An investigation of cloud cover change in response to thermal forcing

The role of cloud cover in determining the sensitivity of climate has been a source of great uncertainty. This article reviews the distributions of cloud cover change from several climate sensitivity experiments conducted at the Geophysical Fluid Dynamics Laboratory of NOAA (GFDL) and other institutions. Two of the sensitivity experiments conducted at GFDL used a general circulation model with a limited computational domain and idealized geography, whereas three other experiments were conducted by the use of a global model with realistic geography. A thermal forcing imposed was either a change of solar constant or that of the CO₂-concentration in the atmosphere. It was found that in all five cases, clouds were decreased in the moist, convectively active regions such as the tropical and middle latitude rainbelts, whereas they increased in the stable region near the model surface from middle to higher latitudes. In addition, cloud also increased in the lower model stratosphere and generally decreased in the middle and upper troposphere for practically all latitudes.A comparison of the cloud changes obtained from investigations carried out at other institutions reveals certain qualitative (but not necessarily quantitative) similarities to the GFDL results. These similarities include a general reduction of tropospheric cloud cover especially in the vicinity of the rainbelts, a general increase of lower stratospheric cloud cover for almost all latitudes and an increase of low stratiform cloud in high latitudes.     

Simulations of 20th and 21st century Arctic cloud amount in the global climate models assessed in the IPCC AR4

Simulations of late 20th and 21st century Arctic cloud amount from 20 global climate models (GCMs) in the Coupled Model Intercomparison Project phase 3 (CMIP3) dataset are synthesized and assessed. Under recent climatic conditions, GCMs realistically simulate the spatial distribution of Arctic clouds, the magnitude of cloudiness during the warmest seasons (summer–autumn), and the prevalence of low clouds as the predominant type. The greatest intermodel spread and most pronounced model error of excessive cloudiness coincides with the coldest seasons (winter–spring) and locations (perennial ice pack, Greenland, and the Canadian Archipelago). Under greenhouse forcing (SRES A1B emissions scenario) the Arctic is expected to become cloudier, especially during autumn and over sea ice, in tandem with cloud decreases in middle latitudes. Projected cloud changes for the late 21st century depend strongly on the simulated modern (late 20th century) annual cycle of Arctic cloud amount: GCMs that correctly simulate more clouds during summer than winter at present also tend to simulate more clouds in the future. The simulated Arctic cloud changes display a tripole structure aloft, with largest increases concentrated at low levels (below 700 hPa) and high levels (above 400 hPa) but little change in the middle troposphere. The changes in cloud radiative forcing suggest that the cloud changes are a positive feedback annually but negative during summer. Of potential explanations for the simulated Arctic cloud response, local evaporation is the leading candidate based on its high correlation with the cloud changes. The polar cloud changes are also significantly correlated with model resolution: GCMs with higher spatial resolution tend to produce larger future cloud increases.     

CloudSat/CALIPSO卫星资料分析云的全球分布及其季节变化特征

全球气候模式（GCM）中云的参数化方案具有不确定性,了解云的时、空变化能为参数化方案提供有效参考。利用搭载在属于A-Train卫星序列的CloudSat和CALIPSO上的94 GHz云廓线雷达（CPR）以及正交极化云-气溶胶激光雷达（CALIOP）联合的2级云分类产品,分析了2007年3月-2010年2月8种云类及三相态的云量地理分布、纬向垂直分布的季节变化特征以及云层分布概率。结果发现,卷云的分布体系与深对流云相似,主要集中在西太平洋暖池、全球各季风区及赤道辐合带,分布格局与气压带、风带季节性移动一致。层云与层积云主要分布在中低纬度非季风区以及中高纬度的洋面上。高积云与高层云的分布形成明显的海陆差异,雨层云与积云的分布形成明显的纬度差异。冰云分布与卷云相似,云高随纬度递增而递减;水云分布与层积云相似,平均分布于2 km高度;混合云集中于高纬度地区及赤道辐合带,中纬度地区随纬度变化集中于海拔0-10 km的弧形带。层状云多以多层云形式出现,积状云多以单、双层云的形式出现,层状云的云重叠现象比积状云更显著。积状和层状云的分布特征与积云和层云降水的分布特征基本一致,验证了不同类型降水的卫星观测结果,同时为气候模式的云量诊断方案提供对比验证的数据。      

On the spread of changes in marine low cloud cover in climate model simulations of the 21st century

In 36 climate change simulations associated with phases 3 and 5 of the Coupled Model Intercomparison Project (CMIP3 and CMIP5), changes in marine low cloud cover (LCC) exhibit a large spread, and may be either positive or negative. Here we develop a heuristic model to understand the source of the spread. The model’s premise is that simulated LCC changes can be interpreted as a linear combination of contributions from factors shaping the clouds’ large-scale environment. We focus primarily on two factors—the strength of the inversion capping the atmospheric boundary layer (measured by the estimated inversion strength, EIS) and sea surface temperature (SST). For a given global model, the respective contributions of EIS and SST are computed. This is done by multiplying (1) the current-climate’s sensitivity of LCC to EIS or SST variations, by (2) the climate-change signal in EIS or SST. The remaining LCC changes are then attributed to changes in greenhouse gas and aerosol concentrations, and other environmental factors. The heuristic model is remarkably skillful. Its SST term dominates, accounting for nearly two-thirds of the intermodel variance of LCC changes in CMIP3 models, and about half in CMIP5 models. Of the two factors governing the SST term (the SST increase and the sensitivity of LCC to SST perturbations), the SST sensitivity drives the spread in the SST term and hence the spread in the overall LCC changes. This sensitivity varies a great deal from model to model and is strongly linked to the types of cloud and boundary layer parameterizations used in the models. EIS and SST sensitivities are also estimated using observational cloud and meteorological data. The observed sensitivities are generally consistent with the majority of models as well as expectations from prior research. Based on the observed sensitivities and the relative magnitudes of simulated EIS and SST changes (which we argue are also physically reasonable), the heuristic model predicts LCC will decrease over the 21st-century. However, to place a strong constraint, for example on the magnitude of the LCC decrease, will require longer observational records and a careful assessment of other environmental factors producing LCC changes. Meanwhile, addressing biases in simulated EIS and SST sensitivities will clearly be an important step towards reducing intermodel spread in simulated LCC changes.     

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。     

Planetary albedo in strongly forced climate, as simulated by the CMIP3 models

In an ensemble of general circulation models, the global mean albedo significantly decreases in response to strong CO₂ forcing. In some of the models, the magnitude of this positive feedback is as large as the CO₂ forcing itself. The models agree well on the surface contribution to the trend, due to retreating snow and ice cover, but display large differences when it comes to the contribution from shortwave radiative effects of clouds. The ??cloud contribution?? defined as the difference between clear-sky and all-sky albedo anomalies and denoted as ??CC is correlated with equilibrium climate sensitivity in the models (correlation coefficient 0.76), indicating that in high sensitivity models the clouds to a greater extent act to enhance the negative clear-sky albedo trend, whereas in low sensitivity models the clouds rather counteract this trend. As a consequence, the total albedo trend is more negative in more sensitive models (correlation coefficient 0.73). This illustrates in a new way the importance of cloud response to global warming in determining climate sensitivity in models. The cloud contribution to the albedo trend can primarily be ascribed to changes in total cloud fraction, but changes in cloud albedo may also be of importance.     

Simulations of Hydrological Cycle Changes Between the LGM and the Present Day over China

Based on the International Satellite Cloud Climatology Project (ISCCP) data in 1983–2006,it is found that there is a high value center of high cloud amount over the Tibetan Plateau (TP),while there is a high value center of middle cloud amount over the Sichuan Basin extending to the coastal area of southeastern China along the same latitude,and a low one over the TP.The present day (PD) and Last Glacial Maximum (LGM) climates are simulated by using the NCAR Community Climate Model (CCM3) nested with a regio...     

The respective roles of surface temperature driven feedbacks and tropospheric adjustment to CO2 in CMIP5 transient climate simulations

An overview of radiative climate feedbacks and ocean heat uptake efficiency diagnosed from idealized transient climate change experiments of 14 CMIP5 models is presented. Feedbacks explain about two times more variance in transient climate response across the models than ocean heat uptake efficiency. Cloud feedbacks can clearly be identified as the main source of inter-model spread. Models with strong longwave feedbacks in the tropics feature substantial increases in cloud ice around the tropopause suggestive of changes in cloud-top heights. The lifting of the tropical tropopause goes together with a general weakening of the tropical circulation. Distinctive inter-model differences in cloud shortwave feedbacks occur in the subtropics including the equatorward flanks of the storm-tracks. Related cloud fraction changes are not confined to low clouds but comprise middle level clouds as well. A reduction in relative humidity through the lower and mid troposphere can be identified as being the main associated large-scale feature. Experiments with prescribed sea surface temperatures are analyzed in order to investigate whether the diagnosed feedbacks from the transient climate simulations contain a tropospheric adjustment component that is not conveyed through the surface temperature response. The strengths of the climate feedbacks computed from atmosphere-only experiments with prescribed increases in sea surface temperatures, but fixed CO₂ concentrations, are close to the ones derived from the transient experiment. Only the cloud shortwave feedback exhibits discernible differences which, however, can not unequivocally be attributed to tropospheric adjustment to CO₂. Although for some models a tropospheric adjustment component is present in the global mean shortwave cloud feedback, an analysis of spatial patterns does not lend support to the view that cloud feedbacks are dominated by their tropospheric adjustment part. Nevertheless, there is positive correlation between the strength of tropospheric adjustment processes and cloud feedbacks across different climate models.     

中国及周边地区多层云垂直结构时空分布特征

利用CALIPSO激光雷达1km水平分辨率的云层产品,计算了中国及周边地区（0°~55°N,70°~140°E）多层云的出现概率,对不同高度多层云的水平分布及其季节变化特征进行了统计分析。结果表明:多层云的出现概率存在显著的区域差异,青藏高原和蒙古高原出现的概率较低,30°N以南的低纬度地区出现的概率较高;多层云系统中双层云占比最大,并且云层发生概率随着云层数的增多而减小;不同高度双层云和三层云的分布特征类似;多层云出现概率夏季最大,冬季最小,其中夏季双层云中“高云+高云”、“高云+中云”和三层云中“高云+高云+高云”、“高云+高云+中云”的配置在青藏高原主体的出现概率最大,而冬季单层云的低云、双层云中“高云+低云”及三层云中少量的“高云+高云+低云”配置在中国东北部海域、南海北部等30°N以北地区的出现概率高于其它季节。      

A quantitative performance assessment of cloud regimes in climate models

Differences in the radiative feedback from clouds account for much of the variation in climate sensitivity amongst General Circulation Models (GCMs). Therefore metrics of model performance which are demonstrated to be relevant to the cloud response to climate change form an important contribution to the overall evaluation of GCMs. In this paper we demonstrate an alternative method for assigning model data to observed cloud regimes obtained from clustering histograms of cloud amount in joint cloud optical depth—cloud top pressure classes. The method removes some of the subjectivity that exists in previous GCM cloud clustering studies. We apply the method to ten GCMs submitted to the Cloud Feedback Model Intercomparison Project (CFMIP), evaluate the simulated cloud regimes and analyse the climate change response in the context of these regimes. We also propose two cloud regime metrics, one of which is specifically targeted at assessing GCMs for the purpose of obtaining the global cloud radiative response to climate change. Most of the global variance in the cloud radiative response between GCMs is due to low clouds, with 47% arising from the stratocumulus regime and 18% due to the regime characterised by clouds undergoing transition from stratocumulus to cumulus. This result is found to be dominated by two structurally similar GCMs. The shallow cumulus regime, though widespread, has a smaller contribution and reduces the variance. For the stratocumulus and transition regimes, part of the variance results from a large model spread in the radiative properties of the regime in the control simulation. Comparison with observations reveals a systematic bias for both the stratocumulus and transition regimes to be overly reflective. If this bias was corrected with all other aspects of the response unchanged, the variance in the low cloud response would reduce. The response of some regimes with high cloud tops differ between the GCMs. These regimes are simulated too infrequently in a few of the models. If the frequency in the control simulation were more realistic and changes within the regimes were unaltered, the variance in the cloud radiative response from high-top clouds would increase. As a result, use of observations of the mean present-day cloud regimes suggests that whilst improvements in the simulation of the cloud regimes would impact the climate sensitivity, the inter-model variance may not reduce. When the cloud regime metric is calculated for the GCMs analysed here, only one model is on average consistent with observations within their uncertainty (and even this model is not consistent with the observations for all regimes), indicating scope for improvement in the simulation of cloud regimes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.     

南半球中高纬度区域不同类型云的辐射特性

利用CloudSat的2B-CLDCLASS-LIDAR云分类产品和2B-FLXHR-LIDAR辐射产品4 a（2007-2010年）的数据,定量分析了单层云（高云、中云、低云）和3种双层云（如:高云与中云共存、高云与低云共存以及中云与低云共存）在南半球中高纬度（40°-65°S）的云量、云辐射强迫和云辐射加热率。其中云辐射加热率定义为有云时的大气加热率廓线与晴空大气加热率廓线的差值。结果表明:研究区域盛行单层低云和单层中云,其云量分别为44.1%和10.3%。并且,中云重叠低云在双层云中云量也是最大（8.7%）。不同类型云的云量也显著影响着其云辐射强迫。单层低云在大气层顶、地表以及大气中的净云辐射强迫分别是-64.8、-56.5和-8.4 W/m2,其绝对值大于其他类型云。虽然单层的中云在大气层顶和地表的净辐射强迫也为负值,但其在大气中的净云辐射强迫为正值（2.3 W/m2）。最后,讨论了不同类型云对大气中辐射能量垂直分布的影响。所有类型云的短波（或长波）云辐射加热率都随高度升高表现为由负值转为正值（或由正值转为负值）。对于大部分云,其净云辐射加热率主要由长波云辐射加热率决定。这些研究结果旨在为模式中云重叠参数化方案在区域的适用性评估及改进提供观测依据。      

基于MODIS产品的中国陆地冰云季节变化特征

利用2011年11月-2016年10月Terra卫星MODIS（moderate-resolution imaging spectroradiometer）3级大气产品数据（MOD08_M3）对中国陆地区域冰云发生概率、有效粒子半径、光学厚度和冰水路径的水平分布与季节变化进行分析。结果表明:冰云特性的水平分布和季节变化特征与东亚季风和强对流天气的发生存在一定联系。近5年冰云发生概率呈上升趋势,季节性变化规律明显,高值区出现在青藏高原东北部;冰云有效粒子水平分布呈现由西南向东北逐渐增加的趋势,总体季节性变化特点不明显,但在纬度较高地区出现随季节变化特征;冰云光学厚度与冰水路径水平分布和季节变化趋势大致相同,呈东南向西北递减趋势,总体季节性变化明显。     

气候模式中云的次网格结构对全球辐射影响的研究

利用一种用于大尺度天气、气候模式的随机云产生器(SCG)和独立气柱近似(ICA)辐射算法,研究了次网格云的水平结构以及垂直重叠结构对全球辐射场的影响.比较了水平非均匀云(IHCLD)和水平均匀云(HCLD)的辐射场差异以及云的最大.随机重叠(MRO)和一般重叠(GenO)的辐射场差异.结果显示,与HCLD相比,IHCLD一方面可增加地面净短波辐射通量,纬向平均最大值(约1W/m~2)和次大值(约0.6 W/m~2)分别位于高纬度低云密集地区和对流旺盛的热带地区;另一方面可增加大气顶的净长波辐射通量,纬向平均最大值(0.3 W/m~2)出现在热带地区.不同的重叠结构对短波和长波辐射收支也有很大的影响.MRO和GenO的短波辐射通量差异在热带辐合带最大.达到30-40W/m~2,在高纬度低云带的纬向平均也可达到5W/m~2左右;长波辐射通量差异具有相似的地区分布,但量值相对较小.不同重叠结构可以造成大气上下层的辐射加热率差异,影响大气热力层结.云的水平和垂直结构对有云区域辐射收支的影响将改变大气热力、动力状况以及水汽条件,从而影响模拟的气候系统的演变.文中采用单向云-辐射计算,排除了与气候系统其他过程复杂的相互作用,从而使其结果具有一定的普适性,可为不同大尺度模式进行次网格云辐射参数化提供参考.     

末次冰期冰盛期中国地区水循环因子变化的模拟研究

ISCCP卫星资料(1983—2006年)的结果显示:青藏高原地区是高云的高值中心;而以四川为中心直到同纬度的中国东南沿海地区是中云的高值区,同时,青藏高原地区是中云的低值中心。利用全球气候模式CCM3嵌套区域模式MM5模拟了现代和末次冰期冰盛期的气候。MM5模拟的结果与ISCCP的卫星资料对比表明:模拟结果再现了中国地区高云和中云分布的主要特征。这暗示云分布的气候特征可能主要由相对湿度决定。同时,通过MM5的结果与NCEP资料的对比也说明,模式可以较好地模拟水汽和温度的垂直分布。在此基础上,研究了末次冰期冰盛期水循环因子的变化。模拟结果显示:末次冰期冰盛期夏季对流层的温度降低,在对流层中上层存在温度降低的中心;而在冬季在南方的对流层中层存在降温中心,在北方的对流层中上层温度升高。大气中水汽含量与温度变化有很好的正相关,除了冬季北方对流层中上层水汽增加外,水汽含量一般降低,而且在近地层降低的最多,随高度增高水汽变化逐渐变小。但是,水汽的相对变化在对流层上层存在降低的高值中心。相对湿度存在变化,最大的变化超过15%,而且有增加,也有减少。在区域尺度相对湿度不是保守的。相对湿度变化与中云和低云的变化一致。在末次冰期冰盛期,中国地区高云量减少,除中国西南地区外,中云和低云量减少,低云量减少的最多。降水的变化与中云和低云的变化相对应,云量增加降水增加,云量减少降水也减少。从相对湿度和有效降水可以看到在西南地区末次冰期冰盛期变得潮湿,在夏季西北地区也变得潮湿。     

基于美国AMF寿县观测的云特性研究

美国能源部大气辐射观测计划移动观测ARMAMF（atmospheric radiation measurement mobile facility）2008年首次在我国寿县开展综合观测,为研究云特性提供了很好的资料平台。本文在此次云雷达等观测资料基础上,研究了寿县秋末冬初云高、云厚、云量及其辐射特性,结果发现,寿县有76．3％的观测日有云出现,54．0％的观测时间有云覆盖,中云（以下简称M云）和高云（以下简称H云）出现频率占全部云系的76．7％,天气系统对寿县云系形成有较大影响;云底高度大于3km的降水性云（以下简称P云）出现频率占全部P云的67．7％,是云底高度小于3kmP云的5．3倍,发生在下午的降水占全部P云的47．8％,气溶胶可能对P云的这种分布有较大影响;云和气溶胶减少地面短波辐射的日均值达一99．1W／m。,其中气溶胶减少约占25．1％。不同高度和厚度云对地面辐射通量的影响有较大差异,P云产生最大的冷却效应（一201．9W／m。）,厚度小于2km的H云对地面辐射通量的减少量最少（一32．9w／m。）。另外,用地面单点云辐射观测与中分辨率成像光谱仪MODIS（moderate resolution imaging spectroradiometer）资料估计结果对比发现,两种资料有较大差异,差异可达-1．9～-36．9W／m。     

Seasonal and latitudinal features of the greenhouse effect in Russia

The annual cycle of air temperature trends is analyzed in comparison with total cloud amount trends. It is shown that during the last 30 years the cloud amount changes were associated with air temperature trends with positive and negative correlation in cold and warm half-year, respectively, which is in perfect agreement with the well-known facts of the influence of clouds on radiation balance and of the latter on temperature regime of the atmosphere. In all cases, the greenhouse effect increases when the radiation budget is nearly zero due to physical-geographic features; the small contribution of the greenhouse effect to the budget change manifests itself in climate changes. A conclusion is drawn on the presence of a greenhouse component of CO₂ in the phenomena described; however, in the high latitudes, this effect is exceeded by long-term changes in cloud amount.     

Variability of summer cloudiness in the Arctic Basin

Summary The extent and thickness of clouds in the Arctic Basin varied considerably in space and time in the late springs and summers of 1977–1979. While, on the average, clouds covered two thirds or more of the basin at any one time, cloud-free episodes were particularly common from the middle of June to late July and persisted locally for several days or even weeks. The central Arctic was less cloudy than the ocean zones closer to the coast in spring, but more cloudy in summer. Most clouds were semi-transparent, allowing recognition of underlying surface features. Optically thick clouds with middle and high level tops were associated with low pressure systems and with atmospheric flows from lower latitudes at the surface and aloft. Cloud-free skies were most frequent in high pressure cells. Climate models used to assess the impact of CO₂ and other trace gases on the radiation budget in the high latitudes should account for the heterogeneity of cloud extent and thickness in the Arctic Basin.With 12 Figures     

On dynamic and thermodynamic components of cloud changes

Clouds are sensitive to changes in both the large-scale circulation and the thermodynamic structure of the atmosphere. In the tropics, temperature changes that occur on seasonal to decadal time scales are often associated with circulation changes. Therefore, it is difficult to determine the part of cloud variations that results from a change in the dynamics from the part that may result from the temperature change itself. This study proposes a simple framework to unravel the dynamic and non-dynamic (referred to as thermodynamic) components of the cloud response to climate variations. It is used to analyze the contrasted response, to a prescribed ocean warming, of the tropically-averaged cloud radiative forcing (CRF) simulated by the ECMWF, LMD and UKMO climate models. In each model, the dynamic component largely dominates the CRF response at the regional scale, but this is the thermodynamic component that explains most of the average CRF response to the imposed perturbation. It is shown that this component strongly depends on the behaviour of the low-level clouds that occur in regions of moderate subsidence (e.g. in the trade wind regions). These clouds exhibit a moderate sensitivity to temperature changes, but this is mostly their huge statistical weight that explains their large influence on the tropical radiation budget. Several propositions are made for assessing the sensitivity of clouds to changes in temperature and in large-scale motions using satellite observations and meteorological analyses on the one hand, and mesoscale models on the other hand.     

Importance of the mixed-phase cloud distribution in the control climate for assessing the response of clouds to carbon dioxide increase: a multi-model study

We have conducted a multi-model intercomparison of cloud-water in five state-of-the-art AGCMs run for control and doubled carbon dioxide climates. The most notable feature of the differences between the control and doubled carbon dioxide climates is in the distribution of cloud-water in the mixed-phase temperature band. The difference is greatest at mid and high latitudes. We found that the amount of cloud ice in the mixed phase layer in the control climate largely determines how much the cloud-water distribution changes for the doubled carbon dioxide climate. Therefore evaluation of the cloud ice distribution by comparison with data is important for future climate sensitivity studies. Cloud ice and cloud liquid both decrease in the layer below the melting layer, but only cloud liquid increases in the mixed-phase layer. Although the decrease in cloud-water below the melting layer occurs at all latitudes, the increase in cloud liquid in the mixed-phase layer is restricted to those latitudes where there is a large amount of cloud ice in the mixed-phase layer. If the cloud ice in the mixed-phase layer is concentrated at high latitudes, doubling of carbon dioxide might shift the center of cloud water distribution poleward which could decrease solar reflection because solar insolation is less at higher latitude. The magnitude of this poleward shift of cloud water appears to be larger for the higher climate sensitivity models, and it is consistent with the associated changes in cloud albedo forcing. For the control climate there is a clear relationship between the differences in cloud-water and relative humidity between the different models, for both magnitude and distribution. On the other hand the ratio of cloud ice to cloud-water follows the threshold temperature which is determined in each model. Improved measurements of relative humidity could be used to constrain the modeled representation of cloud water. At the same time, comparative analysis in global cloud resolving model simulations is necessary for further understanding of the relationships suggested in this paper.