loss more accurately.     

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。