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1.
In a recent paper, Gutiérrez et al. (Nonlinear Process Geophys 15(1):109–114, 2008) introduced a new characterization of spatiotemporal error growth—the so called mean–variance logarithmic (MVL) diagram—and applied it to study ensemble prediction systems (EPS); in particular, they analyzed single-model ensembles obtained by perturbing the initial conditions. In the present work, the MVL diagram is applied to multi-model ensembles analyzing also the effect of model formulation differences. To this aim, the MVL diagram is systematically applied to the multi-model ensemble produced in the EU-funded DEMETER project. It is shown that the shared building blocks (atmospheric and ocean components) impose similar dynamics among different models and, thus, contribute to poorly sampling the model formulation uncertainty. This dynamical similarity should be taken into account, at least as a pre-screening process, before applying any objective weighting method.  相似文献   

2.
The natural sea surface temperature (SST) variability in the global oceans is evaluated in simulations of the Climate Model Intercomparison Project Phase 3 (CMIP3) and CMIP5 models. In this evaluation, we examine how well the spatial structure of the SST variability matches between the observations and simulations on the basis of their leading empirical orthogonal functions-modes. Here we focus on the high-pass filter monthly mean time scales and the longer 5 years running mean time scales. We will compare the models and observations against simple null hypotheses, such as isotropic diffusion (red noise) or a slab ocean model, to illustrate the models skill in simulating realistic patterns of variability. Some models show good skill in simulating the observed spatial structure of the SST variability in the tropical domains and less so in the extra-tropical domains. However, most models show substantial deviations from the observations and from each other in most domains and particularly in the North Atlantic and Southern Ocean on the longer (5 years running mean) time scale. In many cases the simple spatial red noise null hypothesis is closer to the observed structure than most models, despite the fact that the observed SST variability shows significant deviations from this simple spatial red noise null hypothesis. The CMIP models tend to largely overestimate the effective spatial number degrees of freedom and simulate too strongly localized patterns of SST variability at the wrong locations with structures that are different from the observed. However, the CMIP5 ensemble shows some improvement over the CMIP3 ensemble, mostly in the tropical domains. Further, the spatial structure of the SST modes of the CMIP3 and CMIP5 super ensemble is more realistic than any single model, if the relative explained variances of these modes are scaled by the observed eigenvalues.  相似文献   

3.
In order to improve seasonal-to-interannual precipitation forecasts and their application by decision makers, there is a clear need to understand when, where, and to what extent seasonal precipitation anomalies are driven by potentially predictable surface–atmosphere interactions versus to chaotic interannual atmospheric dynamics. Using a simple Monte Carlo approach, interannual variability and linear trends in the SST-forced signal and potential predictability of boreal winter precipitation anomalies is examined in an ensemble of twentieth century AGCM simulations. Signal and potential predictability are shown to be non-stationary over more than 80% of the globe, while chaotic noise is shown to be stationary over most of the globe. Correlation analysis with respect to magnitudes of the four leading modes of global SST variability suggests that interannual variability and trends in signal and potential predictability over 35% of the globe is associated with ENSO-related SST variability; signal and potential predictability are not significantly associated with SST modes characterized by a global SST trend, North Atlantic SST variability, and North Pacific SST variability, respectively. Results suggest that mechanisms other than SST variability contribute to the non-stationarity of signal and noise characteristics of hydroclimatic variability over mid- and high-latitude regions.  相似文献   

4.
A noise reduction technique, namely the interactive ensemble (IE) approach is adopted to reduce noise at the air–sea interface due to internal atmospheric dynamics in a state-of-the-art coupled general circulation model (CGCM). The IE technique uses multiple realization of atmospheric general circulation models coupled to a single ocean general circulation model. The ensembles mean fluxes from the atmospheric simulations are communicated to the ocean component. Each atmospheric simulation receives the same SST coming from the ocean component. The only difference among the atmospheric simulations comes from perturbed initial conditions, thus the atmospheric states are, in principle synoptically independent. The IE technique can be used to better understand the importance of weather noise forcing of natural variability such as El Niño Southern Oscillation (ENSO). To study the impact of weather noise and resolution in the context of a CGCM, two IE experiments are performed at different resolutions. Atmospheric resolution is an important issue since the noise statistics will depend on the spatial scales resolved. A simple formulation to extract atmospheric internal variability is presented. The results are compared to their respective control cases where internal atmospheric variability is left unchanged. The noise reduction has a major impact on the coupled simulation and the magnitude of this effect strongly depends on the horizontal resolution of the atmospheric component model. Specifically, applying the noise reduction technique reduces the overall climate variability more effectively at higher resolution. This suggests that “weather noise” is more important in sustaining climate variability as resolution increases. ENSO statistics, dynamics, and phase asymmetry are all modified by the noise reduction, in particular ENSO becomes more regular with less phase asymmetry when noise is reduced. All these effects are more marked for the higher resolution case. In contrast, ENSO frequency is unchanged by the reduction in the weather noise, but its phase-locking to the annual cycle is strongly dependent on noise and resolution. At low resolution the noise structure is similar to the signal, whereas the spatial structure of the noise deviates from the spatial structure of the signal as resolution increases. It is also suggested that event-to-event differences are largely driven by atmospheric noise as opposed to chaotic dynamics within the context of the large-scale coupled system, suggesting that there is a well-defined “canonical” event.  相似文献   

5.
Research on the forcing of drought and pluvial events over North America is dominated by general circulation model experiments that often have operational limitations (e.g., computational expense, ability to simulate relevant processes, etc). We use a statistically based modeling approach to investigate sea surface temperature (SST) forcing of the twentieth century pluvial (1905?C1917) and drought (1932?C1939, 1948?C1957, 1998?C2002) events. A principal component (PC) analysis of Palmer Drought Severity Index (PDSI) from the North American Drought Atlas separates the drought variability into five leading modes accounting for 62% of the underlying variance. Over the full period spanning these events (1900?C2005), the first three PCs significantly correlate with SSTs in the equatorial Pacific (PC 1), North Pacific (PC 2), and North Atlantic (PC 3), with spatial patterns (as defined by the empirical orthogonal functions) consistent with our understanding of North American drought responses to SST forcing. We use a large ensemble statistical modeling approach to determine how successfully we can reproduce these drought/pluvial events using these three modes of variability. Using Pacific forcing only (PCs 1?C2), we are able to reproduce the 1948?C1957 drought and 1905?C1917 pluvial above a 95% random noise threshold in over 90% of the ensemble members; the addition of Atlantic forcing (PCs 1?C2?C3) provides only marginal improvement. For the 1998?C2002 drought, Pacific forcing reproduces the drought above noise in over 65% of the ensemble members, with the addition of Atlantic forcing increasing the number passing to over 80%. The severity of the drought, however, is underestimated in the ensemble median, suggesting this drought intensity can only be achieved through internal variability or other processes. Pacific only forcing does a poor job of reproducing the 1932?C1939 drought pattern in the ensemble median, and less than one third of ensemble members exceed the noise threshold (28%). Inclusion of Atlantic forcing improves the ensemble median drought pattern and nearly doubles the number of ensemble members passing the noise threshold (52%). Even with the inclusion of Atlantic forcing, the intensity of the simulated 1932?C1939 drought is muted, and the drought itself extends too far into the southwest and southern Great Plains. To an even greater extent than the 1998?C2002 drought, these results suggest much of the variance in the 1932?C1939 drought is dependent on processes other than SST forcing. This study highlights the importance of internal noise and non SST processes for hydroclimatic variability over North America, complementing existing research using general circulation models.  相似文献   

6.
Summary ?This paper is concerned with the chaotic behavior of a coupled system consisting of two components, one representing the atmosphere and the other representing the ocean. The system is expressed as a highly truncated spectral model and for each component, the spectral model is similar to that of Lorenz (1963). Interactions between the two components are permitted, which lead to the temporal variation of surface temperature and hence that of a critical model parameter (the Rayleigh number). The emphasis of the paper is placed upon the chaotic behavior arising from the interactions between the two components and from periodic external forcing. Numerical tests are carried out to show that through interactions, the chaotic behavior of one component may result in chaos of the other even if the latter is otherwise stationary or periodic. It is shown that chaos may also occur if the system is forced periodically at certain frequencies. This study indicates that a new mechanism for chaos exists for coupled systems which are subject not only to internal fluid dynamic nonlinear interactions, but also to interactions between different components and external forcing. Received July 24, 2001; revised March 25, 2002  相似文献   

7.
Dietmar Dommenget 《Climate Dynamics》2011,36(11-12):2129-2145
The observed interannual Indian Ocean sea surface temperature (SST) variability from 1950 to 2008 is analyzed in respect to the spatial structure of the variability. The analysis is based on an objective comparison of the leading empirical orthogonal function modes against the stochastic null hypothesis of spatial red noise (isotropic diffusion). Starting from this red noise assumption, the analysis searches for those structures that are most distinct from the red noise hypothesis. This objective approach will put previously well and less known modes of variability into the context of the multivariate SST variability. The Indian Ocean SST variability is marked by relatively weak SST variability, which is strongly dominated by a basin wide monopole pattern that is caused by different processes. The leading modes of variability are the El Nino Southern Oscillation (ENSO) variability and the warming trend, which both project onto the basin wide monopole structure. Other more characteristic spatial patterns of internal variability are much less dominant in the tropical Indian Ocean, which is quite different from all other ocean basin, where characteristic teleconnection patterns exist. The remaining, ENSO independent, detrended variability is dominated by multi-pole patterns from the southern Indian Ocean reaching into the tropical Indian Ocean, which are probably primarily caused by extra-tropical atmospheric forcings. The large scale tropical Indian Ocean internal variability itself has no dominant structure. The currently often used dipole mode index (DMI) does not appear to present a dominant teleconnection pattern of the Indian Ocean internal SST variability. In the context of the objective analysis presented here, the DMI partly reflects the ENSO variability and is also a representation of the multi-dimensional, chaotic spatial red noise (isotropic diffusion) process. As such the DMI cannot be interpreted as a coherent teleconnection between the two poles.  相似文献   

8.
多模式集成的概率天气预报和气候预测研究进展   总被引：2，自引：2，他引：2       下载免费PDF全文
基于大气的混沌特性,单一的确定性预报逐步向多值的不确定性概率预报转化已成为一种趋势。本文系统地评述了概率天气预报产生的背景,介绍了概率预报的相关概念及国内外的研究状况,着重讨论了多模式集成的概率预报的两种集成方法,即贝叶斯模式平均(Bayesian model averaging,BMA)和多元高斯集合核拟合法(Gaussian ensemble kernel dressing,GEKD),并给出了两个例子的概率预报试验结果。利用BMA方法制作的概率预报的方差较小,减小了预报的不确定性,因此预报结果更接近大气的真实值。作为另一种多模式集成方法,多元高斯集合核拟合法回报的地面气温距平均值及趋势的概率预测结果与实测结果基本一致。利用此方法建立了地面气温年代际变化的概率多模式集合预测模型,并从中提取年代际气候变化特征,对东亚季风区年代际预测具有重要应用价值。  相似文献   

9.
Current ocean reanalysis systems contain considerable uncertainty in estimating the subsurface oceanic state, especially in the tropical Atlantic Ocean. Given this level of uncertainty, it is important to develop useful strategies to identify realistic low-frequency signals optimally from these analyses. In this paper, we present an “ensemble” method to estimate the variability of upper-ocean heat content (HC) in the tropical Atlantic based on multiple-ocean reanalysis products. Six state-of-the-art global ocean reanalaysis products, all of which are widely used in the climate research community, are examined in terms of their HC variability from 1979 to 2007. The conventional empirical orthogonal function (EOF) analysis of the HC anomalies from each individual analysis indicates that their leading modes show significant qualitative differences among analyses, especially for the first modes, although some common characteristics are discernable. Then, the simple arithmetic average (or ensemble mean) is applied to produce an ensemble dataset, i.e., the EM analysis. The leading EOF modes of the EM analysis show quantitatively consistent spatial–temporal patterns with those derived from an alternative EOF technique that maximizes signal-to-noise ratio of the six analyses, which suggests that the ensemble mean generates HC fields with the noise reduced to an acceptable level. The quality of the EM analysis is further validated against AVISO altimetry sea level anomaly (SLA) data and PIRATA mooring station data. A regression analysis with the AVISO SLA data proved that the leading modes in the EM analysis are realistic. It also demonstrated that some reanalysis products might contain higher level of intrinsic noise than others. A quantitative correlation analysis indicates that the HC fields are more realistic in the EM analysis than in individual products, especially over the equatorial regions, with signals contributed from all ensemble members. A direct comparison with the HC anomalies derived from in situ temperature measurements showed that the EM analysis generally gets realistic HC variability at the five chosen PIRATA mooring stations. Overall, these results demonstrate that the EM analysis is a promising alternative for studying physical processes and possibly for initializing climate predictions.  相似文献   

10.
Boreal winter North Atlantic climate change since 1950 is well described by a trend in the leading spatial structure of variability, known as the North Atlantic Oscillation (NAO). Through diagnoses of ensembles of atmospheric general circulation model (AGCM) experiments, we demonstrate that this climate change is a response to the temporal history of sea surface temperatures (SSTs). Specifically, 58 of 67 multi-model ensemble members (87%), forced with observed global SSTs since 1950, simulate a positive trend in a winter index of the NAO, and the spatial pattern of the multi-model ensemble mean trend agrees with that observed. An ensemble of AGCM simulations with only tropical SST forcing further suggests that variations in these SSTs are of primary importance. The probability distribution function (PDF) of 50-year NAO index trends from the forced simulations are, moreover, appreciably different from the PDF of a control simulation with no interannual SST variability, although chaotic atmospheric variations are shown to yield substantial 50-year trends. Our results thus advance the view that the observed linear trend in the winter NAO index is a combination of a strong tropically forced signal and an appreciable noise component of the same phase. The changes in tropical rainfall of greatest relevance include increased rainfall over the equatorial Indian Ocean, a change that has likely occurred in nature and is physically consistent with the observed, significant warming trend of the underlying sea surface.  相似文献   

11.
Michael E. Mann 《Climatic change》2011,107(3-4):267-276
Long Range Dependence (LRD) scaling behavior has been argued to characterize long-term surface temperature time series. LRD is typically measured by the so-called “Hurst” coefficient, “H”. Using synthetic temperature time series generated by a simple climate model with known physics, I demonstrate that the values of H obtained for observational temperature time series can be understood in terms of the linear response to past estimated natural and anthropogenic external radiative forcing combined with the effects of random white noise weather forcing. The precise value of H is seen to depend on the particular noise realization. The overall distribution obtained over an ensemble of noise realizations is seen to be a function of the relative amplitude of external forcing and internal stochastic variability and additionally in climate “proxy” records, the amount of non-climatic noise present. There is no obvious reason to appeal to more exotic physics for an explanation of the apparent scaling behavior in observed temperature data.  相似文献   

12.
Anthropogenic climate forcing will cause the global mean sea level to rise over the 21st century.However,regional sea level is expected to vary across ocean basins,superimposed by the influence of natural internal climate variability.Here,we address the detection of dynamic sea level(DSL)changes by combining the perspectives of a single and a multimodel ensemble approach(the 50-member CanESM5 and a 27-model ensemble,respectively,all retrieved from the CMIP6 archive),under three CMIP6 projected scenarios:SSP1-2.6,SSP3-7.0 and SSP5-8.5.The ensemble analysis takes into account four key metrics:signal(S),noise(N),S/N ratio,and time of emergence(ToE).The results from both sets of ensembles agree in the fact that regions with higher S/N(associated with smaller uncertainties)also reflect earlier ToEs.The DSL signal is projected to emerge in the Southern Ocean,Southeast Pacific,Northwest Atlantic,and the Arctic.Results common for both sets of ensemble simulations show that while S progressively increases with increased projected emissions,N,in turn,does not vary substantially among the SSPs,suggesting that uncertainty arising from internal climate variability has little dependence on changes in the magnitude of external forcing.Projected changes are greater and quite similar for the scenarios SSP3-7.0 and SSP5-8.5 and considerably smaller for the SSP1-2.6,highlighting the importance of public policies towards lower emission scenarios and of keeping emissions below a certain threshold.  相似文献   

13.
 Understanding natural atmospheric decadal variability is an important element of climate research, and here we investigate the geographic and seasonal diversity in the balance between its competing sources. Data are provided by an ensemble of multi-decadal atmospheric general circulation model experiments, forced by observed sea surface temperatures (SSTs), and verified against observations. First, the nature of internal atmospheric variability is studied. By assessing its spectral character, we refute the idea that internal modes may persist or oscillate on multi-annual time-scales, either through mechanisms purely internal to the atmosphere, or via coupling to the land surface; instead, they behave as a white noise process. Second, and more importantly, the role of oceanic forcing, relative to internal variability, is investigated by extending the ‘analysis of variance’ technique to the frequency domain. Significance testing and confidence intervals are also discussed. In the tropics, atmospheric decadal variability is usually dominated by oceanic forcing, although for some regions less so than at interannual time-scales. A moderate oceanic impact is also found for some extratropical regions in some seasons. Verification against observed mean sea-level pressure (MSLP) data suggests that many of these influences are realistic, although some model errors are also revealed. In other mid- and high-latitude regions, local simulated decadal variability is dominated by random processes, i.e. the integrated effects of chaotic weather systems. Third, we focus on the mechanisms of decadal variability in two specific regions (where the model is well behaved). Over the tropical Pacific, the relative impact of SSTs on decadal MSLP is strongly seasonal such that it peaks in September to November (SON). This is explained by noting that the model atmosphere is responsive to SSTs a little farther west in SON than it is in other seasons, and here it picks up relatively more decadal power from the ocean (the western Pacific being less dominated by ENSO time-scales), causing atmospheric ‘signal-to-noise ratios’ to be enhanced at decadal timescales in SON. Over southern North America, a strong SST impact is found in summer and autumn, resulting in an upward trend of MSLP over recent decades. We suggest this is caused by decadal SST variability in the Caribbean (and to some extent the tropical northeast Pacific in summer), which induces anomalous convective heating over these regions and hence the wider MSLP response. Received: 30 November 1998 / Accepted: 22 April 1999  相似文献   

14.
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   

15.
We carry out climate simulations for 1880–2003 with GISS modelE driven by ten measured or estimated climate forcings. An ensemble of climate model runs is carried out for each forcing acting individually and for all forcing mechanisms acting together. We compare side-by-side simulated climate change for each forcing, all forcings, observations, unforced variability among model ensemble members, and, if available, observed variability. Discrepancies between observations and simulations with all forcings are due to model deficiencies, inaccurate or incomplete forcings, and imperfect observations. Although there are notable discrepancies between model and observations, the fidelity is sufficient to encourage use of the model for simulations of future climate change. By using a fixed well-documented model and accurately defining the 1880–2003 forcings, we aim to provide a benchmark against which the effect of improvements in the model, climate forcings, and observations can be tested. Principal model deficiencies include unrealistically weak tropical El Nino-like variability and a poor distribution of sea ice, with too much sea ice in the Northern Hemisphere and too little in the Southern Hemisphere. Greatest uncertainties in the forcings are the temporal and spatial variations of anthropogenic aerosols and their indirect effects on clouds. Electronic supplementary material The online version of this article (doi:) contains supplementary material, which is available to authorized users.  相似文献   

16.
The impact of internal atmospheric variability on North Pacific sea surface temperature (SST) variability is examined based on three coupled general circulation model simulations. The three simulations differ only in the level of atmospheric noise occuring over the ocean at the air-sea interface. The amplitude of atmospheric noise is controlled by use of the interactive ensemble technique. This technique simultaneously couples multiple realizations of a single atmospheric model to a single realization of an ocean model. The atmospheric component models all experience the same SST, but the ocean component is forced by the ensemble averaged fluxes thereby reducing the impact of internal atmospheric dynamics at the air-sea interface. The ensemble averaging is only applied at the air-sea interface so that the internal atmospheric dynamics (i.e., transients) of each atmospheric ensemble member is unaffected. This interactive ensemble technique significantly reduces the SST variance throughout the North Pacific. The reduction in SST variance is proportional to the number of ensemble members indicating that most of the variability can simply be explained as the response to atmospheric stochastic forcing. In addition, the impact of the internal atmospheric dynamics at the air-sea interface masks out much of the tropical-midlatitude SST teleconnections on interannual time scales. Once this interference is reduced (i.e., applying the interactive ensemble technique), tropical-midlatitude SST teleconnections are easily detected.  相似文献   

17.
The new interactive ensemble modeling strategy is used to diagnose how noise due to internal atmospheric dynamics impacts the forced climate response during the twentieth century (i.e., 1870?C1999). The interactive ensemble uses multiple realizations of the atmospheric component model coupled to a single realization of the land, ocean and ice component models in order to reduce the noise due to internal atmospheric dynamics in the flux exchange at the interface of the component models. A control ensemble of so-called climate of the twentieth century simulations of the Community Climate Simulation Model version 3 (CCSM3) are compared with a similar simulation with the interactive ensemble version of CCSM3. Despite substantial differences in the overall mean climate, the global mean trends in surface temperature, 500?mb geopotential and precipitation are largely indistinguishable between the control ensemble and the interactive ensemble. Large differences in the forced response; however, are detected particularly in the surface temperature of the North Atlantic. Associated with the forced North Atlantic surface temperature differences are local differences in the forced precipitation and a substantial remote rainfall response in the deep tropical Pacific. We also introduce a simple variance analysis to separately compare the variance due to noise and the forced response. We find that the noise variance is decreased when external forcing is included. In terms of the forced variance, we find that the interactive ensemble increases this variance relative to the control.  相似文献   

18.
There is increasingly clear evidence that human influence has contributed substantially to the large-scale climatic changes that have occurred over the past few decades. Attention is now turning to the physical implications of the emerging anthropogenic signal. Of particular interest is the question of whether current climate models may be over- or under-estimating the amplitude of the climate system's response to external forcing, including anthropogenic. Evidence of a significant error in a model-simulated response amplitude would indicate the existence of amplifying or damping mechanisms that are inadequately represented in the model. The range of uncertainty in the factor by which we can scale model-simulated changes while remaining consistent with observed change provides an estimate of uncertainty in model-based predictions. With any model that displays a realistic level of internal variability, the problem of estimating this factor is complicated by the fact that it represents a ratio between two incompletely known quantities: both observed and simulated responses are subject to sampling uncertainty, primarily due to internal chaotic variability. Sampling uncertainty in the simulated response can be reduced, but not eliminated, through ensemble simulations. Accurate estimation of these scaling factors requires a modification of the standard "optimal fingerprinting" algorithm for climate change detection, drawing on the conventional "total least squares" approach discussed in the statistical literature. Code for both variants of optimal fingerprinting can be found on .  相似文献   

19.
短期集合降水概率预报试验   总被引：11，自引：2，他引：11       下载免费PDF全文
以MM5模式作为试验模式, 通过选取不同的物理过程参数化方案产生8个集合成员, 分别用平均法、相关法和Rank法对2001年11月至2002年5月期间的22个降水个例进行短期集合降水概率预报试验。试验结果显示对小雨—大暴雨6类降水的概率预报, Rank法的综合预报效果明显好于相关法和平均法, 相关法的综合预报效果与平均法基本相同; 无论从均方误差角度还是从命中率和假警报率的相对大小角度, 对小雨、中雨、大雨和暴雨各量级以上降水的概率预报, Rank法的平均预报效果是三种方法中最好的, 相关法的平均预报效果与平均法相同; Rank法好于平均法的平均幅度从均方误差角度较大, 从命中率和假警报率的相对大小角度则较小。平均而言, 三种方法对各量级以上降水的概率预报都是有技巧预报, 对量级小的降水的概率预报技巧高于对量级大的降水的概率预报技巧。  相似文献   

20.
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   

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