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1.
A new methodology is proposed that allows patterns of interannual covariability, or teleconnections, between the intraseasonal and slow components of seasonal mean Australian rainfall and the corresponding components in the Southern Hemisphere atmospheric circulation to be estimated. In all seasons, the dominant rainfall–circulation teleconnections in the intraseasonal component are shown to have the characteristic features associated with well-known intraseasonal dynamical and statistical atmospheric modes and their relationship with rainfall. Thus, for example, there are patterns of interannual covariability that reflect rainfall relationships with the intraseasonal Southern Annular Mode, the Madden-Julian Oscillation and wavenumber 3 and 4 intraseasonal modes of variability. The predictive characteristics of the atmospheric circulation–rainfall relationship are shown to reside with the slow components. In all seasons, we find rainfall–circulation teleconnections in the slow components related to the El Niño-Southern Oscillation. Each season also has a coupled mode, with a statistically significant trend in the time series of the atmospheric component that appears to be related to recent observed trends in rainfall. The slow Southern Annular Mode also features in association with southern Australian rainfall, especially during austral winter and spring. There is also evidence of an influence of Indian Ocean sea surface temperature variability on rainfall in southeast Australia during austral winter and spring. 相似文献
2.
This study explores potential impacts of the East Asian winter monsoon (EAWM) on summer climate variability and predictability in the Australia–Asian region through Australia–Asia (A-A) monsoon interactions. Observational analysis is conducted for the period of 1959 to 2001 using ERA-40 wind reanalysis and Climate Research Unit rainfall and surface temperature monthly datasets. Statistically significant correlations are established between the Australian summer monsoon and its rainfall variations with cross-equatorial flows penetrating from South China Sea region and northerly flow in the EAWM. The underlying mechanism for such connections is the response of the position and intensity of Hardley circulation to strong/weak EAWM. A strong EAWM is associated with an enhanced cross-equatorial flow crossing the maritime continent and a strengthened Australia summer monsoon westerlies which affect rainfall and temperature variations in northern and eastern part of the Australian continent. Furthermore, partial correlation analysis, which largely excludes El Niño-Southern Oscillation (ENSO) effects, suggests that these connections are the inherent features in the monsoon system. This is further supported by analyzing a global model experiment using persistent sea surface temperatures (SSTs) which, without any SST interannual variations, shows similar patterns as in the observational analysis. Furthermore, such interaction could potentially affect climate predictability in the region, as shown by some statistically significant lag correlations at monthly time scale. Such results are attributed to the impacts of EAWM on regional SST variations and its linkage to surface conditions in the Eurasian continent. Finally, such impacts under global warmed climate are discussed by analyzing ten IPCC AR4 models and results suggest they still exist in the warmed climate even though the EAWM tends to be weaker. 相似文献
3.
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 相似文献
4.
中国月平均温度的气候噪声和潜在可预报性 总被引：9，自引：1，他引：8
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。 相似文献
5.
Potential predictability of the Asian summer monsoon on monthly and seasonal time scales 总被引：1，自引：0，他引：1
Summary ¶The potential predictability of the monthly and seasonal means during the Northern Hemisphere summer and winter is studied by estimating the signal-to-noise ratio. Based on 33 years of daily low-level wind observations and 24 years of satellite observations of outgoing long wave radiation, the predictability of the Asian summer monsoon region is contrasted with that over other tropical regions. A method of separating the contributions from slowly varying boundary forcing and internal dynamics (e.g., intraseasonal oscillations) that determine the predictability of the monthly mean tropical climate is proposed. We show that the Indian monsoon climate is only marginally predictable in monthly time scales as the contribution of the boundary forcing in this region is relatively low and that of the internal dynamics is relatively large. It is shown that excluding the Indian monsoon region, the predictable region is larger and predictability is higher in the tropics during northern summer. Even though the boundary forced variance is large during northern winter, the predictable region is smaller as the internal variance is larger and covers a larger region during northern winter (due to stronger intraseasonal activity). Consistent with the estimates of predictability of monthly means, estimates of potential predictability on seasonal time scales also indicate that predictability of seasonal mean Indian monsoon is limited.Received December 6, 2002; accepted March 16, 2003
Published online: June 12, 2003 相似文献
6.
We propose a method for studying the influence of intraseasonal variability on the interannual variability of seasonal mean fields. The method, using monthly mean data, provides estimates of the interannual variance and covariance, in the seasonal mean field, associated with intraseasonal variability. These estimates can be used to derive patterns of interannual variability associated with meteorological phenomena that vary significantly within a season, such as atmospheric blocking, or intraseasonal oscillations. By removing this intraseasonal component from the total interannual variance/covariance, one can define a slow component of interannual variability that is closely related to very slowly varying (interannual/supra-annual) external forcings and internal dynamics. Together these patterns may help in our understanding of the source of climate predictive skill, and also the influence of intraseasonal variability on interannual variability. To show the efficacy of our methodology, we have tested it on synthetic data, using Monte Carlo simulations of the 500-hPa geopotential heights for boreal winter over the North Pacific/North American region. The synthetic data has been constructed in such a way that the intraseasonal and slow components of interannual variability are known a priori. It is demonstrated that our methodology can effectively separate the spatial patterns of both components of variability. The methodology is also applied to diagnose meteorological phenomena that play major roles in the variability and predictability of DJF New Zealand temperatures. 相似文献
7.
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. 相似文献
8.
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 相似文献
9.
云南地区季降水量和气温的潜在可预报性分析 总被引：1，自引：1，他引：0
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。 相似文献
10.
Recent Advances in Predictability Studies in China （1999-2002） 总被引：10，自引：2，他引：8
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. 相似文献
11.
土壤湿度对东亚夏季气候潜在可预报性影响的数值模拟 总被引：5，自引：0，他引：5
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。 相似文献
12.
Relative contribution of soil moisture and snow mass to seasonal climate predictability: a pilot study 总被引：1，自引：1，他引：0
Hervé Douville 《Climate Dynamics》2010,34(6):797-818
Land surface hydrology (LSH) is a potential source of long-range atmospheric predictability that has received less attention
than sea surface temperature (SST). In this study, we carry out ensemble atmospheric simulations driven by observed or climatological
SST in which the LSH is either interactive or nudged towards a global monthly re-analysis. The main objective is to evaluate
the impact of soil moisture or snow mass anomalies on seasonal climate variability and predictability over the 1986–1995 period.
We first analyse the annual cycle of zonal mean potential (perfect model approach) and effective (simulated vs. observed climate)
predictability in order to identify the seasons and latitudes where land surface initialization is potentially relevant. Results
highlight the influence of soil moisture boundary conditions in the summer mid-latitudes and the role of snow boundary conditions
in the northern high latitudes. Then, we focus on the Eurasian continent and we contrast seasons with opposite land surface
anomalies. In addition to the nudged experiments, we conduct ensembles of seasonal hindcasts in which the relaxation is switched
off at the end of spring or winter in order to evaluate the impact of soil moisture or snow mass initialization. LSH appears
as an effective source of surface air temperature and precipitation predictability over Eurasia (as well as North America),
at least as important as SST in spring and summer. Cloud feedbacks and large-scale dynamics contribute to amplify the regional
temperature response, which is however, mainly found at the lowest model levels and only represents a small fraction of the
observed variability in the upper troposphere. 相似文献
13.
Gustavo Naumann Walter M. Vargas Juan L. Minetti 《Theoretical and Applied Climatology》2011,105(3-4):341-355
The persistence and long-term memories in daily maximum and minimum temperature series during the instrumental period in southern South America were analysed. Here, we found a markedly seasonal pattern both for short- and long-term memories that can lead to enhanced predictability on intraseasonal timescales. In addition, well-defined spatial patterns of these properties were found in the region. Throughout the entire region, the strongest dependence was observed in autumn and early winter. In the Patagonia region only, the temperatures exhibited more memory during the spring. In general, these elements indicate that nonlinear interactions exist between the annual cycles of temperature and its anomalies. Knowledge of the spatiotemporal behaviour of these long-term memories can be used in the building of stochastic models that only use persistence. It is possible to propose two objective forecast models based on linear interactions associated with persistence and one that allows for the use of information from nonlinear interactions that are manifested in the form of forerunners. 相似文献
14.
A study has been made, using the National Centers for Environmental Prediction and National Center for Atmospheric Research
re-analysis 500 hPa geopotential height data, to determine how intraseasonal variability influences, or can generate, coherent
patterns of interannual variability in the extratropical summer and winter Southern Hemisphere atmospheric circulation. In
addition, by separating this intraseasonal component of interannual variability, we also consider how slowly varying external
forcings and slowly varying (interannual and longer) internal dynamics might influence the interannual variability of the
Southern Hemisphere circulation. This slow component of interannual variation is more likely to be potentially predictable.
How sea surface temperatures are related to the slow components is also considered. The four dominant intraseasonal modes
of interannual variability have horizontal structures similar to those seen in both well-known intraseasonal dynamical modes
and statistical modes of intraseasonal variability. In particular, they reflect intraseasonal variability in the high latitudes
associated with the Southern Annular Mode, and wavenumber 4 (summer) and wavenumber 3 (winter) patterns associated with south
Pacific regions of persistent anomalies and blocking, and possibly variability related to the Madden-Julian Oscillation (MJO).
The four dominant slow components of interannual variability, in both seasons, are related to high latitude variability associated
with the Southern Annular Mode, El Nino Southern Oscillation (ENSO) variability, and South Pacific Wave variability associated
with Indian Ocean SSTs. In both seasons, there are strong linear trends in the first slow mode of high latitude variability
and these are shown to be related to similar trends in the Indian Ocean. Once these are taken into account there is no significant
sea surface temperature forcing of these high latitude modes. The second and third ENSO related slow modes, in each season,
have high correlations with tropical sea surface temperature variability in the Pacific and Indian Oceans, both contemporaneously
and at one season lag. The fourth slow mode has a characteristic South Pacific wave structure of either a wavenumber 4 (summer)
or wavenumber 3 (winter) pattern, with strongest loadings in the South Pacific sector, and an association simultaneously with
a dipole SST temperature gradient in the subtropical Indian Ocean. 相似文献
15.
陕西省近40年最高最低温度变化 总被引：6，自引：2，他引：6
利用陕西省1961~2002年的实测资料,在剔除城市热岛效应对气候变化趋势的可能影响之后,使用自然正交函数展开,得到冬、春、夏、秋4季最高、最低温度第1特征向量。第1特征向量均为正值,且方差贡献均超过60%,所对应的时间系数能代表最高、最低温度的时空变化。由此研究了陕西省最高温度、最低温度的时空变化趋势特点:冬、春、夏、秋4季,无论最高、最低温度1980年以后均呈增温趋势,这种变化趋势的中心区域普遍在关中中、西部和渭北;而近10多年来温度日较差除秋季外,有增大的趋势,这可能主要是由于云量的减少所致。 相似文献
16.
Understanding Madden-Julian-Induced sea surface temperature variations in the North Western Australian Basin 总被引：1，自引：1，他引：0
J. Vialard K. Drushka H. Bellenger M. Lengaigne S. Pous J. P. Duvel 《Climate Dynamics》2013,41(11-12):3203-3218
The strongest large-scale intraseasonal (30–110 day) sea surface temperature (SST) variations in austral summer in the tropics are found in the eastern Indian Ocean between Australia and Indonesia (North-Western Australian Basin, or NWAB). TMI and Argo observations indicate that the temperature signal (std. ~0.4 °C) is most prominent within the top 20 m. This temperature signal appears as a standing oscillation with a 40–50 day timescale within the NWAB, associated with ~40 Wm?2 net heat fluxes (primarily shortwave and latent) and ~0.02 Nm?2 wind stress perturbations. This signal is largely related to the Madden-Julian Oscillation. A slab ocean model with climatological observed mixed-layer depth and an ocean general circulation model both accurately reproduce the observed intraseasonal SST oscillations in the NWAB. Both indicate that most of the intraseasonal SST variations in the NWAB in austral winter are related to surface heat flux forcing, and that intraseasonal SST variations are largest in austral summer because the mixed-layer is shallow (~20 m) and thus more responsive during that season. The general circulation model indicates that entrainment cooling plays little role in intraseasonal SST variations. The larger intraseasonal SST variations in the NWAB as compared to the widely-studied thermocline-ridge of the Indian Ocean region is explained by the larger convective and air-sea heat flux perturbations in the NWAB. 相似文献
17.
PREDICTABILITY LEVELS OF MONTHLY FORECAST BASED ON TIME-AVERAGED OCEAN/ATMOSPHERE VARIABLES —A NATURALLY OCCURRING ANALOGUE STUDY 
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下载免费PDF全文 Naturally occurring analogues between the monthly averaged data of 1000,500 and 100 hPa geopotential height and the sea surface temperature (SST) in the Pacific,Atlantic and Indian Oceans during the period January 1956-December 1972 are used to study the potential predictability levels of forecasting the monthly mean ocean/atmosphere variables.It is found that in the ocean-atmosphere system the forecast of geopotential height may be more difficult than SST,and that the predictability level of monthly mean geopotential height anomaly calculated from the corresponding monthly mean SST appears relatively poor,but it can be improved by using the past observational data of monthly mean SST/geopotential fields. 相似文献
18.
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。 相似文献
19.
Daily gridded (1°×1°) temperature data (1969–2005) were used to detect spatial patterns of temporal trends of maximum and minimum temperature (monthly and seasonal), growing degree days (GDDs) over the crop-growing season (kharif, rabi, and zaid) and annual frequencies of temperature extremes over India. The direction and magnitude of trends, at each grid level, were estimated using the Mann–Kendall statistics (α = 0.05) and further assessed at the homogeneous temperature regions using a field significance test (α=0.05). General warming trends were observed over India with considerable variations in direction and magnitude over space and time. The spatial extent and the magnitude of the increasing trends of minimum temperature (0.02–0.04 °C year?1) were found to be higher than that of maximum temperature (0.01–0.02 °C year?1) during winter and pre-monsoon seasons. Significant negative trends of minimum temperature were found over eastern India during the monsoon months. Such trends were also observed for the maximum temperature over northern and eastern parts, particularly in the winter month of January. The general warming patterns also changed the thermal environment of the crop-growing season causing significant increase in GDDs during kharif and rabi seasons across India. The warming climate has also caused significant increase in occurrences of hot extremes such as hot days and hot nights, and significant decrease in cold extremes such as cold days and cold nights. 相似文献
20.
西藏近35年地表湿润指数变化特征及其影响因素 总被引：8，自引：0，他引：8
利用1971-2005年西藏25个气象站月平均最高气温、最低气温、风速、相对湿度、日照时数、降水量等资料,应用Penman-Monteith模犁计算了最大潜在蒸散、地表湿润指数,分析了其空间分布、年际变化特征及季节差异,并讨论了影响地表湿润指数变化的气象因子.研究表明:近35年,西藏年降水量表现为显著的增加趋势,增幅为15.0 mm/(10 a);年最大潜在蒸散呈不同程度的减小趋势,为-4.6--71.6 mm/(10 a).阿里地区西南部、聂拉木年地表湿润指数为不显著的减小趋势,其他各地均呈增大趋势,增幅为0.02-0.09.就西藏平均而言,年地表湿润指数以0.04/10 a的速率显著增大,尤其足近25年增幅更为明显.各季节地表湿润指数也表现为增大趋势,以夏季增幅最明显.20世纪70年代剑80年代主要表现为以低温低湿为主的年际变化特征,进入90年代后,气温持续升高,地表湿润指数明显增加,呈现山暖湿型的气候特征.降水量和相对湿度的明显增加,以及平均气温日较差的显著减小是地表湿润指数显著增加的主要原因,平均风速和日照时数的明显减少,在湿润指数增加趋势中也起着重要作用. 相似文献