BCC_AGCM2.1对中国东部地区云辐射特征模拟的偏差分析

通过与观测及再分析资料的对比,评估了中国国家气候中心大气环流模式BCC_AGCM 2.1对中国东部地区云辐射特征的模拟性能,并着重分析了模拟偏差的原因.在云辐射特征的基本气候态模拟方面,模式能大致再现中国东部中纬度层状云大值带,以及层状云冷季多、暖季少的季节特征,模拟的短波云辐射强迫也具有与观测相对应的季节变化特征.在云辐射强迫和地面温度相互影响过程的模拟方面,模式也能模拟出与观测相近的相互作用过程,即地面温度降低伴随着层状云云量增多以及负的净云辐射强迫加强,升温时层状云云量减少和净云辐射强迫减弱.但模式模拟的大陆层状云云量系统性偏少(尤其在冷季),使得模式在该处的短波云辐射强迫明显偏弱.初步分析表明,造成层状云模拟差异的主要原因是在中国西南地区对流层低层模式模拟的偏南气流明显偏弱以及陆-气潜热通量偏小.偏南气流偏弱导致低层散度和垂直运动条件不利于中层云的形成.同时偏南气流偏弱也不利于向西南地区的水汽输送,再加上模式模拟地表向上潜热通量偏小,这二者都使得模式模拟中国西南区域对流层低层的水汽含量严重偏少,相对湿度偏低,同样不利于层状云生成和发展.水汽偏少进一步导致在冷异常情况下青藏高原下游云辐射-地表温度反馈模拟偏弱,即呈现冷异常时,水汽条件偏弱限制了云量增加,弱化了进一步降低温度的反馈过程.     

基于CloudSat观测资料对WRF模式的冷锋降水过程云参数模拟效果检验

基于WRF数值模式,采用Lin微物理方案,对中国南方地区一次冷锋降水过程进行模拟试验,并用CloudSat观测数据对模式模拟的云量、云液态水和云冰水含量的垂直分布特征进行检验。结果表明:模式模拟云量的垂直分布范围小于CloudSat观测到的分布范围,模拟的云量在低空往往出现缺失,模式可以较好地模拟出CloudSat探测到的深对流云的分布,但对零散分布的小尺度云团模拟效果较差;模式模拟的云液态水分布范围也小于CloudSat观测到的分布范围,云液态水含量值略低于CloudSat观测值,对CloudSat观测的云液态水含量值较低的区域,模式往往不能模拟出云液态水的存在;模式模拟的云冰水垂直分布特征与CloudSat观测结果较为一致,特别是对冰水含量大值中心的位置模拟效果较好,但模式模拟的云冰水含量值远低于CloudSat观测值。整体来看,模式对云冰水垂直分布的模拟效果优于对云液态水的模拟,但Lin微物理方案对云液态水和云冰水的模拟还需进一步改进与完善。     

GRAPES新云量计算方案的引进和影响试验

在GRAPES模式中分别引进了 EC云量计算方案和WRF云量计算方案,并与GRAPES现用的云量计算方案和简单云方案进行了对比数值试验,结果表明:(1)这4种云量计算方案都能较准确地模拟云分布,相对而言,EC云方案和简单云方案模拟地更为准确.(2)简单云方案模拟的总云量较多,GRAPES云方案和WRF云方案模拟的总云量偏少,EC方案模拟的总云量较为接近观测值.(3)在中国东部地区模拟的地面温度与观测基本接近,但在中国西部地区则误差较大;采用EC云方案时模式模拟的地面温度更接近观测值.(4)综合分析模拟的效果,认为EC云量计算方案模拟的效果最佳,可以作为GRAPES新云量计算方案引进的参考.     

不同云重叠参数对全球和东亚地区模拟总云量的影响

在国家气候中心全球大气环流模式BCC_AGCM2.0中引入一组基于CloudSat/CALIPSO卫星观测的、能够体现真实时空变化特征的云垂直重叠参数（抗相关厚度,L_cf）数据,以减小由云的重叠描述造成的辐射场的不确定性。对比了采用时空变化的云重叠参数和采用恒定云重叠参数的气候模拟结果,发现无论在全球还是东亚区域,采用基于卫星观测的云重叠参数对模拟的总云量都有一定程度的改进。采用时空变化的云重叠参数后,冬、夏两季全球平均总云量与云和地球的辐射能量系统（CERES）卫星资料的误差都减少了1.6%,其中热带对流区域总云量的正偏差和副热带地区总云量的负偏差都明显减少,这些有助于正确模拟不同区域间的能量收支差异。在东亚区域,采用时空变化的云重叠参数后,冬、夏两季的东亚区域平均总云量与CERES卫星资料的误差分别减少了1.8%和1.4%。综上所述,基于CloudSat/CALIPSO卫星资料计算得到的L_cf有助于改进大气环流模式对总云量的模拟,从而提高模式对辐射场的模拟精度。      

FGOALS2两个模式版本对青藏高原东侧冬季层云的模拟特征

冬季青藏高原东部（22°N～32°N，102°E～118°E）层云区是唯一存在于副热带陆地的层云密集区，环流特征较为复杂，大多数耦合气候系统模式对该地区层云的模拟存在较大的偏差。对该地区层云模拟能力的系统分析评估是改进模式性能的重要基础。本文基于国际卫星云计划（ISCCP）卫星资料，评估了中国科学院大气物理研究所两个版本的气候系统模式FGOALS-s2和FGOALS-g2的大气环流模式试验（AMIP）对青藏高原东侧层云的模拟能力。通过分析云辐射强迫等相关特征、大气环流、稳定度、以及地表气温和云的关系，探讨了模式偏差的可能原因。结果表明，两个模式都不同程度地低估了青藏高原东侧的低层云量和云水含量。在垂直结构模拟方面，FGOALS-s2模式能较好地模拟出高原东侧低云主导的特征，其模拟的云顶高度与卫星资料更为接近；而FGOALS-g2模式则高估了该地区的平均云顶高度。分析表明，两个模式均低估了高原东侧的低层稳定度，同时不同程度地低估了该地区中低层水平水汽输送，导致层云云量的模拟偏少。此外，FGOALS-g2高估了高原东侧的上升运动和垂直水汽输送，使得模拟的低云偏少而云顶高度偏高。     

中国东部云-降水对应关系的分析与模式评估

为评估和改进模式中不同类型云与降水的对应关系,利用1998—2007年卫星-台站融合降水资料和国际卫星云气候计划的卫星观测云资料,采用诊断方法分析了中国东部季风区冬季层云、夏季对流云、层云与降水的水平分布及季节变化对应关系,并评估了BCC_AGCM模式的T42和T106分辨率版本对云-降水对应关系的模拟能力。观测资料分析结果表明,中国东部冬季云带和雨带都稳定少动,降水主要来自雨层云和高层云,南部沿海层云和层积云也对降水有贡献;夏季,中国东部表现为层积混合云降水特征,对流云带与降水带具有较好的对应关系,并具有一致的移动特征。对流降水主要来自深对流云和卷层云,深对流云云量和降水中心完全吻合,卷层云云带则表现出比深对流云主体和降水带偏北的现象;层云降水主要来自高层云和层积云。模式评估结果表明,中、低分辨率版本的BCC_AGCM模式均模拟出了冬季层云和稳定少动的降水带、夏季深对流云、卷层云和降水带的对应关系及随季风推进的移动特征。与T42模式版本相比,T106模式版本在夏季对流云云量的模拟及其与降水带的对应关系方面有所改善,说明改进的BCC_AGCM积云对流参数化方案与高分辨率模式网格更匹配,但冬季层云云量模拟误差变大,与降水带的对应关系变差,其原因值得进一步分析研究。     

AREM模拟云参数与卫星观测的比较研究

数值模式模拟计算云量与卫星观测云量的定义不同, 难以进行定量比较, 从而限制了对云模拟误差的检验和修正.作者介绍了可把数值模式输出云参数转换成ISCCP卫星反演云参数的云模拟器, 并将其试用于AREM区域模式中, 定量分析了模拟云参数和ISCCP反演云参数的差异.结果表明, AREM模式可以再现中国东部冬季云的基本分布特征, 但与观测相比, 四川地区云量模拟偏大20%, 华东地区则偏小20%左右, 云量中心的云顶高度比ISCCP反演值偏低一个高度段, 云光学厚度偏大一个厚度段.模拟与观测云参数的定量比较, 为评估和改进模式性能提供了依据.     

2004年冬春季中国地区云的模拟分析

文中利用卫星和地面观测资料分析了中国地区总云量和低云量的分布及华南沿海、华东地区和北方地区的云量垂直分布。利用CREM模式模拟了中国地区的云量,并初步分析了模拟误差的原因。模拟表明,模式能较好地再现中国地区云量水平和垂直分布的形势和演变趋势,但对总云量和低云量模拟系统性偏小20％,且模拟的华南沿海云量垂直分布大值中心偏低。初步分析认为这种云量大小的误差是云量诊断方案的经验系数值不适当引起的。     

单双参云微物理方案对强降水过程中云宏微观特征模拟的对比分析

将WRF3.9模式的16种云微物理方案分为单参、双参两组,分别对2016年6月30日—7月4日江淮流域的一次强降水过程进行模拟。首先利用逐小时观测降水对各组模拟降水进行评估,在此基础上利用FY-2G和CALIPSO云产品数据分别评估不同方案对降水过程中总云量、云垂直结构云水含量等宏微观特征的模拟性能。结果表明:选用不同的云微物理参数化方案均能较好地模拟出该次过程的雨带位置和中心降水强度,但不同方案对云宏微观特征的模拟结果有显著差异。分析两组试验对总云量的模拟结果发现,单参方案组除Kessler暖云方案对冰云过程描述不足导致模拟的总云量显著偏高外,其他单参方案和所有双参方案均能模拟出强降水过程中总云量的时空演变特征。从云覆盖率和云水含量的垂直分布特征来看,单参方案组对600 h Pa以下中低层暖云的模拟稳定性整体略优于双参方案组;而对200～500 h Pa的冰相云模拟结果相反,双参方案组比单参试验组模拟的固水含量更加集中。将各层云覆盖率与CALIPSO云产品的对比也发现,对于低层暖云降水过程,选择如WSM3单参云微物理方案比双参方案得到的模拟云量更加接近实况,而对于以深对流为主要运动特征的冷云降水过程,选择如WDM5或WDM6这样双参云微物理方案模拟得出的高云量值更加稳定,误差也更小。     

西北太平洋热带气旋潜势分布和年际变率的数值模拟

热带气旋潜势指数是定量表征影响热带气旋生成的大尺度环境条件指标,在不能显式模拟热带气旋的气候系统模式中,常被作为热带气旋的代用指标。基于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AGCM GAMIL2.0模式在历史海温驱动下的积分结果,评估了该模式对热带气旋潜势气候态、季节循环和年际变率的模拟能力。并分别从影响热带气旋潜势分布的热力因子(相对湿度、热带气旋最大风速)和动力因子(垂直风切变、绝对涡度、垂直抬升速度)的角度,讨论了造成热带气旋潜势模拟误差的原因。结果表明,在西北太平洋地区,模式能够合理再现热带气旋潜势的气候态分布,但由于GAMIL2.0模拟的相对湿度偏大且向东延伸,造成了热带气旋潜势大值区较之再分析资料偏大且偏东10°。由于GAMIL2.0模拟的季风槽位置偏北偏强,导致模拟的热带气旋潜势季节循环北进偏早而南退偏晚。在年际变化方面,GAMIL2.0能合理模拟出热带气旋潜势在ENSO正负位相东西反向的变化特征,但位于20°—30°N的加强和减弱区的分界线偏西,这与模拟的垂直速度和相对湿度的模拟误差有关,进一步分析表明,这是由于模拟中ENSO事件期间的西北太平洋异常上升中心比观测偏西且偏强造成的。     

Evaluation of cloud vertical structure simulated by recent BCC_AGCM versions through comparison with CALIPSO-GOCCP data

ABSTRACT The abilities of BCC-AGCM2.1 and BCC_AGCM2.2 to simulate the annual-mean cloud vertical structure （CVS） were evaluated through comparison with GCM-Oriented CALIPSO Cloud Product （CALIPSO-GOCCP） data. BCC-AGCM2.2 has a dynamical core and physical processes that are consistent with BCC-AGCM2.1, but has a higher horizontal resolution. Results showed that both BCC-AGCM versions underestimated the global-mean total cloud cover （TCC）, middle cloud cover （MCC） and low cloud cover （LCC）, and that BCC_AGCM2.2 underestimated the global-mean high cloud cover （HCC）. The global-mean cloud cover shows a systematic decrease from BCCA-GCM2.1 to BCC_AGCM2.2, especially for HCC. Geographically, HCC is significantly overestimated in the tropics, particularly by BCC_AGCM2,1, while LCC is generally overestimated over extra-tropical lands, but significantly underestimated over most of the oceans, especially for subtropical marine stratocumulus clouds. The leading EOF modes of CVS were extracted. The BCC_AGCMs perform well in reproducing EOF1, but with a larger variance explained. The two models also capture the basic features of EOF3, except an obvious deficiency in eigen- vector peaks. EOF2 has the largest simulation biases in both position and strength of eigenvector peaks. Furthermore, we investigated the effects of CVS on relative shortwave and longwave cloud radiative forcing （RSCRF and RLCRF）. Both BCC_AGCM versions successfully reproduce the sign of regression coefficients, except for RLCRF in PC1. However, the RSCRF relative contributions from PC1 and PC2 are overestimated, while the relative contribution from PC3 is underes timated in both BCC_AGCM versions. The RLCRF relative contribution is underestimated for PC2 and overestimated for PC3.     

BCC_AGCM2.0.1模式系统对江淮梅雨期降水的模拟能力

文章通过累积频率方法定义江淮地区观测和模拟的梅雨期,分析国家气候中心大气模式BCC_AGCM2.0.1对我国东部地区暴雨、大雨、中雨和小雨四种等级降水的模拟能力。由于模式的模拟环流背景与实际不同,导致模拟的降水峰值时期与梅雨期有偏差,因此通过累积频率方法定义模拟的梅雨期,模拟梅雨期为6月1候至6月3候,而实际观测梅雨期6月5候至7月2候,模拟期较实际梅雨期提前了20天。模拟结果表明模拟的我国东部总降水量偏少,总雨日数偏多,主要是由于模拟的暴雨日数偏少,小雨日数偏多;观测的江淮地区近40年来暴雨、大雨和中雨呈上升趋势,小雨呈下降趋势,模式模拟的不同等级降水变化趋势与观测相反,暴雨、大雨和中雨呈下降趋势,小雨呈上升趋势。     

Validation of components of the water cycle in the ECHAM4 general circulation model based on the Newtonian relaxation technique: a case study of an intense winter cyclone

The representation of a simulated synoptic-scale weather system is compared with observations. To force the model to the observed state, the so-called Newtonian relaxation technique (nudging) is applied to relax vorticity, divergence, temperature, and the logarithm of surface pressure to the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis fields. The development of an extraordinary strong cyclone along the East Coast of the USA during 12–14 March 1993 was chosen as the case study. The synoptic-scale features were well represented in the model simulation. However, systematic differences to observations of the International Satellite Cloud Climatology Project (ISCCP) occurred. The model underestimated clouds in lower and middle levels of the troposphere. Low-level clouds were mainly underestimated behind the cold front of the developing cyclone, while the underestimation of mid-level clouds seems to be a more general feature. The reason for the latter is the fact that the relative humidity has to exceed a critical threshold before clouds can develop. In contrast, thin upper-level cirrus clouds in pre-frontal regions were systematically overestimated by the model. Therefore, we investigated the effects of changed physical parameterizations with two sensitivity studies. In the PCI experiment, the standard cloud scheme operated in ECHAM4 was replaced by a more sophisticated one which defines separate prognostic equations for cloud liquid water and cloud ice. The second experiment, RHCRIT, changed the profile of the critical relative humidity threshold for the development of clouds in the standard scheme. Both experiments showed positive changes in the representation of clouds during the development of the cyclone as compared to the ISCCP. PCI clearly reduced the upper-level cloud amounts by intensifying the precipitation flux in the middle troposphere. The changed condensation threshold in the RHCRIT experiment led to a sharper represented cold front and a better represented cloudiness on its rear side as compared to the PCI and the CONTROL simulations.     

Subseasonal variability and predictability of the Arctic Oscillation/North Atlantic Oscillation in BCC_AGCM2.2

The subseasonal variability and predictability of the Arctic Oscillation/North Atlantic Oscillation (AO/NAO) is evaluated using a full set of hindcasts generated from the Beijing Climate Center Atmospheric General Circulation Model version 2.2 (BCC_AGCM2.2). It is shown that the predictability of the monthly mean AO/NAO index varies seasonally, with the highest predictability during winter (December–March) and the lowest during autumn (August–November), with respect to both observations and BCC_AGCM2.2 results. As compared with the persistence prediction skill of observations, the model skillfully predicts the monthly mean AO/NAO index with a one-pentad lead time during all winter months, and with a lead time of up to two pentads in December and January. During winter, BCC_AGCM2.2 exhibits an acceptable skill in predicting the daily AO/NAO index of ∼9 days, which is higher than the persistence prediction skill of observations of ∼4 days. Further analysis suggests that improvements in the simulation of storm track activity, synoptic eddy feedback, and troposphere–stratosphere coupling in the Northern Hemisphere could help to improve the prediction skill of subseasonal AO/NAO variability by BCC_AGCM2.2 during winter. In particular, BCC_AGCM2.2 underestimates storm track activity intensity but overestimates troposphere–stratosphere coupling, as compared with observations, thus providing a clue to further improvements in model performance.     

Cirrus cloud horizontal and vertical inhomogeneity effects in a GCM

Summary A set of the inhomogeneity factor for high-level clouds derived from the ISCCP D1 dataset averaged over a five-year period has been incorporated in the UCLA atmospheric GCM to investigate the effect of cirrus cloud inhomogeneity on climate simulation. The inclusion of this inhomogeneous factor improves the global mean planetary albedo by about 4% simulated from the model. It also produces changes in solar fluxes and OLRs associated with changes in cloud fields, revealing that the cloud inhomogeneity not only affects cloud albedo directly, but also modifies cloud and radiation fields. The corresponding difference in the geographic distribution of precipitation is as large as 7 mm day⁻¹. Using the climatology cloud inhomogeneity factor also produces a warmer troposphere related to changes in the cloudiness and the corresponding radiative heating, which, to some extent, corrects the cold bias in the UCLA AGCM. The region around 14 km, however, is cooler associated with increase in the reflected solar flux that leads to a warmer region above. An interactive parameterization for mean effective ice crystal size based on ice water content and temperature has also been developed and incorporated in the UCLA AGCM. The inclusion of the new parameterization produces substantial differences in the zonal mean temperature and the geographic distribution of precipitation, radiative fluxes, and cloud cover with respect to the control run. The vertical distribution of ice crystal size appears to be an important factor controlling the radiative heating rate and the consequence of circulation patterns, and hence must be included in the cloud-radiation parameterization in climate models to account for realistic cloud processes in the atmosphere.     

A methodology study of the validation of clouds in GCMs using ISCCP satellite observations

The cloudiness fields simulated by a general circulation model and a validation using the International Satellite Cloud Climatology Project (ISCCP) satellite observations are presented. An adapted methodology is developed, in which the issue of the sub-grid scale variability of the cloud fields, and how it may affect the comparison exercise, is considered carefully. In particular different assumptions about the vertical overlap of cloud layers are made, allowing us to reconstruct the cloud distribution inside a model grid column. Carrying out an analysis directly comparable to that of ISCCP then becomes possible. The relevance of this method is demonstrated by its application to the evaluation of the cloud schemes used in Laboratoire de Météoroligie Dynamique (LMD) general circulation model. We compare cloud properties, such as cloud-top height and cloud optical thickness, analysed by ISCCP and simulated by the LMD GCM. The results show that a direct comparison of simulated low cloudiness and that shown from satellites is not possible. They also reveal some model deficiencies concerning the cloud vertical distribution. Some of these features depend little on the cloud overlap assumption and may reveal inadequate parameterisation of the boundary layer mixing or the cloud water precipitation rate. High convective clouds also appear to be too thick.     

Tropical Cyclone Genesis Potential Index over the Western North Pacific Simulated by LASG/IAP AGCM

Tropical cyclone genesis potential index(GPI) is a useful metric for gauging the performance of global climate models in the simulation of tropical cyclone(TC) genesis.The performance of LASG/IAP AGCM GAMIL2.0 in the simulation of GPI over the western North Pacific(WNP) is assessed in this paper.Since GPI depends on large scale environmental factors including low-level vorticity at 850 hPa,relative humidity at 700 hPa,vertical wind shear between 850 and 200 hPa,maximum potential intensity(MPI),and vertical velocity,the bias of GPI simulation is discussed from the perspective of thermal and dynamical factors.The results are compared with the ECMWF reanalysis data(ERA40).The analyses show that both the climatological spatial pattern and seasonal cycle of GPI over the WNP are reasonably simulated by GAMIL2.0,but due to the overestimation of relative humidity,the simulated GPI extends to 170°E,about 10°east to that in the reanalysis data.It is demonstrated that the bias in the simulation of monsoon trough,which is about 5°north to the reanalysis,leads to an overestimation of GPI during May-June and September-October,but an underestimation during July-August.Over the WNP,the response of GPI to ENSO is well captured by GAMIL2.0,including the eastward(westward) shift of TC genesis location during El Nin o(La Nin a) years.However,the anomalous convective center associated with El Nin o shifts westward about 20°in comparison to ERA40,which leads to the biases in both vertical velocity and relative humidity.These eventually result in the westward deflection of the boundary between the positive and negative GPI centers along 20°-30°N.The results from this study provide useful clues for the future improvement of GAMIL2.0.     

Evaluating the Impacts of Cloud Microphysical and Overlap Parameters on Simulated Clouds in Global Climate Models

The improvement of the accuracy of simulated cloud-related variables, such as the cloud fraction, in global climate models (GCMs) is still a challenging problem in climate modeling. In this study, the influence of cloud microphysics schemes (one-moment versus two-moment schemes) and cloud overlap methods (observation-based versus a fixed vertical decorrelation length) on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero. Compared with the fixed decorrelation length method, the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions. The utilization of a two-moment cloud microphysics scheme, on the other hand, notably improved the simulated cloud fraction compared with the one-moment scheme; specifically, the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%. Furthermore, the total cloud fraction bias decreased by 6.6% in the boreal winter (DJF) and 1.64% in the boreal summer (JJA). Cloud radiative forcing globally and in the four regions improved by 0.3%?1.2% and 0.2%?2.0%, respectively. Thus, our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.     

Simulation of marine stratocumulus: effect of precipitation parameterization and sensitivity to droplet number concentration

Marine stratocumulus observations show a large variability in cloud droplet number concentration (CDNC) related to variability in aerosol concentration. Changes in CDNC modify the cloud reflectivity, but also affect cloud water content, cloud lifetime, and cloudiness, through changes in precipitation. In mesoscale models and general circulation models (GCMs), precipitation mechanisms are parameterized. Here we examine how the precipitation parameterization can affect the simulated cloud. Simulations are carried out with the one-dimensional version of the hydrostatic primitive equation model MAR (Modéle Atmosphérique Régional) developed at the Université catholique de Louvain. It includes a E- turbulence closure, a wide-band formulation of the radiative transfer, and a parameterized microphysics including prognostic equations for water vapour, cloud droplets and rain drops concentrations. In a first step, the model is used to simulate a horizontally homogeneous stratocumulus deck observed during the Atlantic Stratocumulus Transition Experiment (ASTEX) on the night of 12–13 June 1992. The observations show that the model is able to realistically reproduce the vertical structure of the cloud-topped boundary layer. In a second step, several precipitation parameterizations commonly used in mesoscale models and GCMs are tested. It is found that most parameterizations tend to overestimate the precipitation, which results in an underestimation of the vertically integrated liquid water content. Afterwards, using those parameterizations that are sensitive to CDNC, several simulations are performed to estimate the effect of CDNC variations on the simulated cloud. Based upon the simulation results, we argue that currently used parameterizations do not enable assessment of such a sensitivity.