非对流云的观测事实及其对大气环流模式设计的意义

通过非对流云的气候资料分析和个例分析表明：(1)非对流云有季节变化,也随海陆分布的不同而变化,还与大气三圈环流及季风等密切相关。由于它们的相关性,在大气环流模式(GCM)中对非对流云的模拟必须与提高模式其他部分的模拟能力相辅相成。(2)产生于中高纬度大范围上升气流的非对流云,由赤道辐合带积云对流所致的高空赤道地区的卷云与卷层云和形成于副热带冷海水上空的层云与层积云是新一代大气环模式显式预报的3类主要非对流云。这3类非对流云均是大尺度的,GCM的网格能显示分辨,但在垂直方向如何提高GCM的分辨率问题仍是一个有待研究的问题。(3)在GCM中如何模拟冷海水上空的层云和赤道ITCZ所对应的大范围卷云和卷层云是十分困难和必要的。(4)通过对东亚及西太平洋区域非对流云系的个例分析,可以认为在新一代大气环流模式中,应显式预报行星大槽及赤道辐合带所对应的非对流云系。在模拟这些非对流云系时,应考虑它们的生消过程、平流过程与辐射过程。由于一段时间内大气环流模式尚难以分辨锋面与α中尺度的气旋,因此有必要在GCM中参数化这些系统,或采用更小的网格距。至于对非对流云所对应的降水参数化问题的研究,需要进一步的观测为基础。     

气候模式模拟气候变化的研究进展

引言在八十年代以前这方面的工作王绍武已经作了全面总结。自八十年代以来,利用气候模式来模拟气候变化已经有了新的发展。从简单的能量平衡模式(EBM),到辐射对流模式(RCM)及一般环流模式,(GCM)。随着高速电子计算机的发展,大气一般环流     

中期预报模式中云预报问题

本文作者是云参数化方面的权威人士。几个有影响的模式,如ECMWF、英国气象局GCM及美国NCAR GCM模式中都用了她的云量参数化方案。本文是1987年10月19—23日在美国哥伦比亚召开的“气候中的云”讨论会上的特邀报告。它全面综述了云在大气环流演变过程中的作用,以及中期数值预报模式中的各种云预报方法。比较了各种方案的优缺点及其检验方法。最后讨论了现行方案的主要问题和今后的发展方向。     

二氧化碳与气候：云变化机制

云分布的变化会对气候变化产生一个重要反馈。大气环流模式模拟表明在气候变暖时高云向上移动,自由对流层云总的说来减少。高云的移动可能是由对流层顶的向上移动引起的。据认为相对湿度及下部云量的减少可以归因于较暖气候条件下垂直运动的厚度增加,并且由于比湿增加大气辐射冷却也会向上移动。关于大气环流模式响应的诊断研究结果与这种机制是一致的。     

积云对流和云物理过程调整对气候模拟的影响

本文利用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 (LASG/IAP) 的大气环流谱模式SAMIL, 结合观测与政府间气候变化专门委员会第四次评估报告 (IPCC AR4) 大气模式集合平均结果, 以大气辐射通量为例, 诊断分析了物理过程调整前后模式对气候模拟的影响。旧版本SAMIL对大气辐射通量的模拟存在较大偏差, 经过大气辐射过程、 积云对流和诊断云等物理过程的调整后, 新版本SAMIL模拟的全球辐射通量的年平均结果与观测的偏差大幅减小, 其中大气顶能量收支的年变化及其平均值与观测更为接近。在积云对流方案调整基础上, 通过对诊断云物理方案的进一步调整, 新版本SAMIL对云物理量模拟更为合理, 在赤道辐合带等区域, 在很大程度上克服了单一积云对流物理过程调整引起的云宏观和微观属性不匹配问题, 能模拟出夏季气候平均辐射通量的全球分布特征, 尤其在东亚区域有较好的模拟能力。研究还表明, 在热带和副热带对流活跃区域, 当前SAMIL中积云对流过程偏差对辐射通量的模拟偏差有很大影响, 而模式中较为简单的诊断云方案也会将云宏观物理量模拟偏差带入云微观量模拟中, 也是主要偏差源之一。本文结果表明, 要继续提高SAMIL的模拟性能, 急需更新云物理参数化方案以改进云辐射过程的模拟, 同时也需要有针对性的研究积云对流和云物理过程之间相互作用, 并作进一步协同调整。     

大气模式比较计划（AMIP）

文章介绍了ＡＭＩＰ及参加ＡＭＩＰ的模式的模拟概况，指出几乎所有模式都能够模拟出大尺度大气环流的平均季节结构，但没有任何一个模式在各方面都好，而且模式一般高估了季节循环的方法，低估了年际变率，并且各个模式在模拟不同气候要素的时空变化时彼此差别也较大，文章还重点讨论了云辐射及陆面水文过程，指出观测与模拟高低云量存在较大差异，而云量可能并不是影响辐射平衡唯一因子，云的季节变化与气温的年循环存在着正相关，     

近年来国外气候动力学过程若干问题数值模拟研究进展

近年来,利用数值模式对大气环流或短期气候动力学过程进行模拟研究已经取得不少进展。无论是简单的理论模式还是复杂的GCM甚至是海气耦合模式均被广泛地应用于气候模拟、预报以及各类敏感性数值试验研究之中。目前的大气数值模式尤其是象GCM这类模式具有很强的模拟能力,一方面,它不仅能成功地模拟气候平均状态,而且能成功地用于天气预报,即准确地描述个别天气系统的逐日演变以及新扰动的发展;而另一方面,也是特别重要的,还能模拟大气低频变化特性,如年际变化特性、季节内尺度振荡、遥相关以及持续性异常等等。气候动力学尤其低频大气动力学过程是近年来热门的也是人们集中力量加以研究的课题,     

P-σ坐标系区域气候模式与GCM的嵌套试验

将P-σ坐标系区域气候模式与大气环流模式（GCM）单向嵌套，对我国1998年夏季长江流域严重洪涝进行模拟试验，并与GCM的模拟结果进行了比较。试验表明，嵌套的区域气候模式对降水场的模拟结果较GCM的结果有明显的改进，这是由于P-σ坐标系区域气候模式能够更真实地描述地形的动力和热力作用，因而能更准确地模拟青藏高原及其邻近地区的气压系统，在一定程度上弥补了低分辨率的GCM模拟在高原地区的不足，文中指出，与GCM嵌套的区域气候模式比GCM能够更有效地模拟区域气候的变化，尤其是对区域气候性特征比较明显的地区。     

利用随机天气模式及多种插值方法生成逐日气候变化情景的研究

针对目前大气环流模式在用于气候变化影响评估研究中时间分辨率较低的局恨性, 以及气候情景的要求和气候变化影响研究的需要, 结合GCM的模拟试验结果, 利用随机天气模式WGEN生成了中国东北地区未来气候变化的逐日情景, 其中包含了可能的气候变率信息, 可与作物动力模式等气候影响模式嵌套, 研究作物生长发育及其产量的可能变化, 及气候变率变化的可能影响等.     

海冰在大气环流模型中的重要作用

文章简要综述了次网格尺度海非均匀性大对大气环流模式性能的影响，南极冰在全球环流和短期气候变化中的作用，以及模式中不同的海冰反照率参数化对地表温度和辐射的影响等研究结果，说明海冰对极地海洋和大气的能量收支及短期气候变化有重要作用，不同的海洋参数化方案对气候模拟结果有重要影响。     

Impact of clouds on the surface radiation balance of the Arctic Ocean

Summary The relationship between clouds and the surface radiative fluxes over the Arctic Ocean are explored by conducting a series of modelling experiments using a one-dimensional thermodynamic sea ice model. The sensitivity of radiative flux to perturbations in cloud fraction and cloud optical depth are determined. These experiments illustrate the substantial effect that clouds have on the state of the sea ice and on the surface radiative fluxes. The effect of clouds on the net flux of radiation at the surface is very complex over the Arctic Ocean particularly due to the presence of the underlying sea ice. Owing to changes in surface albedo and temperature associated with changing cloud properties, there is a strong non-linearity between cloud properties and surface radiative fluxes. The model results are evaluated in three different contexts: 1) the sensitivity of the arctic surface radiation balance to uncertainties in cloud properties; 2) the impact of interannual variability in cloud characteristics on surface radiation fluxes and sea ice surface characteristics; and 3) the impact of climate change and the resulting changes in cloud properties on the surface radiation fluxes and sea ice characteristics.With 11 Figures     

Climate feedbacks in a general circulation model incorporating prognostic clouds

 This study performs a comprehensive feedback analysis on the Bureau of Meteorology Research Centre General Circulation Model, quantifying all important feedbacks operating under an increase in atmospheric CO₂. The individual feedbacks are analysed in detail, using an offline radiation perturbation method, looking at long- and shortwave components, latitudinal distributions, cloud impacts, non-linearities under 2xCO₂ and 4xCO₂ warmings and at interannual variability. The water vapour feedback is divided into terms due to moisture height and amount changes. The net cloud feedback is separated into terms due to cloud amount, height, water content, water phase, physical thickness and convective cloud fraction. Globally the most important feedbacks were found to be (from strongest positive to strongest negative) those due to water vapour, clouds, surface albedo, lapse rate and surface temperature. For the longwave (LW) response the most important term of the cloud ‘optical property’ feedbacks is due to the water content. In the shortwave (SW), both water content and water phase changes are important. Cloud amount and height terms are also important for both LW and SW. Feedbacks due to physical cloud thickness and convective cloud fraction are found to be relatively small. All cloud component feedbacks (other than height) produce conflicting LW/SW feedbacks in the model. Furthermore, the optical property and cloud fraction feedbacks are also of opposite sign. The result is that the net cloud feedback is the (relatively small) product of conflicting physical processes. Non-linearities in the feedbacks are found to be relatively small for all but the surface albedo response and some cloud component contributions. The cloud impact on non-cloud feedbacks is also discussed: greatest impact is on the surface albedo, but impact on water vapour feedback is also significant. The analysis method here proves to be a␣powerful tool for detailing the contributions from different model processes (and particularly those of the clouds) to the final climate model sensitivity. Received: 15 June 2000 / Accepted: 10 January 2001     

一维辐射-对流模式对云辐射强迫的数值模拟研究

利用一维辐射－对流气候模式, 详细研究了云量、云光学厚度以及云高等要素的变化对大气顶和地面太阳短波辐射和红外长波辐射通量以及云的辐射强迫的影响, 给出了计算这些物理量的经验拟合公式。结果表明, 云具有极为重要的辐射－气候效应。云量、云光学厚度以及云高即使只有百分之几的变化, 所带来的辐射强迫也可以与大气二氧化碳浓度加倍所产生的辐射强迫（3.75 W/m2）相比拟。例如, 当分别给它们+3%的扰动时, 即取云量变化0.015, 云光学厚度变化0.27, 以及云高变化0.15 km时（在实际的地球大气中, 这种尺度的变化是完全可能发生的）, 那么,可以得到地气系统的太阳短波辐射强迫-3.10 W/m2以及红外长波辐射强迫-1.77 W/m2, 二者之和为-4.78 W/m2, 已经完全可以抵消大气二氧化碳浓度加倍所产生的辐射强迫。但是, 当云量、云光学厚度以及云高向相反方向产生类似扰动时, 所产生的辐射强迫可能极大地放大二氧化碳浓度增加所产生的增强温室效应。因此, 研究结果揭示出, 不管是为了解释过去的气候变化, 还是预测未来的气候变化, 亟待加强在一个变化了的气候环境（例如地面温度升高）下, 云将发生何种变化的研究。     

A comparison of low-latitude cloud properties and their response to climate change in three AGCMs sorted into regimes using mid-tropospheric vertical velocity

Low-latitude cloud distributions and cloud responses to climate perturbations are compared in near-current versions of three leading U.S. AGCMs, the NCAR CAM 3.0, the GFDL AM2.12b, and the NASA GMAO NSIPP-2 model. The analysis technique of Bony et al. (Clim Dyn 22:71–86, 2004) is used to sort cloud variables by dynamical regime using the monthly mean pressure velocity ω at 500 hPa from 30S to 30N. All models simulate the climatological monthly mean top-of-atmosphere longwave and shortwave cloud radiative forcing (CRF) adequately in all ω-regimes. However, they disagree with each other and with ISCCP satellite observations in regime-sorted cloud fraction, condensate amount, and cloud-top height. All models have too little cloud with tops in the middle troposphere and too much thin cirrus in ascent regimes. In subsidence regimes one model simulates cloud condensate to be too near the surface, while another generates condensate over an excessively deep layer of the lower troposphere. Standardized climate perturbation experiments of the three models are also compared, including uniform SST increase, patterned SST increase, and doubled CO₂ over a mixed layer ocean. The regime-sorted cloud and CRF perturbations are very different between models, and show lesser, but still significant, differences between the same model simulating different types of imposed climate perturbation. There is a negative correlation across all general circulation models (GCMs) and climate perturbations between changes in tropical low cloud cover and changes in net CRF, suggesting a dominant role for boundary layer cloud in these changes. For some of the cases presented, upper-level clouds in deep convection regimes are also important, and changes in such regimes can either reinforce or partially cancel the net CRF response from the boundary layer cloud in subsidence regimes. This study highlights the continuing uncertainty in both low and high cloud feedbacks simulated by GCMs.     

The role of low clouds in determining climate sensitivity in response to a doubling of CO2 as obtained from 16 mixed-layer models

The effects that low clouds in sub-tropical to tropical latitudes have in determining a given model’s climate sensitivity is investigated by analyzing the cloud data produced by 16 “slab” or mixed-layer models submitted to the PCMDI and CFMIP archives and their respective response to a doubling of CO₂. It is found that, within the context of the 16 models analyzed, changes of these low clouds appear to play a major role in determining model sensitivity but with changes of middle cloud also contributing especially from middle to higher latitudes. It is noted that the models with the smallest overall cloud change produce the smallest climate sensitivities and vice versa although the overall signs of the respective cloud feedbacks are positive. It is also found that the amounts of low cloud as simulated by the respective control runs have very little correlation with their respective climate sensitivities. In general, the overall latitude-height patterns of cloud change as derived from these more recent experiments agree quite well with those obtained from much earlier studies which include increases of the highest cloud, decreases of cloud lower down in the middle and lower tropospheric and small increases of low clouds. Finally, other mitigating factors are mentioned which could also affect the spread of the resulting climate sensitivities.     

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。      

Evaluation of a component of the cloud response to climate change in an intercomparison of climate models

Most of the uncertainty in the climate sensitivity of contemporary general circulation models (GCMs) is believed to be connected with differences in the simulated radiative feedback from clouds. Traditional methods of evaluating clouds in GCMs compare time–mean geographical cloud fields or aspects of present-day cloud variability, with observational data. In both cases a hypothetical assumption is made that the quantity evaluated is relevant for the mean climate change response. Nine GCMs (atmosphere models coupled to mixed-layer ocean models) from the CFMIP and CMIP model comparison projects are used in this study to demonstrate a common relationship between the mean cloud response to climate change and present-day variability. Although atmosphere–mixed-layer ocean models are used here, the results are found to be equally applicable to transient coupled model simulations. When changes in cloud radiative forcing (CRF) are composited by changes in vertical velocity and saturated lower tropospheric stability, a component of the local mean climate change response can be related to present-day variability in all of the GCMs. This suggests that the relationship is not model specific and might be relevant in the real world. In this case, evaluation within the proposed compositing framework is a direct evaluation of a component of the cloud response to climate change. None of the models studied are found to be clearly superior or deficient when evaluated, but a couple appear to perform well on several relevant metrics. Whilst some broad similarities can be identified between the 60°N–60°S mean change in CRF to increased CO₂ and that predicted from present-day variability, the two cannot be quantitatively constrained based on changes in vertical velocity and stability alone. Hence other processes also contribute to the global mean cloud response to climate change.     

Climate sensitivity to cloud optical properties

A radiative–convective model was developed to investigate the sensitivity of climate to cloud optical properties and the related feedback processes. This model demonstrates that the Earth's surface temperature increases with cloud optical depth when the clouds are very thin but decreases with cloud optical depth when the cloud shortwave (solar) radiative forcing is larger than the cloud longwave (terrestrial) radiative forcing. When clouds are included in the model, the magnitude of the greenhouse effect due to a doubling of the CO₂ concentration varies with the cloudoptical depth: the thicker the clouds, the weaker the greenhouse warming. In addition, a small variation in the cloud droplet size has a larger impact on the equilibrium state temperature in the lower atmosphere than the warming caused by a doubling of the CO₂ concentration: a 2% increase in the average cloud droplet size per degree increase in temperature doubles the warming caused by the doubling of the CO₂ concentration. These findings suggest that physically reliable correlations between the cloud droplet size and macrophysical meteorological variables such as temperature, wind and water vapor fields are needed on a global climate scale to assess the climate impact of increases in greenhouse gases.     

On the vertical extent of atmospheric feedbacks

This study addresses the question: what vertical regions contribute the most to water vapor, surface temperature, lapse rate and cloud fraction feedback strengths in a general circulation model? Multi-level offline radiation perturbation calculations are used to diagnose the feedback contribution from each model level. As a first step, to locate regions of maximum radiative sensitivity to climate changes, the top of atmosphere radiative impact for each feedback is explored for each process by means of idealized parameter perturbations on top of a control (1?×?CO₂) model climate. As a second step, the actual feedbacks themselves are calculated using the changes modelled from a 2?×?CO₂ experiment. The impact of clouds on water vapor and lapse rate feedbacks is also isolated using `clear sky' calculations. Considering the idealized changes, it is found that the radiative sensitivity to water vapor changes is a maximum in the tropical lower troposphere. The sensitivity to temperature changes has both upper and lower tropospheric maxima. The sensitivity to idealized cloud changes is positive (warming) for upper level cloud increases but negative (cooling) for lower level increases, due to competing long and shortwave effects. Considering the actual feedbacks, it is found that water vapor feedback is a maximum in the tropical upper troposphere, due to the large relative increases in specific humidity which occur there. The actual lapse rate feedback changes sign with latitude and is a maximum (negative) again in the tropical upper troposphere. Cloud feedbacks reflect the general decrease in low- to mid-level low-latitude cloud, with an increase in the very highest cloud. This produces a net positive (negative) shortwave (longwave) cloud feedback. The role of clouds in the strength of the water vapor and lapse rate feedbacks is also discussed.