Long memory, seasonality and time trends in the average monthly temperatures in Alaska

This paper deals with the analysis of monthly temperatures in 19 meteorological stations in Alaska during the last 50?years. For this purpose, we employ a procedure that permits us to examine in a single framework several features observed in climatological time series such as time trends, long-range persistence and seasonality. The results indicate that the highest degrees of persistence are observed in stations located in the southern regions and seasonality appears as a major issue in all cases. Removing the seasonal structure and focussing on the anomalies with respect to the monthly means, the time trend coefficients appear significantly positive in the majority of the cases, implying that temperatures have increased during the last 50?years.     

500 hPa温度场时间序列的年代际突变过程统计特征

本文利用Logistic 模型,推导出描述均值突变的分段函数,用该函数对可能存在突变的时间序列进行拟合,并结合概率分布理论,确定突变幅度最大的时段即为发生突变的过程,由此确定并分析序列中突变的开始时刻、突变幅度、突变变率、突变持续时间、系统不稳定特性等参数。对全球500 hPa 温度场单点时间序列的突变持续过程展开研究:（1）对突变过程的开始时刻进行统计,发现1956～1959、1970～1979、1986～1994、1994～2004 年开始的突变所占比重较大,几次突变过程中,平均来看除了1986～1994 年开始的突变表现为降温,其余几次突变均表现为增温;并且每次突变时,增温幅度比较大的突变,其变率也较大;（2）从突变先后的空间分布上看,1956～1959、1970～1979 年检测到发生突变的格点在海洋上空的突变偏早、欧亚大陆上空偏晚,而1986～1994 和1994～2004 年突变期间这一情况正好相反;（3）针对每次突变过程中的突变幅度,低纬度区域温度发生突变的变化幅度较小、高纬度区域较大;（4）对突变持续时间的检测结果表明,在全球增暖背景下,完成一次突变的持续时间正在逐渐变短;（5）当系统突变正在进行时,系统的不稳定性较强。     

Trends in the annual extreme rainfall events of 1 to 3 days duration over India

Summary In this paper, the annual extreme rainfall series in the time scale of 1 to 3 days duration at 316 stations, well distributed over the Indian region, covering 80-years of rainfall data from 1901 to 1980 were analysed for trend and persistence using standard statistical tests. It has been found that the annual extreme rainfall records of most stations are free from trend and persistence. However, the extreme rainfall series at stations over the west coast north of 12°N and at some stations to the east of the Western Ghats over the central parts of the Peninsula showed a significant increasing trend at 95% level of confidence. Stations over the southern Peninsula and over the lower Ganga valley have been found to exhibit a decreasing trend at the same level of significance. The data series of the stations which showed trends were subjected to a 10-year moving average and the resulting smoothed series have been discussed. It may be said that this increasing or decreasing trend in the annual extreme rainfall events at a few places will have tremendous implications in the hydrologic studies and dam design projects.With 9 Figures     

Probabilistic projections of regional temperature and precipitation extending from observed time series

Probabilistic projections of change in regional temperature and precipitation previously derived allow for the range of sensitivities to global warming simulated by CMIP3 models. However, the changes were relative to an idealized base climate for 1980–1999, disregarding observed trends, such as those in rainfall in some Australian regions. Here we propose a method that represents projections for both forced change and decadal means as time series that extend from the observed series, illustrated using data for central Victoria. The main idea is to estimate the time-evolving underlying (or forced) past climate then convert this to a series of absolute values, by using the mean of the full observational record. We again use the pattern scaling assumption, and combine the CMIP3 sensitivities used for future change with a global warming series beginning at 1900. Like the confidence interval of regression theory, the analysis gives an estimate of the range of the underlying climate at each decade. This range can be augmented to allow for natural variability. A Bayesian theory can be applied to combine the model-based sensitivity with that estimated from observations. The time series are modified and the persistence of current observed anomalies considered, ultimately merging the probabilistic projections with the observed record. For some other cases, such as rainfall in southwest and north Australia and temperature in the state of Iowa, the two sensitivity estimates appear less compatible, and possible additional forcings are considered. Examples of the potential use of such time series are presented.     

Persistence and long-term memories of daily maximum and minimum temperatures in southern South America

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.     

The impact of soil moisture–atmosphere coupling on daily maximum surface temperatures in Southeastern South America

Based on a series of experiments conducted by two regional climate models (RCA4 and LMDZ) with and without soil moisture-atmosphere coupling, we investigate the role of soil moisture on the occurrence of surface air temperature extremes and its persistence in Southeastern South America. Our analysis reveals that both factors, soil moisture-atmosphere coupling and relatively drier soil conditions, enhance the temperature extremes. In addition, the existence of soil-atmosphere coupling and the associated soil moisture variability is crucial for the development of the extremes in SESA. The key role of soil-atmosphere coupling is also reflected in the intrinsic persistence of hot days, which is greater in simulations with interactive soil moisture than in those with prescribed soil conditions. In the absence of soil-atmosphere coupling, the imprint of the anomalous dry (and also wet) soil conditions on the intensity and persistence of hot days is weaker.

     

Changes in Probability of Sequences, Exit Time Distribution and Dynamical Entropy in the Potsdam Temperature Record

Summary  Investigations of the temporal change of the probability of defined sequences and dynamical entropy (Nicolis et al., 1997) are presented using the daily maximum and minimum of the air temperature of the Potsdam time series, which spans the 102 years from 1893–1994. To reduce the number of parameters a nonhierarchical cluster analysis algorithm (Steinhausen and Langer, 1977) was used. This algorithm assigns one of five clusters each day, classified as very cold, cold, temperate, warm or hot. According to the algorithm, each day is described by only one of the symbols vc, c, t, w or h. Subsequently a time series analysis on this series of symbols was performed. The basic analysis length was defined by a 30 year window, which was shifted over the entire time series thus yielding 73 analysis sections. The quantities vary from window to window. Their variations were used for the detection of climate change. One of the major findings was the increased persistence of typical temperature characteristics. Within the windows we investigate relatively long-term correlations which extend over many days. The results show the time series to be markedly non-Markov. Received April 13, 1998 Revised August 28, 1998     

Evaluating persistence and identifying trends and abrupt changes in monthly and annual rainfalls of a semi-arid region in Western India

In this study, 43-year (1965–2007) monthly and annual rainfall time series of ten rainfall stations in a semi-arid region of western India are analyzed by adopting three tests for testing normality and by applying autoregressive technique for exploring persistence. Gradual trends are identified by three tests, and their magnitudes are assessed by the Sen’s slope estimator. Also, abrupt changes are detected by using four tests and they are further confirmed by two tests. Box-whisker plots revealed that the rainfalls of June and September are right skewed for all the stations. The annual rainfalls of Bhinder, Dhariawad, and Gogunda stations are found considerably right skewed. The normality tests indicated that the rainfall of July does not deviate from the normal distribution at all the stations. However, the annual rainfall is found non-normal at five stations. The monthly rainfalls of June, July, and August have persistence respectively at three (Mavli, Salumber, and Sarada), two (Kherwara and Sarada), and one (Mavli) stations, whereas the annual rainfall has persistence at Girwa and Mavli stations. Significantly increasing trend is detected at Mavli in the rainfall of July and in the annual rainfall (p value?>?0.05), while the negative trend in August rainfall at Dhariawad is found significant (p value?>?0.10). This study revealed that the presence of serial correlation does not affect the performance of the Mann-Kendall test. Mean values of trend magnitudes for the rainfalls of June, July, August, and September are 0.3, 0.8, ?0.4, and 0.4 mm year^?1, respectively, and the overall mean value for the annual rainfall is 0.9 mm year^?1. It is found that the standard normal homogeneity test and the Pettitt test are biased towards the end of the series to locate a change point. Conversely, the Bayesian test has a tendency to look for a change point in the beginning of time series. Confirmed abrupt changes in the rainfall time series are found in the year 2003 (Bhinder) in June; years 1974 (Mavli) and 1989 (Dhariawad and Salumber) in July; years 1972 (Sarada), 1990 (Dhariawad), and 2003 (Mavli) in August; years 1977 (Dhariawad), 1991 (Sarada), and 2004 (Kotra) in September; and in the year 1972 (Mavli and Sarada stations) in the annual series. It is emphasized that the significantly increasing trend of rainfall may have linkages with climate change and/or variability. Finally, this study recommends use of multiple statistical tests for analyzing hydrologic time series in order to ensure reliable decisions.     

A resampling scheme for regional climate simulations and its performance compared to a dynamical RCM

Summary A new statistical method for regional climate simulations is introduced. Its simulations are constrained only by the parameters of a linear regression line for a characteristic climatological variable. Simulated series are generated by resampling from segments of observation series such that the resulting series comply with the prescribed regression parameters and possess realistic annual cycles and persistence. The resampling guarantees that the simulated series are physically consistent both with respect to the combinations of different meteorological variables and to their spatial distribution at each time step. The resampling approach is evaluated by means of a cross validation experiment for the Elbe river basin: Its simulations are compared both to an observed climatology and to data simulated by a dynamical RCM. This cross validation shows that the approach is able to reproduce the observed climatology with respect to statistics such as long-term means, persistence features (e.g., dry spells) and extreme events. The agreement of its simulations with the observational data is much closer than for the RCM data. Correspondence: B. Orlowsky, Potsdam Institute for Climate Impact Research, P.O. Box 60 12 03, 14412 Potsdam, Germany     

Siberian high variability and its teleconnections with tropical circulations and surface air temperature over Saudi Arabia

Based on a generated time series for the central pressure of the Siberian High, and on defining a robust Siberian High Index (SHI), the behavior of this atmospheric center of action is examined from 1949 to 2010 with regard to inter-annual variations, persistence, trends, abrupt changes, spectral analysis and interactions. The interannual variability in the central pressure of the Siberian High is considerable. The mean downward linear and non-linear trend over the entire period (1949–2010) is estimated and is found to be statistically significant at the 95 % confidence level. Low frequency variation and linearity within the SHI time series are found from the persistence analysis. Using spectral analysis, the center of action of the Siberian High is characterized by non-periodic behavior; the peaks occur only at the lowest frequency and may be related to the Sea Surface Temperature (SST) over the El Niño region. The Siberian High is affected by the Hadley circulation cell; there is no detectable connection between the Walker circulation cell and the Siberian High. SSTs over the El Niño region may affect the Siberian High. Interactions between the Siberian High and the SSTs over the tropical Atlantic Ocean are absent. The SHI is positively correlated to surface air temperatures over Saudi Arabia, and this is statistically significant in the western and north-western regions.     

A note on fluctuations of the Nile river flood levels (715–1470)

Summary Climate episodes, lasting a century or longer, of low and high annual Nile flood level minima (from 715 to 1470) are defined by a lowpass filter. Their relation to fluctuations of shorter or longer periodicity (less than or greater than 25 years) is deduced from the power spectral density of a hundred year window moving over the time series. Low flood climate and enhanced variance of short period fluctuations are significantly correlated characterizing reduced persistence of fluctuations during dry climate conditions. There is only a qualitative indication of enhanced long period variability (or enhanced persistence) during high flood levels.With 5 Figures     

Some statistical characteristics of air temperature variations at four Mediterranean stations

Summary In this paper some statistical characteristics of air temperature variations over the Mediterranean are presented. The study is based on temperature data at stations in Marseille (1851–1985), Rome (1851–1985), Athens (1858–1985), and Jerusalem (1864–1985). First the homogeneity of temperature data for each station is examined by means of the short-cut Bartlett test. In another step, the existence of abrupt climatic changes towards warm or cold periods at all stations, with the exception of Athens is pointed out. Some statistical significant persistence of the annual and seasonal time series is also shown. Finally, the temperature fluctuations are examined and discussed in respect to possible causes where particularly the role of the Mediterranean Sea and the topography is considered.With 4 Figures     

An ENSO stability analysis. Part I: results from a hybrid coupled model

In this study, we use the Bjerknes stability (BJ) index as a tool to investigate overall El Niño-Southern Oscillation (ENSO) stability in a hybrid-coupled model (HCM) with various atmosphere and ocean background states. This HCM shows that ENSO growth rates as measured by the BJ index and linear growth rates estimated directly with a time series of the Niño 3.4 indices from the coupled model simulations exhibit similar dependence on background states, coupling strength, and thermodynamic damping intensity. That is, the BJ index and linear growth rates increase with a decrease in the intensity of the background wind, an increase in coupling strength, or a decrease in the intensity of thermodynamic damping, although the BJ index tends to overestimate the growth rate. A detailed analysis of the components of the BJ index formula suggests the importance of model climatological background states and oceanic dynamic parameters in determining ENSO stability. We conclude that the BJ index may serve as a useful tool for qualitatively evaluating the overall ENSO stability in coupled models or in observations without a detailed eigen-analysis that is difficult to perform in models more complex than relatively simple models.     

Autoregressive logistic regression applied to atmospheric circulation patterns

Autoregressive logistic regression models have been successfully applied in medical and pharmacology research fields, and in simple models to analyze weather types. The main purpose of this paper is to introduce a general framework to study atmospheric circulation patterns capable of dealing simultaneously with: seasonality, interannual variability, long-term trends, and autocorrelation of different orders. To show its effectiveness on modeling performance, daily atmospheric circulation patterns identified from observed sea level pressure fields over the Northeastern Atlantic, have been analyzed using this framework. Model predictions are compared with probabilities from the historical database, showing very good fitting diagnostics. In addition, the fitted model is used to simulate the evolution over time of atmospheric circulation patterns using Monte Carlo method. Simulation results are statistically consistent with respect to the historical sequence in terms of (1) probability of occurrence of the different weather types, (2) transition probabilities and (3) persistence. The proposed model constitutes an easy-to-use and powerful tool for a better understanding of the climate system.     

Low precipitation over Greece during 1989–1990

Summary The unusual low precipitation observed during 1989–1990 in Greece is the subject of the present study. Statistical comparisons of this drought period with other dry periods in the historical records, possible statistical cycles, (periodicities), or general trends, together with the behaviour of the long time series are investigated. Power spectra of the annual rainfall series are also plotted considering the 95 years of data for the most reliable station in Greece. Furthermore, the drought persistence between successive months is also analysed using standard methods. The analysis identified periodicities of 2.1 and 3.6 years while no significant trends were observed in almost all cases. Finally the persistency of successive dry days was found to be insignificant after the fourth day.With 5 Figures     

Scaling behaviors of precipitation over China

Scaling behaviors in the precipitation time series derived from 1951 to 2009 over China are investigated by detrended fluctuation analysis (DFA) method. The results show that there exists long-term memory for the precipitation time series in some stations, where the values of the scaling exponent α are less than 0.62, implying weak persistence characteristics. The values of scaling exponent in other stations indicate random behaviors. In addition, the scaling exponent α in precipitation records varies from station to station over China. A numerical test is made to verify the significance in DFA exponents by shuffling the data records many times. We think it is significant when the values of scaling exponent before shuffled precipitation records are larger than the interval threshold for 95 % confidence level after shuffling precipitation records many times. By comparison, the daily precipitation records exhibit weak positively long-range correlation in a power law fashion mainly at the stations taking on zonal distributions in south China, upper and middle reaches of the Yellow River, northern part of northeast China. This may be related to the subtropical high. Furthermore, the values of scaling exponent which cannot pass the significance test do not show a clear distribution pattern. It seems that the stations are mainly distributed in coastal areas, southwest China, and southern part of north China. In fact, many complicated factors may affect the scaling behaviors of precipitation such as the system of the east and south Asian monsoon, the interaction between sea and land, and the big landform of the Tibetan Plateau. These results may provide a better prerequisite to long-term predictor of precipitation time series for different regions over China.     

Prediction of Monthly Mean Surface Air Temperature in a Region of China

In conventional time series analysis, a process is often modeled as three additive components： linear trend, seasonal effect, and random noise. In this paper, we perform an analysis of surface air temperature in a region of China using a decomposition method in time series analysis. Applications to the National Centers for Environmental Prediction/the National Center for Atmospheric Research （NCEP/NCAR） Collaborative Reanalysis data in this region of China are discussed. The main finding was that the surface air temperature trend estimated for January 1948 to February 2006 was not statistically significant at 0.5904℃ （100 yr）^-1. Forecasting aspects are also considered.     

Possible causes for the persistence barrier of SSTA in the South China Sea and the vicinity of Indonesia

The persistence barrier refers to the lag correlation of sea surface temperature anomalies (SSTA) showing a rapid and significant decline in a specific season, regardless of the starting month. This implies that there is a decrease in forecast skill for SSTA in this specific season. This paper investigates the possible causes for the persistence barrier of SSTA in the South China Sea (SCS) and its adjacent regions from the perspective of interannual-interdecadal time scales. The results show that the persistence barrier of SSTA exists not only in the SCS, but also in the vicinity of Indonesia south of the equator. The SCS barrier occurs around October--November, while the occurrence of the barrier in the Indonesia region is around November--December. For these two regions, the occurrence of the persistence barrier is closely associated with the interdecadal variability of SSTA, as well as the interannual variability. The persistence barriers in the SCS and the Indonesia region do not exist alone if the interdecadal variability is not considered, because SSTA have a short memory of less than 4 months, regardless of the starting month. Moreover, the influence of the interdecadal variability of SSTA on the persistence barrier of SSTA in the SCS and the Indonesia region may be associated with SSTA in the Indian Ocean and the western Pacific, but is not closely associated with the Pacific Decadal Oscillation. However, compared with the spring persistence barrier (SPB) of ENSO, the close relationship between the persistence barriers in the SCS and the Indonesia region and the interdecadal variability is unique, since the ENSO SPB is not significantly affected by such variability. In addition, although the persistence barriers in both the SCS and the Indonesia region are quite obvious in strong ENSO cases, the interdecadal variability of SSTA also plays a non-negligible role in this relationship.     

Probabilistic analysis of extreme regional meteorological droughts by L-moments in a semi-arid environment

Regional extreme value analyses of drought characteristics provide information on probabilistic nature of drought occurrence, viewed as an essential tool in drought mitigation and planning. In this paper, L-moments are used to investigate the regional characteristics and probabilistic behavior of drought severity levels, represented by the Standardized Precipitation Index (SPI) annual minima (the minimum monthly SPI value). Rainfall data of 3, 6, 12, and 24 month time scales are investigated. A regional watershed in southwestern Iran is used as a case study area. The semi-arid nature of the study area requires appropriate selection of rainfall data. The boxplot approach is used to select those months with adequate data time series for the SPI analysis. Appropriateness of the suggested data time series is discussed in the context of the research by Wu et al. (2007). Based on the results, all of the suggested time scales are found appropriate for SPI investigations. For each time scale of interest regional homogeneity is evaluated and the best regional/sub-regional probability distribution function is selected. Regional quantiles are estimated for different time scales and their variability with respect to return period is discussed.