利用─维时间序列重建动力系统的进一步研究

对月平均气温一维时间序列作相空间拓展，并根据系统的分维数设定重建的动力系统，且在具有二次非线性项的假定下，利用最小二乘法求解相空间状态演化方程中的各项系数，以重建动力系统。结果表明：随相空间维数的增加，重建的动力系统对气候演化过程可作出更精确、细致的描述。     

非平稳时间序列的区域预测研究

基于重构状态空间理论和嵌入定理,给出一个新的非平稳场时间序列的区域预测方法。该方法将外强迫因子引入到预测模型中,并且将区域内预测相点的周围相点所对应的空间信息也引入到预测模型中。然后利用该方法对33模Lorenz系统得到的"理想"的非平稳场时间序列进行预测实验分析。结果表明,嵌入外强迫因子可以更好地重构出原来的动力系统,有效地提高非平稳时间序列的预测精度;同时引入空间和外强迫信息可以利用空间数据弥补时间序列长度的不足,从而进一步提高预测精度。     

Preliminary Study of Reconstruction of a Dynamic System Using an One－Dimensional Time Series

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交叉小波变换在区域气候分析中的应用

将交叉谱与小波变换分析方法相结合,与传统的交叉谱方法相比,交叉小波变换方法用于区域气候变化与大气环流系统之间耦合振荡行为的相关分析更具优越性,不仅可以弥补经典交叉谱分析方法存在的缺陷,而且能够发挥小波变换在时频两域都具有表征气候信号局部化特征的作用;该方法具有较强的耦合信号分辨能力,便于描述耦合信号在时频域中分布状况的优点。采用交叉小波变换分析北极涛动指数(AOI)距平与河南省月平均降水量距平、气温距平序列之间的联合统计特征及其在时频域中的相关关系,根据小波互相关系数、交叉小波凝聚谱和小波位相谱分析北极涛动对河南省气候变化的可能影响。应用结果表明:河南省降水量和气温变化与AOI之间存在着多时间尺度的显著相关振荡,年代际尺度周期上的互相关系数明显大于年际尺度周期,相关程度随耦合振荡频率的增大而减小,相关显著性取决于两者的时频域联合统计特征,时域中小波互相关系数、小波凝聚谱和小波位相谱的分布具有明显的局部化特征;说明北极涛动年际和年代际异常对河南省气候变化具有显著影响。     

Notes of Numerical Simulation of Summer Rainfall in China with a Regional Climate Model REMO

Regional climate models are major tools for regional climate simulation and their output are mostly used for climate impact studies. Notes are reported from a series of numerical simulations of summer rainfall in China with a regional climate model. Domain sizes and running modes are major foci. The results reveal that the model in forecast mode driven by "perfect" boundaries could reasonably represent the inter-annual differences: heavy rainfall along the Yangtze River in 1998 and dry conditions in 1997. Model simulation in climate mode differs to a greater extent from observation than that in forecast mode. This may be due to the fact that in climate mode it departs further from the driving fields and relies more on internal model dynamical processes. A smaller domain in climate mode outperforms a larger one. Further development of model parameterizations including dynamic vegetation are encouraged in future studies.     

一维气候时间序列的拓展及其相空间中浑沌吸引子维数的确定

本文利用上海和广州近百年(1873—1980)月平均气温时间序列资料,将一维气候时间序列拓展到多维相空间上去。计算结果表明,月平均气温所表示的我国季风区短期气候演化,在相空间中存在吸引子,具有分维结构,其维数分别是d=3.4和d=2.3,为奇怪吸引子。由此推论,就我国季风区气候短期变化而言,为了能在多维相空间支撑起上述奇怪吸引子,最好选取四个变量或者建立最低为四阶的动力学模式来进行描述。      

Climate Change Estimates of Summer Temperature and Precipitation in the Alpine Region

Summary Climatic changes of summer temperature and precipitation in the greater Alpine region are assessed by using statistical-dynamical downscaling. The downscaling procedure is applied to two 30-year periods (1971–2000 and 2071–2100, summer months only) taken from the results of a transient coupled ocean/atmosphere climate scenario simulation with increasing greenhouse gas concentrations. The downscaling results for the present-day climate are compared with observations. The estimated regional climate change during the next 100 years shows a general warming. The mean summer temperatures increase by 3 to 5 Kelvin. The most intense climatic warming is predicted in the western parts of the Alps. The amount of summer precipitation decreases in most parts of central Europe by more than 20 percent. Increasing precipitation is simulated only over the Adriatic area and parts of eastern central Europe. The results are compared with observed climate trends for the last decades and results of other regional climate change estimations. The observed trends and the majority of the simulated trends (including ours) have a number of common features. However, there are also climate change estimates of other groups which completely contradict our results. Received April 8, 1999 Revised November 16, 1999     

An Improved Statistical-Dynamical Downscaling Scheme and its Application to the Alpine Precipitation Climatology

Summary An improved statistical-dynamical downscaling method for the regionalization of large-scale climate analyses or simulations is introduced. The method is based on the disaggregation of a multi-year time-series of large-scale meteorological data into multi-day episodes of quasi-stationary circulation. The episodes are subsequently grouped into a defined number of classes. A regional model is used to simulate the evolution of weather during the most typical episode of each class. These simulations consider the effects of the regional topography. Finally, the regional model results are statistically weighted with the climatological frequencies of the respective circulation classes in order to provide regional climate patterns. The statistical-dynamical downscaling procedure is applied to large-scale analyses for a 12-year climate period 1981–1992. The performance of the new method is demonstrated for winter precipitation in the Alpine region. With the help of daily precipitation analyses it was possible to validate the results and to assess the different sources of errors. It appeared that the main error originates from the regional model, whereas the error of the procedure itself was relatively unimportant. This new statistical-dynamical downscaling method turned out to be an efficient alternative to the commonly used method of nesting a regional model continuously within a general circulation model (dynamical downscaling). Received April 8, 1999 Revised July 30, 1999     

Temporal downscaling: a comparison between artificial neural network and autocorrelation techniques over the Amazon Basin in present and future climate change scenarios

Several studies have been devoted to dynamic and statistical downscaling for both climate variability and climate change. This paper introduces an application of temporal neural networks for downscaling global climate model output and autocorrelation functions. This method is proposed for downscaling daily precipitation time series for a region in the Amazon Basin. The downscaling models were developed and validated using IPCC AR4 model output and observed daily precipitation. In this paper, five AOGCMs for the twentieth century (20C3M; 1970–1999) and three SRES scenarios (A2, A1B, and B1) were used. The performance in downscaling of the temporal neural network was compared to that of an autocorrelation statistical downscaling model with emphasis on its ability to reproduce the observed climate variability and tendency for the period 1970–1999. The model test results indicate that the neural network model significantly outperforms the statistical models for the downscaling of daily precipitation variability.     

MODIS卫星遥感北京地区气溶胶光学厚度及与地面光度计遥感的对比

介绍MODIS卫星遥感气溶胶的方法,利用北京大学地面多波段太阳光度计的观测进行了对比,二者的相关性比较好.给出了描述北京地区气溶胶光学厚度分布的几幅图片.卫星遥感对于更好地研究空气污染提供了一种新手段,卫星遥感的气溶胶光学厚度弥补了地面观测空间覆盖不足的缺陷.卫星遥感的气溶胶资料不仅对全球和区域气候研究而且对城市污染分析提供了丰富的资料.     

Probabilistic projections of regional temperature and precipitation extending from observed time series

Probabilistic projections of change in regional temperature and precipitation previously derived allow for the range of sensitivities to global warming simulated by CMIP3 models. However, the changes were relative to an idealized base climate for 1980–1999, disregarding observed trends, such as those in rainfall in some Australian regions. Here we propose a method that represents projections for both forced change and decadal means as time series that extend from the observed series, illustrated using data for central Victoria. The main idea is to estimate the time-evolving underlying (or forced) past climate then convert this to a series of absolute values, by using the mean of the full observational record. We again use the pattern scaling assumption, and combine the CMIP3 sensitivities used for future change with a global warming series beginning at 1900. Like the confidence interval of regression theory, the analysis gives an estimate of the range of the underlying climate at each decade. This range can be augmented to allow for natural variability. A Bayesian theory can be applied to combine the model-based sensitivity with that estimated from observations. The time series are modified and the persistence of current observed anomalies considered, ultimately merging the probabilistic projections with the observed record. For some other cases, such as rainfall in southwest and north Australia and temperature in the state of Iowa, the two sensitivity estimates appear less compatible, and possible additional forcings are considered. Examples of the potential use of such time series are presented.     

Use of general circulation model output in the creation of climate change scenarios for impact analysis

Many scientific studies warn of a rapid global climate change during the next century. These changes are understood with much less certainty on a regional scale than on a global scale, but effects on ecosystems and society will occur at local and regional scales. Consequently, in order to study the true impacts of climate change, regional scenarios of future climate are needed. One of the most important sources of information for creating scenarios is the output from general circulation models (GCMs) of the climate system. However, current state-of-the-art GCMs are unable to simulate accurately even the current seasonal cycle of climate on a regional basis. Thus the simple technique of adding the difference between 2 × CO₂ and 1 × CO₂ GCM simulations to current climatic time series cannot produce scenarios with appropriate spatial and temporal details without corrections for model deficiencies. In this study a technique is developed to allow the information from GCM simulations to be used, while accommodating for the deficiencies. GCM output is combined with knowledge of the regional climate to produce scenarios of the equilibrium climate response to a doubling of the atmospheric CO₂ concentration for three case study regions, China, Sub-Saharan Africa and Venezuela, for use in biological effects models. By combining the general climate change calculated with several GCMs with the observed patterns of interannual climate variability, reasonable scenarios of temperature and precipitation variations can be created. Generalizations of this procedure to other regions of the world are discussed.     

The regional dynamical model of the atmospheric ozonosphere

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中国北方地区旱涝的年代际预测分析研究

基于状态空间重构理论和嵌入定理,给出场时间序列预测模型的建立思路。与单点时间序列预测分析方法相比,场时间序列预测分析方法的优点在于,在寻找吸引子上某个相点的最邻近点及其映象以建立预测模型时,不局限于它自身的时间序列,而是在区域内所有相点的时间序列所构成的整个吸引子上寻找。这样,在一定程度上改进单点时间序列的“遍历性”,以提高预测精度。在此基础上,利用中国北方地区534年旱涝等级资料,对中国北方几个区域年代际尺度的旱涝变化及其极端旱(涝)出现频率进行预测试验分析。     

Abrupt changes as indicators of decadal climate variability

 A statistical test has revealed that abrupt regional climate changes are produced in a coupled atmosphere-ocean general circulation model. Abrupt changes are detected over much of the globe although the occurrence frequency is small over the continents. Over the tropical Pacific Ocean and northern Pacific Ocean, surface air temperature (SAT) and sea level pressure (SLP) shift rapidly on decadal time scales. The regional climate changes presented here have been classified into three types. The first type consists of statistically significant shifts in SLP and statistically significant shifts in SAT which are of opposite sign, and which are reinforced through a positive feedback between the atmosphere and the ocean. The second type is for those occurrences where changes are of the same sign. The third type includes those with a significant shift in only one meteorological element. The second and third types are generally generated by changes in air pressure and wind fields induced by changes of the first type. For example, when SLP increases and sea surface temperature (SST) decreases abruptly in the tropical Pacific Ocean, it triggers abrupt regional changes in middle and high latitudes. The abrupt changes in the model climate have characteristics which are very similar to those of observed rapid shifts. Thus, it is concluded that abrupt changes are a predominant part of regional climate change on decadal time scales. Received: 11 February 1999 / Accepted: 18 May 2000     

The Impact of Agricultural Practices in China on Land-Atmosphere Interactions

Human-induced land use changes and the resulting alterations in vegetation features are major but poorly recognized drivers of regional climatic patterns.In order to investigate the impacts of anthropogenically-induced seasonal vegetation cover changes on regional climate in China,harmonic analysis is applied to 1982-2000 National Oceanic and Atmospheric Administration(NOAA) Advanced Very High Resolution Radiometer(AVVHRR)-derived normalized difference vegetation index(NDVI) time series(ten day interval data).For two climatic divisions of South China,it is shown that the first harmonic term is in phase with air temperature,while the second and third harmonics are in phase with agricultural cultivation.The Penman-Monteith Equation and the Complementary Relationship Areal Evapotranspiration(CRAE) model suggest that monthly mean evapotranspiration is out of phase with temperature and precipitation in regions with signiffcant second or third harmonics.Finally,seasonal vegetation cover changes associated with agricultural cultivation are identiffed:for cropped areas,the temperature and precipitation time series have a single maximum value,while the monthly evapotranspiration time series has a bimodal distribution.It is hypothesized that multi-cropping causes the land surface albedo to sharply increase during harvesting,thereby altering the energy distribution ratio and contributing to observed seasonal vegetation cover changes.     

Role of dynamic vegetation in regional climate predictions over western Africa

This study examines the role of vegetation dynamics in regional predictions of future climate change in western Africa using a dynamic vegetation model asynchronously coupled to a regional climate model. Two experiments, one for present day and one for future, are conducted with the linked regional climate-vegetation model, and the third with the regional climate model standing alone that predicts future climate based on present-day vegetation. These simulations are so designed in order to tease out the impact of structural vegetation feedback on simulated climate and hydrological processes. According to future predictions by the regional climate-vegetation model, increase in LAI is widespread, with significant shift in vegetation type. Over the Guinean Coast in 2084–2093, evergreen tree coverage decreases by 49% compared to 1984–1993, while drought deciduous tree coverage increases by 56%. Over the Sahel region in the same period, grass cover increases by 31%. Such vegetation changes are accompanied by a decrease of JJA rainfall by 2% over the Guinean Coast and an increase by 23% over the Sahel. This rather small decrease or large increase of precipitation is largely attributable to the role of vegetation feedback. Without the feedback effect from vegetation, the regional climate model would have predicted a 5% decrease of JJA rainfall in both the Guinean Coast and the Sahel as a result of the radiative and physiological effects of higher atmospheric CO₂ concentration. These results demonstrate that climate- and CO₂-induced changes in vegetation structure modify hydrological processes and climate at magnitudes comparable to or even higher than the radiative and physiological effects, thus evincing the importance of including vegetation feedback in future climate predictions.