Effect of extreme value loss on long-term correlated time series

Effects of extreme value loss on long-term correlated time series are analyzed by means of detrended fluctuation analysis (DFA) and power spectral density analysis. Weaker memory can be detected after removing of extreme values for the artificial long-term correlated data, indicating the emergence of extreme events may be closely related to long-term memory. For observational temperature records, similar results are obtained, but not in all stations. For example, in some stations, only extending of scaling range to smaller time scales occurs, which may be due to the asymmetric distribution of values in the record. By comparing our findings with previous works, clustered positions of the extreme events are recognized as an important property in long-term correlated records. Through a simple numerical test, close relations between extreme events and long-term memory are discovered, which is helpful for our understanding of the effects of extreme value loss on long-term correlated records.     

Subarea characteristics of the long-range correlations and the index χ for daily temperature records over China

Daily temperature records including daily minimum, maximum, and average temperature from 190 meteorological stations over China during 1951–2000 are analyzed from two perspectives: (a) long-term persistence in direction of time varies, and (b) standard deviation in direction of amplitude varies. By employing the detrended fluctuation analysis (DFA), we find all the temperature records are long-term correlated, while the exponent α obtained from DFA varies from different districts of China due to different climate conditions, such as the southwest monsoon, subtropical high, northeast cold vortex, and the Tibetan plateau, etc. After we take the standard deviation into account, a new index χ?=?α?×?σ, which has been proposed recently, can be obtained. By further rescaling it as $ \chi = \overline \chi - {{1} \left/ {5} \right.} \times {\sigma_{{\overline \chi }}} $ , we find an obvious change of χ for these three kinds of time series, from which the whole China can be divided into two groups, which are comparatively consistent with dry/wet distributions in the south–north areas over China.     

Different multi-fractal behaviors of diurnal temperature range over the north and the south of China

Multi-fractal behaviors of diurnal temperature range (DTR for short) from 100 stations over China during 1956–2010 are analyzed by means of multi-fractal detrended fluctuation analysis. By making a Monte-Carlo simulation, we obtain two criterions which can be used to decide whether a DTR series is significantly multi-fractal or not. With these criterions, different multi-fractal behaviors are found over the north and the south of China, and Yangtze River is roughly the dividing line. Over the north region, nearly all the considered DTR series do not show multi-fractal behaviors, while the results are completely the opposite over the south. The findings are confirmed by the scaling behaviors of the corresponding DTR magnitude series and indicate that more scale-dependent structure differences may be hidden in DTR series over the north and the south of China. Therefore, an extensive analysis of the multi-fractal behaviors are essential for a better understanding of the complex structures of the climate changes.     

热带地区出射长波辐射的长程持续性研究

为了探索热带地区出射长波辐射（OLR）的内在变化规律,利用去趋势波动分析（DFA）方法,对整个热带地区（0~357.5 °E,22.5 °S~22.5 °N）1979—2013年NOAA逐日的OLR资料进行分析。研究结果表明:整个热带地区的OLR存在幂律相关,其标度指数值主要集中在0.65~0.72之间,具有较好的长程相关性（或持续性）。西太平洋、刚果盆地和南美洲因对流发展旺盛,导致上空高云量偏大,OLR值主要取决于云顶温度和云量,而云本身变化较快,使得该地区OLR表现出较弱的长程持续性;中东太平洋、大西洋和撒哈拉沙漠地区上空高云量偏少,OLR值主要取决于海表或陆表的温度,而海表或陆表温度变化相对比较缓慢,使得该地区OLR表现出较强的长程持续性。此外,通过随机打乱逐日OLR时间序列去除趋势和相关性,进一步验证了热带地区OLR的长程持续性是由时间序列的分形特征造成的。      

长江三角洲地区降水资料的均一性检验与订正试验

该文对1961—2002年我国长江三角洲地区36个地面观测站的年、月降水资料序列作均一性检验和订正试验, 以探讨我国年、月降水气候观测序列的均一化方法和思路。该文首先从与待检台站序列相关性较好、台站距离较近的台站中选取了若干站点作为待检台站的参考台站建立参考序列, 采用传统的标准正态检验 (SNHT) 方法, 对所研究的地区年降水的均一性进行了检验。检验结果显示, 该地区降水序列中存在非均一性现象。考虑到所选地区站点密度仍然不够, 文中还采用了一种不依赖于参考台站的思路, 对上述检验结果进行了进一步确认后, 最后对不连续序列进行了逐月订正调整。对比分析表明, 对序列的订正较为合理。在已有的研究基础上, 结合我国台站网络实际特点, 提出结合不同思路来研究我国降水量数据均一性是必要的和可行的, 应用该思路对于我国降水序列进行均一性研究具有一定的合理性和应用前景。     

Non-parametric trend analysis of the aridity index for three large arid and semi-arid basins in Iran

Currently, an important scientific challenge that researchers are facing is to gain a better understanding of climate change at the regional scale, which can be especially challenging in an area with low and highly variable precipitation amounts such as Iran. Trend analysis of the medium-term change using ground station observations of meteorological variables can enhance our knowledge of the dominant processes in an area and contribute to the analysis of future climate projections. Generally, studies focus on the long-term variability of temperature and precipitation and to a lesser extent on other important parameters such as moisture indices. In this study the recent 50-year trends (1955–2005) of precipitation (P), potential evapotranspiration (PET), and aridity index (AI) in monthly time scale were studied over 14 synoptic stations in three large Iran basins using the Mann–Kendall non-parametric test. Additionally, an analysis of the monthly, seasonal and annual trend of each parameter was performed. Results showed no significant trends in the monthly time series. However, PET showed significant, mostly decreasing trends, for the seasonal values, which resulted in a significant negative trend in annual PET at five stations. Significant negative trends in seasonal P values were only found at a number of stations in spring and summer and no station showed significant negative trends in annual P. Due to the varied positive and negative trends in annual P and to a lesser extent PET, almost as many stations with negative as positive trends in annual AI were found, indicating that both drying and wetting trends occurred in Iran. Overall, the northern part of the study area showed an increasing trend in annual AI which meant that the region became wetter, while the south showed decreasing trends in AI.     

数据处理方法对中国区域平均降水序列精度的影响

利用地面观测站点资料研究大范围地区的降水长期变化规律,选取不同的指标会使分析结果出现显著差异。利用中国大陆区域内2139个国家站的逐日降水资料,比较不同数据处理方法得到全国和中国西部、中国东部地区的降水量、降水日数和降水强度的区域平均时间序列,探讨对其变化趋势估算的偏差。研究表明,1951—1957年估算的中国区域平均降水量原始值出现虚假的偏高,使趋势估算出现较大误差;1951—2016年中国西部地区平均降水量距平百分率时间序列的波动幅度显著偏大;区域平均降水量、降水日数和降水强度的距平和标准化距平序列较为可信。全国平均降水量、降水日数的原始值和距平值序列都基本反映了中国东部湿润地区降水的变化,降水量距平百分率的变化主要由西部干燥区域的降水变化构成,降水量标准化距平则可综合反映湿润和干燥地区的降水变化。     

Extreme precipitation events identified using detrended fluctuation analysis (DFA) in Anhui,China

Extreme weather events include unusual, severe or unseasonal weather, and weather at the extremes of the historical distribution. They have become more frequent and intense under global warming, especially in mid-latitude areas. They bring about great agricultural and economic losses. It is important to define the threshold of extreme weather event because it is the starting point of extreme weather event research, though it has been of seldom concern. Taking extreme precipitation events in Anhui, China as an example, the detrended fluctuation analysis (DFA) method is introduced to define the threshold of extreme weather events. Based on it, the spatial and temporal distributions of extreme precipitation events are analyzed. Compared to the traditional percentile method, DFA is based on the long-term correlation of time series. Thresholds calculated by DFA are much higher than the 99th percentile and the values are higher in the south and lower in the north. This spatial pattern is similar to the annual precipitation spatial pattern. There is an obvious increasing trend in the number of days with extreme precipitation, especially after the 1980s. This observation supports the point that more extreme events happen under global warming.     

An analysis of daily and monthly precipitation seasonality and regimes in Iran and the associated changes in 1951–2014

Daily and monthly total precipitation of 155 synoptic stations with relatively regular distribution over Iran, covering the 1990–2014 period, were used to investigate the spatial pattern of precipitation seasonality and regimes over Iran, using a set of precipitation seasonality indices. The results suggest a strong agreement between the indices computed at monthly time scale. The result also shows a latitudinal decreasing gradient from the lower index values in the north to the highest values in the south of Iran, suggesting a strong negative relationship between the latitude and the indices. A weak but statistically significant association was also found between the indices and the longitude, showing a gradual west-east contrast between the mountainous western Iran and the central-eastern lowlands and deserts of the country. The spatial patterns of the indices well agree in revealing different precipitation regimes in Iran, in spite of the observed discrepancies in their areal extent of the regions identified. All the indices characterized northern Iran by a precipitation regime having a moderate seasonality, while the mountainous areas of the western and northern Iran are featured by a marked precipitation regime possessing a longer dry season. However, the most seasonal precipitation regime with the longest dry period describes the southern country and some spot areas of the central-eastern Iran. The spatial distribution of the seasonal precipitation regimes and the month and season of maximum precipitation amounts across Iran was also identified, suggesting that from the 24 possible precipitation regimes over the globe, eight were found in Iran, from which a precipitation regime with the highest precipitation amount in winter, followed by autumn, spring, and summer characterized most parts of the country. January and JFM were also found as the month and season of maximum precipitation in a majority of stations distributed over Iran, respectively. The precipitation concentration index (DPCI) computed using daily precipitation data ranges between 0.56 and 0.76 across the country; nonetheless, the values between 0.64 and 0.70 characterized a majority of stations distributed over most parts of Iran. Contrarily to the indices computed at monthly time scale, the DPCI does not show a clear latitudinal pattern over the country. The Mann–Kendal trend test and the Sen slope estimator were applied to the computed indices relative to 16 stations with the longest and complete precipitation records during 1951–2014 time period. The indices time series showed no significant trend in the majority of the stations, indicating that the precipitation regimes of the studied stations did not change over 1951–2014 period.     

我国东部夏季降水量长年代资料的插补、订正及效果检验

使用均值生成函数、标准正态均一性检验方法和相关分析等方法对我国东部地区96个观测站1931—2020年夏季降水量长年代资料进行了一系列插补、检验、订正及效果分析等工作。结果表明：(1)均值生成函数拟合的1931—2020年各站夏季降水量资料的整体趋势和极值与观测值均有较好的一致性,其中无缺测资料的6个站点观测值和拟合值在距平符号一致率上达到了86.1%,可以满足插补工作的需要。(2)对1931—1950年和1951—2020年2个时段的夏季降水量资料,用平均值和方差2个统计量对插补后的资料进行差异性检验,共有8站具有显著性差异。(3)对插补后的1931—2020年夏季降水量资料进行了均一性检验和均一化订正,其中13站存在非均一性。(4)将订正后的站点资料与CRU＿TS4.05(University of East Anglia Climatic Research Unit Global 0.5°Monthly Time Series)数据库的格点资料进行空间分布相似度分析,2套资料在1931—1950、1951—2020和1931—2020年这3个时段的空间相关系数分别达到了0.90,...     

中国近50a气候变化复杂性分析

分析了我国气温和降水量变化的兰帕尔－齐夫复杂度空间分布特征，结果表明，平均而言，我国平均气温和降水量变化的复杂度约为10－11，小于随机序列的复杂度13，年平衡气温变化序列的复杂度最小，秋季平均气温变化序列的复杂度最大，季节和年平均气温序列的复杂度小于月平均气温变化序列的复杂度，月总降水量变化序列的复杂度为西部，北部大于南部和东部，我国东南沿海地区气候要素变化的复杂度最大。     

北京逐日气温和降水量的长程变化特征

无标度性广泛存在于自然界系统包括气候系统中,其特征之一是可观测量存在幂函数关系,它揭示了气候系统的复杂性.为探索气候可预测性的客观基础,介绍了去趋势涨落分析(DFA)方法,并应用其对北京2003年逐日观测资料序列进行了分析.结果表明,北京日平均气温和降水量均可划分为多个标度不变区域.在特定的标度域内,它们都表现出正长程相关的性质,为制作这些量的短期气候预测提供了理论基础.     

中国台风降水的气候特征

对中国台风降水的时空分布特征进行研究,发现台风降水分布在中国中东部广大地区,台风降水量自东南沿海向西北内陆逐渐减少.台风降水最大值出现在台湾岛的中东部地区和海南岛的个别地区,年平均台风降水量大于700 mm,最小值出现在内蒙古、山西、陕西、四川的部分地区,年平均台风降水量不足10 mm.台风降水一般出现在4～12月,峰值出现在8月.1957～2004年期间台风降水呈下降趋势.台风降水的异常主要由于亚洲地区大气环流和赤道中东太平洋沃克环流的异常变化所引起.进一步分析发现,台风降水在中国大部分地区为减少趋势,且这种趋势在台湾岛、海南岛、东南沿海部分地区和东北南部较显著.台风暴雨是我国东南沿海及部分内陆地区的极端强降水事件之一,这些地区的暴雨和大暴雨很大程度上是台风带来的.     

USCRN气候基准站网布局理论在我国的应用

基于美国气候基准站网 (USCRN,US Climate Reference Network) 的均匀正三角网格布局模式,以站网对我国平均降水量和气温的方差解释量分别能够达到95%和98%为标准,利用我国2416个观测站的降水量和气温数据研究了我国气候观测站网的布局设计,得到能反映我国整体气候特征的站网最少台站数。结果表明:为显著减少全国尺度上的气候不确定性,需要新建边长为3°纬距的正三角均匀网格 (103个站点) 站网,并在全国范围内开展站址考察、总结评估和运行检验工作,才能得到新的气候基准站网;在已有气候观测系统基础上改进完善而不新建系统,则改进的气候基准站网为最接近边长为2°纬距的正三角网格分布 (229个站点),其中199个预期位置或其附近已建有观测站点,没有对应实际站点的30个预期位置主要分布在青藏高原西南大部,这些地区将是未来建站的重点。     

Spatial and temporal variability of precipitation indices during 1961–2010 in Hunan Province,central south China

Based on daily precipitation records at 75 meteorological stations in Hunan Province, central south China, the spatial and temporal variability of precipitation indices is analyzed during 1961–2010. For precipitation extremes, most of precipitation indices suggest that both the amount and the intensity of extreme precipitation are increasing, especially the mean precipitation amount on a wet day, showing a significant positive trend. Meanwhile, both of the monthly rainfall heterogeneity and the contribution of the days with the greatest rainfall show an upward trend. When it comes to rainfall erosivity, most of this province is characterized by high values of annual rainfall erosivity. Although the directions of trends in annual rainfall erosivity at most stations are upward, only 6 of the 75 stations have significant trends. Furthermore, the spatial and temporal variation of dryness/wetness has been assessed by the standardized precipitation index (SPI). The principal component analysis (PCA) was applied to the SPI series computed on 24-month time scales. The results demonstrated a noticeable spatial variability with three subregions characterized by different trends: a remarkable wet tendency prevails in the central and southern areas, while the northern areas are dominated by a remarkable dry tendency.     

全球地面降水月值历史数据集研制

全球降水历史数据是开展气候、水循环等研究的基础。收集整理全球12个数据源降水历史月值资料,通过站号、站名甄别不同数据源中相同台站,对344个通过相关系数、一致率、均值t检验、方差F检验的台站多源资料进行拼接,尽可能多地融合各套数据产品优势,最终形成全球降水历史月值数据集(CMA Global PrecipitationV1.0, CGP)。数据集重点解决当前国际数据产品在东亚地区站点稀少、同时应用多套数据应用门槛较高等问题。数据集收录3.1万个台站共计1.87×107组月降水记录, 4152个台站序列长度达百年。与美国大气海洋局(NOAA)的全球降水数据集(GHCN-M V2.0)对比,CGP新增1万个站点、0.5×107组有效观测记录和1030条百年序列,其中141条百年序列通过多源整合技术获取。CGP的站点和数据量优势主要体现在东亚、东欧、西伯利亚等站点稀疏地区。基于CGP分析的全球降水时空特征与国际同类产品的结果较一致。新增的数据虽然没有改变全球降水分布的总体特征,但对区域性的百年降水变化检测有一定影响。基于CGP的全球降水百年序列结果显示,20世纪前半叶全球降水量偏小,近20年是1900年以来全球降水量最大的时期,各纬度带、各个国家或地区的降水长期变化趋势呈现显著的差异。      

四川地区降水幂律指数研究

基于四川地区5个站点1960~2004年的日降水观测资料,对其在等级分析的基础上探讨了幂律分布的规律,结果如下:(1)这5个站点日降水存在的一个共同特征—幂律尾分布,且不同等级的降水量对应不同的幂律尾指数,在一定程度上反映了不同雨型具有不同的气候背景和物理机理;(2)小雨幂指数随时间变化很小,中雨幂指数随时间变化较大,从80年代末期开始,四川盆地西部与南部地区中雨幂指数增加,中雨降水减少,川西高原、四川盆地东部与北部地区中雨幂指数减小,中雨降水增加。因此,中雨的变化可能会引起总降水量的变化。      

Homogenized Daily Relative Humidity Series in China during 1960?2017

Surface relative humidity(RH)is a key element for weather and climate monitoring and research.However,RH is not as commonly applied in studying climate change,partly because the observation series of RH are prone to inhomogeneous biases due to non-climate changes in the observation system.A homogenized dataset of daily RH series from 746 stations in Chinese mainland for the period 1960–2017,ChinaRHv1.0,has been developed.Most(685 or 91.82%of the total)station time series were inhomogeneous with one or more break points.The major breakpoints occurred in the early 2000s for many stations,especially in the humid and semi-humid zones,due to the implementation of automated observation across the country.The inhomogeneous biases in the early manual records before this change are positive relative to the recent automatic records,for most of the biased station series.There are more break points detected by using the MASH(Multiple Analysis of Series for Homogenization)method,with biases mainly around?0.5%and 0.5%.These inhomogeneous biases are adjusted with reference to the most recent observations for each station.Based on the adjusted observations,the regional mean RH series of China shows little long-term trend during 1960–2017[0.006%(10 yr)^?1],contrasting with a false decreasing trend[?0.414%(10 yr)?1]in the raw data.It is notable that ERA5 reanalysis data match closely with the interannual variations of the raw RH series in China,including the jump in the early 2000s,raising a caveat for its application in studying climate change in the region.     

近60年来中国主要流域极端降水演变特征

在全球增暖背景下,中国极端降水事件及洪涝、干旱等次生灾害近年来频发,严重影响生态系统、人民的生产生活和社会经济发展。本文基于气候变化检测和指数专家组（ETCCDI）定义的10个降水指数,利用中国台站日降水资料,系统分析了1961～2017年中国及九大流域片降水变化情况,并利用空间场显著性检验考察不同降水指数的显著变化是否与外强迫作用有关。结果表明,各降水指数的变化具有区域性特征。整体而言,全国范围内平均降水、降水强度、极端强降水和连续性强降水呈增强趋势的台站数多于呈减弱趋势的台站数,呈显著增强趋势的台站占比不可能仅由气候系统内部变率引起,还受到外强迫的影响。此外,中国大部分站点连续干旱日数（CDD）减少,观测中CDD呈显著减弱趋势的台站占比也与外强迫作用有关。九大流域片中,内陆河片能够观测到平均降水、降水强度、极端强降水和连续性强降水的增多以及连续干旱日数的减少,有洪涝灾害增多的风险,且上述变化可归因为外强迫的作用。长江流域片、东南诸河片和珠海流域片平均降水、极端强降水和连续性强降水均增强,其中强降水的变化与外强迫作用有关。西南诸河片极端强降水增强,但大部分站点CDD呈增加趋势,有干旱增加的风险。黄河流域片、海河流域片、淮河流域片及松辽河流域片的大部分站点及区域平均结果中,降水指数多无显著变化趋势。增暖背景下,不同流域片呈现出不同的降水变化特征,将面临不同的气候灾害风险。     

Long-term Correlations and Extreme Wind Speed Estimations

In this paper, we use fluctuation analysis to study statistical correlations in wind speed time series. Each time series used here was recorded hourly over 40 years. The fluctuation functions of wind speed time series were found to scale with a universal exponent approximating to 0.7, which means that the wind speed time series are long-term correlated. In the classical method of extreme estimations, data are commonly assumed to be independent (without correlations). This assumption will lead to an overestimation if data are long-term correlated. We thus propose a simple method to improve extreme wind speed estimations based on correlation analysis. In our method, extreme wind speeds are obtained by simply scaling the mean return period in the classical method. The scaling ratio is an analytic function of the scaling exponent in the fluctuation analysis.