1857—1987年南方涛动指数

本文利用塔希提和达尔文的气压差将美国气候分析中心的SOI序列从1935年向前接补到1857年,并用SOI周期及其与SST的关系进行了检验,证明可以将两段SOI接续起来。     

基于神经网络奇异谱分析的ENSO指数预测

采用奇异谱分析方法研究了ENSO指数及相关序列, 结果表明奇异谱分析能很好的对原始序列进行信噪分离, 增大了ENSO指数的可预报性.在此基础上, 提出了人工神经网络和奇异谱分析相结合的ENSO指数预测方法, 进行了不同因子组合的预报试验, 预报效果明显优于持续性预报, 超前4季的Nio 3区、 Nio 4区预报相关系数仍高于0.5.     

热带有关海域海表温度、南方涛动和我国东部气温的谱分析

本文采用波谱分析法对热带有关海域海表温度、南方涛动指数和我国东部气温作了分析。通过功率谱计算表明:(1)南海、赤道西太平洋和印度洋月海表温度变北分别存在3.4、6.8和1.4年振动周期。(2)我国华北、长江中、下游及华南地区月气温变化分别存在1.1、3.4和3.4年振动周期。交叉谱分析表明:在3.4年周期振动上,南方涛动依次超前南海月海表温度、长江中、下游及华南地区月气温变化;南海月海表温度依次超前于华南月气温及赤道东太平洋海表温度变化,这在某种意义上可把南海水温低频振动作为厄·尼诺监测器是有价值的。本文还对我国东部气温年际变化与厄·尼诺现象作了简单分析。     

南方涛动与我国夏季降水

本文分析了南方涛动、赤道东太平洋海温与我国夏季降水的关系。分析表明,前一年春季到冬季,赤道东太平洋海温偏高,南方涛动偏弱,夏季我国主要雨带位置偏北,位于黄河流域及其以北地区(即1类雨型),反之,夏季我国主要雨带位置偏南,位于黄河与长江之间(即2类雨型)。由此得出了预报我国夏季1、2类雨型的预报方案。并分析了南方涛动和北半球大气环流的遥相关联系及对北太平洋海温场热源异常的响应。     

基于奇异谱分析的江淮降水场预测模型研究

中国的降水预报主要是对汛期(夏季)的单站降水量或区域的降水型态进行预报,而主要的预报方法为动力学方法和统计学方法。据此,利用江淮地区的月降水量标准化序列,得到江淮地区降水时空分布型态。并在此基础上,利用奇异谱分析和时间序列的分析方法,设计了统计学的降水预测模型,对江淮地区的月降水量进行了预测试验。结果表明,模型能对江淮降水场的趋势作出较好的预报,且递推时间短时预报效果较好。     

A Data-Adaptive Filter of the Tahiti-Darwin Southern Oscillation Index and the Associate Scheme of Filling Data Gaps

The Tahiti-Darwin Southern Oscillation index provided by Climate Analysis Center of USA has been used in numerous studies. But, it has some deficiency. It contains noise mainly due to high month-to-month variability. In order to reduce the level of noise in the SO index, this paper introduces a fully data-adaptive filter based on singular spectrum analysis. Another interesting aspect of the filter is that it can be used to fill data gaps of the SO index by an iterative process. Eventually, a noiseless long-period data series without any gaps is obtained.     

用奇异谱分析作汛期降水的中期气候预测

用奇异谱分析方法对哈尔滨汛期降水进行中期气候预测。其结果表明，此方法对汛期降水趋势有较好的预测能力，并且预报效果比较稳定，经5次试报的平均准确率达76％，说明奇异谱分析模式是一种有效的中期气候预测手段，其趋势预测结果可信度高。     

基于SSA-MGF的BP神经网络多步预测模型

采用奇异谱分析（Singular Spectrum Analysis，SSA）方法对标准化样本序列进行准周期信号分量重建，将重建序列构造均值生成函数（Mean Generating Function．MGF）延拓矩阵作为输入因子，原样本序列作为输出因子，构建BP神经网络多步预测模型。通过实际建模并与逐步回归等方法进行对比预测试验，结果表明，基于SSA-MGF的BP神经网络多步预测模型预测效果优于其他3种模型，说明SSA的去噪及BP神经网络预报模型对于提高预测准确率是相对有效的，是一种具有较高应用价值的多步预测方法。     

基于多种代用资料的南极涛动指数重建

Based on multiple proxies from the Southern Hemisphere, an austral summer (December-January-February: DJF) Antarctic Oscillation Index (AAO) since 1500 A.D. was reconstructed with a focus on interannual to interdecadal variability (< 50 a). By applying a multivariate regression method, the observational AAO-proxy relations were calibrated and cross-validated for the period of 1957-89. The regressions were employed to compute the DJF-AAO index for 1500-1956. To verify the results, the authors checked the explained variance (r2), the reduction of error (RE), and the standard error (SE). Cross-validation was performed by applying a leave-one-out validation method. Over the entire reconstruction period, the mean values of r2, RE, and SE are 59.9%, 0.47, and 0.67, respectively. These statistics indicate that the DJF-AAO reconstruction is relatively skillful and reliable for the last ~460 years. The reconstructed AAO variations on the interannual and interdecadal timescales compare favorably with those of several shorter sea level pressure (SLP)-based AAO indices. The leading periods of the DJF-AAO index over the last 500 years are ~2.4, ~2.6, ~6.3, ~24.1, and ~37.6 years, all of which are significant at the 95% level as estimated by power spectral analysis.     

A Statistical Analysis of the North Atlantic Oscillation and its Impact on European Temperature

In this study the behaviour of the North Atlantic Oscillation (NAO) and its impact on the surface air temperature in Europe 1891-1990 is analysed using statistical time series analysis techniques. For this purpose, both the NAO index (NAOI) and the surface air temperature time series from 41 European stations are split up into typical variation components. Various measures of correlation indicate that the NAOI-temperature relationships are approximately linear and most pronounced in winter. The spatial correlation patterns show a correlation decrease from North West to South East (winter) exceeding correlation coefficients of 0.6 in the Scotland-South Norwegian area. In summer, these correlations are very weak, in spring and autumn stronger but smaller than in winter. These correlations change significantly in time indicating increasing correlations in Central and North Europe and decreasing correlations in the North West. Low-frequent episodic components represented by related polynomials of different order are very outstanding in both NAO and temperatures showing up in all seasons, except summer, relative maxima roughly 1900 and in recent times, relative minima in the beginning ( ca . 1870) and roughly 1960-1970. Periodogramm analysis reveals a dominant cycle of 7.5 years (NAOI and a majority of temperature time series) whereas in case of the polynomial component one may speculate about a 80-90 year cycle.     

我国西北地区气候变化与北极涛动的交叉小波分析

运用交叉小波方法分析了近56 a来我国西北地区气候变化与北极涛动指数（AOI）、欧亚纬向环流指数之间的联系,讨论了西北地区冬季气温和夏季降水变化与同期AOI之间的多时间尺度相关。结果表明,AOI、西北地区降水和气温变化都存在着不同尺度的周期振荡,西北地区降水与AOI之间存在准3 a尺度的显著共振周期,而AOI与西北地区气温变化的多时间尺度相关表现为准2a、3-5 a和8-11 a左右的显著相关振荡且以8-11 a共振周期的凝聚性最强;AO对西北地区气温变化的影响比对降水的影响更显著,与其年代际尺度的相互作用有关。当冬季北极涛动处于正相位时,欧亚纬向环流偏强,西北地区冬季气温偏高。夏季西北地区降水变化与前期北极涛动指数异常密切相关,这对于西北地区夏季降水变化预测具有重要的参考意义。     

观测分析非洲南部地区土壤湿度异常对南半球大气环流的显著影响

本文基于44年ERA40再分析月平均土壤湿度资料和大气环流变量场资料,去除ENSO遥相关以及趋势影响后,利用滞后最大协方差方法分析非洲南部地区土壤湿度分布与南半球大气环流异常之间的线性耦合。第一最大协方差模态的结果表明:在南半球冬季（Jun-Jul-Aug,6～8月）和夏季（Jan-Feb-Mar,1～3月）,大气中类似南极涛动（Antarctic Oscillation,简称AAO）正位相的环流型与超前月份（最长时间达到5个月）的非洲南部地区土壤湿度的异常分布显著相关。基于土壤湿度变率中心的线性回归分析方法证实非洲南部地区其北部土壤湿度正异常、中南部土壤湿度负异常的空间分布对后期夏季和冬季的大气有显著的反馈作用。诊断结果显示由于夏秋季节和春季初夏非洲南部地区土壤湿度异常均有显著的持续性,同时对后期AAO产生持续增强作用,所以滞后最大协方差方法可以检测出它们对后期AAO的显著影响。以上非洲南部地区土壤湿度异常超前于南极涛动的信号,将有助于加强对土壤湿度反馈机制及其对南半球大尺度环流变率影响的认识。