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。     

我国的降水资源及其稳定性与潜在可预报性（Ⅱ）潜在可预报性

本文探讨了年降水量气候噪声估计的方法，并利用我国分布较均匀的１６２个测站１９６０－１９９１年降水资料，讨论了年降水量的潜在可预报性，以便进一步研究月，季降水量的可预报性。结果得出；黄河以南和长江流域的广大地区，特别是四川东部和江淮地区，是我国降水量气候噪声最大的地区；华北，西北以及华南地区降水量的潜在可预报性较大黄河以南和长江流域中下游地区降水量的潜在可预报性较小。     

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。     

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 %。     

吉林省夏季降水量潜在可预报性估计

短期气候预测的主要对象是气温和降水的月、季平均量和总量，需要做预报是因为它们有年际变率，但是年际变率究竟有多少是可以被预报出来的呢？通常情况下将总的年际变率划分为主要来源于大气下边界条件持续性外源强迫引起的可预报成分和由于大气内部不稳定性产生的日际天气振荡引起的不可预报成分，前者称气候信号，后者称气候噪声，我们用两个成分的方差之比给出潜在可预报性的测度。本文用低频白噪声延伸法估计了吉林省夏季（6～8月）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，我省各地均存在潜在可预报性信号。以绝对误差小于均方差0．68倍做为预报正确的标准，我省70%的站预报正确率上限达60％以上。     

我国月降水量气候噪声的估计

利用我国大陆均匀分布的70个测站1961~1991年逐日降水量资料, 讨论了月降水量的气候噪声的3种估计方法, 并分别用改进后的方法估计了1、4、7、10月四个有代表性月份降水量的气候噪声.结果表明:我国月降水量的气候噪声随着降水量增加, 具有明显的季节变化, 一般夏季月份的噪声显著大于冬季月份, 秋季月份稍高于春季月份.从空间分布看, 春、夏、秋、冬全国绝大部分地区的气候噪声由南向北、由沿海向内陆明显减小.     

使用ECMWF集合预报数据集估计全球季节平均气候异常的潜在可预报性

利用ECMWF PROVOST项目产生的在给定海表温度强迫下的150(1979～1993)季节集合预报数据集，分析揭示了季节平均气候异常潜在可预报性的全球分布。首先，利用可再现的强迫模态重建集合资料场，在Kolmogorov-Smirnov(K-S)检验的基础上定义潜在可预报性指数PU^k，然后，将重建场的PU^k与重建场贡献于集合平均的方差比结合，提出了定量估计局地潜在可预报性的指数PI。以全球850hPa温度季节平均异常场为例，对PI进行定量计算表明：不仅大部分热带地区，而且热带外一些地区的季节平均气候异常具有潜在可预报性，主要分布在北美、南非和亚洲部分季风区；全球大部分潜在可预报地区主要受ENSO型强迫控制，而部分温带地区如中国华北、中亚、北美南部主要受非ENSO型强迫控制；局地潜在可预报性具有季节性，夏季可预报性较强，冬季较弱。通过与其他几种估计季节潜在可预报性的方法进行比较表明，本文提出的PI方法能更好地把热带外地区受外强迫控制的可预报信号提取出来。     

北半球海冰强迫作用下大气可预报性研究

使用LASG/IAP GOALS耦合模式中的全球大气环流模式分量AGCMR15L9的计算结果，在其他外强迫维持气候值不变的情况下，用方差分析的方法，以外部方差与总方差之比R_e作为衡量标准，考察该模式关于海冰的季节和跨季节潜在可预报性的大小。结果发现，从总体上看，北半球海冰变化所造成的潜在可预报性较小，只有在大气低层的一些气候要素，如温度、湿度的结果中，才存在R_e＞0.5的现象。潜在可预报性结果的局地特征比较明显，高值往往发生在海冰年际变率大的区域里。与中低纬海温在中高纬地区的影响相比，不排除海冰的作用更大的可能性。另外，如果分区域看，北半球某些区域的海冰，在若干挑选出的其区域海冰面积发生大异常年份中的潜在可预报性可能会比不做挑选的总体结果要大。这说明北半球某些区域海冰在面积发生较大异常的时候，可能对同期或（及）后期环流有着比较重要的影响。     

土壤湿度对东亚夏季气候潜在可预报性影响的数值模拟

利用全球大气环流模式NCARCAM3进行了在给定的观测海温条件下的22a（1979—2000年）5—8月的2组集合试验。运用方差分析方法,分析了在气候态和年际变化的表层土壤湿度情况下,CAM3模式模拟的东亚夏季气候潜在可预报性及其差异。结果表明：在给定的观测海温条件下,采用气候态的土壤湿度时,CAM3模式模拟的东亚夏季气候的潜在可预报性偏低;而采用年际变化的土壤湿度时,模拟的夏季气候潜在可预报性有所提高,尤其是在中国西北地区;后者模拟的中国西北地区夏季降水和气温的潜在可预报性比前者的模拟结果提高0．1以上。其原因可能是：采用年际变化的土壤湿度时,模式可以更好地模拟出中国西北地区的地表蒸发量和湍流热通量的年际变化,进而使得模式对该地区夏季气候的预报技巧得到提高。     

1982~1999年中国地区叶面积指数变化及其与气候变化的关系

利用1982～1999年AVHRR Pathfinder卫星遥感观测的植被叶面积指数（leaf area index,LAI）资料和中国730个气象台站的温度、降水观测资料,研究了中国不同地区（东北地区、华北地区、长江流域、华南地区和西南地区）LAI的季节、生长季和年变化,及其与气候变化（温度、降水）的关系。结果表明,在中国大部分地区,年平均LAI和生长季平均LAI均是增加的。由于区域和季节气候的差异,LAI变化趋势具有明显的空间和季节非均一性。从区域平均的角度来看,不同地区年和生长季平均LAI都有增加趋势,并且在华南地区增加最快。因而,在全球变化背景下,华南地区可能是潜在的碳汇。在季节尺度上,各地区区域平均LAI基本上都是增加的,并且都在春季增加最快。温度变化是LAI变化的主要原因。但是人类活动如农业活动、城市化等对华北平原、长江三角洲和珠江三角洲等地区LAI变化的作用不容忽视。     

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.     

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.     

Climatic noise and potential predictability of monthly mean temperature over China

Summary In this paper, based on the data at 162 stations selected over China from 1960 to 1991 the climatic noise and potential predictability of monthly mean temperature have been studied. The method of estimating climatic noise is based on the idea of Yamamoto et al. (1985) and the potential predictability is expressed by the ratio of the estimated inter-annual variation to the estimated natural variation (or climatic noise). Generally the climatic noise of monthly mean temperature increases with latitude and altitude and varies with season. The continental air from Siberia and Mongolia plays a significant role and the ocean acts as an adjustor and a reductor in the climatic noise except for the tropical Pacific ocean in transitional season. The potential predictability is diversified from month to month and one station to another, but generally the monthly mean temperature over China is potentially predictable at statistical significance level 0.10. The results suggest that we could not ask a climate model to predict the climate with satisfactory results worldwide in all seasons and that the regional model could be a hopeful way to predict the climate.With 3 Figures     

Non-stationarity of the signal and noise characteristics of seasonal precipitation anomalies

In order to improve seasonal-to-interannual precipitation forecasts and their application by decision makers, there is a clear need to understand when, where, and to what extent seasonal precipitation anomalies are driven by potentially predictable surface–atmosphere interactions versus to chaotic interannual atmospheric dynamics. Using a simple Monte Carlo approach, interannual variability and linear trends in the SST-forced signal and potential predictability of boreal winter precipitation anomalies is examined in an ensemble of twentieth century AGCM simulations. Signal and potential predictability are shown to be non-stationary over more than 80% of the globe, while chaotic noise is shown to be stationary over most of the globe. Correlation analysis with respect to magnitudes of the four leading modes of global SST variability suggests that interannual variability and trends in signal and potential predictability over 35% of the globe is associated with ENSO-related SST variability; signal and potential predictability are not significantly associated with SST modes characterized by a global SST trend, North Atlantic SST variability, and North Pacific SST variability, respectively. Results suggest that mechanisms other than SST variability contribute to the non-stationarity of signal and noise characteristics of hydroclimatic variability over mid- and high-latitude regions.     

Seasonal differences of model predictability and the impact of SST in the Pacific

Both seasonal potential predictability and the impact of SST in the Pacific on the forecast skill over China are investigated by using a 9-level global atmospheric general circulation model developed at the Institute of Atmospheric Physics under the Chinese Academy of Sciences (IAP9L-AGCM). For each year during 1970 to 1999, the ensemble consists of seven integrations started from consecutive observational daily atmospheric fields and forced by observational monthly SST. For boreal winter, spring and summer,the variance ratios of the SST-forced variability to the total variability and the differences in the spatial correlation coefficients of seasonal mean fields in special years versus normal years are computed respectively. It follows that there are slightly inter-seasonal differences in the model potential predictability in the Tropics. At northern middle and high latitudes, prediction skill is generally low in spring and relatively high either in summer for surface air temperature and middle and upper tropospheric geopotential height or in winter for wind and precipitation. In general, prediction skill rises notably in western China, especially in northwestern China, when SST anomalies (SSTA) in the Nino-3 region are significant. Moreover,particular attention should be paid to the SSTA in the North Pacific (NP) if one aims to predict summer climate over the eastern part of China, i.e., northeastern China, North China and southeastern China.     

The seasonal predictability of the Asian summer monsoon in a two-tiered forecast system

An extensive set of boreal summer seasonal hindcasts from a two tier system is compared with corresponding seasonal hindcasts from two other coupled ocean–atmosphere models for their seasonal prediction skill (for precipitation and surface temperature) of the Asian summer monsoon. The unique aspect of the two-tier system is that it is at relatively high resolution and the SST forcing is uniquely bias corrected from the multi-model averaged forecasted SST from the two coupled ocean–atmosphere models. Our analysis reveals: (a) The two-tier forecast system has seasonal prediction skill for precipitation that is comparable (over the Southeast Asian monsoon) or even higher (over the South Asian monsoon) than the coupled ocean–atmosphere. For seasonal anomalies of the surface temperature the results are more comparable across models, with all of them showing higher skill than that for precipitation. (b) Despite the improvement from the uncoupled AGCM all models in this study display a deterministic skill for seasonal precipitation anomalies over the Asian summer monsoon region to be weak. But there is useful probabilistic skill for tercile anomalies of precipitation and surface temperature that could be harvested from both the coupled and the uncoupled climate models. (c) Seasonal predictability of the South Asian summer monsoon (rainfall and temperature) does seem to stem from the remote ENSO forcing especially over the Indian monsoon region and the relatively weaker seasonal predictability in the Southeast Asian summer monsoon could be related to the comparatively weaker teleconnection with ENSO. The uncoupled AGCM with the bias corrected SST is able to leverage this teleconnection for improved seasonal prediction skill of the South Asian monsoon relative to the coupled models which display large systematic errors of the tropical SST’s.