应用九层全球大气格点模式进行跨季度短期气候预测系统性试验

利用中国科学院大气物理研究所9层大气环流模式（IAP9L AGCM）对夏季气候进行了30年（ 1970～1999年）集合回报试验,并采用统计学分析方法对跨季度夏季短期气候的可预测性问 题进行了初步探讨. 结果表明,该模式对对流层中、高层大气环流的预测能力强于低层,位 势高度场和表面气温的可预测性最大,而降水的可预测性则相对较小. 对流层中、高层位势 高度场的可预测性基本呈带状分布,越靠近赤道可预测性越高;而降水的可预测性基本局限 于赤道东太平洋及热带个别区域. 由此可见,降水的预测极为困难和复杂,订正系 统的研究和寻找新的预报物理因子非常重要.      

大气初始场对短期气候数值预测的影响

利用中国科学院大气物理研究所发展的九层大气环流模式(简称IAP 9L2°×2.5°-AGCM)分析了大气初始场对短期气候数值预测的影响,分别从实际观测海温、海冰的外强迫和气候态海温、海冰的外强迫出发,进行两组集合回报试验,每组包含3个试验,分别将实时NCEP-Ⅱ资料和对NCEP-Ⅱ资料经5 d平滑、11 d平滑后的资料作为大气初始场,进行17 a(1988—2004年)集合回报试验,采用相关分析方法对试验结果进行对比分析。结果表明,比较相关系数定量检验出大气初始场对热带地区可预报性影响较小,而对中、高纬度地区影响很大。通过对6个试验中对应气象要素在对流层各层距平时间相关系数以及17 a空间异常相关系数均值比较分析发现对东亚(中国)地区夏季气候而言,NCEP-Ⅱ资料经5 d平滑后生成的大气初始场对应回报试验结果相对最好。     

基于岩石变形声发射实验结果讨论岩石失稳的可预测性

根据室内岩石变形声发射实验结果,计算了声发射序列的地震非均匀度GL值,并以此探讨了岩石破裂失稳的可预测性。在双轴压缩、等速率加载条件下,含障碍体断层、挤压雁列断层和Ⅲ型剪切断层等非连续岩石标本在临近滑动失稳前,声发射活动均出现了显著的均匀—非均匀的状态变化,表现为GL值出现持续大于1的异常变化,GL值异常的位置与破裂成核部位相重合,并与断层走向一致;改变统计窗长、窗内分割数及能级统计下限计算声发射序列的GL值,结果表明,GL值计算结果具有一定的稳定性。综合分析认为,地震非均匀度(GL值)可以较好地刻画岩石失稳前夕因破裂成核导致的声发射活动在时间分布上的状态变化,GL值持续异常是岩石失稳的显著性标志,对预测岩石失稳具有较好的指示意义。     

Progress in Predictability Studies in China （2003-2006）

Since the last International Union of Geodesy and Geophysics General Assembly(2003),predictability studies in China have made significant progress.For dynamic forecasts,two novel approaches of conditional nonlinear optimal perturbation and nonlinear local Lyapunov exponents were proposed to cope with the predictability problems of weather and climate,which are superior to the corresponding linear theory.A possible mechanism for the"spring predictability barrier"phenomenon for the El Ni(?)o-Southern Oscillation (ENSO)was provided based on a theoretical model.To improve the forecast skill of an intermediate coupled ENSO model,a new initialization scheme was developed,and its applicability was illustrated by hindcast experiments.Using the reconstruction phase space theory and the spatio-temporal series predictive method, Chinese scientists also proposed a new approach to improve dynamical extended range(monthly)prediction and successfully applied it to the monthly-scale predictability of short-term climate variations.In statistical forecasts,it was found that the effects of sea surface temperature on precipitation in China have obvious spatial and temporal distribution features,and that summer precipitation patterns over east China are closely related to the northern atmospheric circulation.For ensemble forecasts,a new initial perturbation method was used to forecast heavy rain in Guangdong and Fujian Provinces on 8 June 1998.Additionally, the ensemble forecast approach was also used for the prediction of a tropical typhoons.A new downscaling model consisting of dynamical and statistical methods was provided to improve the prediction of the monthly mean precipitation.This new downsealing model showed a relatively higher score than the issued operational forecast.     

地震预报的研究为何进步不快？

地震作为一种物理现象，认识到一定的程度肯定是能够预报的。但是，它是一个困难的课题，因为地球中的震源体不能直接观测到，并且最重要的参量——应力的强度也不能直接测出。同时，地震学是一门年轻的学科，地震的起因只是到了20世纪60年代才被揭示出来：先进的地震台网以及测量地壳形变的技术，象地球定位系统(GPS)和合成孔径雷达干涉测量技术(lnSAR)仅仅是最近才开始在线布设。布设足够密度的测点，才能在了解破裂的过程时有重大进展，也只有日本才能做到。可是在美国还没有一个实用的地震预报研究程序。主要原因是资金赞助机构和同级的评审机构都回避非专业化的领域，但却促成了一些单项的活动。虽然一些成功的预报意见看来理由并不充分，确实有少量准确预报的例子。多数的预报都有几年的时间间隔，但有时也精确到几天，并且能提供准备活动的时间，为取得更大的进步，我们必须创造一个严格的科学的探讨范畴，在这里允许非专业人员大胆尝试。领导者应当把必要的资金投入到这项工作中来，包括把最优秀的智力投入到这个充满希望的有益于社会，但也颇具争论的课题中来。     

Reversal of monthly East Asian winter air temperature in 2020/21 and its predictability

在全球变暖的背景下,近年来东亚冬季气温存在复杂的季节内变化.本文研究了2020/21年东亚冬季气温的月际转折及可预测性.结果 表明,2020/21年东亚冬季气温前冬(2020年12月-2021年1月中旬)偏冷,后冬(2021年1月中旬-2月)偏暖.西伯利亚高压强度在前冬和后冬也出现转折变化.在前冬,由于2020年9月巴...     

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.     

The Mesoscale Predictability of a Heavy Precipitation Event

Using the mesoscale model MM5, the development of initial condition uncertainties at different scales and amplitudes and their influences on the mesoscale predictability of the ＂0185＂ Shanghai heavy precipitation event are investigated. It is found that different initial conditions obtained from different globe model analyses lead to large variations in the simulated location and strength of the heavy precipitation, and the scales and amplitudes of the initial condition perturbations significantly influence the model error growth. The power spectrum evolution of the difference total energy （DTE） between a control simulation and a sensitivity experiment indicates that the error growth saturates after 12 h, which is the predictable time limit of the heavy precipitation event. The power spectrum evolution of the accumulated precipitation difference between the control and sensitivity simulations suggests a loss of the mesoscale predictability for precipitation systems of scales smaller than 300 kin, i.e., the predictable space for the heavy precipitation event is beyond 300 km. The results also show that the initial uncertainties at larger scales and amplitudes generally result in larger forecast divergence than the uncertainties at smaller scales and amplitudes. The predictable forecasting time and space can be expanded （e.g., from 12 to 15 h, and from beyond 300 kin to beyond 200 km） under properly prescribed initial perturbations at smaller scales and amplitudes.     

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.     

城市植被覆盖变化可预测性及其空间格局的定量递归分析——以深圳市为例

由于人口的快速增加和经济要素的大量聚集,城市是下垫面性质改变最为显著的区域。本文应用定量递归分析技术对1999~2006年逐旬的1km SPOT-VEGETATION NDVI数据进行分析,以期揭示NDVI时间系列的非线性动态特征及其空间格局。研究结果表明,深圳市的NDVI时间系列的RQA特征值居于随机系列和确定性系列之间,属于含有随机成分的确定性系列。不同覆被类型下NDVI系列的特性不同,林地区的NDVI系列规则性最高,农地区次之,建设用地区最差,表明人类活动作为一种噪声对于NDVI系列性质的扰动。用二阶Rényi熵作为NDVI系列的可预测性指标,其空间格局表现为海拔高、坡度大、林地分布集中的西北和东南部可预测性高,而海拔相对低、建设用地相对集中的中南部地区可预测性低。