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.     

Changes in Arctic clouds during intervals of rapid sea ice loss

We investigate the behavior of clouds during rapid sea ice loss events (RILEs) in the Arctic, as simulated by multiple ensemble projections of the 21st century in the Community Climate System Model (CCSM3). Trends in cloud properties and sea ice coverage during RILEs are compared with their secular trends between 2000 and 2049 during summer, autumn, and winter. The results suggest that clouds promote abrupt Arctic climate change during RILEs through increased (decreased) cloudiness in autumn (summer) relative to the changes over the first half of the 21st century. The trends in cloud characteristics (cloud amount, water content, and radiative forcing) during RILEs are most strongly and consistently an amplifying effect during autumn, the season in which RILEs account for the majority of the secular trends. The total cloud trends in every season are primarily due to low clouds, which show a more robust response than middle and high clouds across RILEs. Lead-lag correlations of monthly sea ice concentration and cloud cover during autumn reveal that the relationship between less ice and more clouds is enhanced during RILEs, but there is no evidence that either variable is leading the other. Given that Arctic cloud projections in CCSM3 are similar to those from other state-of-the-art GCMs and that observations show increased autumn cloudiness associated with the extreme 2007 and 2008 sea ice minima, this study suggests that the rapidly declining Arctic sea ice will be accentuated by changes in polar clouds.     

Mixed-phase clouds cause climate model biases in Arctic wintertime temperature inversions

Temperature inversions are a common feature of the Arctic wintertime boundary layer. They have important impacts on both radiative and turbulent heat fluxes and partly determine local climate-change feedbacks. Understanding the spread in inversion strength modelled by current global climate models is therefore an important step in better understanding Arctic climate and its present and future changes. Here, we show how the formation of Arctic air masses leads to the emergence of a cloudy and a clear state of the Arctic winter boundary layer. In the cloudy state, cloud liquid water is present, little to no surface radiative cooling occurs and inversions are elevated and relatively weak, whereas surface radiative cooling leads to strong surface-based temperature inversions in the clear state. Comparing model output to observations, we find that most climate models lack a realistic representation of the cloudy state. An idealised single-column model experiment of the formation of Arctic air reveals that this bias is linked to inadequate mixed-phase cloud microphysics, whereas turbulent and conductive heat fluxes control the strength of inversions within the clear state.     

Atmospheric inversion strength over polar oceans in winter regulated by sea ice

Low-level temperature inversions are a common feature of the wintertime troposphere in the Arctic and Antarctic. Inversion strength plays an important role in regulating atmospheric processes including air pollution, ozone destruction, cloud formation, and negative longwave feedback mechanisms that shape polar climate response to anthropogenic forcing. The Atmospheric Infrared Sounder (AIRS) instrument provides reliable measures of spatial patterns in mean wintertime inversion strength when compared with available radiosonde observations and reanalysis products. Here, we examine the influence of sea ice concentration on inversion strength in the Arctic and Antarctic. Correlation of inversion strength with mean annual sea ice concentration, likely a surrogate for the effective thermal conductivity of the wintertime ice pack, yields strong, linear relationships in the Arctic (r?=?0.88) and Antarctic (r?=?0.86). We find a substantially greater (stronger) linear relationship between sea ice concentration and surface air temperature than with temperature at 850?hPa, lending credence to the idea that sea ice controls inversion strength through modulation of surface heat fluxes. As such, declines in sea ice in either hemisphere may imply weaker mean inversions in the future. Comparison of mean inversion strength in AIRS and global climate models (GCMs) suggests that many GCMs poorly characterize mean inversion strength at high latitudes.     

Radiative Forcing and Climate Response Due to Black Carbon in Snow and Ice

The radiative forcing and climate response due to black carbon(BC) in snow and/or ice were investigated by integrating observed effects of BC on snow/ice albedo into an atmospheric general circulation model(BCC AGCM2.0.1) developed by the National Climate Center(NCC) of the China Meteorological Administration(CMA).The results show that the global annual mean surface radiative forcing due to BC in snow/ice is +0.042 W m 2,with maximum forcing found over the Tibetan Plateau and regional mean forcing exceeding +2.8 W m 2.The global annual mean surface temperature increased 0.071 C due to BC in snow/ice.Positive surface radiative forcing was clearly shown in winter and spring and increased the surface temperature of snow/ice in the Northern Hemisphere.The surface temperatures of snow-covered areas of Eurasia and North America in winter(spring) increased by 0.83 C(0.6 C) and 0.83 C(0.46 C),respectively.Snowmelt rates also increased greatly,leading to earlier snowmelt and peak runoff times.With the rise of surface temperatures in the Arctic,more water vapor could be released into the atmosphere,allowing easier cloud formation,which could lead to higher thermal emittance in the Arctic.However,the total cloud forcing could decrease due to increasing cloud cover,which will offset some of the positive feedback mechanism of the clouds.     

A transitioning Arctic surface energy budget: the impacts of solar zenith angle, surface albedo and cloud radiative forcing

Snow surface and sea-ice energy budgets were measured near 87.5°N during the Arctic Summer Cloud Ocean Study (ASCOS), from August to early September 2008. Surface temperature indicated four distinct temperature regimes, characterized by varying cloud, thermodynamic and solar properties. An initial warm, melt-season regime was interrupted by a 3-day cold regime where temperatures dropped from near zero to ?7°C. Subsequently mean energy budget residuals remained small and near zero for 1 week until once again temperatures dropped rapidly and the energy budget residuals became negative. Energy budget transitions were dominated by the net radiative fluxes, largely controlled by the cloudiness. Variable heat, moisture and cloud distributions were associated with changing air-masses. Surface cloud radiative forcing, the net radiative effect of clouds on the surface relative to clear skies, is estimated. Shortwave cloud forcing ranged between ?50 W m^?2 and zero and varied significantly with surface albedo, solar zenith angle and cloud liquid water. Longwave cloud forcing was larger and generally ranged between 65 and 85 W m^?2, except when the cloud fraction was tenuous or contained little liquid water; thus the net effect of the clouds was to warm the surface. Both cold periods occurred under tenuous, or altogether absent, low-level clouds containing little liquid water, effectively reducing the cloud greenhouse effect. Freeze-up progression was enhanced by a combination of increasing solar zenith angles and surface albedo, while inhibited by a large, positive surface cloud forcing until a new air-mass with considerably less cloudiness advected over the experiment area.     

Twenty-first century Arctic climate change in the CCSM3 IPCC scenario simulations

Arctic climate change in the Twenty-first century is simulated by the Community Climate System Model version 3.0 (CCSM3). The simulations from three emission scenarios (A2, A1B and B1) are analyzed using eight (A1B and B1) or five (A2) ensemble members. The model simulates a reasonable present-day climate and historical climate trend. The model projects a decline of sea-ice extent in the range of 1.4–3.9% per decade and 4.8–22.2% per decade in winter and summer, respectively, corresponding to the range of forcings that span the scenarios. At the end of the Twenty-first century, the winter and summer Arctic mean surface air temperature increases in a range of 4–14°C (B1 and A2) and 0.7–5°C (B1 and A2) relative to the end of the Twentieth century. The Arctic becomes ice-free during summer at the end of the Twenty-first century in the A2 scenario. Similar to the observations, the Arctic Oscillation (AO) is the dominant factor in explaining the variability of the atmosphere and sea ice in the 1870–1999 historical runs. The AO shifts to the positive phase in response to greenhouse gas forcings in the Twenty-first century. But the simulated trends in both Arctic mean sea-level pressure and the AO index are smaller than what has been observed. The Twenty-first century Arctic warming mainly results from the radiative forcing of greenhouse gases. The 1st empirical orthogonal function (explains 72.2–51.7% of the total variance) of the wintertime surface air temperature during 1870–2099 is characterized by a strong warming trend and a “polar amplification”-type of spatial pattern. The AO, which plays a secondary role, contributes to less than 10% of the total variance in both surface temperature and sea-ice concentration.     

Evaluation of an ensemble of Arctic regional climate models: spatiotemporal fields during the SHEBA year

Simulations of eight different regional climate models (RCMs) have been performed for the period September 1997–September 1998, which coincides with the Surface Heat Budget of the Arctic Ocean (SHEBA) project period. Each of the models employed approximately the same domain covering the western Arctic, the same horizontal resolution of 50 km, and the same boundary forcing. The models differ in their vertical resolution as well as in the treatments of dynamics and physical parameterizations. Both the common features and differences of the simulated spatiotemporal patterns of geopotential, temperature, cloud cover, and long-/shortwave downward radiation between the individual model simulations are investigated. With this work, we quantify the scatter among the models and therefore the magnitude of disagreement and unreliability of current Arctic RCM simulations. Even with the relatively constrained experimental design we notice a considerable scatter among the different RCMs. We found the largest across-model scatter in the 2 m temperature over land, in the surface radiation fluxes, and in the cloud cover which implies a reduced confidence level for these variables. An erratum to this article can be found at     

An evaluation of Arctic cloud and radiation processes during the SHEBA year: simulation results from eight Arctic regional climate models

Eight atmospheric regional climate models (RCMs) were run for the period September 1997 to October 1998 over the western Arctic Ocean. This period was coincident with the observational campaign of the Surface Heat Budget of the Arctic Ocean (SHEBA) project. The RCMs shared common domains, centred on the SHEBA observation camp, along with a common model horizontal resolution, but differed in their vertical structure and physical parameterizations. All RCMs used the same lateral and surface boundary conditions. Surface downwelling solar and terrestrial radiation, surface albedo, vertically integrated water vapour, liquid water path and cloud cover from each model are evaluated against the SHEBA observation data. Downwelling surface radiation, vertically integrated water vapour and liquid water path are reasonably well simulated at monthly and daily timescales in the model ensemble mean, but with considerable differences among individual models. Simulated surface albedos are relatively accurate in the winter season, but become increasingly inaccurate and variable in the melt season, thereby compromising the net surface radiation budget. Simulated cloud cover is more or less uncorrelated with observed values at the daily timescale. Even for monthly averages, many models do not reproduce the annual cycle correctly. The inter-model spread of simulated cloud-cover is very large, with no model appearing systematically superior. Analysis of the co-variability of terms controlling the surface radiation budget reveal some of the key processes requiring improved treatment in Arctic RCMs. Improvements in the parameterization of cloud amounts and surface albedo are most urgently needed to improve the overall performance of RCMs in the Arctic.     

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.     

气候模式分辨率对气溶胶气候效应模拟结果的影响

气候模式分辨率作为影响模式模拟结果的重要因素,其对气溶胶与云相互作用的影响尚未全面认识。利用公共大气模型CAM5.3在3种分辨率（2°、1°、0.5°）下,分别采用2000年和1850年气溶胶排放情景进行试验,检验提高分辨率是否能改进气候模式的模拟能力,分析不同分辨率下气溶胶气候效应的异同,探索模式分辨率对气溶胶气候效应数值模拟结果的影响。通过观测资料与模式结果对比发现,提高分辨率可以明显改进模式对总云量、云短波辐射强迫的模拟能力,0.5°分辨率下模拟结果与观测更接近,其他变量并无明显改善。在不同分辨率下,全球平均的气溶胶气候效应较为一致,总云量、云水路径均增加,云短波和长波辐射强迫均加强,而云顶的云滴有效半径和降水均减小,地面气温降低。不同分辨率下,气溶胶增加引起的气溶胶光学厚度、云水路径、地面温度、云短波和长波辐射强迫变化的纬向平均分布相似但大小存在差异;而降水和云量变化的纬向分布与大小均存在较大差异,在区域尺度上还存在较大的不确定性。全球平均而言, 0.5°分辨率下气溶胶的间接辐射强迫相比1°分辨率下的结果降低了2.5%,相比2°分辨率下的结果降低了6.4%。提高模式分辨率可以部分改进模式模拟能力,同时,气溶胶的间接效应随着模式分辨率的提高而减弱。但气溶胶引起的云量、降水的变化在不同分辨率下差异较大,存在较大的不确定性。      

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.     

CMIP5全球气候模式对青藏高原地区气候模拟能力评估

青藏高原是气候变化的敏感和脆弱区,全球气候模式对于这一地区气候态的模拟能力如何尚不清楚。为此,本文使用国际耦合模式比较计划第五阶段（CMIP5）的历史模拟试验数据,评估了44 个全球气候模式对1986~2005 年青藏高原地区地表气温和降水两个基本气象要素的模拟能力。结果表明,CMIP5 模式低估了青藏高原地区年和季节平均地表气温,年均平均偏低2.3℃,秋季和冬季冷偏差相对更大;模式可较好地模拟年和季节平均地表气温分布型,但模拟的空间变率总体偏大;地形效应校正能够有效订正地表气温结果。CMIP5 模式对青藏高原地区降水模拟能力较差。尽管它们能够模拟出年均降水自西北向东南渐增的分布型,但模拟的年和季节降水量普遍偏大,年均降水平均偏多1.3 mm d-1,这主要是源于春季和夏季降水被高估。同时,模式模拟的年和季节降水空间变率也普遍大于观测值,尤其表现在春季和冬季。相比较而言,44 个模式集合平均性能总体上要优于大多数单个模式;等权重集合平均方案要优于中位数平均;对择优挑选的模式进行集合平均能够提高总体的模拟能力,其中对降水模拟的改进更为显著。     

Cloud climate investigations in the Nordic region using NOAA AVHRR data

Summary A method to estimate monthly cloud conditions (monthly cloud frequencies) from multispectral satellite imagery is described. The operational cloud classification scheme SCANDIA (the SMHI Cloud ANalysis model using DIgital AVHRR data), based on high resolution imagery from the polar orbiting NOAA-satellites, has been used to produce monthly cloud frequencies for the entire year of 1993 and some additional months in 1991, 1992, 1994 and 1995. Cloud analyses were made for an area covering the Nordic countries with a horizontal resolution of four km. Examples of seasonal, monthly and diurnal variation in cloud conditions are given and an annual mean for 1993 is presented.Comparisons with existing surface observations showed very good agreement for horizontal cloud distributions but approximately 5% smaller cloud amounts were found in the satellite estimations. The most evident problems were encountered in the winter season due to difficulties in identifying low-level cloudiness at very low sun elevations. The underestimation in the summer season was partly fictious and caused by the overestimation of convective cloud cover by surface observers.SCANDIA results were compared to ISCCP (International Satellite Cloud Climatology Project) cloud climatologies for two selected months in 1991 and 1992. ISCCP cloudiness was indicated to be higher, especially during the month with anticyclonic conditions where a cloudiness excess of more than 10% were found. The regional variation of cloud conditions in the area was found to be inadequately described by ISCCP cloud climatologies. An improvement of the horizontal resolution of ISCCP data seems necessary to enable use for regional applications.The SCANDIA model is proposed as a valuable tool for local and regional monitoring of the cloud climatology at high latitudes. More extensive comparisons with ISCCP cloud climatologies are suggested as well as comparisons with modelled cloudiness from atmospheric general circulation models and climate models. Special studies of cloud conditions in the Polar areas are also proposed.With 14 Figures     

Intercomparsion of regional biases and doubled CO2-sensitivity of coupled atmosphere-ocean general circulation model experiments

 We compared regional biases and transient doubled CO₂ sensitivities of nine coupled atmosphere-ocean general circulation models (GCMs) from six international climate modeling groups. We evaluated biases and responses in winter and summer surface air temperatures and precipitation for seven subcontinental regions, including those in the 1990 Intergovernmental Panel on Climate Change (IPCC) Scientific Assessment. Regional biases were large and exceeded the variance among four climatological datasets, indicating that model biases were not primarily due to uncertainty in observations. Model responses to altered greenhouse forcing were substantial (average temperature change=2.7±0.9 ^°C, range of precipitation change =−35 to +120% of control). While coupled models include more climate system feedbacks than earlier GCMs implemented with mixed-layer ocean models, inclusion of a dynamic ocean alone did not improve simulation of long-term mean climatology nor increase convergence among model responses to altered greenhouse gas forcing. On the other hand, features of some of the coupled models including flux adjustment (which may have simply masked simulation errors), high horizontal resolution, and estimation of screen height temperature contributed to improved simulation of long-term surface climate. The large range of model responses was partly accounted for by inconsistencies in forcing scenarios and transient-simulation averaging periods. Nonetheless, the models generally had greater agreement in their sensitivities than their controls did with observations. This suggests that consistent, large-scale response features from an ensemble of model sensitivity experiments may not depend on details of their representation of present-day climate. Received: 9 September 1996 / Revised: 31 July 1997     

On the interpretation of inter-model spread in CMIP5 climate sensitivity estimates

This study diagnoses the climate sensitivity, radiative forcing and climate feedback estimates from eleven general circulation models participating in the Fifth Phase of the Coupled Model Intercomparison Project (CMIP5), and analyzes inter-model differences. This is done by taking into account the fact that the climate response to increased carbon dioxide (CO₂) is not necessarily only mediated by surface temperature changes, but can also result from fast land warming and tropospheric adjustments to the CO₂ radiative forcing. By considering tropospheric adjustments to CO₂ as part of the forcing rather than as feedbacks, and by using the radiative kernels approach, we decompose climate sensitivity estimates in terms of feedbacks and adjustments associated with water vapor, temperature lapse rate, surface albedo and clouds. Cloud adjustment to CO₂ is, with one exception, generally positive, and is associated with a reduced strength of the cloud feedback; the multi-model mean cloud feedback is about 33 % weaker. Non-cloud adjustments associated with temperature, water vapor and albedo seem, however, to be better understood as responses to land surface warming. Separating out the tropospheric adjustments does not significantly affect the spread in climate sensitivity estimates, which primarily results from differing climate feedbacks. About 70 % of the spread stems from the cloud feedback, which remains the major source of inter-model spread in climate sensitivity, with a large contribution from the tropics. Differences in tropical cloud feedbacks between low-sensitivity and high-sensitivity models occur over a large range of dynamical regimes, but primarily arise from the regimes associated with a predominance of shallow cumulus and stratocumulus clouds. The combined water vapor plus lapse rate feedback also contributes to the spread of climate sensitivity estimates, with inter-model differences arising primarily from the relative humidity responses throughout the troposphere. Finally, this study points to a substantial role of nonlinearities in the calculation of adjustments and feedbacks for the interpretation of inter-model spread in climate sensitivity estimates. We show that in climate model simulations with large forcing (e.g., 4 × CO₂), nonlinearities cannot be assumed minor nor neglected. Having said that, most results presented here are consistent with a number of previous feedback studies, despite the very different nature of the methodologies and all the uncertainties associated with them.     

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。     

Cloud radiative forcing of subtropical low level clouds in global models

Simulations of subtropical marine low clouds and their radiative properties by nine coupled ocean-atmosphere climate models participating in the fourth assesment report (AR4) of the intergovernmental panel on climate change (IPCC) are analyzed. Satellite observations of cloudiness and radiative fluxes at the top of the atmosphere (TOA) are utilized for comparison. The analysis is confined to the marine subtropics in an attempt to isolate low cloudiness from tropical convective systems. All analyzed models have a negative bias in the low cloud fraction (model mean bias of −15%). On the other hand, the models show an excess of cloud radiative cooling in the region (model mean excess of 13 W m⁻²). The latter bias is shown to mainly originate from too much shortwave reflection by the models clouds rather than biases in the clear-sky fluxes. These results confirm earlier studies, thus no major progress in simulating the marine subtropical clouds is noted. As a consequence of the combination of these two biases, this study suggests that all investigated models are likely to overestimate the radiative response to changes in low level subtropical cloudiness.     

近50年我国西部地区气象要素的变化特征

利用1951-2000年全国194站地面观测资料和高空观测资料,对近50年我国西部地区的气候变化特征进行分析。结果表明:从20世纪70年代开始,我国西部地区年平均气温呈上升趋势,其中河套区和新疆区气温上升最为明显,其次为青藏高原区和河西区,西南区气温增幅最不明显,地表温度变化与气温的变化基本同步,但地温变化要比气温变化更加剧烈一些。西南区的地温从70年代中期开始回升,但始终未达到50年代初期的水平,因此从线性变化上表现为下降趋势。西部地区除了河套区外,其他4个区的年平均降水量均增加,增加最明显的是新疆区和青藏高原区。我国整个西部地区年平均总云量和低云量均呈线性减少趋势,减少最明显的是西南区和河套区。在辐射变化上,我国西部总辐射呈减少趋势,青藏高原区减少最多;西南区的散射辐射呈增加趋势,其他4个区减少,其中新疆区和青藏高原区散射辐射减幅明显。散射辐射的大小与天空中云量和气溶胶含量的多少成正比,西南区散射辐射呈增加趋势,而总云量和低云量呈下降趋势,可以推测是气溶胶含量增加导致了散射辐射的增加。