Climate change projections of the North American Regional Climate Change Assessment Program (NARCCAP)

We investigate major results of the NARCCAP multiple regional climate model (RCM) experiments driven by multiple global climate models (GCMs) regarding climate change for seasonal temperature and precipitation over North America. We focus on two major questions: How do the RCM simulated climate changes differ from those of the parent GCMs and thus affect our perception of climate change over North America, and how important are the relative contributions of RCMs and GCMs to the uncertainty (variance explained) for different seasons and variables? The RCMs tend to produce stronger climate changes for precipitation: larger increases in the northern part of the domain in winter and greater decreases across a swath of the central part in summer, compared to the four GCMs driving the regional models as well as to the full set of CMIP3 GCM results. We pose some possible process-level mechanisms for the difference in intensity of change, particularly for summer. Detailed process-level studies will be necessary to establish mechanisms and credibility of these results. The GCMs explain more variance for winter temperature and the RCMs for summer temperature. The same is true for precipitation patterns. Thus, we recommend that future RCM-GCM experiments over this region include a balanced number of GCMs and RCMs.     

Addressing sources of uncertainty in runoff projections for a data scarce catchment in the Ecuadorian Andes

Future climate projections from general circulation models (GCMs) predict an acceleration of the global hydrological cycle throughout the 21st century in response to human-induced rise in temperatures. However, projections of GCMs are too coarse in resolution to be used in local studies of climate change impacts. To cope with this problem, downscaling methods have been developed that transform climate projections into high resolution datasets to drive impact models such as rainfall-runoff models. Generally, the range of changes simulated by different GCMs is considered to be the major source of variability in the results of such studies. However, the cascade of uncertainty in runoff projections is further elongated by differences between impact models, especially where robust calibration is hampered by the scarcity of data. Here, we address the relative importance of these different sources of uncertainty in a poorly monitored headwater catchment of the Ecuadorian Andes. Therefore, we force 7 hydrological models with downscaled outputs of 8 GCMs driven by the A1B and A2 emission scenarios over the 21st century. Results indicate a likely increase in annual runoff by 2100 with a large variability between the different combinations of a climate model with a hydrological model. Differences between GCM projections introduce a gradually increasing relative uncertainty throughout the 21st century. Meanwhile, structural differences between applied hydrological models still contribute to a third of the total uncertainty in late 21st century runoff projections and differences between the two emission scenarios are marginal.     

Quantifying sources of uncertainty in projected wheat yield changes under climate change in eastern Australia

Future climate projections and impact analyses are pivotal to evaluate the potential change in crop yield under climate change. Impact assessment of climate change is also essential to prepare and implement adaptation measures for farmers and policymakers. However, there are uncertainties associated with climate change impact assessment when combining crop models and climate models under different emission scenarios. This study quantifies the various sources of uncertainty associated with future climate change effects on wheat productivity at six representative sites covering dry and wet environments in Australia based on 12 soil types and 12 nitrogen application rates using one crop model driven by 28 global climate models (GCMs) under two representative concentration pathways (RCPs) at near future period 2021–2060 and far future period 2061–2100. We used the analysis of variance (ANOVA) to quantify the sources of uncertainty in wheat yield change. Our results indicated that GCM uncertainty largely dominated over RCPs, nitrogen rates, and soils for the projections of wheat yield at drier locations. However, at wetter sites, the largest share of uncertainty was nitrogen, followed by GCMs, soils, and RCPs. In addition, the soil types at two northern sites in the study area had greater effects on yield change uncertainty probably due to the interaction effect of seasonal rainfall and soil water storage capacity. We concluded that the relative contributions of different uncertainty sources are dependent on climatic location. Understanding the share of uncertainty in climate impact assessment is important for model choice and will provide a basis for producing more reliable impact assessment.     

CLIMBER-2: a climate system model of intermediate complexity. Part II: model sensitivity

 A set of sensitivity experiments with the climate system model of intermediate complexity CLIMBER-2 was performed to compare its sensitivity to changes in different types of forcings and boundary conditions with the results of comprehensive models (GCMs). We investigated the climate system response to changes in freshwater flux into the Northern Atlantic, CO₂ concentration, solar insolation, and vegetation cover in the boreal zone and in the tropics. All these experiments were compared with the results of corresponding experiments performed with different GCMs. Qualitative, and in many respects, quantitative agreement between the results of CLIMBER-2 and GCMs demonstrate the ability of our climate system model of intermediate complexity to address diverse aspects of the climate change problem. In addition, we used our model for a series of experiments to assess the impact of some climate feedbacks and uncertainties in model parameters on the model sensitivity to different forcings. We studied the role of freshwater feedback and vertical ocean diffusivity for the stability properties of the thermohaline ocean circulation. We show that freshwater feedback plays a minor role, while changes of vertical diffusivity in the ocean considerably affect the circulation stability. In global warming experiments we analysed the impact of hydrological sensitivity and vertical diffusivity on the long-term evolution of the thermohaline circulation. In the boreal and tropical deforestation experiments we assessed the role of an interactive ocean and showed that for both types of deforestation scenarios, an interactive ocean leads to an additional cooling due to albedo and water vapour feedbacks. Received: 28 May 2000 / Accepted: 9 November 2000     

Comparison of uncertainty sources for climate change impacts: flood frequency in England

This paper investigates the uncertainty in the impact of climate change on flood frequency in England, through the use of continuous simulation of river flows. Six different sources of uncertainty are discussed: future greenhouse gas emissions; Global Climate Model (GCM) structure; downscaling from GCMs (including Regional Climate Model structure); hydrological model structure; hydrological model parameters and the internal variability of the climate system (sampled by applying different GCM initial conditions). These sources of uncertainty are demonstrated (separately) for two example catchments in England, by propagation through to flood frequency impact. The results suggest that uncertainty from GCM structure is by far the largest source of uncertainty. However, this is due to the extremely large increases in winter rainfall predicted by one of the five GCMs used. Other sources of uncertainty become more significant if the results from this GCM are omitted, although uncertainty from sources relating to modelling of the future climate is generally still larger than that relating to emissions or hydrological modelling. It is also shown that understanding current and future natural variability is critical in assessing the importance of climate change impacts on hydrology.     

两次暴雨过程模拟对陆面参数化方案的敏感性研究

选取发生在江西和福建境内的两次暴雨个例,利用NCEP再分析资料在对暴雨发生前、后的环境场和物理量场进行诊断和对比分析的基础上,采用中尺度模式WRF V3.3,通过数值模拟探讨了陆面过程对两次暴雨过程的可能影响及其相关的物理过程。结果表明,2012年5月12日江西大暴雨主要受大尺度环流和中尺度天气系统影响,具有范围大、持续时间长等特点,属于大尺度降水为主的暴雨;而2011年8月23日福建暴雨发生在副热带高压控制下的午后,局地下垫面强烈的感热和潜热通量使低层大气不稳定性增强,触发了此次对流性降水为主的暴雨。通过资料诊断分析,可以判断陆面过程对福建暴雨个例的影响程度明显强于江西暴雨个例。通过关闭地表通量试验发现,陆面过程对暴雨模拟十分重要,尤其是对于该个例中对流性降水的发生起到关键性的作用。通过陆面参数化方案的敏感性试验发现,两次暴雨过程对陆面参数化方案均较为敏感。江西暴雨对陆面过程的敏感性主要体现在对流降水的模拟上,而福建暴雨则体现在大尺度降水的模拟方面,即福建暴雨对陆面参数化方案的敏感性强于江西暴雨。敏感性产生机制与降水类型关系紧密,大尺度降水对陆面过程的敏感性主要来源于不同参数化模拟的中高空对流系统的差异,而对流降水的敏感性则与不同参数化模拟的地表通量的差异有关。通过陆面参数的扰动试验进一步发现,相比于地表粗糙度和最小叶孔阻抗,土壤孔隙度和地表反照率则是影响对流降水对陆面过程敏感的关键因子,这在本质上与地表通量是否受到扰动有关。地表通量较风场而言,受扰动引起变化的空间范围广、时间响应快,变化具有明显规律性。所得结果可为深入理解陆面过程影响暴雨等天气过程和改进数值模式对暴雨的模拟能力提供一定的参考。     

Assessing uncertainties in climate change impact analyses on the river flow regimes in the UK. Part 1: baseline climate

Assessing future climate and its potential implications on river flows is a key challenge facing water resource planners. Sound, scientifically-based advice to decision makers also needs to incorporate information on the uncertainty in the results. Moreover, existing bias in the reproduction of the ‘current’ (or baseline) river flow regime is likely to transfer to the simulations of flow in future time horizons, and it is thus critical to undertake baseline flow assessment while undertaking future impacts studies. This paper investigates the three main sources of uncertainty surrounding climate change impact studies on river flows: uncertainty in GCMs, in downscaling techniques and in hydrological modelling. The study looked at four British catchments’ flow series simulated by a lumped conceptual rainfall–runoff model with observed and GCM-derived rainfall series representative of the baseline time horizon (1961–1990). A block-resample technique was used to assess climate variability, either from observed records (natural variability) or reproduced by GCMs. Variations in mean monthly flows due to hydrological model uncertainty from different model structures or model parameters were also evaluated. Three GCMs (HadCM3, CCGCM2, and CSIRO-mk2) and two downscaling techniques (SDSM and HadRM3) were considered. Results showed that for all four catchments, GCM uncertainty is generally larger than downscaling uncertainty, and both are consistently greater than uncertainty from hydrological modelling or natural variability. No GCM or downscaling technique was found to be significantly better or to have a systematic bias smaller than the others. This highlights the need to consider more than one GCM and downscaling technique in impact studies, and to assess the bias they introduce when modelling river flows.     

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.     

Evaluating historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang Province,China

Assessing the regional impact of climate change on agriculture, hydrology, and forests is vital for sustainable management. Trustworthy projections of climate change are needed to support these assessments. In this paper, 18 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) are evaluated for their ability to simulate regional climate change in Zhejiang Province, Southeast China. Simple graphical approaches and three indices are used to evaluate the performance of six key climatic variables during simulations from 1971 to 2000. These variables include maximum and minimum air temperature, precipitation, wind speed, solar radiation, and relative humidity. These variables are of great importance to researchers and decision makers in climate change impact studies and developing adaptation strategies. This study found that most GCMs failed to reproduce the observed spatial patterns, due to insufficient resolution. However, the seasonal variations of the six variables are simulated well by most GCMs. Maximum and minimum air temperatures are simulated well on monthly, seasonal, and yearly scales. Solar radiation is reasonably simulated on monthly, seasonal, and yearly scales. Compared to air temperature and solar radiation, it was found that precipitation, wind speed, and relative humidity can only be simulated well at seasonal and yearly scales. Wind speed was the variable with the poorest simulation results across all GCMs.     

A new way of quantifying GCM water vapour feedback

The water vapour feedback probably makes the largest contribution to climate sensitivity, and the second-largest contribution to its uncertainty, in the sense of disagreement between General Circulation Models (GCMs, the most physically detailed models of climate we have). Yet there has been no quantification of it which allows these differences to be attributed physically with the aim of constraining the true value. This paper develops a new breakdown of the non-cloud LW (longwave) response to climate change, which avoids the problems of the conventional breakdown, and applies it to a set of 4 GCMs. The basic physical differences are that temperature is used as the vertical coordinate, and relative humidity as the humidity variable. In this framework the different GCMs’ feedbacks look more alike, consistent with our understanding that their water vapour responses are physically very similar. Also, in the global mean all the feedback components have the same sign, allowing us to conveniently attribute the overall response fractionally (e.g. about 60% from the “partly-Simpsonian” component). The systematic cancellation between different feedback components in the conventional breakdown is lost, so now a difference in a feedback component actually contributes to a difference in climate sensitivity, and the differences between these GCMs in the non-cloud LW part of this can be traced to differences in formulation, mean climate and climate change response. Physical effects such as those due to variations in the formulation of LW radiative transfer become visible. Differences in the distribution of warming no longer dominate comparison of GCMs. The largest component depends locally only on the GCM’s mean climate, so it can in principle be calculated for the real world and validated. However, components dependent on the climate change response probably account for most of the variation between GCMs. The effect of simply changing the humidity variable in the conventional breakdown is also examined. It gives some of this improvement—the loss of the cancellations that leave the conventional breakdown of no use to understand differences between GCMs’ climate sensitivities—but not the link to mean climate.     

A regional climate model simulation over West Africa: parameterization tests and analysis of land-surface fields

The West African Monsoon has been simulated with the regional climate model PROMES, coupled to the land-surface model ORCHIDEE and nested in ECMWF analysis, within AMMA-EU project. Three different runs are presented to address the influence of changes in two parameterizations (moist convection and radiation) on the simulated West African Monsoon. Another aim of the study is to get an insight into the relationship of simulated precipitation and 2-m temperature with land-surface fluxes. To this effect, data from the AMMA land-surface model intercomparison project (ALMIP) have been used. In ALMIP, offline simulations have been made using the same land-surface model than in the coupled simulation presented here, which makes ALMIP data particularly relevant for the present study, as it enables us to analyse the simulated soil and land-surface fields. The simulation of the monsoon depends clearly on the two analysed parameterizations. The inclusion of shallow convection parametrization affects the intensity of the simulated monsoon precipitation and modifies some dynamical aspects of the monsoon. The use of a fractional cloud-cover parameterization and a more complex radiation scheme is important for better reproducing the amplitude of the latitudinal displacement of the precipitation band. This is associated to an improved simulation of the surface temperature field and the easterly jets. However, the parameterization changes do not affect the timing of the main rainy and break periods of the monsoon. A better representation of downward solar radiation is associated with a smaller bias in the surface heat fluxes. The comparison with ALMIP land-surface and soil fields shows that precipitation and temperature biases in the regional climate model simulation are associated to certain biases in land-surface fluxes. The biases in soil moisture seem to be driven by atmospheric biases as they are strongly affected by the parameterization changes in atmospheric processes.     

百分位统计降尺度方法及在GCMs日降水订正中的应用

在格点观测的逐日降水量数据基础上,采用百分位统计降尺度方法对全球气候模式(GCM)输出的日降水量进行了订正处理。5种订正方案的比较结果表明,取12个百分位数进行日降水量订正是合理的。观测资料与3个GCMs订正前后全国平均年、季降水量空间分布以及主要流域平均年、月和日降水序列多年平均、变化趋势及概率密度的对比分析表明:(1)统计降尺度处理可在一定程度上降低GCMs模拟的降水量偏差,特别是中国中部、长江以南和东北部分地区,对德国马普研究所的海气耦合模式(MPI/ECHAM5)模拟的降水量订正效果最显著;(2)GCMs统计降尺度处理的降水量季节分布特征与观测更为接近,所有流域MPI/ECHAM5订正的降水量优于或接近直接输出结果;(3)与GCM直接输出的降水相比,部分流域经统计降尺度处理后降水量变化趋势与观测的一致性有所增加,但不明显;(4)当日降水量<30mm时,订正的降水量与观测的偏差明显减小;当日降水量>30mm时,部分流域由负偏差转为正偏差。由于GCMs结构和降尺度方法的局限性,在用于具体流域未来气候变化预估及气候变化影响评估时,应选择尽可能多的、模拟能力强的GCMs数据,以包含尽可能多的模拟气候情景。     

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.     

Evaluating the Impacts of Cloud Microphysical and Overlap Parameters on Simulated Clouds in Global Climate Models

The improvement of the accuracy of simulated cloud-related variables, such as the cloud fraction, in global climate models (GCMs) is still a challenging problem in climate modeling. In this study, the influence of cloud microphysics schemes (one-moment versus two-moment schemes) and cloud overlap methods (observation-based versus a fixed vertical decorrelation length) on the simulated cloud fraction was assessed in the BCC_AGCM2.0_CUACE/Aero. Compared with the fixed decorrelation length method, the observation-based approach produced a significantly improved cloud fraction both globally and for four representative regions. The utilization of a two-moment cloud microphysics scheme, on the other hand, notably improved the simulated cloud fraction compared with the one-moment scheme; specifically, the relative bias in the global mean total cloud fraction decreased by 42.9%–84.8%. Furthermore, the total cloud fraction bias decreased by 6.6% in the boreal winter (DJF) and 1.64% in the boreal summer (JJA). Cloud radiative forcing globally and in the four regions improved by 0.3%?1.2% and 0.2%?2.0%, respectively. Thus, our results showed that the interaction between clouds and climate through microphysical and radiation processes is a key contributor to simulation uncertainty.     

用于气候研究的雪盖模型参数化方案敏感性研究

为了得到一个适用于气候研究简化的季节性雪盖模式最佳方案,必须对雪盖内部的重要物理过程、其与上大气相互作用、相应模型的参数化方案和有关的参数选取以及模型的分层结构进行深入研究.利用作者的雪盖模型(SAST),对其中的一些关键性过程的有关参数化方案(如压实、相变、融化雪水流动及分层方案考虑等)及关键的参数(如雪面反照率、有效热传导系数及持水能力等)进行了分析和敏感性试验,得到若干有意义的结论,为雪盖模式改进提供有用的结论.     

Influence of SST biases on future climate change projections

We use a quantile-based bias correction technique and a multi-member ensemble of the atmospheric component of NCAR CCSM3 (CAM3) simulations to investigate the influence of sea surface temperature (SST) biases on future climate change projections. The simulations, which cover 1977?C1999 in the historical period and 2077?C2099 in the future (A1B) period, use the CCSM3-generated SSTs as prescribed boundary conditions. Bias correction is applied to the monthly time-series of SSTs so that the simulated changes in SST mean and variability are preserved. Our comparison of CAM3 simulations with and without SST correction shows that the SST biases affect the precipitation distribution in CAM3 over many regions by introducing errors in atmospheric moisture content and upper-level (lower-level) divergence (convergence). Also, bias correction leads to significantly different precipitation and surface temperature changes over many oceanic and terrestrial regions (predominantly in the tropics) in response to the future anthropogenic increases in greenhouse forcing. The differences in the precipitation response from SST bias correction occur both in the mean and the percent change, and are independent of the ocean?Catmosphere coupling. Many of these differences are comparable to or larger than the spread of future precipitation changes across the CMIP3 ensemble. Such biases can affect the simulated terrestrial feedbacks and thermohaline circulations in coupled climate model integrations through changes in the hydrological cycle and ocean salinity. Moreover, biases in CCSM3-generated SSTs are generally similar to the biases in CMIP3 ensemble mean SSTs, suggesting that other GCMs may display a similar sensitivity of projected climate change to SST errors. These results help to quantify the influence of climate model biases on the simulated climate change, and therefore should inform the effort to further develop approaches for reliable climate change projection.     

Characterizing climate change risks by linking robust decision frameworks and uncertain probabilistic projections

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning.     

A quantitative performance assessment of cloud regimes in climate models

Differences in the radiative feedback from clouds account for much of the variation in climate sensitivity amongst General Circulation Models (GCMs). Therefore metrics of model performance which are demonstrated to be relevant to the cloud response to climate change form an important contribution to the overall evaluation of GCMs. In this paper we demonstrate an alternative method for assigning model data to observed cloud regimes obtained from clustering histograms of cloud amount in joint cloud optical depth—cloud top pressure classes. The method removes some of the subjectivity that exists in previous GCM cloud clustering studies. We apply the method to ten GCMs submitted to the Cloud Feedback Model Intercomparison Project (CFMIP), evaluate the simulated cloud regimes and analyse the climate change response in the context of these regimes. We also propose two cloud regime metrics, one of which is specifically targeted at assessing GCMs for the purpose of obtaining the global cloud radiative response to climate change. Most of the global variance in the cloud radiative response between GCMs is due to low clouds, with 47% arising from the stratocumulus regime and 18% due to the regime characterised by clouds undergoing transition from stratocumulus to cumulus. This result is found to be dominated by two structurally similar GCMs. The shallow cumulus regime, though widespread, has a smaller contribution and reduces the variance. For the stratocumulus and transition regimes, part of the variance results from a large model spread in the radiative properties of the regime in the control simulation. Comparison with observations reveals a systematic bias for both the stratocumulus and transition regimes to be overly reflective. If this bias was corrected with all other aspects of the response unchanged, the variance in the low cloud response would reduce. The response of some regimes with high cloud tops differ between the GCMs. These regimes are simulated too infrequently in a few of the models. If the frequency in the control simulation were more realistic and changes within the regimes were unaltered, the variance in the cloud radiative response from high-top clouds would increase. As a result, use of observations of the mean present-day cloud regimes suggests that whilst improvements in the simulation of the cloud regimes would impact the climate sensitivity, the inter-model variance may not reduce. When the cloud regime metric is calculated for the GCMs analysed here, only one model is on average consistent with observations within their uncertainty (and even this model is not consistent with the observations for all regimes), indicating scope for improvement in the simulation of cloud regimes. Electronic supplementary material  The online version of this article (doi:) contains supplementary material, which is available to authorized users.