A nonlinear impulse response model of the coupled carbon cycle-climate system (NICCS)

 Impulse-response-function (IRF) models are designed for applications requiring a large number of climate change simulations, such as multi-scenario climate impact studies or cost-benefit integrated-assessment studies. The models apply linear response theory to reproduce the characteristics of the climate response to external forcing computed with sophisticated state-of-the-art climate models like general circulation models of the physical ocean-atmosphere system and three-dimensional oceanic-plus-terrestrial carbon cycle models. Although highly computer efficient, IRF models are nonetheless capable of reproducing the full set of climate-change information generated by the complex models against which they are calibrated. While limited in principle to the linear response regime (less than about 3 ^∘C global-mean temperature change), the applicability of the IRF model presented has been extended into the nonlinear domain through explicit treatment of the climate system's dominant nonlinearities: CO₂ chemistry in ocean water, CO₂ fertilization of land biota, and sublinear radiative forcing. The resultant nonlinear impulse-response model of the coupled carbon cycle-climate system (NICCS) computes the temporal evolution of spatial patterns of climate change for four climate variables of particular relevance for climate impact studies: near-surface temperature, cloud cover, precipitation, and sea level. The space-time response characteristics of the model are derived from an EOF analysis of a transient 850-year greenhouse warming simulation with the Hamburg atmosphere-ocean general circulation model ECHAM3-LSG and a similar response experiment with the Hamburg carbon cycle model HAMOCC. The model is applied to two long-term CO₂ emission scenarios, demonstrating that the use of all currently estimated fossil fuel resources would carry the Earth's climate far beyond the range of climate change for which reliable quantitative predictions are possible today, and that even a freezing of emissions to present-day levels would cause a major global warming in the long term. Received: 28 January 2000 / Accepted: 9 March 2001     

A nonlinear stochastic low-order energy balance climate model

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.     

Hierarchical evaluation of IPCC AR4 coupled climate models with systematic consideration of model uncertainties

The capability of reproducing observed surface air temperature (SAT) changes for the twentieth century is assessed using 22 multi-models which contribute to the Intergovernmental Panel on Climate Change Fourth Assessment Report. A Bayesian method is utilized for model evaluation by which model uncertainties are considered systematically. We provide a hierarchical analysis for global to sub-continental regions with two settings. First, regions of different size are evaluated separately at global, hemispheric, continental, and sub-continental scales. Second, the global SAT trend patterns are evaluated with gradual refinement of horizontal scales (higher dimensional analysis). Results show that models with natural plus anthropogenic forcing (MME_ALL) generally exhibit better skill than models with anthropogenic only forcing (MME_ANTH) at all spatial scales for different trend periods (entire twentieth century and its first and second halves). This confirms previous studies that suggest the important role of natural forcing. For the second half of the century, we found that MME_ANTH performs well compared to MME_ALL except for a few models with overestimated warming. This indicates not only major contributions of anthropogenic forcing over that period but also the applicability of both MMEs to observationally-constrained future predictions of climate changes. In addition, the skill-weighted averages with the Bayes factors [Bayesian model averaging (BMA)] show a general superiority over other error-based weighted averaging methods, suggesting a potential advantage of BMA for climate change predictions.     

Comparison of statistically downscaled precipitation in terms of future climate indices and daily variability for southern Ontario and Quebec,Canada

Given the coarse resolution of global climate models, downscaling techniques are often needed to generate finer scale projections of variables affected by local-scale processes such as precipitation. However, classical statistical downscaling experiments for future climate rely on the time-invariance assumption as one cannot know the true change in the variable of interest, nor validate the models with data not yet observed. Our experimental setup involves using the Canadian regional climate model (CRCM) outputs as pseudo-observations to estimate model performance in the context of future climate projections by replacing historical and future observations with model simulations from the CRCM, nested within the domain of the Canadian global climate model (CGCM). In particular, we evaluated statistically downscaled daily precipitation time series in terms of the Peirce skill score, mean absolute errors, and climate indices. Specifically, we used a variety of linear and nonlinear methods such as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k-nearest neighbors to generate present and future daily precipitation occurrences and amounts. We obtained the predictors from the CGCM 3.1 20C3M (1971–2000) and A2 (2041–2070) simulations, and precipitation outputs from the CRCM 4.2 (forced with the CGCM 3.1 boundary conditions) as predictands. Overall, ANN models and tree ensembles outscored the linear models and simple nonlinear models in terms of precipitation occurrences, without performance deteriorating in future climate. In contrast, for the precipitation amounts and related climate indices, the performance of downscaling models deteriorated in future climate.     

Contributions of solar and greenhouse gases forcing during the present warm period

Due to the dramatic increase in the global mean surface temperature (GMST) during the twentieth century, the climate science community has endeavored to determine which mechanisms are responsible for global warming. By analyzing a millennium simulation (the period of 1000–1990 ad) of a global climate model and global climate proxy network dataset, we estimate the contribution of solar and greenhouse gas forcings on the increase in GMST during the present warm period (1891–1990 ad). Linear regression analysis reveals that both solar and greenhouse gas forcing considerably explain the increase in global mean temperature during the present warm period, respectively, in the global climate model. Using the global climate proxy network dataset, on the other hand, statistical approach suggests that the contribution of greenhouse gas forcing is slightly larger than that of solar forcing to the increase in global mean temperature during the present warm period. Overall, our result indicates that the solar forcing as well as the anthropogenic greenhouse gas forcing plays an important role to increase the global mean temperature during the present warm period.     

全球年平均人为热释放气候强迫的估算

利用能源经济领域具有权威性的英国石油公司(BP)世界能源统计资料和联合国人口统计资料,通过一些简单的数值计算,初步估算了人为热释放的全球气候强迫。结果表明:当前(2008年)全球年平均人为热释放的气候强迫还不是很大,约为0.031W/m2;但随着人口及能源消费总量的增加,未来人为热释放产生的全球年平均气候强迫将有可能达0.30W/m2。     

Climate Change Prediction

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.     

Estimation of the Distribution of Global Anthropogenic Heat Flux

The radiance lights data in 2006 from the National Oceanic and Atmospheric Administration Air Force Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) and authoritative energy data distributed by the United State Energy Information Administration were applied to estimate the global distribution of anthropogenic heat flux.A strong linear relationship was found to exist between the anthropogenic heat flux and the DMSP/OLS radiance data.On a global scale,the average value of anthropogenic heat flux is approximately 0.03 W m 2 and 0.10 W m 2 for global land area.The results indicate that global anthropogenic heat flux was geographically concentrated and distributed,fundamentally correlating to the economical activities.The anthropogenic heat flux concentrated in the economically developed areas including East Asia,Europe,and eastern North America.The anthropogenic heat flux in the concentrated regions,including the northeastern United States,Central Europe,United Kingdom,Japan,India,and East and South China is much larger than global average level,reaching a large enough value that could affect regional climate.In the center of the concentrated area,the anthropogenic heat flux density may exceed 100 W m 2,according to the results of the model.In developing areas,including South America,Central and North China,India,East Europe,and Middle East,the anthropogenic heat flux can reach a level of more than 10 W m 2 ;however,the anthropogenic heat flux in a vast area,including Africa,Central and North Asia,and South America,is low.With the development of global economy and urban agglomerations,the effect on climate of anthropogenic heat is essential for the research of climate change.     

Checking for model consistency in optimal fingerprinting

 Current approaches to the detection and attribution of an anthropogenic influence on climate involve quantifying the level of agreement between model-predicted patterns of externally forced change and observed changes in the recent climate record. Analyses of uncertainty rely on simulated variability from a climate model. Any numerical representation of the climate is likely to display too little variance on small spatial scales, leading to a risk of spurious detection results. The risk is particularly severe if the detection strategy involves optimisation of signal-to-noise because unrealistic aspects of model variability may automatically be given high weight through the optimisation. The solution is to confine attention to aspects of the model and of the real climate system in which the model simulation of internal climate variability is adequate, or, more accurately, cannot be shown to be deficient. We propose a simple consistency check based on standard linear regression which can be applied to both the space-time and frequency domain approaches to optimal detection and demonstrate the application of this check to the problem of detection and attribution of anthropogenic signals in the radiosonde-based record of recent trends in atmospheric vertical temperature structure. The influence of anthropogenic greenhouse gases can be detected at a high confidence level in this diagnostic, while the combined influence of anthropogenic sulphates and stratospheric ozone depletion is less clearly evident. Assuming the time-scales of the model response are correct, and neglecting the possibility of non-linear feedbacks, the amplitude of the observed signal suggests a climate sensitivity range of 1.2–3.4 K, although the upper end of this range may be underestimated by up to 25% due to uncertainty in model-predicted response patterns. Received: 9 December 1997 / Accepted: 24 December 1998     

On the relationship between vegetation and climate in tropical and northern Africa

Vegetation cover is a crucial component of the Earth’s climate system but, still, our understanding of the mechanisms governing the reciprocal influence between atmosphere and vegetation is limited. In this study, we investigate the unilateral atmospheric impact on vegetation cover in tropical and northern Africa, differentiated into regions with different circulation regimes and into detailed land-cover classes. In contrast to former studies, climate predictors from a regional climate model are used as input for a multiple regression model. Climate models provide consistent data without gaps at high spatial resolution, a considerably larger set of available climate variables and the perspective to transfer the statistical relationships to future projections, e.g., in the context of anthropogenic climate change. Indeed, robust climate predictors which drive up to 70 % of observed interannual vegetation variability could be extracted from the climate model. Besides precipitation and temperature, global radiation, and relative humidity play an important role. The statistical transfer functions are plausible in terms of the affected regions and land-cover classes and draw a rather complex picture of the atmosphere–vegetation relation in Africa.     

Dynamic responses of atmospheric carbon dioxide concentration to global temperature changes between 1850 and 2010

Changes in Earth's temperature have significant impacts on the global carbon cycle that vary at different time scales, yet to quantify such impacts with a simple scheme is traditionally deemed difficult. Here, we show that, by incorporating a temperature sensitivity parameter(1.64 ppm yr~(-1) ?C~(-1)) into a simple linear carbon-cycle model, we can accurately characterize the dynamic responses of atmospheric carbon dioxide(CO_2) concentration to anthropogenic carbon emissions and global temperature changes between 1850 and 2010(r~2 0.96 and the root-mean-square error 1 ppm for the period from 1960onward). Analytical analysis also indicates that the multiplication of the parameter with the response time of the atmospheric carbon reservoir(～12 year) approximates the long-term temperature sensitivity of global atmospheric CO_2concentration(～15 ppm?C~(-1)), generally consistent with previous estimates based on reconstructed CO_2 and climate records over the Little Ice Age. Our results suggest that recent increases in global surface temperatures, which accelerate the release of carbon from the surface reservoirs into the atmosphere, have partially offset surface carbon uptakes enhanced by the elevated atmospheric CO_2 concentration and slowed the net rate of atmospheric CO_2 sequestration by global land and oceans by ～30%since the 1960 s. The linear modeling framework outlined in this paper thus provides a useful tool to diagnose the observed atmospheric CO_2 dynamics and monitor their future changes.     

A global perspective on African climate

We describe the global climate system context in which to interpret African environmental change to support planning and implementation of policymaking action at national, regional and continental scales, and to inform the debate between proponents of mitigation v. adaptation strategies in the face of climate change. We review recent advances and current challenges in African climate research and exploit our physical understanding of variability and trends to shape our outlook on future climate change. We classify the various mechanisms that have been proposed as relevant for understanding variations in African rainfall, emphasizing a “tropospheric stabilization” mechanism that is of importance on interannual time scales as well as for the future response to warming oceans. Two patterns stand out in our analysis of twentieth century rainfall variability: a drying of the monsoon regions, related to warming of the tropical oceans, and variability related to the El Niño–Southern Oscillation. The latest generation of climate models partly captures this recent continent-wide drying trend, attributing it to the combination of anthropogenic emissions of aerosols and greenhouse gases, the relative contribution of which is difficult to quantify with the existing model archive. The same climate models fail to reach a robust agreement regarding the twenty-first century outlook for African rainfall, in a future with increasing greenhouse gases and decreasing aerosol loadings. Such uncertainty underscores current limitations in our understanding of the global climate system that it is necessary to overcome if science is to support Africa in meeting its development goals.     

Signatures of a universal spectrum for atmospheric interannual variability in some disparate climatic regimes

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     

Natural and anthropogenic climate change: incorporating historical land cover change,vegetation dynamics and the global carbon cycle

This study explores natural and anthropogenic influences on the climate system, with an emphasis on the biogeophysical and biogeochemical effects of historical land cover change. The biogeophysical effect of land cover change is first subjected to a detailed sensitivity analysis in the context of the UVic Earth System Climate Model, a global climate model of intermediate complexity. Results show a global cooling in the range of –0.06 to –0.22 °C, though this effect is not found to be detectable in observed temperature trends. We then include the effects of natural forcings (volcanic aerosols, solar insolation variability and orbital changes) and other anthropogenic forcings (greenhouse gases and sulfate aerosols). Transient model runs from the year 1700 to 2000 are presented for each forcing individually as well as for combinations of forcings. We find that the UVic Model reproduces well the global temperature data when all forcings are included. These transient experiments are repeated using a dynamic vegetation model coupled interactively to the UVic Model. We find that dynamic vegetation acts as a positive feedback in the climate system for both the all-forcings and land cover change only model runs. Finally, the biogeochemical effect of land cover change is explored using a dynamically coupled inorganic ocean and terrestrial carbon cycle model. The carbon emissions from land cover change are found to enhance global temperatures by an amount that exceeds the biogeophysical cooling. The net effect of historical land cover change over this period is to increase global temperature by 0.15 °C.     

Simple indices of global climate variability and change Part II: attribution of climate change during the twentieth century

Five simple indices of surface temperature are used to investigate the influence of anthropogenic and natural (solar irradiance and volcanic aerosol) forcing on observed climate change during the twentieth century. These indices are based on spatial fingerprints of climate change and include the global-mean surface temperature, the land-ocean temperature contrast, the magnitude of the annual cycle in surface temperature over land, the Northern Hemisphere meridional temperature gradient and the hemispheric temperature contrast. The indices contain information independent of variations in global-mean temperature for unforced climate variations and hence, considered collectively, they are more useful in an attribution study than global mean surface temperature alone. Observed linear trends over 1950–1999 in all the indices except the hemispheric temperature contrast are significantly larger than simulated changes due to internal variability or natural (solar and volcanic aerosol) forcings and are consistent with simulated changes due to anthropogenic (greenhouse gas and sulfate aerosol) forcing. The combined, relative influence of these different forcings on observed trends during the twentieth century is investigated using linear regression of the observed and simulated responses of the indices. It is found that anthropogenic forcing accounts for almost all of the observed changes in surface temperature during 1946–1995. We found that early twentieth century changes (1896–1945) in global mean temperature can be explained by a combination of anthropogenic and natural forcing, as well as internal climate variability. Estimates of scaling factors that weight the amplitude of model simulated signals to corresponding observed changes using a combined normalized index are similar to those calculated using more complex, optimal fingerprint techniques.     

The impact of atmospheric nonlinearities on the fastest growth of ENSO prediction error

In this paper we explore the impact of atmospheric nonlinearities on the optimal growth of initial condition error of El Niño and the Southern Oscillation (ENSO) prediction using singular vector (SV) analysis. This is performed by comparing and analyzing SVs of two hybrid coupled models (HCMs), one composed of an intermediate complexity dynamical ocean model coupled with a linear statistical atmospheric model, and the other one with the same ocean model coupled with a nonlinear statistical atmosphere. Tangent linear and adjoint models for both HCMs are developed. SVs are computed under the initial conditions of seasonal background and actual ENSO cycle simulated by the ocean model forced with the real wind data of 1980–1999. The optimization periods of 3, 6 and 9 months are individually considered. The results show that the first SVs in both HCMs are very similar to each other, characterized by a central east-west dipole pattern spanning over the entire tropical Pacific. The spatial patterns of the leading SV in both HCMs are not sensitive to optimization periods and initial time. However, the first singular value, indicating the optimal growth rate of prediction error, displays considerable differences between the two HCMs, indicating a significant impact of atmospheric nonlinearities on the optimal growth of ENSO prediction error. These differences are greater with increasing optimization time, suggesting that the impact of atmospheric nonlinearities on the optimal growth of prediction error becomes larger for a longer period of prediction.     

一种局地非线性气候动力统计模型及其预报试验

根据反演建模理论，在引入一次样条函数的基础上，设计了一种局地非线性气候动力统计模型及其一整套反演方案。其实质是用逐段线性化的研究局地气候演化的非线性特征。作为初步试验，利用云南省18个测站1956年1月－1990年12月逐月温度距平和逐月雨量距平率的时间序列，反演得到一组近似描述云南省局地气候系统的非线性动力统计方程，应用反演获得的此非线性动力统计方程进行了云南省18个测站1991年1月－1994年12月逐月温度距平和逐月雨量距平率的预报试验。试验结果表明，温度距平和雨量距平率的拟合准确率分别约为78％和64％；温度距平和雨量距平率的外推预报准确率分别为75％和63％。表明此模型具有一定的拟合和预报能力，同时具有良好的稳定性。     

A statistical model to downscale local daily temperature extremes from synoptic-scale atmospheric circulation patterns in the Australian region

 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     

IPCC AR6报告解读：气候系统对太阳辐射干预响应

IPCC第六次评估报告（AR6）第一工作组报告评估了太阳辐射干预（Solar radiation modification,SRM）对气候系统和碳循环的影响。在大幅度减排基础上,太阳辐射干预有潜力作为应对气候变化的备用措施。目前,对于太阳辐射干预气候影响的评估都是基于模式模拟结果。评估主要结论如下：太阳辐射干预可以在全球和区域尺度上抵消一部分温室气体增加造成的气候变化（高信度);但是太阳辐射干预无法在全球和区域尺度上完全抵消温室气体增加引起的气候变化（几乎确定);有可能通过适当的太阳辐射干预设计,同时实现多个温度变化减缓目标（中等信度);在高强度温室气体排放情景下,如果太阳辐射干预实施后突然终止,并且这种终止长时间持续,将会造成快速的气候变化（高信度);如果在减排和CO₂移除的情况下,太阳辐射干预的实施强度逐渐减小至零,将显著降低太阳辐射干预突然终止产生的快速气候变化风险（中等信度);太阳辐射干预会通过降温作用,促进陆地和海洋对大气CO₂的吸收（中等信度),但是太阳辐射干预无法缓解海洋酸化（高信度);太阳辐射干预对其他生物化学循环影响的不确定性大。由于对云-气溶胶-辐射过程的相互作用和微物理过程认知有限,目前对平流层气溶胶注入、海洋低云亮化、高层卷云变薄等太阳辐射干预方法的冷却潜力和气候效应的认知还有很大的不确定性。