中国月平均温度的气候噪声和潜在可预报性

利用中国74个测站1960～1991年日平均温度研究了中国月平均温度的气候噪声和潜在可预报性。气候噪声是在Yamamoto等人的思想基础上设计的方法估计的,而潜在可预报性则是用月平均温度的年际变化与自然变化（气候噪声）之比表示的。一般情况下中国月平均温度的气候噪声随纬度和高度增加而增加,并随季节变化而变化。来自西伯利亚和蒙古的变性大陆干冷气团对气候噪声有很大的影响,一般而言,海洋对气候噪声起着调节和减弱作用（除了热带海洋在春秋过渡季节外）。月平均温度的潜在可预报性有较大的季节和区域差异。但总的来说中国月平均温度在α＝0．10的统计显著性水平上是潜在可预报的。这些结果表明由于气候噪声和潜在可预报性有季节和区域的差异,所以不能要求用一个气候模式在任何时候对每一地区都得到满意的结果。要对各月的气候进行预报,需根据不同月份至少不同季节建立区域气候模式可能更有发展前景。     

POTENTIAL PREDICTABILITY OF MONTHLY PRECIPITATION OVER CHINA*

In this paper,based on the data at 70 stations selected evenly over China for 31 years from 1961-1991.three methods to estimate climatic noise have been discussed and then the climatic noise and potential predictability of monthly precipitation(January.July.April and October)have been examined.The estimating of climatic noise is based on the method of Madden and improved methods of Trenberth and Yamamoto et al.(1985).The potential predictability is approximated by the ratio of the estimated interannual variation to the natural variation.Generally.the climatic noise of monthly precipitation over China has obvious seasonal variation and it is greater in summer than in winter,a bit greater in autumn than in spring.In most areas,the climatic noise is prominently decreasing from south to north and from coast to inland.The potential predictability of monthly precipitation also has obvious seasonal and regional difference,but the potential predictability is greater in winter than in summer in most parts of China.Whereas the comparison of spring and autumn is not obvious.Comparing with the method of Madden,the estimated values of climatic noise based on the improved methods of Trenberth and Yamamoto et al.are relatively lower.     

中国季降水量的气候噪声和潜在可预报性估计

利用中国130个测站1961—2004年的日降水量资料,使用低频白噪声延伸法和方差分析法估计了中国季降水量的气候噪声方差和潜在可预报性。结果表明：中国季降水量的气候噪声方差由南向北、由沿海向内陆逐渐减小,且有明显的季节变化,夏季最高,其次是春秋季,冬季最小,而且内陆的季节变化比东南沿海的季节变化显著。季降水量的潜在可预报性有较大的季节和区域差异,但总体来说,全国大部分地区的季降水量是潜在可预报的。以绝对误差小于均方差0.68倍作为预测正确标准,全国大部分地区季降水量的预报正确率上限为50%-60%。     

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。     

英国CRU高分辨率格点资料揭示的20世纪中国气候变化

中国覆盖比较完整的台站观测始于1951年,1951年之前虽然有一些观测记录,但是残缺不全.所以要建立更长的气候序列就要吸收代用资料,但是代用资料可能与气候要素仅有一定程度的相关,不可能一一对应,因此应用代用资料重建的气候序列有一定的不确定性.英国East Anglia大学的Climatic Research Unit(简称CRU)通过整合已有的若干个知名数据库,重建了一套覆盖完整、高分辨率、且无缺测的月平均地表气候要素数据集,时间范围覆盖1901～2003年,空间为0.5°×0.5°经纬网格覆盖所有陆地.这套资料和中国已有的气候数据相比具有如下优点: 第一,中国西部20世纪前半期非常缺少观测,CRU资料尽管包含插值带来的误差,经比较仍可作为有一定信度的参考; 第二,中国现有的百年温度序列只是年或季分辨率,而CRU资料达到月分辨率; 第三,建立这个序列仅使用观测结果,做统计内插,不包括代用资料所带来的不确定性.因此,CRU的序列与用代用资料补充得到的序列在资料方面有较大不同,比较这两个序列,不仅可以进一步确认中国气候变化的特征,也可以彼此校正.结果表明:(1)CRU资料反映的全国年平均温度年际变化和考虑代用资料重建的序列吻合得很好,相关系数达到0.84;(2)区域尺度上,两者在10个典型分区的气温变率也相当一致,相关整体保持在0.8左右,仅新疆西南部和西藏西北部两者差异较大;(3)CRU资料揭示的中国年总降水量在1951～2000年的变化与160站观测吻合,相关系数达到0.93;(4)CRU资料的中国东部四季降水量和重建资料十分一致,秋季一致性最好,相关0.93;(5)CRU资料和重建的序列比较一致地表现出中国温度和降水年代际变化的主要特征,其给出的20世纪20年代中国大旱和20世纪40年代中国高温的空间分布与作者过去的结论相一致.这表明,作者过去重建的中国气候序列有比较大的可靠性,而CRU资料也提供了新的信息,特别是在20世纪前半期和中国西部.     

Estimating the potential predictability of Australian surface maximum and minimum temperature

A study is made of the potential predictability of seasonal means in Australian surface maximum and minimum temperature using monthly data from December 1950 to November 2000. Because the usual assumption of stationarity cannot be applied to the observations at all stations and for all seasons, a modification to an existing methodology is proposed. Here, we show that, to a first order, monthly mean variances within a season can be modeled by a linear relationship, and inter-monthly correlations can be assumed to be stationary. The intraseasonal component of variability can then be estimated using monthly data. Removing the intraseasonal variance from the total interannual variance allows an estimate of the potential predictability to be made. Surface maximum and minimum temperature has high potential predictability over most of northern Australia in the four main seasons. However, there is high potential predictability only in some of the four seasons for the centre and south of Australia. Surface minimum temperature is generally more potentially predictable than surface maximum temperature. The spatial and temporal patterns of potential predictability are generally consistent with published patterns of hindcast skill from a statistical forecast scheme. A comparison between the intraseasonal variance of Australian surface maximum and minimum temperature estimated using the stationary variance assumption and the linear assumptions showed qualitatively and quantitatively similar patterns of distribution.     

STABILITY AND POTENTIAL PREDICTABILITY OF ANNUAL PERCIPITATION OVER CHINA

The precipitation is a primary element which directly affects the agricultural production of thecountry with one fifth of the world population.With the economic development the water resourcestress is getting greater.In this paper,based on the data at 162 stations selected evenly over Chinafrom 1960 to 1991 the stability and potential predictability of annual precipitation have been stud-ied.The eastern and southern parts of the country having abundant precipitation enjoy more stableprecipitation.The north and northwest parts of the country where the precipitations are deficienthave unstable precipitations.The potential predictability approximates to the ratio of the estimatedinterannual variance to the climatic noise.Generally the annual precipitation over China is poten-tially predictable.In the area between the Huanghe River and Changjiang River and the east ofnortheastern China the potential predictability is the lowest in the country.In the north and north-west of the country the potential predictability is greater.The southeastern coast has relatively lowvalues of potential predictability.Also,the method of estimating climatic noise of annual precipita-tion has been discussed from the idea of Yamamoto et al.(1985)in order to estimate the potentialpredictability.     

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.     

A Regression-based Assessment of the Predictability of New Zealand Climate Anomalies

Summary Estimates of the predictability of New Zealand monthly and seasonal temperature and rainfall anomalies are calculated using a cross-validated linear regression procedure. Predictors are indices of the large scale circulation, sea-surface temperatures, the Southern Oscillation Index and persistence. Statistical significance is estimated through a series of Monte Carlo trials. No significant forecast relationships are found for rainfall anomalies at either the monthly or seasonal time scale. Temperature forecasts are however considered to exhibit significant skill, with variance reductions of the order of 10–20% in independent trials. Temperature anomalies are most skilfully predicted over the North Island, and skill is greatest in Spring and Summer in most areas. At the monthly time scale, predictors local to the New Zealand region account for most of the forecast skill, while at the seasonal time scale, skill depends strongly upon “remote” predictors defined over regions of the southern hemisphere distant from New Zealand. Indices of meridional flow over the Tasman Sea/New Zealand region are found to be useful predictors, especially for monthly forecasts, perhaps as a proxy for atmospherically-forced sea surface temperature anomalies. Sea surface temperature anomalies to the west of New Zealand and in the tropical Indian Ocean are also useful, especially for seasonal predictions. Forecast skill is more reliably estimated at the monthly time scale than at the seasonal time scale, as a result of the larger sample size of monthly mean data. While long-term mean levels of skill may be estimated reliably over the whole data set, statistically significant decadal-scale variations are found in the predictability of temperature anomalies. Therefore, even if long-term forecast skill levels are reliably estimated, it may be impossible to predict the short-term skill of operational seasonal climate forecasts. Implications for operational climate predictions in mid-latitudes are discussed. Received July 18, 1997 Revised April 2, 1998     

Relevance of soil moisture for seasonal atmospheric predictions: is it an initial value problem?

Ensembles of boreal summer atmospheric simulations, spanning a 15-year period (1979–1993), are performed with the ARPEGE climate model to investigate the influence of soil moisture on climate variability and potential predictability. All experiments are forced with observed monthly mean sea surface temperatures. In addition to a control experiment with interactive soil moisture boundary conditions, two sensitivity experiments are performed. In the first, the interannual variability of the deep soil moisture is removed during the whole season, through a relaxation toward the monthly mean model climatology. In the second, only the variability of the initial soil moisture conditions is suppressed. While it is shown that soil moisture strongly contributes to the climate variability simulated in the control experiment, an analysis of variance indicates that soil moisture does not represent a significant source of predictability in most continental areas. The main exception is the North American continent, where climate predictability is clearly reduced through the use of climatological initial conditions. Using climatological soil moisture boundary conditions does not lead to strong and homogeneous impacts on potential predictability, thereby suggesting that the climate signals driven by the sea surface temperature variability are not generally amplified by interactive soil moisture and that the relevance of soil moisture for seasonal forecasting is mainly an initial value problem.     

华东地区热带气旋汛频数异常时的环流分析

该文是为应用统计释用预报方法对影响华东地区的热带气旋（ATC）汛频数预报所做的前期研究工作。研究发现:（1）影响华东地区的热带气旋汛频数的局地性异常变化是汛平均环流半球性调整的结果;（2）汛平均环流中的大气长波既反映了月平均环流的特征又和汛频数的变化存在着较好的关系,特别在异常年,其天气系统和相关地区也比较稳定;（3） 利用影响华东地区的热带气旋汛频数和环流变化的关系建立了汛频数的统计预报方程。在短期气候数值形势预报准确的基础上,应用统计释用预报方法所得的准确率为70%左右,若能预报出汛平均环流的异常变化,则可确定ATC汛频数的特多年或特少年。     

ENSO事件对中国气候的可能影响

利用 1 95 1 -1 998年的北太平洋海温和中国的温度、降水月平均资料 ,分析了ENSO事件对中国夏季主要雨带、长江中下游梅雨、华北雨季和中国秋冬季降水、温度、台风之间的可能影响 ,结果表明ENSO事件是影响中国气候的重要因素之一。     

Influence of non-feedback variations of radiation on the determination of climate feedback

Recent studies have estimated the magnitude of climate feedback based on the correlation between time variations in outgoing radiation flux and sea surface temperature (SST). This study investigates the influence of the natural non-feedback variation (noise) of the flux occurring independently of SST on the determination of climate feedback. The observed global monthly radiation flux is used from the Clouds and the Earth's Radiant Energy System (CERES) for the period 2000–2008. In the observations, the time lag correlation of radiation and SST shows a distorted curve with low statistical significance for shortwave radiation while a significant maximum at zero lag for longwave radiation over the tropics. This observational feature is explained by simulations with an idealized energy balance model where we see that the non-feedback variation plays the most significant role in distorting the curve in the lagged correlation graph, thus obscuring the exact value of climate feedback. We also demonstrate that the climate feedback from the tropical longwave radiation in the CERES data is not significantly affected by the noise. We further estimate the standard deviation of radiative forcings (mainly from the noise) relative to that of the non-radiative forcings, i.e., the noise level from the observations and atmosphere–ocean coupled climate model simulations in the framework of the simple model. The estimated noise levels in both CERES (>13 %) and climate models (11–28 %) are found to be far above the critical level (~5 %) that begins to misrepresent climate feedback.     

云南地区季降水量和气温的潜在可预报性分析

利用云南地区42年气候资料,使用低频白噪声延伸法和方差分析方法,估计了该地区季节降水量和季节气温的气候噪声方差和潜在可预报性。分析结果表明：（1）云南季降水量的气候噪声方差随着季节降水量的增加而增加,空间上主要是由南往北减小,夏季降水量的气候噪声方差显著大于其他季节,季气温的气候噪声方差则随着季节气温的减小而增加,空间上春、冬季由东往西减小而夏、秋季由南往北增加;冬季气温的气候噪声方差显著大于其他季节;（2）云南季降水量和季气温的潜在可预报性同样具有显著的季节变化和空间变化,云南春季的降水量和气温的潜在可预报性均显著大于其他季节,夏季降水量和气温的潜在可预报性均较其他三个季节小;春、秋季降水量潜在可预报性西部大于东部,夏季北部大于南部,冬季则是南部大于北部,云南季气温除夏季外均是西部大于东部。（3）季风和冷空气活动可能对云南地区的季降水量和气温的潜在可预报性有重要影响。     

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.     

Reconstruction of paleovegetation and paleoclimate in the Early and Middle Eocene, Hainan Island, China

The Early–Middle Eocene palynoflora and paleoclimate of Changchang Basin, Hainan Island, South China, is described in the present paper and is compared with that of the Middle–Late Eocene, Hunchun City, Jilin Province, North China. The nearest living relatives (NLRs) of the recovered palynotaxa suggest a subtropical evergreen or deciduous broad-leaved forest at the center of the basin but a temperate evergreen or deciduous broad-leaved forest and needle-leaved forest growing in the peripheral part of the basin. Based on the climatic preferences of the NLRs, the climate in the Changchang Basin during the Early–Middle Eocene was warm and humid subtropical with a mean annual temperature of 14.2–19.8°C, a mean temperature of the warmest month of 22.5–29.1°C, a mean temperature of the coldest month of 1.7–11.9°C, a difference of temperature between coldest and warmest months of 12.1–24.6°C, a mean annual precipitation of 784.7–1,113.3 mm, a mean maximum monthly precipitation of 141.5–268.1 mm and a mean minimum monthly precipitation of 6.9–14.1 mm. A comparison of the palynoflora and paleoclimate between the Changchang Basin and Hunchun City, suggests essentially a similar climate in South and North China during Eocene time in contrast to the oceanic tropical climate in South China and cool dry temperate climate in North China as at present.     

广西夏季降水量潜在可预报性估计

研究和评价短期气候预测方法必须了解月或季平均量的年际变率有多少是可以被预报出来的。通常认为, 总的年际变率能够被划分为主要来源于大气下边界条件持续性强迫的可预报成分和来源于“天气噪声”的不可预报成分, 这两个成分的方差之比给出潜在预报性的测度。文章用低频白噪声延伸法及日降水量独立和相关假设下的方差分析方法估计了广西夏季降水量潜在可预报的气候信号方差和天气噪声方差。结果表明:全区都存在潜在可预报信号, 在中部和东部气候信号最强, 南部最弱。以绝对误差小于均方差0.68倍作为预报正确的标准, 则预报正确率上限在自治区中部和东部约为72 %, 南部约为59 %, 北部和西部约为62 %。     

Predicting Western Pacific Subtropical High Using a Combined Tropical Indian Ocean Sea Surface Temperature Forecast

Weather and climate in East China are closely related to the variability of the western Pacific subtropical high（WPSH）, which is an important part of the Asian monsoon system. The WPSH prediction in spring and summer is a critical component of rainfall forecasting during the summer flood season in China. Although many attempts have been made to predict WPSH variability, its predictability remains limited in practice due to the complexity of the WPSH evolution. Many studies have indicated that the sea surface temperature（SST） over the tropical Indian Ocean has a significant effect on WPSH variability. In this paper, a statistical model is developed to forecast the monthly variation in the WPSH during the spring and summer seasons on the basis of its relationship with SST over the tropical Indian Ocean. The forecasted SST over the tropical Indian Ocean is the predictor in this model, which differs significantly from other WPSH prediction methods. A 26-year independent hindcast experiment from 1983 to 2008 is conducted and validated in which the WPSH prediction driven by the combined forecasted SST is compared with that driven by the persisted SST. Results indicate that the skill score of the WPSH prediction driven by the combined forecasted SST is substantial.     

Statistical Analysis and Prediction of KwaZulu-Natal Climate

Summary. Climatic fluctuations in KwaZulu-Natal, southeastern South Africa, are analysed using statistical techniques. Moist easterly winds sweep in from the Indian Ocean during all seasons except winter, producing a balance between evaporative losses and precipitation. The seasonal cycle is unimodal with a peak of rainfall and temperature in the summer months (December to February) with a 1–2 month lag for streamflow and vegetation growth. Rainfall and temperature departures in recent decades exhibit a 3 year cycle and a 3–6 month persistence of cool/wet or warm/dry phases. The predictability of summer rainfall, temperature, crop yield, inflow to dams and malaria incidence is explored. Multivariate linear regression models with lead-times of one season account for two-thirds of the variance in most cases. Climatic signals which enable predictability include winds over the tropical east Atlantic and north Indian Ocean. El Ni?o signals from tropical Pacific sea surface temperatures and the Southern Oscillation Index are also important predictors for KwaZulu-Natal’s climate. These relationships suggest that local circulation responses to large scale tropical-polar temperature gradients govern climatic fluctuations over KwaZulu-Natal. Received August 27, 1997. Revised November 10, 1997     

本世纪我国气温变化的某些特征

将本世纪以来我国137个站每个站逐月的月平均气温划分为五个等级,绘制了全国范围的历年逐月温度等级分布图。根据图中各月冷暖区范围大小,分为暖月、偏暖月、冷月、偏冷月和正常月五种类型。计算了全国范围和东北、华北、长江中下游、华南、西北(主要指陕西、甘肃、宁夏、青海等四省区。以下同)、西南和新疆等七个区的温度等级值。根据这些资料,统计分析了五类温度月份出现的气候特征及不同季节冷暖月次的多年变化,研究了全国和各区温度等级值的演变规律及其相互联系,以及近30年来我国气温演变的某些特征。