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1.
Through the analysis of ensembles of coupled model simulations and projections collected from CMIP3 and CMIP5, we demonstrate that a fundamental spatial scale limit might exist below which useful additional refinement of climate model predictions and projections may not be possible. That limit varies among climate variables and from region to region. We show that the uncertainty (noise) in surface temperature predictions (represented by the spread among an ensemble of global climate model simulations) generally exceeds the ensemble mean (signal) at horizontal scales below 1000 km throughout North America, implying poor predictability at those scales. More limited skill is shown for the predictability of regional precipitation. The ensemble spread in this case tends to exceed or equal the ensemble mean for scales below 2000 km. These findings highlight the challenges in predicting regionally specific future climate anomalies, especially for hydroclimatic impacts such as drought and wetness.  相似文献   

2.
An analysis is presented of the dependence of the regional temperature and precipitation change signal on systematic regional biases in global climate change projections. The CMIP3 multi-model ensemble is analyzed over 26 land regions and for the A1B greenhouse gas emission scenario. For temperature, the model regional bias has a negligible effect on the projected regional change. For precipitation, a significant correlation between change and bias is found in about 30% of the seasonal/regional cases analyzed, covering a wide range of different climate regimes. For these cases, a performance-based selection of models in producing climate change scenarios can affect the resulting change estimate, and it is noted that a minimum of four to five models is needed to obtain robust precipitation change estimates. In a number of cases, models with largely different precipitation biases can still produce changes of consistent sign. Overall, it is assessed that in the present generation of models the regional bias does not appear to be a dominant factor in determining the simulated regional change in the majority of cases.  相似文献   

3.
One of the main sources of uncertainty in estimating climate projections affected by global warming is the choice of the global climate model (GCM). The aim of this study is to evaluate the skill of GCMs from CMIP3 and CMIP5 databases in the north-east Atlantic Ocean region. It is well known that the seasonal and interannual variability of surface inland variables (e.g. precipitation and snow) and ocean variables (e.g. wave height and storm surge) are linked to the atmospheric circulation patterns. Thus, an automatic synoptic classification, based on weather types, has been used to assess whether GCMs are able to reproduce spatial patterns and climate variability. Three important factors have been analyzed: the skill of GCMs to reproduce the synoptic situations, the skill of GCMs to reproduce the historical inter-annual variability and the consistency of GCMs experiments during twenty-first century projections. The results of this analysis indicate that the most skilled GCMs in the study region are UKMO-HadGEM2, ECHAM5/MPI-OM and MIROC3.2(hires) for CMIP3 scenarios and ACCESS1.0, EC-EARTH, HadGEM2-CC, HadGEM2-ES and CMCC-CM for CMIP5 scenarios. These models are therefore recommended for the estimation of future regional multi-model projections of surface variables driven by the atmospheric circulation in the north-east Atlantic Ocean region.  相似文献   

4.
The projected changes of precipitation and temperature in the Yangtze River Basin in the 20th Century from 20 models of the CMIP3 (phase 3 of the Coupled Model Inter-comparison Project) dataset are analyzed based on the observed precipitation and temperature data of 147 meteorological stations in the Yangtze River Basin. The results show that all models tend to underestimate the annual mean temperature over the Yangtze River Basin, and to overestimate the annual mean precipitation. The temporal changes of simulated annual mean precipitation and temperature are broadly comparable with the observations, but with large variability among the results of the models. Most of the models can reproduce maximum precipitation during the monsoon season, while all models tend to underestimate the mean temperature of each month over the Yangtze River Basin. The Taylor diagram shows that the differences between modeled and observed temperature are relatively smaller as compared to differences in precipitation. For a detailed investigation of regional characteristics of climate change in the Yangtze River Basin during 2011–2050, the multi-model ensembles produced by an upgraded REA method are carried out for more reliable projections. The projected precipitation and temperature show large spatial variability in the Yangtze River Basin. Mean precipitation will increase under the A1B and B1 scenarios and decrease under the A2 scenario, with linear trends ranging from ?21 to 28.5?mm/decade. Increasing mean temperature can be found in all scenarios with linear trends ranging from 0.15 to 0.48°C/decade. Grids in the head region of the Jingshajiang catchment show distinct increasing trends for all scenarios. Some physical processes associated with precipitation are not well represented in the models.  相似文献   

5.
This study aims at sharpening the existing knowledge of expected seasonal mean climate change and its uncertainty over Europe for the two key climate variables air temperature and precipitation amount until the mid-twentyfirst century. For this purpose, we assess and compensate the global climate model (GCM) sampling bias of the ENSEMBLES regional climate model (RCM) projections by combining them with the full set of the CMIP3 GCM ensemble. We first apply a cross-validation in order to assess the skill of different statistical data reconstruction methods in reproducing ensemble mean and standard deviation. We then select the most appropriate reconstruction method in order to fill the missing values of the ENSEMBLES simulation matrix and further extend the matrix by all available CMIP3 GCM simulations forced by the A1B emission scenario. Cross-validation identifies a randomized scaling approach as superior in reconstructing the ensemble spread. Errors in ensemble mean and standard deviation are mostly less than 0.1 K and 1.0 % for air temperature and precipitation amount, respectively. Reconstruction of the missing values reveals that expected seasonal mean climate change of the ENSEMBLES RCM projections is not significantly biased and that the associated uncertainty is not underestimated due to sampling of only a few driving GCMs. In contrast, the spread of the extended simulation matrix is partly significantly lower, sharpening our knowledge about future climate change over Europe by reducing uncertainty in some regions. Furthermore, this study gives substantial weight to recent climate change impact studies based on the ENSEMBLES projections, since it confirms the robustness of the climate forcing of these studies concerning GCM sampling.  相似文献   

6.
通过对15组CMIP3和CMIP5两代模式集合平均对中国西北干旱区气温和降水的模拟能力比较,发现CMIP5模式对气温和降水的模拟更接近观测值。CMIP5模式模拟年、春季、夏季、秋季平均气温的相关系数比CMIP3模式分别提升了0.15、0.13、0.24和0.02,冬季下降了0.07。CMIP5模式对西北干旱区的平均气温变化趋势的模拟效果比CMIP3有所提高,对年、春季、夏季、秋季、冬季趋势的模拟偏差比CMIP3分别减少了0.03℃/10a、0.10℃/10a、0.01℃/10a、0.06℃/10a、0.14℃/10a。对西北干旱区平均气温年、季的模拟偏差分布上,CMIP5模式的偏差均比CMIP3低1~2℃。但是天山区年、季节平均气温的模拟与整体模拟偏低情况相反,CMIP3和CMIP5分别偏高3~6℃和1~4℃,对夏季的模拟偏高最严重,分别达到6℃和4℃。CMIP5模式整体对西北干旱区降水量的模拟结果与观测值的平均相关系数与CMIP3相差不大,均不超过0.1,而且偏差仍然较大。CMIP5模式对西北干旱区的降水量的变化趋势模拟效果比CMIP3有所降低,对年、春季、夏季、秋季、冬季趋势的模拟偏差比CMIP3增加了0.67 mm/10a、0.23 mm/10a、0.51 mm/10a、0.11 mm/10a、0.14 mm/10a。CMIP5模式对年、春季、夏季、秋季和冬季的降水量模拟的均方根误差相比CMIP3分别减少77.6 mm、25.5 mm、25.0 mm、18.8 mm和13.9 mm。在空间上,CMIP5模式对年、季节降水模拟仍然偏高,但是比CMIP3有明显缓解;CMIP3和CMIP5模式对夏季天山区年降水量和夏季降水量的模拟也与大部分区域偏高的趋势明显相反,两代模式对夏季天山区的降水模拟均偏低50 mm左右。  相似文献   

7.
Regional and seasonal temperature and precipitation over land are compared across two generations of global climate model ensembles, specifically, CMIP5 and CMIP3, through historical twentieth century skills and multi-model agreement, and twenty first century projections. A suite of diagnostic and performance metrics, ranging from spatial bias or model-consensus maps and aggregate time series plots, to measures of equivalence between probability density functions and Taylor diagrams, are used for the intercomparisons. Pairwise and multi-model ensemble comparisons were performed for 11 models, which were selected based on data availability and resolutions. Results suggest little change in the central tendency or variability or uncertainty of historical skills or consensus across the two generations of models. However, there are regions and seasons, at different levels of aggregation, where significant changes, performance improvements, and even degradation in skills, are suggested. The insights may provide directions for further improvements in next generations of climate models, and in the meantime, help inform adaptation and policy.  相似文献   

8.
For the construction of regional climate change scenarios spanning a relevant fraction of the spread in climate model projections, an inventory of major drivers of regional climate change is needed. For the Netherlands, a previous set of regional climate change scenarios was based on the decomposition of local temperature/precipitation changes into components directly linked to the level of global warming, and components related to changes in the regional atmospheric circulation. In this study this decomposition is revisited utilizing the extensive modelling results from the CMIP5 model ensemble in support for the 5th IPCC assessment. Rather than selecting a number of GCMs based on performance metrics or relevant response features, a regression technique was developed to utilize all available model projections. The large number of projections allows a quantification of the separate contributions of emission scenarios, systematic model responses and natural variability to the total likelihood range. Natural variability plays a minor role in modelled differences in the global mean temperature response, but contributes for up to 50 % to the range of mean sea level pressure responses and local precipitation. Using key indicators (“steering variables”) for the temperature and circulation response, the range in local seasonal mean temperature and precipitation responses can be fairly well reproduced.  相似文献   

9.
J. Bhend  P. Whetton 《Climatic change》2013,118(3-4):799-810
There is increasing pressure from stakeholders for highly localised climate change projections. A comprehensive assessment of climate model performance at the grid box scale in simulating recent change, however, is not available at present. Therefore, we compare observed changes in near-surface temperature, sea level pressure (SLP) and precipitation with simulations available from the Coupled Model Intercomparison Projects 3 and 5 (CMIP3 and CMIP5). In both multi-model datasets we find coherent areas of inconsistency between observed and simulated local trends per degree global warming in both temperature and SLP in the majority of models. Localised projections should thus take into account the possibility of regional biases shared across models. In contrast, simulated changes in precipitation are not significantly different from observations due to low signal-to-noise ratio of local precipitation changes. Therefore, recent regional rainfall change is likely not providing useful constraints for future projections as of yet. Comparing the two most recent sets of internationally coordinated climate model experiments, we find no indication of improvement in the models’ ability to reproduce local trends in temperature, SLP and precipitation.  相似文献   

10.

This paper presents the first multi-model ensemble of 10-year, “convection-permitting” kilometer-scale regional climate model (RCM) scenario simulations downscaled from selected CMIP5 GCM projections for historical and end of century time slices. The technique is to first downscale the CMIP5 GCM projections to an intermediate 12–15 km resolution grid using RCMs, and then use these fields to downscale further to the kilometer scale. The aim of the paper is to provide an overview of the representation of the precipitation characteristics and their projected changes over the greater Alpine domain within a Coordinated Regional Climate Downscaling Experiment Flagship Pilot Study and the European Climate Prediction system project, tasked with investigating convective processes at the kilometer scale. An ensemble of 12 simulations performed by different research groups around Europe is analyzed. The simulations are evaluated through comparison with high resolution observations while the complementary ensemble of 12 km resolution driving models is used as a benchmark to evaluate the added value of the convection-permitting ensemble. The results show that the kilometer-scale ensemble is able to improve the representation of fine scale details of mean daily, wet-day/hour frequency, wet-day/hour intensity and heavy precipitation on a seasonal scale, reducing uncertainty over some regions. It also improves the representation of the summer diurnal cycle, showing more realistic onset and peak of convection. The kilometer-scale ensemble refines and enhances the projected patterns of change from the coarser resolution simulations and even modifies the sign of the precipitation intensity change and heavy precipitation over some regions. The convection permitting simulations also show larger changes for all indices over the diurnal cycle, also suggesting a change in the duration of convection over some regions. A larger positive change of frequency of heavy to severe precipitation is found. The results are encouraging towards the use of convection-permitting model ensembles to produce robust assessments of the local impacts of future climate change.

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11.
Changes in temperature and precipitation extremes in the CMIP5 ensemble   总被引：6，自引：1，他引：5  
Twenty-year temperature and precipitation extremes and their projected future changes are evaluated in an ensemble of climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5), updating a similar study based on the CMIP3 ensemble. The projected changes are documented for three radiative forcing scenarios. The performance of the CMIP5 models in simulating 20-year temperature and precipitation extremes is comparable to that of the CMIP3 ensemble. The models simulate late 20th century warm extremes reasonably well, compared to estimates from reanalyses. The model discrepancies in simulating cold extremes are generally larger than those for warm extremes. Simulated late 20th century precipitation extremes are plausible in the extratropics but uncertainty in extreme precipitation in the tropics and subtropics remains very large, both in the models and the observationally-constrained datasets. Consistent with CMIP3 results, CMIP5 cold extremes generally warm faster than warm extremes, mainly in regions where snow and sea-ice retreat with global warming. There are tropical and subtropical regions where warming rates of warm extremes exceed those of cold extremes. Relative changes in the intensity of precipitation extremes generally exceed relative changes in annual mean precipitation. The corresponding waiting times for late 20th century extreme precipitation events are reduced almost everywhere, except for a few subtropical regions. The CMIP5 planetary sensitivity in extreme precipitation is about 6 %/°C, with generally lower values over extratropical land.  相似文献   

12.
CMIP5模式对中国年平均气温模拟及其与CMIP3模式的比较   总被引：5，自引：0，他引：5  
利用CRUT3v和CN05两套观测资料,评估25个CMIP5模式对1906-2005年中国年平均气温变化的模拟能力,并与CMIP3模式对比。结果表明：1906-2005年中国平均温升速率为0.84℃/100a,CMIP5多模式集合平均模拟的增温率为0.77℃/100a。模式对20世纪后期温升模拟好于前期,仅有两个模式能模拟中国20世纪40年代异常增暖。模式对气温气候态空间分布模拟较好,但在中国西部地区存在最大模拟冷偏差和不确定性。1961-1999年,中国北方增暖大于南方。多模式集合平均可以较好地模拟气温变化线性趋势的空间分布,但对南北气温变化趋势的差异模拟过小。总体说来,在中国平均气温变化趋势、气温气候态空间分布和气温变化趋势空间分布三方面,CMIP5模式都较CMIP3模式有所提高。  相似文献   

13.
使用多种观测资料和43个参加耦合模式比较计划第五阶段（CMIP5）的全球气候模式模拟数据,评估分析了全球气候模式对中国地区1980-2005年降水特征的模拟能力。结果表明：多数CMIP5模式能够模拟出中国降水由西北向东南递增的分布特点,这与耦合模式比较计划第三阶段（CMIP3）的模式模拟结果类似,但华南地区降水模拟偏少,西部高原地区降水模拟偏多。模式能够较好地模拟出降水冬弱夏强的季节变化特征,但降水模拟系统性偏多。从EOF分析结果来看,多数CMIP5模式可以再现中国地区年平均降水的时空变化特征,集合平均的表现优于CMIP3。多模式集合在月、季、年时间尺度下模拟的平均值优于大部分单个模式的结果。CMIP5中6个中国模式的模拟能力与其他模式相当,其中FGOALS-g2、BCC-CSM1-1-m的模拟能力相对较好。  相似文献   

14.
It is well accepted within the scientific community that a large ensemble of different projections is required to achieve robust climate change information for a specific region. For this purpose we have compiled a state-of-the-art multi-model multi-scenario ensemble of global and regional precipitation projections. This ensemble combines several global projections from the CMIP3 and CMIP5 databases, along with some recently downscaled regional CORDEX-Africa projections. Altogether daily precipitation data from 77 different climate change projections is analysed; separated into 31 projections for a high and 46 for a low emission scenario. We find a robust indication that, independent of the underlying emission scenario, annual total precipitation amounts over the central African region are not likely to change severely in the future. However some robust changes in precipitation characteristics, like the intensification of heavy rainfall events as well as an increase in the number of dry spells during the rainy season are projected for the future. Further analysis shows that over some regions the results of the climate change assessment clearly depend on the size of the analyzed ensemble. This indicates the need of a “large-enough” ensemble of independent climate projections to allow for a reliable climate change assessment.  相似文献   

15.
ENSO representation in climate models: from CMIP3 to CMIP5   总被引：4，自引：2，他引：2  
We analyse the ability of CMIP3 and CMIP5 coupled ocean–atmosphere general circulation models (CGCMs) to simulate the tropical Pacific mean state and El Niño-Southern Oscillation (ENSO). The CMIP5 multi-model ensemble displays an encouraging 30 % reduction of the pervasive cold bias in the western Pacific, but no quantum leap in ENSO performance compared to CMIP3. CMIP3 and CMIP5 can thus be considered as one large ensemble (CMIP3 + CMIP5) for multi-model ENSO analysis. The too large diversity in CMIP3 ENSO amplitude is however reduced by a factor of two in CMIP5 and the ENSO life cycle (location of surface temperature anomalies, seasonal phase locking) is modestly improved. Other fundamental ENSO characteristics such as central Pacific precipitation anomalies however remain poorly represented. The sea surface temperature (SST)-latent heat flux feedback is slightly improved in the CMIP5 ensemble but the wind-SST feedback is still underestimated by 20–50 % and the shortwave-SST feedbacks remain underestimated by a factor of two. The improvement in ENSO amplitudes might therefore result from error compensations. The ability of CMIP models to simulate the SST-shortwave feedback, a major source of erroneous ENSO in CGCMs, is further detailed. In observations, this feedback is strongly nonlinear because the real atmosphere switches from subsident (positive feedback) to convective (negative feedback) regimes under the effect of seasonal and interannual variations. Only one-third of CMIP3 + CMIP5 models reproduce this regime shift, with the other models remaining locked in one of the two regimes. The modelled shortwave feedback nonlinearity increases with ENSO amplitude and the amplitude of this feedback in the spring strongly relates with the models ability to simulate ENSO phase locking. In a final stage, a subset of metrics is proposed in order to synthesize the ability of each CMIP3 and CMIP5 models to simulate ENSO main characteristics and key atmospheric feedbacks.  相似文献   

16.
Based on climate extreme indices calculated from a high-resolution daily observational dataset in China during1961–2005, the performance of 12 climate models from phase 6 of the Coupled Model Intercomparison Project(CMIP6),and 30 models from phase 5 of CMIP(CMIP5), are assessed in terms of spatial distribution and interannual variability. The CMIP6 multi-model ensemble mean(CMIP6-MME) can simulate well the spatial pattern of annual mean temperature,maximum daily maximum temperature, and minimum daily minimum temperature. However, CMIP6-MME has difficulties in reproducing cold nights and warm days, and has large cold biases over the Tibetan Plateau. Its performance in simulating extreme precipitation indices is generally lower than in simulating temperature indices. Compared to CMIP5, CMIP6 models show improvements in the simulation of climate indices over China. This is particularly true for precipitation indices for both the climatological pattern and the interannual variation, except for the consecutive dry days. The arealmean bias for total precipitation has been reduced from 127%(CMIP5-MME) to 79%(CMIP6-MME). The most striking feature is that the dry biases in southern China, very persistent and general in CMIP5-MME, are largely reduced in CMIP6-MME. Stronger ascent together with more abundant moisture can explain this reduction in dry biases. Wet biases for total precipitation, heavy precipitation, and precipitation intensity in the eastern Tibetan Plateau are still present in CMIP6-MME, but smaller, compared to CMIP5-MME.  相似文献   

17.
This paper includes a comprehensive assessment of 40 models from the Coupled Model Intercomparison Project phase 5 (CMIP5) and 33 models from the CMIP phase 6 (CMIP6) to determine the climatological and seasonal variation of ocean salinity from the surface to 2000 m. The general pattern of the ocean salinity climatology can be simulated by both the CMIP5 and CMIP6 models from the surface to 2000-m depth. However, this study shows an increased fresh bias in the surface and subsurface salinity in the CMIP6 multimodel mean, with a global average of ?0.44 g kg?1 for the sea surface salinity (SSS) and ?0.26 g kg?1 for the 0–1000-m averaged salinity (S1000) compared with the CMIP5 multimodel mean (?0.25 g kg?1 for the SSS and ?0.07 g kg?1 for the S1000). In terms of the seasonal variation, both CMIP6 and CMIP5 models show positive (negative) anomalies in the first (second) half of the year in the global average SSS and S1000. The model-simulated variation in SSS is consistent with the observations, but not for S1000, suggesting a substantial uncertainty in simulating and understanding the seasonal variation in subsurface salinity. The CMIP5 and CMIP6 models overestimate the magnitude of the seasonal variation of the SSS in the tropics in the region 20°S–20°N but underestimate the magnitude of the seasonal change in S1000 in the Atlantic and Indian oceans. These assessments show new features of the model errors in simulating ocean salinity and support further studies of the global hydrological cycle.  相似文献   

18.
A verification framework for interannual-to-decadal predictions experiments   总被引：2，自引：1，他引：1  
Decadal predictions have a high profile in the climate science community and beyond, yet very little is known about their skill. Nor is there any agreed protocol for estimating their skill. This paper proposes a sound and coordinated framework for verification of decadal hindcast experiments. The framework is illustrated for decadal hindcasts tailored to meet the requirements and specifications of CMIP5 (Coupled Model Intercomparison Project phase 5). The chosen metrics address key questions about the information content in initialized decadal hindcasts. These questions are: (1) Do the initial conditions in the hindcasts lead to more accurate predictions of the climate, compared to un-initialized climate change projections? and (2) Is the prediction model’s ensemble spread an appropriate representation of forecast uncertainty on average? The first question is addressed through deterministic metrics that compare the initialized and uninitialized hindcasts. The second question is addressed through a probabilistic metric applied to the initialized hindcasts and comparing different ways to ascribe forecast uncertainty. Verification is advocated at smoothed regional scales that can illuminate broad areas of predictability, as well as at the grid scale, since many users of the decadal prediction experiments who feed the climate data into applications or decision models will use the data at grid scale, or downscale it to even higher resolution. An overall statement on skill of CMIP5 decadal hindcasts is not the aim of this paper. The results presented are only illustrative of the framework, which would enable such studies. However, broad conclusions that are beginning to emerge from the CMIP5 results include (1) Most predictability at the interannual-to-decadal scale, relative to climatological averages, comes from external forcing, particularly for temperature; (2) though moderate, additional skill is added by the initial conditions over what is imparted by external forcing alone; however, the impact of initialization may result in overall worse predictions in some regions than provided by uninitialized climate change projections; (3) limited hindcast records and the dearth of climate-quality observational data impede our ability to quantify expected skill as well as model biases; and (4) as is common to seasonal-to-interannual model predictions, the spread of the ensemble members is not necessarily a good representation of forecast uncertainty. The authors recommend that this framework be adopted to serve as a starting point to compare prediction quality across prediction systems. The framework can provide a baseline against which future improvements can be quantified. The framework also provides guidance on the use of these model predictions, which differ in fundamental ways from the climate change projections that much of the community has become familiar with, including adjustment of mean and conditional biases, and consideration of how to best approach forecast uncertainty.  相似文献   

19.
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20.
Uncertainties in the climate response to a doubling of atmospheric CO2 concentrations are quantified in a perturbed land surface parameter experiment. The ensemble of 108 members is constructed by systematically perturbing five poorly constrained land surface parameters of global climate model individually and in all possible combinations. The land surface parameters induce small uncertainties at global scale, substantial uncertainties at regional and seasonal scale and very large uncertainties in the tails of the distribution, the climate extremes. Climate sensitivity varies across the ensemble mainly due to the perturbation of the snow albedo parameterization, which controls the snow albedo feedback strength. The uncertainty range in the global response is small relative to perturbed physics experiments focusing on atmospheric parameters. However, land surface parameters are revealed to control the response not only of the mean but also of the variability of temperature. Major uncertainties are identified in the response of climate extremes to a doubling of CO2. During winter the response both of temperature mean and daily variability relates to fractional snow cover. Cold extremes over high latitudes warm disproportionately in ensemble members with strong snow albedo feedback and large snow cover reduction. Reduced snow cover leads to more winter warming and stronger variability decrease. As a result uncertainties in mean and variability response line up, with some members showing weak and others very strong warming of the cold tail of the distribution, depending on the snow albedo parametrization. The uncertainty across the ensemble regionally exceeds the CMIP3 multi-model range. Regarding summer hot extremes, the uncertainties are larger than for mean summer warming but smaller than in multi-model experiments. The summer precipitation response to a doubling of CO2 is not robust over many regions. Land surface parameter perturbations and natural variability alter the sign of the response even over subtropical regions.  相似文献   

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