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1.
Stochastic representation of forecast uncertainties has been taken into account to improve dynamical seasonal prediction. In this study, perturbing the dynamic tendency by a random number is introduced to account for inherent uncertainties associated with computational representations of the underlying partial differential equations that govern the atmospheric motion. Compared to the traditional approach to perturb the physical tendency, the sensitivity of fluctuations in forecast variables to the magnitude of random forcing is found to be greater in the case of perturbing the dynamical tendency. Realizing that the major advantage of stochastic tendency in traditional approaches lies in the increase in ensemble spread, our approach manifests a greater potential in the field of dynamical ensemble prediction. An evaluation of a simulated climate for a boreal summer demonstrates a significant enhancement in forecast skill in terms of the large-scale features and precipitation, when both the dynamical and physical tendencies are simultaneously perturbed. This finding implies that model uncertainties could be addressed in terms of not only the physical parameterization but also the dynamical portion that used to be regarded as deterministically solved. 相似文献
2.
The effects of stochastic forcing on a one-dimensional, energy balance climate model are considered. A linear, stochastic model is reviewed in analogy with the Brownian motion problem from classical statistical mechanics. An analogous nonlinear model is studied and shows different behavior from the linear model. The source of the nonlinearity is the dynamical heat transport. The role of nonlinearity in coupling different temporal and spatial scales of the atmosphere is examined. The Fokker-Planck equation from statistical mechanics is used to obtain a time evolution equation for the probability density function for the climate, and the climatic potential function is calculated. Analytical solutions to the steady-state Fokker-Planck equation are obtained, while the time-dependent solution is obtained numerically. The spread of the energy produced by a stochastic forcing element is found to be characterized by movement mainly from smaller to larger scales. Forced and free variations of climate are also explicitly considered. 相似文献
3.
An analytic solution of an energy balance model (EBM) is presented which can beused as a recursive filter for time series analysis. It is shown that the EBM can reproduce the solution of a coupled atmosphere-ocean general circulation model (AOGCM) experiment. Contrary to the AOGCM, the EBM easily allows for variations in climate sensitivity to satisfy the full range of uncertainty concerned with this parameter. The recursive filter is applied to two natural and two anthropogenic forcing mechanisms which are expressed in terms of heating rate anomaly time series: volcanism, solar activity, greenhouse gases (GHG), and anthropogenic tropospheric aerosols. Thus, we obtain modelled global mean temperature variations as a response to the different forcings and with respect to the uncertainty in the forcing approximations and climate sensitivity. In addition, it is shown that the observed (ENSO-corrected) global mean temperature time series within the period from 1866 to 1997 can be explained by the external forcings which have been considered and an additional white noise forcing. In this way we are able to separate different signals and compare them. As a result, global anthropogenic climate change due to GHG forcing can be detected at a high level of significance without considering spatial patterns of climate change but including natural forcing, which is usually not done. Furthermore, it is shown that solar forcing alone does not lead to significantclimate change, whereas solar and volcanic forcing together lead to a significant natural climate change signal. Anthropogenic climate change due to GHG forcing may partly be masked by anthropogenic aerosol cooling. 相似文献
4.
A new framework for isolating individual feedback processes in coupled general circulation climate models. Part I: formulation 总被引：1，自引：1，他引：0
This paper proposes a coupled atmosphere–surface climate feedback–response analysis method (CFRAM) as a new framework for
estimating climate feedbacks in coupled general circulation models with a full set of physical parameterization packages.
The formulation of the CFRAM is based on the energy balance in an atmosphere–surface column. In the CFRAM, the isolation of
partial temperature changes due to an external forcing or an individual feedback is achieved by solving the linearized infrared
radiation transfer model subject to individual energy flux perturbations (external or due to feedbacks). The partial temperature
changes are addable and their sum is equal to the (total) temperature change (in the linear sense). The decomposition of feedbacks
is based on the thermodynamic and dynamical processes that directly affect individual energy flux terms. Therefore, not only
those feedbacks that directly affect the TOA radiative fluxes, such as water vapor, clouds, and ice-albedo feedbacks, but
also those feedbacks that do not directly affect the TOA radiation, such as evaporation, convections, and convergence of horizontal
sensible and latent heat fluxes, are explicitly included in the CFRAM. In the CFRAM, the feedback gain matrices measure the
strength of individual feedbacks. The feedback gain matrices can be estimated from the energy flux perturbations inferred
from individual parameterization packages and dynamical modules. The inter-model spread of a feedback gain matrix would help
us to detect the origins of the uncertainty of future climate projections in climate model simulations. 相似文献
5.
偶然性与必然性过程及其相互转化是世界事物变化复杂性的根源。根据布朗运动统计理论,提出了分子热运动是不稳定流体中湍流形成之源,由此形成不同宏观尺度的随机运动是大气运动固有的属性。观测事实表明,太阳辐射作为决定大气运动与变化的主要因子,它的变化具有随机性,是大气的随机强迫因子,它对气候变化具有决定性影响。地-气相互作用是一个时变的非线性相互反馈的耦合过程,形成了下边界对大气复杂的随机强迫作用,其界面交换耦合随机动力学模式尚待建立。由于大气过程固有的随机性以及随机的外强迫耦合作用,大气确定性预报的时效是有界的,它决定于预报对象的不确定性及其空间尺度与时间尺度,以及预报时效内的大气不确定性。由此,客观存在着大气过程的报不准关系。 相似文献
6.
Cornelia Schwierz Christof Appenzeller Huw C. Davies Mark A. Liniger Wolfgang Müller Thomas F. Stocker Masakazu Yoshimori 《Climatic change》2006,79(1-2):31-63
The tasks of providing multi-decadal climate projections and seasonal plus sub-seasonal climate predictions are of significant societal interest and pose major scientific challenges. An outline is presented of the challenges posed by, and the approaches adopted to, tracing the possible evolution of the climate system on these various time-scales. First an overview is provided of the nature of the climate system’s natural internal variations and the uncertainty arising from the complexity and non-linearity of the system. Thereafter consideration is given sequentially to the range of extant approaches adopted to study and derive multi-decadal climate projections, seasonal predictions, and significant sub-seasonal weather phenomena. For each of these three time-scales novel results are presented that indicate the nature (and limitations) of the models used to forecast the evolution, and illustrate the techniques adopted to reduce or cope with the forecast uncertainty. In particular, the contributions (i) appear to exemplify that in simple climate models uncertainties in radiative forcing outweigh uncertainties associated with ocean models, (ii) examine forecast skills for a state-of-the-art seasonal prediction system, and (iii) suggest that long-lived weather phenomena can help shape intra-seasonal climate variability. Finally, it is argued, that co-consideration of all these scales can enhance our understanding of the challenges associated with uncertainties in climate prediction. 相似文献
7.
Probabilistic climate change projections using neural networks 总被引：5，自引：0，他引：5
Anticipated future warming of the climate system increases the need for accurate climate projections. A central problem are the large uncertainties associated with these model projections, and that uncertainty estimates are often based on expert judgment rather than objective quantitative methods. Further, important climate model parameters are still given as poorly constrained ranges that are partly inconsistent with the observed warming during the industrial period. Here we present a neural network based climate model substitute that increases the efficiency of large climate model ensembles by at least an order of magnitude. Using the observed surface warming over the industrial period and estimates of global ocean heat uptake as constraints for the ensemble, this method estimates ranges for climate sensitivity and radiative forcing that are consistent with observations. In particular, negative values for the uncertain indirect aerosol forcing exceeding –1.2 Wm–2 can be excluded with high confidence. A parameterization to account for the uncertainty in the future carbon cycle is introduced, derived separately from a carbon cycle model. This allows us to quantify the effect of the feedback between oceanic and terrestrial carbon uptake and global warming on global temperature projections. Finally, probability density functions for the surface warming until year 2100 for two illustrative emission scenarios are calculated, taking into account uncertainties in the carbon cycle, radiative forcing, climate sensitivity, model parameters and the observed temperature records. We find that warming exceeds the surface warming range projected by IPCC for almost half of the ensemble members. Projection uncertainties are only consistent with IPCC if a model-derived upper limit of about 5 K is assumed for climate sensitivity. 相似文献
8.
《Global Environmental Change》2007,17(1):59-72
Projections of future climate change are plagued with uncertainties, causing difficulties for planners taking decisions on adaptation measures. This paper presents an assessment framework that allows the identification of adaptation strategies that are robust (i.e. insensitive) to climate change uncertainties. The framework is applied to a case study of water resources management in the East of England, more specifically to the Anglian Water Services’ 25 year Water Resource Plan (WRP). The paper presents a local sensitivity analysis (a ‘one-at-a-time’ experiment) of the various elements of the modelling framework (e.g., emissions of greenhouse gases, climate sensitivity and global climate models) in order to determine whether or not a decision to adapt to climate change is sensitive to uncertainty in those elements.Water resources are found to be sensitive to uncertainties in regional climate response (from general circulation models and dynamical downscaling), in climate sensitivity and in climate impacts. Aerosol forcing and greenhouse gas emissions uncertainties are also important, whereas uncertainties from ocean mixing and the carbon cycle are not. Despite these large uncertainties, Anglian Water Services’ WRP remains robust to the climate change uncertainties sampled because of the adaptation options being considered (e.g. extension of water treatment works), because the climate model used for their planning (HadCM3) predicts drier conditions than other models, and because ‘one-at-a-time’ experiments do not sample the combination of different extremes in the uncertainty range of parameters. This research raises the question of how much certainty is required in climate change projections to justify investment in adaptation measures, and whether such certainty can be delivered. 相似文献
9.
在气候影响研究中引入随机天气发生器的方法和不确定性 总被引：1，自引：0，他引：1
通过采用不同的随机天气发生器生成一定气候背景下各种气候变率情景，许多学者在最近的研究中已经认识到气候变率对农作物生长发育影响的重要性。传统的气候影响评估方法直接以大气环流模式的模拟试验结果作为未来气候情景，这样不可能理解如上的重要性。本文着重评述将随机天气发生器应用于气候变化影响研究的一般方法框架，以及作者的具体个例研究方法。文中最后分析了目前该领域研究中还存在的一些不确定性。 在当前的气候变化影响研究中，有不同的方法用来研制一种称为WGEN的典型随机天气发生器的参数化方案及其随机试验方法。不同的研究者也有不同的参数调控方法。通常的思路是通过气候控制试验和2×CO2试验之间的气候变量平均值和方差的变化来扰动随机天气发生器的参数，以生成未来逐日气候变化情景。本文作者根据短期气候预测模式的输出产品建立了一套WGEN的参数化方案及其随机试验方法，并且在时间和空间两个尺度上检验和评估了此参数化方案下WGEN的模拟能力。另外，作者由未来降水的变化，调试随机天气发生器参数，生成了气候变率变化情景。这些参数调节可以产生各种不同类型和定性大小的气候变率变化，用于气候影响评估的敏感性分析。通过如上方法，作为一个个例，文中评估了未来气候变率变化 相似文献
10.
A flexible climate model for use in integrated assessments 总被引：2，自引：0，他引：2
Because of significant uncertainty in the behavior of the climate system, evaluations of the possible impact of an increase
in greenhouse gas concentrations in the atmosphere require a large number of long-term climate simulations. Studies of this
kind are impossible to carry out with coupled atmosphere ocean general circulation models (AOGCMs) because of their tremendous
computer resource requirements. Here we describe a two dimensional (zonally averaged) atmospheric model coupled with a diffusive
ocean model developed for use in the integrated framework of the Massachusetts Institute of Technology (MIT) Joint Program
on the Science and Policy of Global Change. The 2-D model has been developed from the Goddard Institute for Space Studies
(GISS) GCM and includes parametrizations of all the main physical processes. This allows it to reproduce many of the nonlinear
interactions occurring in simulations with GCMs. Comparisons of the results of present-day climate simulations with observations
show that the model reasonably reproduces the main features of the zonally averaged atmospheric structure and circulation.
The model’s sensitivity can be varied by changing the magnitude of an inserted additional cloud feedback. Equilibrium responses
of different versions of the 2-D model to an instantaneous doubling of atmospheric CO2 are compared with results of similar simulations with different AGCMs. It is shown that the additional cloud feedback does
not lead to any physically inconsistent results. On the contrary, changes in climate variables such as precipitation and evaporation,
and their dependencies on surface warming produced by different versions of the MIT 2-D model are similar to those shown by
GCMs. By choosing appropriate values of the deep ocean diffusion coefficients, the transient behavior of different AOGCMs
can be matched in simulations with the 2-D model, with a unique choice of diffusion coefficients allowing one to match the
performance of a given AOGCM for a variety of transient forcing scenarios. Both surface warming and sea level rise due to
thermal expansion of the deep ocean in response to a gradually increasing forcing are reasonably reproduced on time scales
of 100–150 y. However a wide range of diffusion coefficients is needed to match the behavior of different AOGCMs. We use results
of simulations with the 2-D model to show that the impact on climate change of the implied uncertainty in the rate of heat
penetration into the deep ocean is comparable with that of other significant uncertainties.
Received: 10 March 1997 / Accepted: 20 October 1997 相似文献
11.
12.
Development of climate change projections for small watersheds using multi-model ensemble simulation and stochastic weather generation 总被引：1，自引：0，他引：1
Regional climate models (RCMs) have been increasingly used for climate change studies at the watershed scale. However, their performance is strongly dependent upon their driving conditions, internal parameterizations and domain configurations. Also, the spatial resolution of RCMs often exceeds the scales of small watersheds. This study developed a two-step downscaling method to generate climate change projections for small watersheds through combining a weighted multi-RCM ensemble and a stochastic weather generator. The ensemble was built on a set of five model performance metrics and generated regional patterns of climate change as monthly shift terms. The stochastic weather generator then incorporated these shift terms into observed climate normals and produced synthetic future weather series at the watershed scale. This method was applied to the Assiniboia area in southern Saskatchewan, Canada. The ensemble led to reduced biases in temperature and precipitation projections through properly emphasizing models with good performance. Projection of precipitation occurrence was particularly improved through introducing a weight-based probability threshold. The ensemble-derived climate change scenario was well reproduced as local daily weather series by the stochastic weather generator. The proposed combination of dynamical downscaling and statistical downscaling can improve the reliability and resolution of future climate projection for small prairie watersheds. It is also an efficient solution to produce alternative series of daily weather conditions that are important inputs for examining watershed responses to climate change and associated uncertainties. 相似文献
13.
Hervé Douville 《Climate Dynamics》2005,24(4):373-391
While time-slice simulations with atmospheric general circulation models (GCMs) have been used for many years to regionalize climate projections and/or assess their uncertainties, there is still no consensus about the method used to prescribe sea surface temperature (SST) in such experiments. In the present study, the response of the Indian summer monsoon to increasing amounts of greenhouse gases and sulfate aerosols is compared between a reference climate scenario and three sets of time-slice experiments, consisting of parallel integrations for present-day and future climates. Different monthly mean SST boundary conditions have been tested in the present-day integrations: raw climatological SST derived from the reference scenario, observed climatological SST, and observed SST with interannual variability. For future climate, the SST forcing has been obtained by superimposing climatological monthly mean SST anomalies derived from the reference scenario onto the present-day SST boundary conditions. None of these sets of time-slice experiments is able to capture accurately the response of the Indian summer monsoon simulated in the transient scenario. This finding suggests that the ocean–atmosphere coupling is a fundamental feature of the climate system. Neglecting the SST feedback and variability at the intraseasonal to interannual time scales has a significant impact on the projected monsoon response to global warming. Adding interannual variability in the prescribed SST boundary conditions does not mitigate the problem, but can on the contrary reinforce the discrepancies between the forced and coupled experiments. The monsoon response is also shown to depend on the simulated control climate, and can therefore be sensitive to the use of observed rather than model-derived SSTs to drive the present-day atmospheric simulation, as well as to any approximation in the prescribed radiative forcing. While such results do not challenge the use of time-slice experiments for assessing uncertainties and understanding mechanisms in transient scenarios, they emphasize the need for high-resolution coupled atmosphere-ocean GCMs for dynamical downscaling, or at least for high-resolution atmospheric GCMs coupled with a slab or a regional ocean model. 相似文献
14.
Numerical convergence of the dynamics of a GCM 总被引：1，自引：0，他引：1
Atmospheric general circulation models (GCMs) are characterized by many features but especially by: (1) the manner of discretizing the governing equations and of representing the variables involved at a given resolution, and (2) the manner of parameterizing unresolved physical processes in terms of those resolved variables. These two aspects of model formulation are not independent
and it is difficult to untangle their intertwined effects when assessing model performance. The attempt here is to separate
these aspects of GCM behaviour and to ask, “Given a perfect parameterization of the physical processes in a model, what resolution is needed to capture the dominant dynamical aspects
of the atmospheric climate?” Alternatively, “At what resolution do the dynamics of a GCM converge”? The perfect parameterization
approach assumes that the calculation of the physical terms returns the “correct” result at all resolutions. In the idealized
case, a time-independent forcing is one of the simplest that satisfies this condition. However, experiments show that it is
difficult for the dynamics of a GCM to balance a time-independent forcing with atmosphere-like flows and structures. The model
requires, and the atmosphere presumably includes, physical feedback mechanisms which act so as to maintain the kinds of flows
and structures that are observed. Resolution experiments are performed with a simplified forcing function for the thermodynamic
equation which combines a dominant time-independent specified forcing with a weak linear relaxation feedback. These experiments
show that the dynamics of the GCM have essentially converged at T32 and certainly by T63 which is the next resolution considered.
This is shown by the constancy of structures, variances, covariances, transports and energy budgets with increasing resolution.
Experiments with an alternative forcing proposed by Held and Suarez, which has the form of a linear relaxation, show somewhat
less evidence of convergence at these resolutions. In both cases the “physics” are known by assumption. However, the form
and nature of the forcing is different, as is the behaviour with resolution. The implication for the real system is that the
resolution required for simulating the dynamical aspects of climate is rather modest. The simulated climate does, however, apparently depend on the ability to correctly and
consistently parameterize the physical processes in a GCM, involving both forcing and feedback mechanisms, as a function of
resolution.
Received 19 January 1996/Accepted 22 August 1996 相似文献
15.
Climate Change Prediction 总被引：4，自引：0，他引：4
The concept of climate change prediction in response to anthropogenic forcings at multi-decadal time scales is reviewed. This
is identified as a predictability problem with characteristics of both first kind and second kind (due to the slow components
of the climate system). It is argued that, because of the non-linear and stochastic aspects of the climate system and of the
anthropogenic and natural forcings, climate change contains an intrinsic level of uncertainty. As a result, climate change
prediction needs to be approached in a probabilistic way. This requires a characterization and quantification of the uncertainties
associated with the sequence of steps involved in a climate change prediction. A review is presented of different approaches
recently proposed to produce probabilistic climate change predictions. The additional difficulties found when extending the
prediction from the global to the regional scale and the implications that these have on the choice of prediction strategy
are finally discussed. 相似文献
16.
In order to study the physical nature of the Arctic Oscillation (AO) or the Northern Hemisphere annular mode (NAM), the linear stochastic model, constructed empirically from the output of a GCM run, is used to investigate the modal aspects of this climate variability mode. Theoretical analysis on the dominant modal response to the stochastic forcing is performed in terms of the maximum variance contribution. As the modal aspect of AO/NAM, two dominant normal modes are found to resemble the spatial structure of the AO/NAM represented by the leading EOF. These stochastically forced normal modes are regarded to be physical modes and thus can explain many fundamental features of the AO/NAM such as the barotropic annular dipole, tropospher–stratosphere coupling, and its dominance over the wintertime Northern Hemisphere. It then follows that the origin or physical nature of the AO/NAM can be attributed to the behaviors of the dominant annular normal modes. A key distinction of this study from previous eddy driving theories is that, to drive the variability, eddy forcing needs not to have a spatial structure completely coinciding with the pattern of the NAM, since the latter is mainly decided by the linear normal modes. 相似文献
17.
Xiaoran ZHUANG Jinzhong MIN Liu ZHANG Shizhang WANG Naigeng WU Haonan ZHU 《大气科学进展》2020,37(8):893-911
This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB) of East China. The scale-dependent error growth(ensemble variability) and associated impact on precipitation forecasts(precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale,while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing. Meanwhile, larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale. Consequently, these results can be used to explain underdispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB. 相似文献
18.
The dynamics of glacial cycles is studied in terms of the dynamical systems theory. We explore the dependence of the climate state on the phase of the astronomical forcing by examining five conceptual models of glacial cycles proposed in the literature. The models can be expressed as quasiperiodically forced dynamical systems. It is shown that four of them exhibit a strange nonchaotic attractor (SNA), which is an intermediate regime between quasiperiodicity and chaos. Then, the dependence of the climate state on the phase of the astronomical forcing is not given by smooth relations, but constitutes a geometrically strange set. Our result suggests that SNA is a candidate for representing the dynamics of glacial cycles, in addition to well-known quasiperiodicity and chaos. 相似文献
19.
Changes in climate are expected to lead to changes in the characteristics extreme rainfall frequency and intensity. In this study, we propose an integrated approach to explore potential changes in intensity-duration-frequency (IDF) relationships. The approach incorporates uncertainties due to both the short simulation periods of regional climate models (RCMs) and the differences in IDF curves derived from multiple RCMs in the North American Regional Climate Change Assessment Program (NARCCAP). The approach combines the likelihood of individual RCMs according to the goodness of fit between the extreme rainfall intensities from the RCMs’ historic runs and those from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) data set and Bayesian model averaging (BMA) to assess uncertainty in IDF predictions. We also partition overall uncertainties into within-model uncertainty and among-model uncertainty. Results illustrate that among-model uncertainty is the dominant source of the overall uncertainty in simulating extreme rainfall for multiple locations in the U.S., pointing to the difficulty of predicting future climate, especially extreme rainfall regimes. For all locations a more intense extreme rainfall occurs in future climate; however the rate of increase varies among locations. 相似文献
20.
Assessment of a stochastic downscaling methodology in generating an ensemble of hourly future climate time series 总被引：2，自引：0，他引：2
This study extends a stochastic downscaling methodology to generation of an ensemble of hourly time series of meteorological variables that express possible future climate conditions at a point-scale. The stochastic downscaling uses general circulation model (GCM) realizations and an hourly weather generator, the Advanced WEather GENerator (AWE-GEN). Marginal distributions of factors of change are computed for several climate statistics using a Bayesian methodology that can weight GCM realizations based on the model relative performance with respect to a historical climate and a degree of disagreement in projecting future conditions. A Monte Carlo technique is used to sample the factors of change from their respective marginal distributions. As a comparison with traditional approaches, factors of change are also estimated by averaging GCM realizations. With either approach, the derived factors of change are applied to the climate statistics inferred from historical observations to re-evaluate parameters of the weather generator. The re-parameterized generator yields hourly time series of meteorological variables that can be considered to be representative of future climate conditions. In this study, the time series are generated in an ensemble mode to fully reflect the uncertainty of GCM projections, climate stochasticity, as well as uncertainties of the downscaling procedure. Applications of the methodology in reproducing future climate conditions for the periods of 2000–2009, 2046–2065 and 2081–2100, using the period of 1962–1992 as the historical baseline are discussed for the location of Firenze (Italy). The inferences of the methodology for the period of 2000–2009 are tested against observations to assess reliability of the stochastic downscaling procedure in reproducing statistics of meteorological variables at different time scales. 相似文献