气候系统可预报性的全局研究

胞映射方法是对非线性系统进行全局分析的强有力工具。文中基于一个最大简化的地－气耦合模式,引入胞映射全局分析方法,对气候系统的可预报性进行了全局的研究。气候系统存在最大可预报期限,对于超过逐日预报可预报期限后的预报,用胞映射思想证明了平均值的可预报性,并得到了定量的结果。研究表明,耦合机制具有延长可预报期限的作用,观测精度的提高亦可延长可预报期限     

月尺度降水可预报性的实验研究

利用信噪方差比探讨了山东省月尺度降水可预报性,发现２～３月、９～１０月不可预报,其它月份可预报性强。一般大气环流系统冬夏转换季节可预报性差。与１３个代表站预报评分进行了比较,发现可预报性指数越高,则实际预报评分也相对较高,两者有较好的对应关系。     

数值天气预报和气候预测可预报性研究的若干动力学方法

简要回顾了数值天气预报和气候预测可预报性研究的若干动力学方法,包括用于研究第一类可预报性问题的线性奇异向量（LSV）和条件非线性最优初始扰动（CNOP-I）方法,以及Lyapunov指数和非线性局部Lyapunov指数方法。前两种方法用于研究预报或预测的预报误差问题,可以用于估计天气预报和气候预测的最大预报误差,而且根据导致最大预报误差的初始误差结构的信息,这两种方法可以用于确定预报或预测的初值敏感区。应该指出的是,LSV是基于线性化模式,对于描述非线性大气和海洋的运动具有局限性。因而,对于非线性模式,应该选择使用CNOP-I估计最大预报误差。Lyapunov指数和非线性局部Lyapunov指数可以用于研究第一类可预报性问题中的预报时限问题,前者是基于线性模式,不能解释非线性对预报时限的影响,而非线性局部Lyapunov指数方法则考虑了非线性的影响,能够较好地估计实际天气和气候的预报时限。第二类可预报性问题的研究方法相对较少,本文仅介绍了由我国科学家提出的关于模式参数扰动的条件非线性最优参数扰动（CNOP-P）方法,该方法可以用于寻找到对预报有最大影响的参数扰动,并可以进一步确定哪些参数最应该利用观测资料进行校准。另一方面,通过对比CNOP-I和CNOP-P对预报误差的影响,可以判断导致预报不确定性的主要误差因子,进而指导人们着力改进模式或者初始场。     

延伸期预报中的可预报性浅析

延伸期（10-30天）预报是无缝隙集约化预报预测业务体系的重要一环,一直以来在防灾减灾科学决策中起着重要作用。然而实际预报中,其准确率较中短期天气预报和气候预测均明显偏低,原因在于随着预报时效延长,预报结果存在明显的不确定性。因此,业务预报中需要充分考虑预报对象的可预报性。本文通过总结延伸期时效可预报性的来源、主流数值模式预报性能的现状,介绍了一线业务中的预报思路及常用的可预报性参考产品,揭示了可预报性理论在延伸期预报中的应用。同时还展望了延伸期预报未来的发展方向。     

条件非线性最优扰动在可预报性问题研究中的应用

本文总结了近年来条件非线性最优扰动方法的应用研究的主要进展.主要包括四个方面:(1)将条件非线性最优扰动(CNOP)方法拓展到既考虑初始扰动又考虑模式参数扰动,形成了拓展的CNOP方法.拓展的CNOP方法不仅能够应用于研究分别由初始误差和模式参数误差导致的可预报性问题,而且能够用于探讨初始误差和模式参数误差同时存在的情形;(2)将拓展的CNOP方法分别应用于ENSO和黑潮可预报性研究,考察了初始误差和模式参数误差对其可预报性的影响,揭示了初始误差在导致ENSO和黑潮大弯曲路径预报不确定性中的重要作用;(3)考察了阻塞事件发生的最优前期征兆(OPR)以及导致其预报不确定性的最优增长初始误差(OGR),揭示了OPR和OGR空间模态及其演变机制的相似性及其局地性特征;(4)研究了台风预报的目标观测问题,用CNOP方法确定了台风预报的目标观测敏感区,通过观测系统模拟试验(OSSEs)和/或观测系统试验(OSEs),表明了CNOP敏感区在改进台风预报中的有效性.具体地,台风OGR的主要误差分布在某一特定区域,空间分布具有明显的局地性特征,在台风OGR的局地性区域增加观测,有效改进了台风的预报技巧,该区域代表了台风预报的初值敏感区.事实上,上述El Ni(n)o事件、黑潮路径变异以及阻塞事件的OGR的空间分布也具有明显的局地性特征,这些事件的OGR刻画的局地性区域可能也代表了初值敏感区.     

月尺度气温可预报性对资料长度的依赖及可信度

利用全国518个站1960—2011年逐日气温观测资料和160个站1983—2012年月尺度气温客观预测数据,基于非线性局部Lyapunov指数和非线性误差增长理论,研究中国区域月尺度气温可预报性期限对资料序列长度的依赖性。结果表明:气温可预报性期限对资料序列的长度有一定程度的依赖性,在西北、东北及华中地区尤为明显。平均而言,45年的资料序列长度才能够得到稳定合理的可预报性期限。为了验证气温可预报期限计算结果的可信度,将月尺度气温的可预报性期限与客观气候预测方法的预报评分技巧进行对比,发现两者结果非常一致。其中,由观测资料得到的1月气温的可预报性期限明显低于7月,1月客观气候预测方法的预报评分技巧也明显低于7月,且1月 (7月) 预报评分的空间分布型与1月 (7月) 气温可预报性期限的空间分布型较为一致。因此,利用非线性局部Lyapunov指数和台站逐日观测资料分析气温的可预报性期限结果是可信的。     

热带气旋异常运动可预报性问题的理论研究

简要讨论了３个问题：（１）热带气旋移动与大尺度波动流型演变两者可预报性的差别。（２）热带气旋移动可预报性问题的两类方法。（３）近几年来热带气旋移动可预报性研究的若干结果。     

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.     

因子的可预报性和预报模型适用性研究初探

本文从因子场与预报量场之间的整体相关性着手，通过典型相关分析来提取因子信息，用求得的典型变量作为新预报因子，经试用表明，新因子的可预报性比原因子有明显提高。 在预报模型的选择上，本文提出了依据预报模型对历史样本实际预测精度的优劣来衡量预报模型的预测能力，从而选用对历史样本预测精度较高的预报模型作未来预报。     

BCC二代气候系统模式的季节预测评估和可预报性分析

本文利用国家气候中心（BCC）第二代季节预测模式系统历史回报数据,从确定性预报和概率预报两个方面系统地评估了该模式对气温、降水和大气环流的季节预报性能,并与BCC一代气候预测模式的结果进行了对比,重点分析了二代模式的季节可预报性问题。结果显示,BCC二代模式对全球气温、降水和环流的预报性能整体上优于一代模式,特别在热带中东太平洋、印度洋和海洋大陆地区的温度和降水的预报效果改进尤为明显。这些热带地区降水预报的改进,可以通过激发太平洋—北美型（PNA）、东亚—太平洋型（EAP）等遥相关波列提升该模式在中高纬地区的季节预报技巧。分析表明,厄尔尼诺和南方涛动（ENSO）信号在热带和热带外地区均是模式季节可预报性的重要来源,BCC二代模式能够较好把握全球大气环流对ENSO信号的响应特征,从而通过对ENSO预报技巧的改进有效地提升了模式整体的预报性能。从概率预报来看,BCC二代模式对我国冬季气温和夏季降水具备一定的预报能力,特别是对我国东部大部分地区冬季气温正异常和负异常事件预报的可靠性和辨析度相对较高。因此,进一步提高模式对热带大尺度异常信号和大气主要模态的预报能力、加强概率预报产品释用对提高季节气候预测水平具有重要意义。     

A Preliminary Study on the Polar Climate Predictability

Studies have revealed that predictability of the atmospheric general circulation is generally high in the tropics throughout the year and that there is some predictability in the Northern extra-tropical winter at-mospheric circulation through some patterns of tele connection. Predictability of the general circulation at the polar regions has still remained as a ‘cold’ topic and little has been known about this question. Based on a preliminary study on the predictability by using the Institute of Atmospheric Physics (IAP) general circulation model, it is found that the SST-related predictability of the Southern winter lower atmospheric circulation in Antarctica is reasonably high and that there is some predictability in the 500 hPa and 200 hPa geopotential height fields over Europe and the Okhotsk Sea region during the Northern winter. It is sug-gested that more researches on this issue based on data analysis and model simulations are needed to obtain better understanding.     

Assessing the Impacts of Eurasian Snow Conditions on Climate Predictability with a Global Climate Model

On the basis of two ensemble experiments conducted by a general atmospheric circulation model (Institute of Atmospheric Physics nine-level atmospheric general circulation model coupled with land surface model, hereinafter referred to as IAP9L_CoLM), the impacts of realistic Eurasian snow conditions on summer climate predictability were investigated. The predictive skill of sea level pressures (SLP) and middle and upper tropospheric geopotential heights at mid-high latitudes of Eurasia was enhanced when improved Eurasian snow conditions were introduced into the model. Furthermore, the model skill in reproducing the interannual variation and spatial distribution of the surface air temperature (SAT) anomalies over China was improved by applying realistic (prescribed) Eurasian snow conditions. The predictive skill of the summer precipitation in China was low; however, when realistic snow conditions were employed, the predictability increased, illustrating the effectiveness of the application of realistic Eurasian snow conditions. Overall, the results of the present study suggested that Eurasian snow conditions have a significant effect on dynamical seasonal prediction in China. When Eurasian snow conditions in the global climate model (GCM) can be more realistically represented, the predictability of summer climate over China increases.     

来自大气内部的季节气候可预测性初探

这是一个十分初步的工作结果.通常认为,气候的季节-年际可预测性主要来自缓慢变化的大气外强迫的异常(如海表温度、积雪、土壤温度和湿度等).作者从大气月平均风场异常的持续性以及与东亚季风年际异常相关的全球大气环流异常特征两个方面初步探讨东亚夏季气候的可预测性.初步结果说明,大气月平均风场在对流层上层和平流层下层的热带区域及一些关键区域可持续性可以超过一个季节,并且与东亚季风有显著相关,从而使得从大气内部变化来考虑东亚夏季风气候仍然有一定的可预测性.     

Progress in the Study of Nonlinear Atmospheric Dynamics and Predictability of Weather and Climate in China (2007--2011)

Recent progress in the study of nonlinear atmospheric dynamics and related predictability of weather and climate in China (2007-2011) are briefly introduced in this article. Major achievements in the study of nonlinear atmospheric dynamics have been classified into two types:(1) progress based on the analysis of solutions of simplified control equations, such as the dynamics of NAO, the optimal precursors for blocking onset, and the behavior of nonlinear waves, and (2) progress based on data analyses, such as the nonlinear analyses of fluctuations and recording-breaking temperature events, the long-range correlation of extreme events, and new methods of detecting abrupt dynamical change. Major achievements in the study of predictability include the following:(1) the application of nonlinear local Lyapunov exponents (NLLE) to weather and climate predictability; (2) the application of condition nonlinear optimal perturbation (CNOP) to the studies of El Nin o-Southern Oscillation (ENSO) predictions, ensemble forecasting, targeted observation, and sensitivity analysis of the ecosystem; and (3) new strategies proposed for predictability studies. The results of these studies have provided greater understanding of the dynamics and nonlinear mechanisms of atmospheric motion, and they represent new ideas for developing numerical models and improving the forecast skill of weather and climate events.     

欧亚大陆积雪对我国春季气候可预报性的影响

利用大气环流模式IAP9L_CoLM,通过两组集合后报试验,考察了欧亚大陆积雪对我国春季气候可预报性的影响。一组试验为常规后报试验,积雪是由模式陆面过程预报得到的,另一组试验为积雪试验,模式积分过程中欧亚大陆雪水当量由微波遥感积雪资料替代,一天替换一次。通过分析两组试验后报结果的差异,来考察欧亚大陆积雪对我国春季（3~5月）气候可预报性的影响。分析表明:欧亚大陆积雪模拟水平的改善能提高春季欧亚大陆中高纬环流场（海平面气压场和中、高层位势高度场）的可预报性,模式对我国春季气温异常的年际变化和空间分布的可预报能力也有显著增强。对我国春季降水,虽然预报技巧较低,但引入较真实的欧亚积雪作用后,由于中高纬环流场预报技巧的改进导致降水的预测能力也有所改进。个例分析也表明,欧亚中高纬春季积雪异常模拟水平的改善导致了欧亚中高纬贝加尔湖及以南区域环流场可预报性的提高,最终使中国东部区域春季气候异常模拟技巧得以改善。以上结果也证实,欧亚大陆积雪是影响东亚区域春季气候的一个重要因子,要提高模式对中国春季气候的预报技巧,积雪模拟水平的改进是非常必要的。     

目前短期气候预测可预报性的研究概况

气候的可预报性研究是气候变化研究的一个重要方面，作者首先介绍了短期气候预测之所以可行的两个主要原因，以及短期气候预测可预报性问题的实质；然后，较详细地介绍了目前研究月、季尺度时间平均可预报性的方差分析方法，包括了对实际大气和模式大气可预报性的研究；最后，对时间平均可预报性的研究结论进行了总结。     

Response of a Grassland Ecosystem to Climate Change in a Theoretical Model

The response of a grassland ecosystem to climate change is discussed within the context of a theoretical model.An optimization approach,a conditional nonlinear optimal perturbation related to parameter(CNOP-P) approach,was employed in this study.The CNOP-P,a perturbation of moisture index in the theoretical model,represents a nonlinear climate perturbation.Two kinds of linear climate perturbations were also used to study the response of the grassland ecosystem to different types of climate changes.The results show that the extent of grassland ecosystem variation caused by the CNOP-P-type climate change is greater than that caused by the two linear types of climate change.In addition,the grassland ecosystem affected by the CNOP-P-type climate change evolved into a desert ecosystem,and the two linear types of climate changes failed within a specific amplitude range when the moisture index recovered to its reference state.Therefore,the grassland ecosystem response to climate change was nonlinear.This study yielded similar results for a desert ecosystem seeded with both living and wilted biomass litter.The quantitative analysis performed in this study also accounted for the role of soil moisture in the root zone and the shading effect of wilted biomass on the grassland ecosystem through nonlinear interactions between soil and vegetation.The results of this study imply that the CNOP-P approach is a potentially effective tool for assessing the impact of nonlinear climate change on grassland ecosystems.     

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.     

一个海气耦合模式对东亚夏季气候预测潜力的评估

利用一个具有较高分辨率的海气耦合模式SINTEX-F(Scale Interaction Experiment-Frontier Research Center for Global Change coupled GCM)的多年回报结果,评估了该海气耦合模式对东亚区域,尤其是中国地区气候异常的预测潜力.与观测实况的比较结果表明:SINTEX-F模式对夏季降水、500 hPa高度场和地表气温都有一定的预测技巧,但是相比而言降水与高度场的回报技巧要高于地表气温;而且耦合模式对东亚地区气候异常的主要空间分布和年际变化特征也有较好的预测潜力,对500 hPa高度场效果较好;对降水异常的年际变化也有一定的预测潜力,尤其是我国中部地区效果较好,但是模式预测的降水异常的幅值较观测相对偏弱;此外对我国西部的极端气候也有一定的预测潜力.     

A New Strategy for Solving a Class of Constrained Nonlinear Optimization Problems Related to Weather and Climate Predictability

There are three common types of predictability problems in weather and climate, which each involve different constrained nonlinear optimization problems: the lower bound of maximum predictable time, the upper bound of maximum prediction error, and the lower bound of maximum allowable initial error and parameter error. Highly efficient algorithms have been developed to solve the second optimization problem. And this optimization problem can be used in realistic models for weather and climate to study the upper bound of the maximum prediction error. Although a filtering strategy has been adopted to solve the other two problems, direct solutions are very time-consuming even for a very simple model, which therefore limits the applicability of these two predictability problems in realistic models. In this paper, a new strategy is designed to solve these problems, involving the use of the existing highly efficient algorithms for the second predictability problem in particular. Furthermore, a series of comparisons between the older filtering strategy and the new method are performed. It is demonstrated that the new strategy not only outputs the same results as the old one, but is also more computationally efficient. This would suggest that it is possible to study the predictability problems associated with these two nonlinear optimization problems in realistic forecast models of weather or climate.