CAMS-CSM模式及其参与CMIP6的方案

世界气候研究计划(WCRP)组织开展的耦合模式比较计划已实施到第六阶段（CMIP6),中国气象科学研究院发展的气候系统模式CAMS-CSM是注册参加CMIP6的模式之一。除CMIP6要求的气候诊断、评估和描述试验（DECK）以及历史气候模拟试验（Historical）外,CAMS-CSM还计划参加情景模式比较计划（ScenarioMIP)、云反馈模式比较计划（CFMIP)、全球季风模式比较计划（GMMIP）和高分辨率模式比较计划（HighResMIP)这4个模式比较子计划（MIPs)。文中通过介绍CAMS-CSM的基本情况和模拟性能,以及计划参加的CMIP6试验及MIPs,为模式试验数据使用者提供参考。     

CFMIP大气环流模式模拟的东亚云辐射强迫特征

通过与卫星观测云和辐射资料的比较,检验了10个大气环流模式对东亚地区云量、垂直结构、光学属性以及辐射特征的模拟能力.10个模式的模拟结果均来自"云反馈模式比较计划"(CFMIP),为便于与国际卫星云气候计划(ISCCP)资料的比较,所有模式都引入了ISCCP模拟器.结果表明,10个模式均能模拟出东亚地区冬、夏两季云量及...     

GRAPES模式不同云物理方案对短期气候模拟的影响

在胡志晋、刘奇俊云物理方案的基础上，研制了GRAPES模式的云降水显式方案。用不同云物理方案开展了短期气候（月尺度）过程的模拟试验，并与地面观测资料和NECP再分析资料进行了对比分析。模拟的结果表明，耦合了云降水显式方案的GRAPES模式较好地模拟出了中国地区降水、温度、云量、长短波辐射的特点和分布规律。气候模拟中冰相过程和暖云过程模拟的降水、温度、云量和辐射差异较大，不同相态的水凝物及其分布对辐射特征有较大的影响，混合相云物理方案的模拟结果与实况更为吻合，应使用混合相云物理方案进行短期气候的模拟。     

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.     

祁连山云系云微物理结构和人工增雨催化个例模拟研究

改进了胡志晋、刘奇俊的云物理方案,并实现了与GRAPES模式的耦合;利用包括了新云物理方案的GRAPES模式对祁连山地区一次山区云降水过程进行了数值模拟,研究了祁连山地区云系的微物理结构,并开展了人工催化的数值试验研究。结果表明：（1）耦合的双参数方案能够较好的模拟此次降水范围、强度及云场分布的特点和规律;（2）新方案给出了祁连山云系的合理微观结构和它的特征;（3）播撒冰晶可以增加降雨,在云初始阶段播撒增雨范围较广,在云发展阶段播撒增雨范围较集中;（4）播撒冰晶后,云的动力结构发生了改变。     

Ka波段固态发射机体制云雷达和激光云高仪探测青藏高原夏季云底能力和效果对比分析

激光云高仪和云雷达是探测云底的两种设备,但其探测能力和探测结果有一定的差异,对比分析两种设备的测云效果有助于正确认识它们的探测优势,推进我国云雷达在云探测中的应用。本文提出了基于云雷达数据的云底和云顶高度分析方法,利用2014年夏季第三次青藏高原大气科学试验云雷达、激光雷达和激光云高仪数据,统计了三种设备探测青藏高原低云、中云和高云的云底高度偏差、探测率,分析了激光云高仪探测云底偏高的原因,根据探测结果提出了固态发射机体制雷达探测青藏高原低云的优化观测模式,模拟分析了探测效果。结果表明:（1）云雷达对高云的探测能力要明显优于激光云高仪,但其对低云的探测能力有待改进,激光云高仪探测云底下部的边界层内的云雷达回波信号可能是非云降水回波;低层云的遮挡作用明显降低了激光云高仪对多层云的观测能力;与激光云高仪相比,云雷达仍然会漏掉一些高云和中云。（2）激光云高仪探测的中云和高云的云底很多在云雷达回波内部,云雷达和激光云高仪观测的云底的时空对应关系比较差。（3）增大激光发射功率和优化固态发射机体制云雷达观测模式可提高云的观测能力,微波和激光雷达数据融合可全面了解不同类型云的宏观特征。这一工作为云雷达和激光雷达数据的应用,评估激光云高仪和云雷达探测青藏高原云的能力,讨论设计优化的云观测方案,为推进我国云观测技术的发展提供了重要参考依据。     

IPCC AR6对地球气候系统中反馈机制的新认识

气候反馈反映了气候系统内部对外界干扰的适应过程,在很大程度上影响对未来气候变化的预估。本文对政府间气候变化专门委员会（IPCC）第六次评估报告（AR6）中有关气候反馈的内容进行了梳理。相比第五次评估报告（AR5）,AR6对云反馈的认识有了较大提高,尤其是副热带海洋上空低云的反馈。AR6认为在高信度上云反馈参数为正值,即对气候变化起到一种放大效应。不过,云反馈的不确定范围在所有反馈机制中依然是最大的。除了普朗克反馈外,其他反馈机制（包括水汽、温度直减率、地表反照率、云、生物地球物理和非CO₂生物地球化学反馈）均在正值区间或零附近,总体上对气候变化起到放大效应。AR6对总的气候反馈的估计值为-1.16 W·m^-2·℃^-1,5%～95%的置信区间为[-1.81,-0.51] W·m^-2·℃^-1。随着气候平均态的增暖,气候反馈参数很可能会更靠近正值。     

耦合模式热带太平洋云—气候反馈模拟误差评估

云—气候反馈是热带海气相互作用的重要过程, 同时也是气候模拟的难点。本文利用IPCC AR4提供的耦合模式20世纪模拟试验结果和观测资料, 通过滤波和经验正交展开 (EOF) 的方法将热带太平洋海表温度的年际变化和年代际变化信号分别提取出来, 然后再分别计算观测和模式在年际和年代际时间尺度上云—辐射和热通量反馈特征, 发现在上述两个时间尺度上, 耦合模式模拟的云—辐射和热通量的反馈都要比观测和再分析资料的偏弱。反馈偏弱的可能原因是模式中热带大气对流和云对海表温度变化的敏感性比真实大气要偏弱。值得注意的是, 尽管耦合模式热带太平洋年代际热力反馈偏弱, 但是耦合模式模拟的热带太平洋南北纬10°之间海表温度的年代际增温趋势与观测相当。进一步分析表明, 只用年代际热力反馈来解释热带太平洋的气候变化是不够的, 还必须考虑动力反馈对于海表温度变化的调节作用。     

背景云凝结核对台风“莫拉克”降水微物理过程影响的数值研究

利用WRF模式设计两个数值试验:敏感性试验（背景场云凝结核浓度为2 000 cm-3）和控制试验（背景场云凝结核浓度为300 cm-3）模拟气溶胶对登陆台风“莫拉克”(2009年第8号台风)降水的影响。结果显示大量气溶胶进入外围雨带中起到云凝结核作用,凝结水汽生成水滴,释放潜热有利于对流发展;气溶胶含量增多引起水滴半径减小,雨滴生成的时间加长,降水时间延后;大量液态水上升至冻结高度以上发生相变释放潜热,固态水物质含量增多;台风外围雨带的中小尺度对流云团增强,降水增加。      

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。     

On the contribution of local feedback mechanisms to the range of climate sensitivity in two GCM ensembles

Global and local feedback analysis techniques have been applied to two ensembles of mixed layer equilibrium CO₂ doubling climate change experiments, from the CFMIP (Cloud Feedback Model Intercomparison Project) and QUMP (Quantifying Uncertainty in Model Predictions) projects. Neither of these new ensembles shows evidence of a statistically significant change in the ensemble mean or variance in global mean climate sensitivity when compared with the results from the mixed layer models quoted in the Third Assessment Report of the IPCC. Global mean feedback analysis of these two ensembles confirms the large contribution made by inter-model differences in cloud feedbacks to those in climate sensitivity in earlier studies; net cloud feedbacks are responsible for 66% of the inter-model variance in the total feedback in the CFMIP ensemble and 85% in the QUMP ensemble. The ensemble mean global feedback components are all statistically indistinguishable between the two ensembles, except for the clear-sky shortwave feedback which is stronger in the CFMIP ensemble. While ensemble variances of the shortwave cloud feedback and both clear-sky feedback terms are larger in CFMIP, there is considerable overlap in the cloud feedback ranges; QUMP spans 80% or more of the CFMIP ranges in longwave and shortwave cloud feedback. We introduce a local cloud feedback classification system which distinguishes different types of cloud feedbacks on the basis of the relative strengths of their longwave and shortwave components, and interpret these in terms of responses of different cloud types diagnosed by the International Satellite Cloud Climatology Project simulator. In the CFMIP ensemble, areas where low-top cloud changes constitute the largest cloud response are responsible for 59% of the contribution from cloud feedback to the variance in the total feedback. A similar figure is found for the QUMP ensemble. Areas of positive low cloud feedback (associated with reductions in low level cloud amount) contribute most to this figure in the CFMIP ensemble, while areas of negative cloud feedback (associated with increases in low level cloud amount and optical thickness) contribute most in QUMP. Classes associated with high-top cloud feedbacks are responsible for 33 and 20% of the cloud feedback contribution in CFMIP and QUMP, respectively, while classes where no particular cloud type stands out are responsible for 8 and 21%.     

Origins of differences in climate sensitivity, forcing and feedback in climate models

We diagnose climate feedback parameters and CO₂ forcing including rapid adjustment in twelve atmosphere/mixed-layer-ocean (“slab”) climate models from the CMIP3/CFMIP-1 project (the AR4 ensemble) and fifteen parameter-perturbed versions of the HadSM3 slab model (the PPE). In both ensembles, differences in climate feedbacks can account for approximately twice as much of the range in climate sensitivity as differences in CO₂ forcing. In the AR4 ensemble, cloud effects can explain the full range of climate sensitivities, and cloud feedback components contribute four times as much as cloud components of CO₂ forcing to the range. Non-cloud feedbacks are required to fully account for the high sensitivities of some models however. The largest contribution to the high sensitivity of HadGEM1 is from a high latitude clear-sky shortwave feedback, and clear-sky longwave feedbacks contribute substantially to the highest sensitivity members of the PPE. Differences in low latitude ocean regions (30°N/S) contribute more to the range than those in mid-latitude oceans (30–55°N/S), low/mid latitude land (55°N/S) or high latitude ocean/land (55–90°N/S), but contributions from these other regions are required to account fully for the higher model sensitivities, for example from land areas in IPSL CM4. Net cloud feedback components over the low latitude oceans sorted into percentile ranges of lower tropospheric stability (LTS) show largest differences among models in stable regions, mainly due to their shortwave components, most of which are positive in spite of increasing LTS. Differences in the mid-stability range are smaller, but cover a larger area, contributing a comparable amount to the range in climate sensitivity. These are strongly anti-correlated with changes in subsidence. Cloud components of CO₂ forcing also show the largest differences in stable regions, and are strongly anticorrelated with changes in estimated inversion strength (EIS). This is qualitatively consistent with what would be expected from observed relationships between EIS and low-level cloud fraction. We identify a number of cases where individual models show unusually strong forcings and feedbacks compared to other members of the ensemble. We encourage modelling groups to investigate unusual model behaviours further with sensitivity experiments. Most of the models fail to correctly reproduce the observed relationships between stability and cloud radiative effect in the subtropics, indicating that there remains considerable room for model improvements in the future.     

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.     

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.     

气候模式中云的垂直重叠及其辐射传输问题研究进展

概述了全球气候模式中云的垂直重叠的处理方法及其辐射物理过程的最新研究进展。从云垂直重叠模型的构造、模型在气候模式中的实现方式,得到与观测一致的云重叠结构所采用的数据和方法、重叠云的辐射传输等方面,给出了针对这一国际研究难点问题的最新研究进展。关于气候模式中云的垂直重叠问题的研究至今已取得了许多成果,表现在:重叠模型上有了更为科学的描述形式(如指数衰减重叠);重叠云的辐射传输也有了更快速的处理方法(如蒙特卡洛独立柱近似)并被广泛应用;连续的三维云遥感观测(如CloudSat/CALIPSO)和云分辨尺度的三维云模式的发展为在气候模式中精确描述云的垂直结构提供了丰富的观测资料和模式数据。但是,气候模式中现有的云重叠结构处理及其辐射传输方法还远不够完善,仍然存在很多没有解决的问题需要在未来进行探索。     

单层不同类型云系统的特性及高云重叠的影响

通过时空匹配主动遥感产品（2B-CLDCLASS-LIDAR）的云类型参数与被动遥感产品（Clouds and the Earth's Radiant Energy System-Single Scanner Footprint: CERES-SSF）的云宏微观物理特性和大气层顶处辐射通量参数（2007/01~2010/12），本文在全球尺度上统计分析了不同类型单层云以及高云重叠的双层云的云特性和大气层顶云辐射强迫。尽管主动和被动卫星遥感产品相互独立，但被动遥感反演的云光学厚度和云相态等参数能够合理反映不同类型云之间的差异。不同类型单层云表现出的云特性差异能进一步影响大气层顶的辐射能量收支。本文通过对比不同类型单层云的Clouds and the Earth's Radiant Energy System（CERES）视场样本数量随短波和长波云辐射强迫的分布，发现它们的高值区形状、位置和面积存在差异，反映了其各自独特的辐射特性。层积云的CERES视场样本数量密集区类似椭球结构，而其它云类型则趋于指数曲线结构。当进一步考虑高云重叠的影响时，统计结果表明:重叠的高云比高云单层状态有更蔽光和更厚的云体；高云重叠的底层云类型则比其单层状态有更透光和更薄的云体；高云重叠的双层云（除了高云重叠积云）对地气系统的净冷却效应要弱于其低层云类型单层状态。本文研究结果旨在加深理解云辐射反馈机制，并为改进模式中云重叠参数化方案提供一定观测依据。     

Cloud-radiation feedbacks in a general circulation model and their dependence on cloud modelling assumptions

The general circulation model (GCM) used in this study includes a prognostic cloud scheme and a rather detailed radiation scheme. In a preceding paper, we showed that this model was more sensitive to a global perturbation of the sea surface temperatures than most other models with similar physical parametrization. The experiments presented here show how this feature might depend on some of the cloud modelling assumptions. We have changed the temperature at which the water clouds are allowed to become ice clouds and analyzed separately the feedbacks associated with the variations of cloud cover and cloud radiative properties. We show that the feedback effect associated with cloud radiative properties is positive in one case and negative in the other. This can be explained by the elementary cloud radiative forcing and has implications concerning the use of the GCMs for climate sensitivity studies.     

Towards evaluating cloud response to climate change using clustering technique identification of cloud regimes

Most of the discrepancies in the climate sensitivity of general circulation models (GCMs) are believed to be due to differences in cloud radiative feedback. Analysis of cloud response to climate change in different ‘regimes’ may offer a more detailed understanding of how the cloud response differs between GCMs. In which case, evaluation of simulated cloud regimes against observations in terms of both their cloud properties and frequency of occurrence will assist in assessing confidence in the cloud response to climate change in a particular GCM. In this study, we use a clustering technique on International Satellite Cloud Climatology Project (ISCCP) data and on ISCCP-like diagnostics from two versions of the Hadley Centre GCM to identify cloud regimes over four different geographical regions. The two versions of the model are evaluated against observational data and their cloud response to climate change compared within the cloud regime framework. It is found that cloud clusters produced by the more recent GCM, HadSM4, compare more favourably with observations than HadSM3. In response to climate change, although the net cloud response over particular regions is often different in the two models, in several instances the same basic processes may be seen to be operating. Overall, both changes in the frequency of occurrence of cloud regimes and changes in the properties (optical depth and cloud top height) of the cloud regimes contribute to the cloud response to climate change.     

GRAPES全球模式云方案的诊断研究

在对GRAPES全球预报系统（GRAPES_GFS）云预报性能进行诊断评估的基础上,对凝结（华）和蒸发等物理过程及对流卷出对云的影响过程进行改进和优化,旨在提高GRAPES_GFS云量及其特征量和降水的预报精度。通过研究GRAPES全球模式、欧洲中期天气预报中心（ECMWF）和美国环境预报中心（NCEP）全球模式中3种云方案原理上的区别和联系,并将GRAPES_GFS预报结果与ECMWF第5代全球再分析资料（ERA5）做对比,分析云方案改进前后云量、云含水量、柱含水量等云宏观、微观物理量,降水量、向外长波辐射等基本物理量以及物理过程对温度和湿度的反馈倾向的预报性能,在此基础上对GRAPES_GFS原云方案进行改进。结果显示,云方案改进后,GRAPES_GFS在热带地区液水预报增大,云系和水成物垂直分布更合理;云量预报与ERA5预报结果更接近,特别是高云量预报的改进,使得热带向外长波辐射预报略有改进;低纬度地区日平均总降水量略有增大,特别是热带地区格点尺度降水增多较为明显,缓解了热带区域格点尺度降水预报偏小的现象。