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1.
Realistic simulation of the internal variability of the climate system is important both for climate change detection and as an indicator of whether the physics of the climate system is well-represented in a climate model. In this work zonal mean atmospheric temperatures from a control run of the second Hadley Centre coupled GCM are compared with gridded radiosonde observations for the past 38 years to examine how well modelled and observed variability agree. On time scales of between six months and twenty years, simulated and observed variability of global mean temperatures agree well for the troposphere, but in the equatorial stratosphere variability is lower in the model than in the observations, particularly at periods of two years and seven to twenty years. We find good agreement between modelled and observed variability in the mass-weighted amplitude of a forcing-response pattern, as used for climate change detection, but variability in a signal-to-noise optimised fingerprint pattern is significantly greater in the observations than in a model control run. This discrepancy is marginally consistent with anthropogenic forcing, but more clearly explained by a combination of solar and volcanic forcing, suggesting these should be considered in future `vertical detection' studies. When the relationship between tropical lapse rate and mean temperature was examined, it was found that these quantities are unrealistically coherent in the model at periods above three years. However, there is a clear negative lapse rate feedback in both model and observations: as the tropical troposphere warms, the mid-tropospheric lapse rate decreases on all the time scales considered. Received: 11 August 1998 / Accepted: 20 July 1999  相似文献   

2.
CMIP1 evaluation and intercomparison of coupled climate models   总被引：10，自引：1，他引：10  
 The climates simulated by 15 coupled atmosphere/ocean climate models participating in the first phase of the Coupled Model Intercomparison Project (CMIP1) are intercompared and evaluated. Results for global means, zonal averages, and geographical distributions of basic climate variables are assembled and compared with observations. The current generation of climate models reproduce the major features of the observed distribution of the basic climate parameters, but there is, nevertheless, a considerable scatter among model results and between simulated and observed values. This is particularly true for oceanic variables. Flux adjusted models generally produce simulated climates which are in better accord with observations than do non-flux adjusted models; however, some non-flux adjusted model results are closer to observations than some flux adjusted model results. Other model differences, such as resolution, do not appear to provide a clear distinction among model results in this generation of models. Many of the systematic differences (those differences common to most models), evident in previous intercomparison studies are exhibited also by the CMIP1 group of models although often with reduced magnitudes. As is characteristic of intercomparison results, different climate variables are simulated with different levels of success by different models and no one model is “best” for all variables. There is some evidence that the “mean model” result, obtained by averaging over the ensemble of models, provides an overall best comparison to observations for climatological mean fields. The model deficiencies identified here do not suggest immediate remedies and the overall success of the models in simulating the behaviour of the complex non-linear climate system apparently depends on the slow improvement in the balance of approximations that characterize a coupled climate model. Of course, the results of this and similar studies provide only an indication, at a particular time, of the current state and the moderate but steady evolution and improvement of coupled climate models. Received: 26 January 2000 / Accepted: 9 June 2000  相似文献   

3.
Evaluating the response of climate to greenhouse gas forcing is a major objective of the climate community, and the use of large ensemble of simulations is considered as a significant step toward that goal. The present paper thus discusses a new methodology based on neural network to mix ensemble of climate model simulations. Our analysis consists of one simulation of seven Atmosphere–Ocean Global Climate Models, which participated in the IPCC Project and provided at least one simulation for the twentieth century (20c3m) and one simulation for each of three SRES scenarios: A2, A1B and B1. Our statistical method based on neural networks and Bayesian statistics computes a transfer function between models and observations. Such a transfer function was then used to project future conditions and to derive what we would call the optimal ensemble combination for twenty-first century climate change projections. Our approach is therefore based on one statement and one hypothesis. The statement is that an optimal ensemble projection should be built by giving larger weights to models, which have more skill in representing present climate conditions. The hypothesis is that our method based on neural network is actually weighting the models that way. While the statement is actually an open question, which answer may vary according to the region or climate signal under study, our results demonstrate that the neural network approach indeed allows to weighting models according to their skills. As such, our method is an improvement of existing Bayesian methods developed to mix ensembles of simulations. However, the general low skill of climate models in simulating precipitation mean climatology implies that the final projection maps (whatever the method used to compute them) may significantly change in the future as models improve. Therefore, the projection results for late twenty-first century conditions are presented as possible projections based on the “state-of-the-art” of present climate modeling. First, various criteria were computed making it possible to evaluate the models’ skills in simulating late twentieth century precipitation over continental areas as well as their divergence in projecting climate change conditions. Despite the relatively poor skill of most of the climate models in simulating present-day large scale precipitation patterns, we identified two types of models: the climate models with moderate-to-normal (i.e., close to observations) precipitation amplitudes over the Amazonian basin; and the climate models with a low precipitation in that region and too high a precipitation on the equatorial Pacific coast. Under SRES A2 greenhouse gas forcing, the neural network simulates an increase in precipitation over the La Plata basin coherent with the mean model ensemble projection. Over the Amazonian basin, a decrease in precipitation is projected. However, the models strongly diverge, and the neural network was found to give more weight to models, which better simulate present-day climate conditions. In the southern tip of the continent, the models poorly simulate present-day climate. However, they display a fairly good convergence when simulating climate change response with a weak increase south of 45°S and a decrease in Chile between 30 and 45°S. Other scenarios (A1B and B1) strongly resemble the SRES A2 trends but with weaker amplitudes.  相似文献   

4.
统计降尺度法对华北地区未来区域气温变化情景的预估   总被引：32，自引：1，他引：31  
迄今为止,大部分海气耦合气候模式（AOGCM）的空间分辨率还较低,很难对区域尺度的气候变化情景做合理的预测。降尺度法已广泛用于弥补AOGCM在这方面的不足。作者采用统计降尺度方法对1月和7月华北地区49个气象观测站的未来月平均温度变化情景进行预估。采用的统计降尺度方法是主分量分析与逐步回归分析相结合的多元线性回归模型。首先,采用1961～2000年的 NCEP再分析资料和49个台站的观测资料建立月平均温度的统计降尺度模型,然后把建立的统计降尺度模型应用于HadCM3 SRES A2 和 B2 两种排放情景, 从而生成各个台站1950～2099年1月份和7月份温度变化情景。结果表明:在当前气候条件下,无论1月还是7月,统计降尺度方法模拟的温度与观测的温度有很好的一致性,而且在大多数台站,统计降尺度模拟气温与观测值相比略微偏低。对于未来气候情景的预估方面,无论1月还是7月,也无论是HadCM3 SRES A2 还是B2排放情景驱动统计模型,结果表明大多数的站点都存在温度的明显上升趋势,同时7月的上升趋势与1月相比偏低。  相似文献   

5.
Summary A parameterization of shortwave and longwave radiation fluxes derived from detailed radiative transfer models is included in a global primitive equation statistical-dynamical model (SDM) with two bulk atmospheric layers. The model is validated comparing the model simulations with the observed mean annual and seasonal zonally averaged climate. The results show that the simulation of the shortwave and longwave radiation fluxes matches well with the observations. The SDM variables such as surface and 500 hPa temperatures, zonal winds at 250 hPa and 750 hPa, vertical velocity at 500 hPa and precipitation are also in good agreement with the observations. A comparison between the results obtained with the present SDM and those with the previous version of the model indicates that the model results improved when the parameterization of the radiative fluxes based on detailed radiative transfer models are included into the SDM.The SDM is used to investigate its response to the greenhouse effect. Sensitivity experiments regarding the doubling of CO2 and the changing of the cloud amount and height are performed. In the case 2×CO2 the model results are consistent with those obtained from GCMs, showing a warming of the climate system. An enhancement of the greenhouse effect is also noted when the cloud layer is higher. However, an increase of the cloud amount in all the latitude belts provokes an increase of the surface temperature near poles and a decrease in all the other regions. This suggests that the greenhouse effect overcomes the albedo effect in the polar latitudes and the opposite occurs in other regions. In all the experiments the changes in the surface temperature are larger near poles, mainly in the Southern Hemisphere.With 8 Figures  相似文献   

6.
Regional climate projections using climate models commonly use an “all-model” ensemble based on data sets such as the Intergovernmental Panel on Climate Change’s (IPCC) 4th Assessment (AR4). Some regional assessments have omitted models based on specific criteria. We use a criteria based on the capacity of climate models to simulate the observed probability density function calculated using daily data, model-by-model and region-by-region for each of the AR4 models over Australia. We demonstrate that by omitting those climate models with relatively weak skill in simulating the observed probability density functions of maximum and minimum temperature and precipitation, different regional projections are obtained. Differences include: larger increases in the mean maximum and mean minimum temperatures, but smaller increases in the annual maximum and minimum temperatures. There is little impact on mean precipitation but the better models simulate a larger increase in the annual rainfall event combined with a larger decrease in the number of rain days. The weaker models bias the amount of mean warming towards lower increases, bias annual maximum temperatures to excessive warming and bias precipitation such that the amount of the annual rainfall event is under-estimated. We suggest that omitting weak models from regional scale estimates of future climate change helps clarify the nature and scale of the projected impacts of global warming.  相似文献   

7.
CMIP5全球气候模式对青藏高原地区气候模拟能力评估   总被引：9，自引：4，他引：5  
胡芩  姜大膀  范广洲 《大气科学》2014,38(5):924-938
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。  相似文献   

8.
Probability distributions of daily maximum and minimum temperatures in a suite of ten RCMs are investigated for (1) biases compared to observations in the present day climate and (2) climate change signals compared to the simulated present day climate. The simulated inter-model differences and climate changes are also compared to the observed natural variability as reflected in some very long instrumental records. All models have been forced with driving conditions from the same global model and run for both a control period and a future scenario period following the A2 emission scenario from IPCC. We find that the bias in the fifth percentile of daily minimum temperatures in winter and at the 95th percentile of daily maximum temperature during summer is smaller than 3 (±5°C) when averaged over most (all) European sub-regions. The simulated changes in extreme temperatures both in summer and winter are larger than changes in the median for large areas. Differences between models are larger for the extremes than for mean temperatures. A comparison with historical data shows that the spread in model predicted changes in extreme temperatures is larger than the natural variability during the last centuries.  相似文献   

9.
EC-Earth, a new Earth system model based on the operational seasonal forecast system of the European Centre for Medium-Range Weather Forecasts (ECMWF), is presented. The performance of version 2.2 (V2.2) of the model is compared to observations, reanalysis data and other coupled atmosphere–ocean-sea ice models. The large-scale physical characteristics of the atmosphere, ocean and sea ice are well simulated. When compared to other coupled models with similar complexity, the model performs well in simulating tropospheric fields and dynamic variables, and performs less in simulating surface temperature and fluxes. The surface temperatures are too cold, with the exception of the Southern Ocean region and parts of the Northern Hemisphere extratropics. The main patterns of interannual climate variability are well represented. Experiments with enhanced CO2 concentrations show well-known responses of Arctic amplification, land-sea contrasts, tropospheric warming and stratospheric cooling. The global climate sensitivity of the current version of EC-Earth is slightly less than 1?K/(W?m?2). An intensification of the hydrological cycle is found and strong regional changes in precipitation, affecting monsoon characteristics. The results show that a coupled model based on an operational seasonal prediction system can be used for climate studies, supporting emerging seamless prediction strategies.  相似文献   

10.
Since single-integration climate models only provide one possible realization of climate variability, ensembles are a promising way to estimate the uncertainty in climate modeling. A statistical model is presented that extracts information from an ensemble of regional climate simulations to estimate probability distributions of future temperature change in Southwest Germany in the following two decades. The method used here is related to kernel dressing which has been extended to a multivariate approach in order to estimate the temporal autocovariance in the ensemble system. It has been applied to annual and seasonal mean temperatures given by ensembles of the coupled general circulation model ECHAM5/MPI-OM as well as the regional climate simulations using the COSMO-CLM model. The results are interpreted in terms of the bivariate probability density of mean and trend within the period 2011–2030 with respect to 1961–1990. Throughout the study region one can observe an average increase in annual mean temperature of approximately +0.6K and a corresponding trend of +0.15K/20a. While the increase in 20-year mean temperature is virtually certain, the 20-year trend still shows a 20% chance for negative values. This indicates that the natural variability of the climate system, as far as it is reflected by the ensemble system, can produce negative trends even in the presence of longer-term warming. Winter temperatures are clearly more affected and for both quantities we observe a north-to-south pattern where the increase in the very southern part is less intense.  相似文献   

11.
An ensemble of regional climate model simulations from the European framework project ENSEMBLES is compared with observations of low precipitation events across a number of European regions. We characterize precipitation deficits in terms of two drought indices, the Standardized Precipitation Index and the self-calibrated Palmer Drought Severity Index. Models that robustly describe the observations for the period 1961–2000 in given regions are identified and an assessment of the overall performance of the ensemble is provided. The results show that in general, models capture the most severe drought events and that the ensemble mean model also performs well. Some regions that appear to be more problematic to simulate well are also identified. These are relatively small regions and have rather complex topographical features. The analysis suggests that assessment of future drought occurrence based on climate change experiments in general would appear to be robust. But due to the heterogeneous and often fine-scaled structure of drought occurrence, quantitative results should be used with great care, particularly in regions with complex terrain and limited information about past drought occurrence.  相似文献   

12.
Estimates of impact of climate change on crop production could be biased depending upon the uncertainties in climate change scenarios, region of study, crop models used for impact assessment and the level of management. This study reports the results of a study where the impact of various climate change scenarios has been assessed on grain yields of irrigated rice with two popular crop simulation models- Ceres-Rice and ORYZA1N at different levels of N management. The results showed that the direct effect of climate change on rice crops in different agroclimatic regions in India would always be positive irrespective of the various uncertainties. Rice yields increased between 1.0 and 16.8% in pessimistic scenarios of climate change depending upon the level of management and model used. These increases were between 3.5 and 33.8% in optimistic scenarios. At current as well as improved level of management, southern and western parts of India which currently have relatively lower temperatures compared to northern and eastern regions, are likely to show greater sensitivity in rice yields under climate change. The response to climate change is small at low N management compared to optimal management. The magnitude of this impact can be biased upto 32% depending on the uncertainty in climate change scenario, level of management and crop model used. These conclusions are highly dependent on the specific thresholds of phenology and photosynthesis to change in temperature used in the models. Caution is needed in using the impact assessment results made with the average simulated grain yields and mean changes in climatic parameters.  相似文献   

13.
We investigate the influence of clouds on the surface energy budget and surface temperature in the sea-ice covered parts of the ocean north of the Arctic circle in present-day climate in nine global climate models participating in the Coupled Model Intercomparison Project phase 3, CMIP3. Monthly mean simulated surface skin temperature, radiative fluxes and cloud parameters are evaluated using retrievals from the extended AVHHR Polar Pathfinder (APP-x) product. We analyzed the annual cycle but the main focus is on the winter, in which large parts of the region experience polar night. We find a smaller across-model spread as well as better agreement with observations during summer than during winter in the simulated climatological annual cycles of total cloudiness and surface skin temperature. The across-model spread in liquid and ice water paths is substantial during the whole year. These results qualitatively agree with earlier studies on the present-day Arctic climate in GCMs. The climatological ensemble model mean annual cycle of surface cloud forcing shows good agreement with observations in summer. However, during winter the insulating effect of clouds tends to be underestimated in models. During winter, most of the models as well as the observations show higher monthly mean total cloud fractions, associated with larger positive surface cloud forcing. Most models also show good correlation between the surface cloud forcing and the vertically integrated ice and liquid cloud condensate. The wintertime ensemble model mean total cloud fraction (69%) shows excellent agreement with observations. The across-model spread in the winter mean cloudiness is substantial (36?C94%) however and several models significantly underestimate the cloud liquid water content. If the two models not showing any relationship between cloudiness and surface cloud forcing are disregarded, a tentative across-model relation exists, in such a way that models that simulate large winter mean cloudiness also show larger surface cloud forcing. Even though the across-model spread in wintertime surface cloud forcing is large, no clear relation to the surface temperature is found. This indicates that other processes, not explicitly cloud related, are important for the simulated across-model spread in surface temperature.  相似文献   

14.
Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity (absolute indices and threshold indices), duration (duration indices), and frequency (percentile indices) of extreme temperature. The overall performance of each model is summarized by a "portrait" diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986-2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961-2005.  相似文献   

15.
Methods are proposed to estimate the monthly relative humidity and wet bulb temperature based on observations from a dynamical downscaling coupled general circulation model with a regional climate model (RCM) for a quantitative assessment of climate change impacts. The water vapor pressure estimation model developed was a regression model with a monthly saturated water vapor pressure that used minimum air temperature as a variable. The monthly minimum air temperature correction model for RCM bias was developed by stepwise multiple regression analysis using the difference in monthly minimum air temperatures between observations and RCM output as a dependent variable and geographic factors as independent variables. The wet bulb temperature was estimated using the estimated water vapor pressure, air temperature, and atmospheric pressure at ground level both corrected for RCM bias. Root mean square errors of the data decreased considerably in August.  相似文献   

16.
Changing climate may impact wildlife populations in national parks and conservation areas. We used logistic and non-linear matrix population models and 35 years of historic weather and population data to investigate the effects of climate on the population dynamics of elk in Rocky Mountain National Park (RMNP), Colorado, U.S.A. We then used climate scenarios derived from Hadley and Canadian Climate Center (CCC) global climate models to project the potential impact of future climate on the elk population. All models revealed density-dependent effects of population size on growth rates. The best approximating logistic population model suggested that high levels of summer precipitation accelerated elk population growth, but higher summer minimum temperatures slowed growth. The best approximating non-linear matrix model indicated that high mean winter minimum temperatures enhanced recruitment of juveniles, while high summer precipitation enhanced the survival of calves. Warmer winters and wetter summers predicted by the Hadley Model could increase the equilibrium population size of elk by about 100%. Warmer winters and drier summers predicted by the CCC Model couldraise the equilibrium population size of elk by about 50%. Managers of national parks have relied on effects of weather, particularly severe winters, to regulate populations of native ungulates and prevent harmful effects of overabundance. Our results suggest that these regulating effects of severe winter weather may weaken if climate changes occur as those that are widely predicted in most climate change scenarios.  相似文献   

17.
哈尔滨气温的长期变化及基本态特征   总被引：1，自引：0，他引：1  
王永波  张治  周秀杰 《高原气象》2012,31(2):492-497
利用1881—2009年的气温观测资料,研究了哈尔滨年及四季平均气温的气候基本态和气候变率特征。结果表明,20世纪80年代以来哈尔滨夏季气温变化的异常程度显著增加。在冬季,近期哈尔滨气温处于暖背景及小变率的时段,反映了哈尔滨很长时间内大多数年份还将维持暖冬天气。近49年哈尔滨平均气温、平均最高(低)气温和平均日较差的时间变化特征显示,哈尔滨的年及四季平均气温都呈上升趋势,冬季增暖幅度最大;最高(低)气温变化趋势与平均气温一致,但无论年及四季,平均最高气温的变化速率都小于平均气温,平均最低气温的变化速率都大于平均气温。  相似文献   

18.
Numerical Simulation of Long-Term Climate Change in East Asia   总被引：1，自引：0，他引：1       下载免费PDF全文
A 10-yr regional climate simulation was performed using the fifth-generation PSU/NCAR Mesoscale Model Version 3 (MM5V3) driven by large-scale NCEP/NCAR reanalyses. Simulations of winter and summer mean regional climate features were examined against observations. The results showed that the model could well simulate the 10-yr winter and summer mean circulation, temperature, and moisture transport at middle and low levels. The simulated winter and summer mean sea level pressure agreed with the NCAR/NCEP reanalysis data. The model could well simulate the distribution and intensity of winter mean precipitation rates as well as the distribution of summer mean precipitation rates, but it overestimated the summer mean precipitation over North China. The model's ability to simulate the regional climate change in winter was superior to that in summer. In addition, the model could simulate the inter-annual variation of seasonal precipitation and surface air temperature. Geopotential heights and temperature at middle and high levels between simulations and observations exhibited high anomaly correlation coefficients. The model also showed large variability to simulate the regional climate change associated with the El Nino events. The MM5V3 well simulated the anomalies of summer mean precipitation in 1992 and 1995, while it demonstrated much less ability to simulate that in 1998. Generally speaking, the MM5V3 is capable of simulating the regional climate change, and could be used for long-term regional climate simulation.  相似文献   

19.
Summary A coupled biosphere-atmosphere statistical-dynamical model (SDM) is used to study the climatic effects of Amazonian deforestation. A soil moisture model based on BATS has been incorporated into the SDM in order to study the biogeophysical feedback of change in surface characteristics to regional climate due to the deforestation. In the control experiment, the mean annual and mean seasonal climate is well simulated by the model when compared with NCEP/NCAR reanalysis data. In the deforestation experiment, the evergreen broadleaf trees in the Amazonian region are substituted by short grass. The effects of Amazonian deforestation on regional climate are analysed taking into account the model simulations for the land portion of the latitude belts comprising the tropical region. Amazonian deforestation results in regional climate changes such as a decrease in evaporation, precipitation, available surface net radiation and soil moisture content, and an increase in temperatures and sensible heat flux. The reduction in transpiration was responsible for the most part of the decrease in total evapotranspiration. The reduction in precipitation was larger than the decrease in evapotranspiration so that runoff was reduced. The simulation of the diurnal cycle of the surface temperature shows an increase in temperature during the day and a decrease at night, which is in agreement with observations, whereas earlier GCM experiments showed an increase both during the day and night. In general, the changes in temperature and energy fluxes are in good agreement with GCM experiments, showing that the SDM is able to simulate the characteristics of the tropical climate that are associated with the substitution of forest by pasture areas.  相似文献   

20.
Two 30-year simulations corresponding to 1960-1989 and 2070-2099 have been performed with a variable resolution atmospheric model. The model has a maximum horizontal resolution of 0.5° over the Mediterranean Sea. Simulations are driven by IPCC-B2 scenario radiative forcing. Sea surface temperatures (SSTs) are prescribed from monthly observations for the present climate simulation, and from a blend of observations and coupled simulations for the scenario. Another pair of forced atmospheric simulations has been run under these forcings with the same uniform low resolution as the coupled model. Comparisons with observations show that the variable resolution model realistically reproduces the main climate characteristics of the Mediterranean region. At a global scale, changes in latitudinal temperature profiles are similar for the forced and coupled models, justifying the time-slice approach. The 2 m temperature and precipitation responses predict a warming and drying of the Mediterranean region. A comparison with the coupled simulation and forced low-resolution simulation shows that this pattern is robust. The decrease in mean precipitation is associated with a significant decrease in soil wetness, and could involve considerable impact on water resources around the Mediterranean basin.  相似文献   

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