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1.
A Bayesian Statistical Analysis of the Enhanced Greenhouse Effect   总被引：1，自引：1，他引：0  
This paper demonstrates that there is a robust statistical relationship between the records of the global mean surface air temperature and the atmospheric concentration of carbon dioxide over the period 1870–1991. As such, the enhanced greenhouse effect is a plausible explanation for the observed global warming. Long term natural variability is another prime candidate for explaining the temperature rise of the last century. Analysis of natural variability from paleo-reconstructions, however, shows that human activity is so much more likely an explanation that the earlier conclusion is not refuted. But, even if one believes in large natural climatic variability, the odds are invariably in favour of the enhanced greenhouse effect. The above conclusions hold for a range of statistical models, including one that is capable of describing the stabilization of the global mean temperature from the 1940s to the 1970s onwards. This model is also shown to be otherwise statistically adequate. The estimated climate sensitivity is about 3.8 °C with a standard deviation of 0.9 °C, but depends slightly on which model is preferred and how much natural variability is allowed. These estimates neglect, however, the fact that carbon dioxide is but one of a number of greenhouse gases and that sulphate aerosols may well have dampened warming. Acknowledging the fact that carbon dioxide is used as a proxy for all human induced changes in radiative forcing brings a lot of additional uncertainty. Prior knowledge on both climate sensitivity and radiative forcing is needed to say anything about the respective sizes. A fully Bayesian approach is used to combine expert knowledge with information from the observations. Prior knowledge on the climate sensitivity plays a dominant role. The data largely exclude climate sensitivity to be small, but cannot exclude climate sensitivity to be large, because of the possibility of strong negative sulphate forcing. The posterior of climate sensitivity has a strong positive skewness. Moreover, its mode (again 3.8 °C; standard deviation 2.4 °C) is higher than the best guess of the IPCC.  相似文献   

2.
Considerable controversy has been generated by the observation that the Earth's climate has warmed over the last century. Public policy decisions hinge on the question of whether this trend is natural climate variability or the result of the increase in atmospheric concentrations of greenhouse gases. The strength of the enhanced greenhouse effect depends, in large part, on the uncertain value of climate sensitivity. In this paper climate sensitivity is estimated from the global temperature record by assuming models for greenhouse forcing, climate response to forcing, and climate variability. We find optimal estimates of climate sensitivity are remarkably insensitive to assumptions, at least for forcing excluding the effect of aerosols, and these values are considerably less than most predictions arising from General Circulation Models (GCM's). It is, however, the statistical significance of these estimates that is sensitive to assumptions about climate variability. Assuming climate variability with a time scale of a decade or less, climate sensitivity is estimated to be significantly greater than zero, but also significantly lower than that predicted by GCM's. Climate variability with a century time scale is consistent with both the recent temperature record and the pre-instrumental record for the last millenium; if this type of variability is assumed, the estimate of climate sensitivity has a confidence band wide enough to encompass both zero and typical values obtained by GCM's. With century time-scale variability it will be several decades before confident estimates can be made.  相似文献   

3.
We use recent advances in time series econometrics to estimate the relation among emissions of CO2 and CH4, the concentration of these gases, and global surface temperature. These models are estimated and specified to answer two questions; (1) does human activity affect global surface temperature and; (2) does global surface temperature affect the atmospheric concentration of carbon dioxide and/or methane. Regression results provide direct evidence for a statistically meaningful relation between radiative forcing and global surface temperature. A simple model based on these results indicates that greenhouse gases and anthropogenic sulfur emissions are largely responsible for the change in temperature over the last 130 years. The regression results also indicate that increases in surface temperature since 1870 have changed the flow of carbon dioxide to and from the atmosphere in a way that increases its atmospheric concentration. Finally, the regression results for methane hint that higher temperatures may increase its atmospheric concentration, but this effect is not estimated precisely.  相似文献   

4.
Interpretation of the effects of increasing atmospheric carbon dioxide on temperature is made more difficult by the fact that it is unclear whether sufficient global warming has taken place to allow a statistically significant finding of any upward trend in the temperature series. We add to the few existing statistical results by reporting tests for both deterministic and stochastic non-stationarity (trends) in time series of global average temperature. We conclude that the statistical evidence is sufficient to reject the hypothesis of a stochastic trend; however, there is evidence of a trend which could be approximated by a deterministic linear model.The authors are grateful to the SSHRC (Green) and FCAR (Galbraith) for financial support under grants 10-89-0205 and NC-0047.  相似文献   

5.
Summary Changes in solar activity are regularly forwarded as an hypothesis to explain the observed global warming over the last century. The support of such claims is largely statistical, as knowledge of the physical relationships is limited. The statistical evidence is revisited. Changing solar activity is a statistically plausible hypothesis for the observed warming, if short-term natural variability is the only alternative explanation. Compared to the enhanced greenhouse effect, the solar hypothesis looses a substantial part of its plausibility. Reversely, the size and significance of the estimated impact of the enhanced greenhouse effect on the global mean temperature is hardly affected by solar activity. Received January 9, 1998 Revised June 23, 1998  相似文献   

6.
 The study seeks to describe one method of deriving information about local daily temperature extremes from larger scale atmospheric flow patterns using statistical tools. This is considered to be one step towards downscaling coarsely gridded climate data from global climate models (GCMs) to finer spatial scales. Downscaling is necessary in order to bridge the spatial mismatch between GCMs and climate impact models which need information on spatial scales that the GCMs cannot provide. The method of statistical downscaling is based on physical interaction between atmospheric processes with different spatial scales, in this case between synoptic scale mean sea level pressure (MSLP) fields and local temperature extremes at several stations in southeast Australia. In this study it was found that most of the day-to-day spatial variability of the synoptic circulation over the Australian region can be captured by six principal components. Using the scores of these PCs as multivariate indicators of the circulation a substantial part of the local daily temperature variability could be explained. The inclusion of temperature persistence noticeably improved the performance of the statistical model. The model established and tested with observations is thought to be finally applied to GCM-simulated pressure fields in order to estimate pressure-related changes in local temperature extremes under altered CO2 conditions. Received: 26 March 1996 / Accepted: 20 September 1996  相似文献   

7.
By construction, the time series for radiative forcing that are used to run the 20c3m experiments, which are implemented by climate models, impart non-stationary movements (either stochastic or deterministic) to the simulated time series for global surface temperature. Here, we determine whether stochastic or deterministic trends are present in the simulated time series for global surface temperature by examining the time series for radiative forcing. Statistical tests indicate that the forcings contain a stochastic trend against the alternative hypothesis that the series are trend stationary with a one-time structural change. This result is consistent with the economic processes that impart a stochastic trend to anthropogenic emissions and the physical processes that integrate emissions in the atmosphere. Furthermore, the stochastic trend in the aggregate measure of radiative forcing also is present in the simulated time series for global surface temperature, which is consistent with the relation between these two variables that is represented by a zero dimensional energy balance model. Finally, we propose that internal weather variability imposed on the stochastic trend in radiative forcings is responsible for statistical results, which gives the impression that global surface temperature is trend stationary with a one-time structural change. We conclude that using the ideas of stochastic trends, cointegration, and error correction can generate reliable conclusions regarding the causes of changes in global surface temperature during the instrumental temperature record.  相似文献   

8.
The East Asian summer monsoon (EASM) circulation and summer rainfall over East China have experienced large decadal changes during the latter half of the 20th century. To investigate the potential causes behind these changes, a series of simulations using the national center for atmospheric research (NCAR) community atmospheric model version 3 (CAM3) and the geophysical fluid dynamics laboratory (GFDL) atmospheric model version 2.1 (AM2.1) are analyzed. These simulations are forced separately with different historical forcing, namely tropical sea surface temperature (SSTs), global SSTs, greenhouse gases plus aerosols, and a combination of global SSTs and greenhouse gases plus aerosols. This study focuses on the relative roles of these individual forcings in causing the observed monsoon and rainfall changes over East Asia during 1950–2000. The simulations from both models show that the SST forcing, primarily from the Tropics, is able to induce most of the observed weakening of the EASM circulation, while the greenhouse gas plus (direct) aerosol forcing increases the land-sea thermal contrast and thus enhances the EASM circulation. The results suggest that the recent warming in the Tropics, especially the warming associated with the tropical interdecadal variability centered over the central and eastern Pacific, is a primary cause for the weakening of the EASM since the late 1970s. However, a realistic simulation of the relatively small-scale rainfall change pattern over East China remains a challenge for the global models.  相似文献   

9.
 Two simulations with a global coupled ocean-atmosphere circulation model have been carried out to study the potential impact of solar variability on climate. The Hoyt and Schatten estimate of solar variability from 1700 to 1992 has been used to force the model. Results indicate that the near-surface temperature simulated by the model is dominated by the long periodic solar fluctuations (Gleissberg cycle), with global mean temperatures varying by about 0.5 K. Further results indicate that solar variability and an increase in greenhouse gases both induce to a first approximation a comparable pattern of surface temperature change, i.e., an increase of the land-sea contrast. However, the solar-induced warming pattern in annual means and summer is more centered over the subtropics, compared to a more uniform warming associated with the increase in greenhouse gases. The observed temperature rise over the most recent 30 and 100 years is larger than the trend in the solar forcing simulation during the same period, indicating a strong likelihood that, if the model forcing and response is realistic, other factors have contributed to the observed warming. Since the pattern of the recent observed warming agrees better with the greenhouse warming pattern than with the solar variability response, it is likely that one of these factors is the increase of the atmospheric greenhouse gas concentration. Received: 14 October 1996 / Accepted: 9 May 1997  相似文献   

10.
Summary Atmospheric flows exhibit long-range spatiotemporal correlations manifested as the fractal geometry to the global cloud cover pattern concomitant with inverse power law form for power spectra of temporal fluctuations on all space-tie scales ranging from turbulence (centimetersseconds) to climate (kilometers-years). Long-range spatiotemporal correlations are ubiquitous to dynamical systems in nature and are identified as signatures ofself-organized criticality. Standard models in meteorological theory cannot explain satisfactorily the observed self-organized criticality in atmospheric flows. Mathematical models for simulation and prediction of atmospheric flows are nonlinear and do not possess analytical solutions. Finite precision computer realizations of nonlinear models give unrealistic solutions because ofdeterministic chaos, a direct consequence of round-off error growth in iterative numerical computations. Recent studies show that roundoff error doubles on an average for each iteration of iterative computations. Round-off error propagates to the main stream computation and gives unrealistic solutions in numerical weather prediction (NWP) and climate models which incorporate thousands of iterative computations in long-term numerical integration schemes. An alternative non-deterministic cell dynamical system model for atmospheric flows described in this paper predicts the observed self-organized criticality as intrinsic to quantumlike mechanics governing flow dynamics. The model provides universal quantification for self-organized criticality in terms of the statistical normal distribution. Model predictions are in agreement with a majority of observed spectra of time series of several standard climatological data sets representative of disparate climatic regimes. Universal spectrum for natural climate variability rules out linear trends. Man-made greenhouse gas related atmospheric warming will result in intensification of natural climate variability, seen immediately in high frequency fluctuations such as QBO and ENSO and even shorter timescales. Model concepts and results of analyses are discussed with reference to possible prediction of climate change.With 11 Figures  相似文献   

11.
全球增暖对ENSO影响的数值模拟研究   总被引：4，自引：0，他引：4       下载免费PDF全文
胡博  李维京  陈鲜艳 《大气科学》2007,31(2):214-221
利用日本东京大学气候系统研究所、日本环境研究所和日本地球环境研究中心联合开发的海气耦合模式MIROC3.2，研究了全球变暖对ENSO年际变率的影响。该模式较好地模拟了ENSO循环的不同阶段表层和次表层海水温度变化，海表温度最大振幅出现在120°W以东，与观测一致，表明模式可以较好反映热带地区大气、海洋的动力、热力特征。研究还比较了控制试验和CO2浓度年增长1%的瞬时试验，结果表明，在全球变暖的大环境下ENSO事件发生频率没有显著变化，但ENSO事件强度增大，年际变率变大；热带太平洋呈现整体增暖趋势，表层温度尤其是热带中太平洋地区温度升高显著。敏感性分析表明，年际ENSO变率的振幅增大的主要贡献来自于海洋。海水增温导致热带太平洋海温垂直梯度增大，在热带西太平洋海温垂直温度梯度变化最为明显；次表层海温对单位大气风应力变化的响应大于表层海温响应。当这种响应与热带太平洋赤道地区径向温度梯度变化的共同作用导致温室效应下ENSO振幅增大。  相似文献   

12.
As an example of the technique of fingerprint detection of greenhouse climate change, a multivariate signal or fingerprint of the enhanced greenhouse effect is defined using the zonal mean atmospheric temperature change as a function of height and latitude between equilibrium climate model simulations with control and doubled CO2 concentrations. This signal is compared with observed atmospheric temperature variations over the period 1963 to 1988 from radiosonde-based global analyses. There is a significant increase of this greenhouse signal in the observational data over this period.These results must be treated with caution. Upper air data are available for a short period only, possibly too short to be able to resolve any real greenhouse climate change. The greenhouse fingerprint used in this study may not be unique to the enhanced greenhouse effect and may be due to other forcing mechanisms. However, it is shown that the patterns of atmospheric temperature change associated with uniform global increases of sea surface temperature, with El NinoSouthern Oscillation events and with decreases of stratospheric ozone concentrations individually are different from the greenhouse fingerprint used here.  相似文献   

13.
The responses of the climate system to increase of atmospheric carbon dioxide(CO_2)arestudied by using a new version of the Bureau of Meteorological Research Centre(BMRC)globalcoupled general circulation model(CGCM).Two simulations are run:one with atmospheric CO_2concentration held constant at 330 ppm,the other with a tripling of atmospheric CO_2(990 ppm).Results from the 41-year control coupled integration are applied to analyze the mean state,seasonal cycle and interannual variability in the model.Comparisons between the greenhouseexperiment and the control experiment then provide estimations of the influence of increased CO_2on climate changes and climate variability.Especially discussed is the question on whether theclimate changes concerned with CO_2 inerease will impact interannual variability in tropical Pacific,such as ENSO.  相似文献   

14.
Trends in global temperature   总被引：2，自引：1，他引：2  
Statistical models consisting of a trend plus serially correlated noise may be fitted to observed climate data such as global surface temperature, the trend and noise representing systematic change and other variations, respectively. When such a model is fitted, the estimated character of the noise determines the precision of the estimated trend, and hence the precision of the estimate of the magnitude of the systematic change in the variable considered. The results of fitting such models to global temperature imply that there is uncertainty in the amount of temperature change over the past century of up to ± 0.2 °C, but that the change of around one half of a degree Celsius is significantly different from zero.The statistical models for climate variability also imply that the observed temperature data provide only imprecise information about the climate sensitivity. This is defined here as the equilibrium response of global temperature to a doubling of the atmospheric concentration of carbon dioxide. The temperature changes observed to date are compatible with a wide range of climate sensitivities, from 0.7 °C to 2.2 °C. When data uncertainties are taken into account, the interval widens even further.  相似文献   

15.
T. J. Osborn 《Climate Dynamics》2004,22(6-7):605-623
Analysis of simulations with seven coupled climate models demonstrates that the observed variations in the winter North Atlantic Oscillation (NAO), particularly the increase from the 1960s to the 1990s, are not compatible with either the internally generated variability nor the response to increasing greenhouse gas forcing simulated by these models. The observed NAO record can be explained by a combination of internal variability and greenhouse gas forcing, though only by the models that simulate the strongest variability and the strongest response. These models simulate inter-annual variability of the NAO index that is significantly greater than that observed, and can no longer explain the observed record if the simulated NAO indices are scaled so that they have the same high-frequency variance as that observed. It is likely, therefore, that other external forcings also contributed to the observed NAO index increase, unless the climate models are deficient in their simulation of inter-decadal NAO variability or their simulation of the response to greenhouse gas forcing. These conclusions are based on a comprehensive analysis of the control runs and transient greenhouse-gas-forced simulations of the seven climate models. The simulations of mean winter circulation and its pattern of inter-annual variability are very similar to the observations in the Atlantic half of the Northern Hemisphere. The winter atmospheric circulation response to increasing greenhouse gas forcing shows little inter-model similarity at the regional scale, and the NAO response is model-dependent and sensitive to the index used to measure it. At the largest scales, however, sea level pressure decreases over the Arctic Ocean in all models and increases over the Mediterranean Sea in six of the seven models, so that there is an increase of the NAO in all models when measured using a pattern-based index.  相似文献   

16.
Accurate decadal climate predictions could be used to inform adaptation actions to a changing climate. The skill of such predictions from initialised dynamical global climate models (GCMs) may be assessed by comparing with predictions from statistical models which are based solely on historical observations. This paper presents two benchmark statistical models for predicting both the radiatively forced trend and internal variability of annual mean sea surface temperatures (SSTs) on a decadal timescale based on the gridded observation data set HadISST. For both statistical models, the trend related to radiative forcing is modelled using a linear regression of SST time series at each grid box on the time series of equivalent global mean atmospheric CO2 concentration. The residual internal variability is then modelled by (1) a first-order autoregressive model (AR1) and (2) a constructed analogue model (CA). From the verification of 46 retrospective forecasts with start years from 1960 to 2005, the correlation coefficient for anomaly forecasts using trend with AR1 is greater than 0.7 over parts of extra-tropical North Atlantic, the Indian Ocean and western Pacific. This is primarily related to the prediction of the forced trend. More importantly, both CA and AR1 give skillful predictions of the internal variability of SSTs in the subpolar gyre region over the far North Atlantic for lead time of 2–5 years, with correlation coefficients greater than 0.5. For the subpolar gyre and parts of the South Atlantic, CA is superior to AR1 for lead time of 6–9 years. These statistical forecasts are also compared with ensemble mean retrospective forecasts by DePreSys, an initialised GCM. DePreSys is found to outperform the statistical models over large parts of North Atlantic for lead times of 2–5 years and 6–9 years, however trend with AR1 is generally superior to DePreSys in the North Atlantic Current region, while trend with CA is superior to DePreSys in parts of South Atlantic for lead time of 6–9 years. These findings encourage further development of benchmark statistical decadal prediction models, and methods to combine different predictions.  相似文献   

17.
We use the method of surrogates to test the structure of variability in nine paleoclimate reconstructions and to compare temperature trends with that of the modern temperature record in the Northern hemisphere. Three different algorithms are used to generate surrogate time series: the iterated amplitude adjusted Fourier transform (IAAFT), the statically transformed autoregressive process (STAP) and a modification of STAP, which generates surrogates of arbitrary length (STAPL). We assessed through formal statistical tests that the surrogates preserve the LTP structure of the reconstructed time series of global temperature, using different measures (Hurst exponent, DFA exponent and R/S analysis). Then using the same surrogates we tested for the presence of a linear trend at least as great as the trend of the modern time series against the null hypothesis that the observed trend is only due to LTP. The null hypothesis could be rejected at the lowest possible significance level for all but two of the reconstructions. The result from the non-parametric test adds further statistical evidence to that of earlier parametric studies that the observed global warming trend in the modern time series cannot be adequately explained by natural agents of variability.  相似文献   

18.
 The Canadian Centre for Climate Modelling and Analysis (CCCma) global coupled model is used to investigate the potential climate effects of increasing greenhouse gas (GHG) concentrations and changes in sulfate aerosol loadings. The forcing scenario adopted closely resembles that of Mitchell et al. for both the greenhouse gas and aerosol components. Its implementation in the model and the resulting changes in forcing are described. Five simulations of 200 years in length, nominally for the years 1900 to 2100, are available for analysis. They consist of a control simulation without change in forcing, three independent simulations with the same greenhouse gas and aerosol changes, and a single simulation with greenhouse gas only forcing. Simulations of the evolution of temperature and precipitation from 1900 to the present are compared with available observations. Temperature and precipitation are primary climate variables with reasonable temporal and spatial coverage in the observational record for the period. The simulation of potential climate change from the present to the end of the twenty-first century, based on projected GHG and aerosol forcing changes, is discussed in a companion paper. For the historical period dealt with here, the GHG and aerosol forcing has changed relatively little compared to the forcing changes projected to the end of the twenty-first century. Nevertheless, the forced climate signal for temperature in the model is reasonably consistent with the observed global mean temperature from the instrumental record. This is true also for the trend in zonally averaged temperature as a function of latitude and for some aspects of the geographical and regional distributions of temperature. Despite the modest change in overall forcing, the difference between GHG+aerosol and GHG-only forcing is discernible in the temperature response for this period. Changes in precipitation, on the other hand, are much less evident in both the instrumental and simulated record. There is an apparent increasing trend in average precipitation in both the observations and the model results over that part of the land for which observations are available. Regional and geographical changes and trends (which are less affected by sampling considerations), if they exist, are masked by the large natural variability of precipitation in both model and observations. Received: 24 September 1998 / Accepted: 8 October 1999  相似文献   

19.
The IAP/LASG GOALS coupled model is used to simulate the climate change during the 20th century using historical greenhouse gases concentrations, the mass mixing ratio of sulfate aerosols simulated by a CTM model, and reconstruction of solar variability spanning the period 1900 to 1997. Four simulations, including a control simulation and three forcing simulations, are conducted. Comparison with the observational record for the period indicates that the three forcing experiments simulate reasonable temporal and spatial distributions of the temperature change. The global warming during the 20th century is caused mainly by increasing greenhouse gas concentration especially since the late 1980s; sulfate aerosols offset a portion of the global warming and the reduction of global temperature is up to about 0.11℃ over the century; additionally, the effect of solar variability is not negligible in the simulation of climate change over the 20th century.  相似文献   

20.
Based on the principles of the probability theory a statistical model has been developed assessing the likelihood of occurrence of extreme temperature events from the knowledge of the statistical characteristics of the daily temperature extremes. It is demonstrated that the probability of such events is more sensitive to changes in the variability of climate than to changes in its average. Further, this sensitivity increases at a nonlinear rate the more extreme the event. The applicability of the model has been verified by comparing the simulated frequencies of a large spectrum of temperature events with the observed numbers derived from a long time series of daily temperature extremes at Potsdam. Accordingly, the relative simulation errors increase significantly as the events become more extreme. A correction is possible, because most of these errors are systematic rather than random. Moreover, in accordance with the climate observations the simulations reveal statistically significant linear trends in the number of extreme events since the end of the last century. Local scenarios of extreme temperature events have been derived for the city of Berlin by considering both hypothetical new climate states and climate changes simulated by a General Circulation Model (GCM). As a consequence of an increase in the atmospheric concentration of greenhouse gases up to the end of the next century according to the IPCC Scenario A the repetition rate of extreme events in summer (e.g., hot days) is expected to rise considerably relative to the current climate. Moreover, in the winter season cold days will become extremely rare.  相似文献   

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