Predictability of the atmosphere

This paper makes a review on the predictability of the atmosphere. The essential problems of predictability theo-ry, i.e., how a deterministic system changes to an undeterministic system (chaos) and how is the opposite (order within chaos), are discussed. Some applications of predictability theory are given.     

Climate predictability on interannual to decadal time scales: the initial value problem

Any initial value forecast of climate will be subject to errors originating from poorly known initial conditions, model imperfections, and by "chaos" in the sense that, even if the initial conditions were perfectly known, infinitesimal errors can amplify and spoil the forecast at some lead time. Here the latter source of error is examined using a "perfect model" approach whereby small perturbations are made to a coupled atmosphere-ocean general circulation model and the spread of nearby model trajectories, on time and space scales appropriate to seasonal-decadal climate variability, is measured to assess the lead time at which the error saturates. The study therefore represents an estimate of the upper limit of the predictability of climate (appropriate to the initial value problem) given a perfect model and near perfect knowledge of the initial conditions. It is found that, on average, surface air temperature anomalies are potentially predictable on seasonal to interannual time scales in the tropical regions and are potentially predictable on decadal time scales over the ocean in the North Atlantic. For mid-latitude surface air temperature anomalies over land, model trajectories rapidly diverge and there is little sign of any potential predictability on time scales greater than a season or so. For mean sea level pressure anomalies, there is potential predictability on seasonal time scales in the tropics, and for some global scale annual-decadal anomalies, although not those associated with the North Atlantic Oscillation. For precipitation, the only potential for predictability is for seasonal time anomalies associated with the El-Niño Southern Oscillation. For the majority of the highly populated regions of the world, climate predictability on interannual to decadal time scales based in the initial value approach is likely to be severely limited by chaotic error growth. It is found however that there can be cases in which the potential predictability can be higher than average indicating that there is perhaps some utility in making initial value forecasts of climate in those regions which show low predictability on average.     

Determination of the Backward Predictability Limit and Its Relationship with the Forward Predictability Limit

In this work, two types of predictability are proposed—forward and backward predictability—and then applied in the nonlinear local Lyapunov exponent approach to the Lorenz63 and Lorenz96 models to quantitatively estimate the local forward and backward predictability limits of states in phase space. The forward predictability mainly focuses on the forward evolution of initial errors superposed on the initial state over time, while the backward predictability is mainly concerned with when the given state can be predicted before this state happens. From the results, there is a negative correlation between the local forward and backward predictability limits. That is, the forward predictability limits are higher when the backward predictability limits are lower, and vice versa. We also find that the sum of forward and backward predictability limits of each state tends to fluctuate around the average value of sums of the forward and backward predictability limits of sufficient states.Furthermore, the average value is constant when the states are sufficient. For different chaotic systems, the average value is dependent on the chaotic systems and more complex chaotic systems get a lower average value. For a single chaotic system,the average value depends on the magnitude of initial perturbations. The average values decrease as the magnitudes of initial perturbations increase.     

非线性误差增长理论在大气可预报性中的应用

为了能从非线性误差增长动力学的角度来研究大气的可预报性问题,在非线性动力系统的理论和方法基础上,文中引入了可预报性研究的新方法--非线性局部Lyapunov指数.非线性局部Lyapunov指数及其相关统计量能够用来定量地确定混沌系统可预报性的大小,真正地实现了对可预报性的定量化研究.首先给出了利用大气单个变量的实际观测资料获得其可预报期限估计的计算方法,因而解决了将非线性误差增长理论应用到大气实际的可预报性研究中的问题.然后,以位势高度场为例,详细讨论了逐日时间尺度上全球可预报性的时空分布,得到的主要结论为:(1)在水平方向上,全球位势高度场可预报性表现为一定的南北纬向带状分布,赤道地区和南极地区的可预报期限最长,可以达到两周左右;北极地区次之,可预报期限大约为9-12 d;北半球中高纬度地区可预报期限相对较短,可预报期限大约为6-9 d;而在南半球的中纬度地区最短,可预报期限仅为4-6 d.此外,500 hPa位势高度场可预报性分市随季节有明显变化,季节不同一些可预报期限的高值区和低值区所在的纬度和经度也会不同,总体来说,全球大部分地区的可预报性冬季都大于夏季,尤其在南极地区、热带印度洋以及北太平洋地区.(2)在垂直方向上,位势高度场可预报期限随高度升商而增加,可预报期限从对流层下层的两周以下增加到平流层下层的1个月左右,对流层和平流层天气尺度运动的可预报期限与其时间尺度是十分一致的.     

Review of the Study of Nonlinear Atmospheric Dynamics in China （1999-2002）

Researches on nonlinear atmospheric dynamics in China (1999-2002) are briefly surveyed. This review includes the major achievements in the following branches of nonlinear dynamics: nonlinear stability theory,nonlinear blocking dynamics, 3D spiral structure in the atmosphere, traveling wave solution of the nonlinear evolution equation, numerical predictability in a chaotic system, and global analysis of climate dynamics.Some applications of nonlinear methods such as hierarchy structure of climate and scaling invariance, the spatial-temporal series predictive method, the nonlinear inverse problem, and a new difference scheme with multi-time levels are also introduced.     

Non-stationarity of the signal and noise characteristics of seasonal precipitation anomalies

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.     

随机误差对混沌系统可预报性的影响

根据非线性局部Lyapunov指数的方法, 以Logistic映射和Lorenz系统的试验数据序列为例, 研究了在初始误差存在的情况下, 随机误差对混沌系统可预报性的影响。结果表明: 初始误差和随机误差对可预报期限影响所起的作用大小主要取决于两者的相对大小。当初始误差远大于随机误差时, 系统的可预报期限主要由初始误差决定, 可以不考虑随机误差对预报模式可预报性的影响; 反之, 当随机误差远大于初始误差时, 系统的可预报期限主要由随机误差决定; 当初始误差和随机误差量级相当时, 两者都对系统的可预报期限起重要作用。在后两种情况下, 在考虑初始误差对可预报性影响的同时还必须考虑随机误差的作用。此外, 我们在已知系统精确的控制方程和误差演化方程的条件下, 研究了随机误差对可预报性的影响, 理论所得结果与试验数据所得结果相似。这表明在随机误差较小的情况下, 对系统可预报期限的估计相对准确, 但在随机误差较大的情况下, 可预报期限的估计误差也较大。本文利用三种不同的滤波方法对序列进行了试验, 结果表明, Lanczos高通滤波得到的高频序列与原始加入的噪声序列无论是在强度上还是在演变趋势上都表现得相当一致, 其能有效地去除高频噪音继而提高对系统的可预报期限的估计, 这对实际气象观测资料如何有效地去除噪音具有一定的启发意义。     

LOCALIZED FEATURES OF CHAOTIC SYSTEM AND ATMOSPHERIC PREDICTABILITY

The localized features on chaotic attractor in phase space and predictability are investigated in thepresent study.It will be suggested that the localized features in phase space have to be considered indetermining the predictability.The notions of the local instability including the finite-time and local-time instabilities which determine the growth rate of error are introduced,and the calculation methodsare discussed in detail.The results from the calculation of the 3-component Lorenz model show thatsuch instability,correspondingly the growth rate of error,varies dramatically as the trajectoriesevolve on the chaotic attractor.The region in which the growth rate of error is small is localizedconsiderably,and is separable from the region in which the growth rate is large.The localpredictability is of important interest.It is also suggested that such localized features may be the maincause for a great deal of case-to-case variability of the predictive skill in the operational forecasts.     

当代大气科学方法论及其展望述评

从方法论的高度上阐述大气科学中关于"确定论"和"随机论"两种主导思想的研究方法。指出,确定论不再是唯一占统治地位的方法论,不确定性的客观存在已经为愈来愈多的科学事实所证明。天气气候变化过程,既有确定性的一面,又存在着不确定性的一面。必须正确认识两者对立统一的辩证关系。文中指出,由于"新三论"的异军突起,必须重新认识自然界历来就存在的必然性现象及其动力学规律、偶然性现象及其统计规律、既必然又偶然的混沌现象及其非线性规律及其三者的相互联系。因而在大气科学研究中,确定性方法和随机性方法必须有机结合,而其新的结合点正是混沌理论及其非线性科学。     

混沌系统单变量可预报性研究

对于n维的混沌系统, 不同变量的可预报性是不同的。为了研究混沌系统中单个变量的可预报性, 本文在以前提出的混沌系统整体的非线性局部Lyapunov指数基础上(李建平等, 2006), 引入了单变量的非线性局部Lyapunov指数及其相关统计量, 进一步完善了非线性误差增长理论。通过应用到几个混沌个例, 结果表明单变量的非线性局部Lyapunov指数及其相关统计量可以用来定量地研究多维混沌系统中不同变量的可预报性, 系统不同变量的可预报性之间不是相互独立的, 而是单个变量的可预报期限与系统整体的可预报期限之比都近似保持一个常数, 但各个变量的常数值有所不同。     

非线性动力学和湍流

湍流的发生和发展湍流的描述是一个基本问题。浑沌运动的最近研究表明,确定性的系统可以有不规则的浑沌结果,因此浑沌的研究对解释湍流现象是非常重要的。 浑沌和湍流的特点是敏感初条件,并且长时间以后是不可预测的。 非线性耗散系统中运动的形态是非常复杂的。可以有定常吸引子,周期吸引子,拟周期吸引子和奇怪吸引子。 当控制参数变化穿过分岔点,吸引子的拓扑性质就要发生变化。 一般,湍流经过极少数几次分岔即可出现。     

误差非线性的增长理论及可预报性研究

对非线性系统的误差发展方程不作线性化近似,直接用原始的误差发展方程来研究初始误差的发展,提出了误差非线性的增长理论。首先,在相空间中定义一个非线性误差传播算子,初始误差在这个算子的作用下,可以非线性发展成任意时刻的误差;然后,在此基础上,引入了非线性局部Lyapunov指数的概念。由平均非线性局部Lyapunov指数可以得到误差平均相对增长随时间的演变情况;对于一个混沌系统,误差平均相对增长被证明将趋于一个饱和值,利用这个饱和值,混沌系统的可预报期限可以被定量地确定。误差非线性的增长理论可以应用于有限尺度大小初始扰动的可预报性研究,较误差的线性增长理论有明显的优越性。     

Relationships between the Limit of Predictability and Initial Error in the Uncoupled and Coupled Lorenz Models

In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems:the Lorenz model, which possesses a single characteristic time scale, and the coupled Lorenz model, which possesses two different characteristic time scales. The limit of predictability is defined here as the time at which the error reaches 95% of its saturation level; nonlinear behaviors of the error growth are therefore involved in the definition of the limit of predictability. Our results show that the logarithmic function performs well in describing the relationship between the limit of predictability and initial error in both models, although the coefficients in the logarithmic function were not constant across the examined range of initial errors. Compared with the Lorenz model, in the coupled Lorenz model-in which the slow dynamics and the fast dynamics interact with each other-there is a more complex relationship between the limit of predictability and initial error. The limit of predictability of the Lorenz model is unbounded as the initial error becomes infinitesimally small; therefore, the limit of predictability of the Lorenz model may be extended by reducing the amplitude of the initial error. In contrast, if there exists a fixed initial error in the fast dynamics of the coupled Lorenz model, the slow dynamics has an intrinsic finite limit of predictability that cannot be extended by reducing the amplitude of the initial error in the slow dynamics, and vice versa. The findings reported here reveal the possible existence of an intrinsic finite limit of predictability in a coupled system that possesses many scales of time or motion.     

Chaos of a simple coupled system generated by interaction and external forcing

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     

非线性局部Lyapunov指数方法在目标观测中的应用初探

本文初步探讨了非线性局部Lyapunov指数方法(NLLE)在目标观测中的应用。首先, 在NLLE理论基础上研究了非线性动力系统内局部平均误差相对增长(LAGRE)特征, 证明了在误差发展进入随机状态前, LAGRE与初始误差大小无关而是与初始状态有关;在演化进入随机状态后, LAGRE的饱和值由初始误差大小决定这一特征。同时利用三个变量的常微分方程模型Lorenz63验证了这一结论。其次, 从非线性局部误差增长理论出发, 在局部动力演化相似方法(LDA)的基础上提出向前局部动力演化相似方法(FLDA)的概念, 并通过两个混沌个例来说明LDA和FLDA方法能够有效的利用历史资料还原任意初始状态的LAGRE。这些方法的提出为NLLE理论应用于观测资料研究目标观测问题提供了依据。     

天气可预报性的时空分布

为了能从非线件误差增长动力学的角度研究大气的可预报性问题,文章引入了可预报性研究的新方法--非线性局部Lyapunov指数.非线性局部Lyapunov指数及其相关统计量能够被用来定量地确定混沌系统可预报性的大小,真正地实现对可预报性的定量化研究.为了把非线性局部Lyapunov指数方法应用到实际的大气可预报性研究中,给出了一种利用大气的实际观测资料估计非线性局部Lyapunov指数的计算方法.存非线性局部Lyapunov指数方法的基础上,文中利用NCEP/NCAR再分析资料,对大气位势高度场、温度场、纬向风场、经向风场等要素场可预报性的时空分布进行了研究,结果表明:(1)在500 hPa高度层上,对于不同的要素场,其可预报期限的大小以及时空分布规律都不一样;全球大部分地区位势高度场可预报期限最大,温度场和纬向风场次之,而经向风场的可预报期限最小.(2)在500 hPa高度层七,位势高度场和温度场的纬向平均可预报期限基本上表现为一定的南北纬向带状分布,热带地区和南极地区的可预报期限最大,北极地区次之,南北半球中高纬度地区可预报期限相对较小.纬向风场可预报期限在热带地区最高,但是南北极地区可预报期限与邻近的中高纬度地区差别不大.经向风场可预报期限在南北两极地区最高,南北半球的中纬度和赤道附近地区可预报期限最小.(3)在垂直方向上,纬向平均高度场、温度场以及纬向风场可预报期限基本上都是随高度升高而增加,高层的可预报期限明显大于低层;经向风场可预报期限随高度的变化比较复杂,不同的纬度有所不同.(4)可预报性有明显的季节变化,不同要素场可预报期限高低值区的位置和强度随季节鄙有明显变化,对于全球大部分地区来说,冬季可预报性都大于夏季的.     

Recent Advances in Predictability Studies in China （1999-2002）

Since the last International Union of Geodesy and Geophysics (IUGG) General Assembly (1999), the predictability studies in China have made further progress during the period of 1999-2002. Firstly, three predictability sub-problems in numerical weather and climate prediction are classified, which are concerned with the maximum predictability time, the maximum prediction error, and the maximum allowable initial error, and then they are reduced into three nonlinear optimization problems. Secondly, the concepts of the nonlinear singular vector (NSV) and conditional nonlinear optimal perturbation (CNOP) are proposed,which have been utilized to study the predictability of numerical weather and climate prediction. The results suggest that the nonlinear characteristics of the motions of atmosphere and oceans can be revealedby NSV and CNOP. Thirdly, attention has also been paid to the relations between the predictability and spatial-temporal scale, and between the model predictability and the machine precision, of which the investigations disclose the importance of the spatial-temporal scale and machine precision in the study of predictability. Also the cell-to-cell mapping is adopted to analyze globally the predictability of climate,which could provide a new subject to the research workers. Furthermore, the predictability of the summer rainfall in China is investigated by using the method of correlation coefficients. The results demonstrate that the predictability of summer rainfall is different in different areas of China. Analysis of variance, which is one of the statistical methods applicable to the study of predictability, is also used to study the potential predictability of monthly mean temperature in China, of which the conclusion is that the monthly mean temperature over China is potentially predictable at a statistical significance level of 0.10. In addition,in the analysis of the predictability of the T106 objective analysis/forecasting field, the variance and the correlation coefficient are calculated to explore the distribution characteristics of the mean-square errors.Finally, the predictability of short-term climate prediction is investigated by using statistical methods or numerical simulation methods. It is demonstrated that the predictability of short-term climate in China depends not only on the region of China being investigated, but also on the time scale and the atmospheric internal dynamical process.     

33模Lorenz系统的混沌特征及其可预报性分析

简述了33模Lorenz系统的导出及求解过程,并从功率谱、关联维数和Lyapunov指数等三方面验证其33个谱模分量及流场和扰动温度场得到的时空序列具有混沌特性,并对其进行可预报性分析。结果表明,对于混沌系统而言,对其时空序列取平均并不能延长可预报时效。     

Extended Range(10–30 Days) Heavy Rain Forecasting Study Based on a Nonlinear Cross-Prediction Error Model

Extended range(10–30 d) heavy rain forecasting is difficult but performs an important function in disaster prevention and mitigation. In this paper,a nonlinear cross prediction error(NCPE) algorithm that combines nonlinear dynamics and statistical methods is proposed. The method is based on phase space reconstruction of chaotic single-variable time series of precipitable water and is tested in 100 global cases of heavy rain. First,nonlinear relative dynamic error for local attractor pairs is calculated at different stages of the heavy rain process,after which the local change characteristics of the attractors are analyzed. Second,the eigen-peak is defined as a prediction indicator based on an error threshold of about 1.5,and is then used to analyze the forecasting validity period. The results reveal that the prediction indicator features regarded as eigenpeaks for heavy rain extreme weather are all reflected consistently,without failure,based on the NCPE model; the prediction validity periods for 1–2 d,3–9 d and 10–30 d are 4,22 and 74 cases,respectively,without false alarm or omission. The NCPE model developed allows accurate forecasting of heavy rain over an extended range of 10–30 d and has the potential to be used to explore the mechanisms involved in the development of heavy rain according to a segmentation scale. This novel method provides new insights into extended range forecasting and atmospheric predictability,and also allows the creation of multi-variable chaotic extreme weather prediction models based on high spatiotemporal resolution data.