河南省东亚飞蝗发生的时间序列分析

利用1953～2000年河南省东亚飞蝗的发生时间序列资料,做了飞蝗大发生年的游程检验,结果表明,1953～2000年间河南省东亚飞蝗的大发生年是随机出现的.     

河南省东亚飞蝗发生的时间序列分析

利用1953—2000年河南省东亚飞蝗的发生时间序列资料，做了飞蝗大发生年的游程检验，结果表明，1953—2000年间河南省东亚飞蝗的大发生年是随机出现的。     

干旱前期温度偏高对河南省东亚飞蝗发生的影响

用灾变规律的关键时方法分析了干旱和前期高温对河南省东亚飞蝗发生的影响，结果表明，4月份降水量偏少(干旱)是河南省东亚飞蝗蝗灾发生的主要诱导因素之一，当年4月份区域平均降水量对夏蝗的影响比对秋蝗敏感。上年11月平均温度偏高是当年河南省东亚飞蝗蝗灾发生的又一主要诱导因素。     

环渤海夏蝗发生程度气象集成预报方法

东亚飞蝗发生程度与气象条件关系密切,该文使用1980-2008年的飞蝗资料和气象资料,选择了环渤海4种典型(沿海、水库、洼淀、内涝)蝗区,利用秩相关系数法筛选影响飞蝗发生程度的气象因子,确定了影响不同蝗区夏蝗发生程度的气象因子指标集。利用权重修正气象距离法,建立了气象距离指标预报模型;依据飞蝗自身生物学特性,建立了生物学预报模型。在此基础上,基于蝗虫生物学和气象条件影响的共同作用,建立了夏蝗发生程度的生物一气象集成长期预报模型。结果表明:在环渤海飞蝗区域,不同类型蝗区影响气象因子有一定差别,集成预报模型趋势预报准确率高于其他模型。     

El Nio、La Nia事件与太阳活动对河南省东亚飞蝗大发生的影响

用研究灾变规律的关键时方法分析了El Nio、La Nia事件和太阳活动对河南省东亚飞蝗 (Locus ta migratoria manilensis Meyen) 的影响。结果发现, El No和La Nia事件不会促使河南省沿黄地区东亚飞蝗的大发生, 并得出El Ni no和La Ni na事件对飞蝗大发生的影响主要通过影响其发生地的天气、气候 (主要是降水), 从而影响飞蝗的大发生。在太阳黑子相对数高年的后一年, 河南省东亚飞蝗大发生的可能性超过90%。     

东亚飞蝗生育气象条件分析及发生度预报

对滨州蝗区１９８１－１９９５年虫情观测资料及同期气象资料,用相关分析的方法,对东亚飞蝗生育期气象条件进行了系统分析,在统计基础上,用逐步回归的方法建立了东亚飞蝗发生度及几个主要发育特征期的预报方程。     

El Ni(n)o、La Ni(n)a事件与太阳活动对河南省东亚飞蝗大发生的影响

用研究灾变规律的关键时方法分析了El Ni(n)o、La Ni(n)a事件和太阳活动对河南省东亚飞蝗(Locusta migratoria manilensis Meyen)的影响.结果发现,El Ni(n)o和La Ni(n)a事件不会促使河南省沿黄地区东亚飞蝗的大发生,并得出El Ni(n)o和La Ni(n)a事件对飞蝗大发生的影响主要通过影响其发生地的天气、气候(主要是降水),从而影响飞蝗的大发生.在太阳黑子相对数高年的后一年,河南省东亚飞蝗大发生的可能性超过90%.     

El Ni?o、La Ni?a事件与太阳活动对河南省东亚飞蝗大发生的影响

用研究灾变规律的关键时方法分析了El Ni(n)o、La Ni(n)a事件和太阳活动对河南省东亚飞蝗(Locusta migratoria manilensis Meyen)的影响.结果发现,El Ni(n)o和La Ni(n)a事件不会促使河南省沿黄地区东亚飞蝗的大发生,并得出El Ni(n)o和La Ni(n)a事件对飞蝗大发生的影响主要通过影响其发生地的天气、气候(主要是降水),从而影响飞蝗的大发生.在太阳黑子相对数高年的后一年,河南省东亚飞蝗大发生的可能性超过90%.       

中国冬半年极端低温事件的时空特征及其与东亚冬季风的关系

利用1961～2012年全国553站逐日最低气温资料,根据百分位值法,定义了不同台站的极端低温阈值,并统计分析了逐年逐站发生极端低温事件的频数,并且使用经验正交函数分解(EOF)的方法对其时空变化特征进行了研究。研究结果表明:近51年来,我国冬半年极端低温事件总体呈明显的减少趋势,但仍存在明显的时空差异。EOF分析结果显示我国极端低温事件发生频率主要存在3种空间模态,即空间一致型和南北振荡型以及东西振荡型。并且对PC1(第一主分量)时间序列作突变检验的结果表明,我国冬半年极端低温事件频数在1986年前后存在明显突变现象,这与1986年前后东亚冬季风发生年代际突变时间点一致。进一步相关回归分析结果表明,我国冬半年极端低温事件频数与东亚冬季风指数存在很好的相关性,这可能与西伯利亚高压的变化有显著的联系,即冬半年西伯利亚高压强时,极端低温事件发生较多,反之亦然。分析的结果表明,1986年前后,中国冬半年极端低温事件发生频次的突变可能与西伯利亚高压的南移并且减弱有关。     

气候变暖背景下西藏高原雪灾变化及其与大气环流的关系

利用西藏高原高海拔牧区1979年10月至2013年4月19个气象站逐日积雪资料、月降水量和月平均温度资料,依据积雪深度和积雪持续日数组合的雪灾指标,分析了不同等级雪灾的气候变化特征及温度和降雪对雪灾变化的影响。结果表明,11月至翌年2月是西藏高原雪灾多发期且灾害程度重,全年发生中度以上雪灾的概率大,占总站次的54%;1979年以来雪灾变化总体呈现减少趋势,但存在着年代际变化,即1980 1997年代表现为波动性上升,1998 2007年呈显著减少趋势,此后略有回升,突变点在1999年和2008年;温度与雪灾相关性比降雪更高,温度具有显著增暖而降雪变化不明显,温度对雪灾气候变化起着决定性作用。分析了大气环流系统与雪灾变化关系得出:西风带南支槽活跃和东半球极涡偏强、东亚大槽和副热带高压偏西时雪灾易发生,反之则然;西风带南支槽和东半球极涡的变化趋势及突变点与西藏高原雪灾的变化趋势和突变点非常一致,是西藏高原雪灾变化的一个可能原因。     

气候变化趋势分析中自相关的检验与去除

受资料本身、分析方法及未来排放情景假设等因素影响, 气候变暖幅度尚存在较大的不确定性。从分析方法入手, 探讨气象观测序列可能存在的自相关及其对气候变化趋势分析的不确定性影响。引入了Durbin-Watson一阶自相关检验方法对气象观测序列进行检验, 并用Cochrane-Orcutt方法去除存在的自相关。分析发现:浙江省平湖市气温序列存在的自相关放大了该站气温的升温趋势, 并且虚高了气温变化趋势的显著性水平。因此, 对资料序列进行自相关检验与去除是十分必要的。     

利用多模式对中国气温序列中不连续点的检测

发展了一种对元数据依赖程度较小的气候序列均一化思路,在缺乏元数据的基础上,采取3种不同的方法,对中国东南部地区年平均最高、最低气温序列进行了均一性检验;并对其中不连续的气温序列进行了订正.对比表明,订正后序列较订正前更为合理.在检验过程中发现,对于最低气温检验可以明显得出比最高气温更多的不连续点.为了进一步认识这个问题,对此进行了进一步分析,认为最低温度对变化更为敏感主要是由于其物理特征:最低温度一般出现在夜间,夜间大气较为稳定,因此一些变化(如仪器,迁站)可能引起明显的不连续现象,而最高气温往往出现在白天,因此大气混合比较充分,空间的均一性要明显强于最低温度.另外,热岛现象、台站密度等因素均是影响气温序列均一化的因素.     

气侯时间序列变点的推断

本文研究时间序列的变点问题。所给出的统计方法可用来推断一个时间序列是否存在变点，存在几个变点以及变点在什么位置。这种统计推断方法可用于气候阶段的划分。     

Homogeneity tests and non-parametric analyses of tendencies in precipitation time series in Keszthely,Western Hungary

This study aims to investigate the precipitation trends in Keszthely (Western Hungary, Central Europe) through an examination of historical climate data covering the past almost one and a half centuries. Pettitt’s test for homogeneity was employed to detect change points in the time series of monthly, seasonal and annual precipitation records. Change points and monotonic trends were analysed separately in annual, seasonal and monthly time series of precipitation. While no break points could be detected in the annual precipitation series, a significant decreasing trend of 0.2–0.7 mm/year was highlighted statistically using the autocorrelated Mann-Kendall trend test. Significant change points were found in those time series in which significant tendencies had been detected in previous studies. These points fell in spring and winter for the seasonal series, and October for the monthly series. The question therefore arises of whether these trends are the result of a shift in the mean. The downward and upward shift in the mean in the case of spring and winter seasonal amounts, respectively, leads to a suspicion that changes in precipitation are also in progress in these seasons. The study concludes that homogeneity tests are of great importance in such analyses, because they may help to avoid false trend detections.     

Robust non-Gaussian statistics and long-range correlation of total ozone

Three long-term total ozone time series at Camborne, Lerwick and Arosa are examined for their statistical properties. Non-Gaussian behaviour is seen for all locations. There are large interannual fluctuations in the higher moments of the probability distribution. However, only the mean for all stations and summer standard deviation at Lerwick show significant trends. This suggests that there has been no long-term change in the stratospheric circulation, but there are decadal variations. The time series can be also characterised as scale invariant with a Hurst exponent of about 0.8 for all three sites. The Arosa time series was found to be weakly intermittent, in agreement with the non-Gaussian characteristics of the data set     

The impact of water resources development projects on water vapor pressure trends in a semi-arid region, Turkey

The aim of this study was to investigate long-term seasonal trends and decadal change patterns of monthly mean water vapor pressure (WVP) observation series at 16 meteorological stations scattered point-wisely over the Southeastern Anatolian Project (GAP) area in Turkey, where large-scale soil and water development projects have been put into practice since the 1970s. The record length of WVP observation series of each station varied between 31- and 41-years between 1962 and 2002. The monthly mean WVP observation series of each station was rearranged on seasonal basis. Sequential Mann–Kendall trend test, Sen’s slope estimator, and Spearman’s rank–order correlation tests were employed for detection of likely trends, and Kruskall–Wallis test was used to detect decadal variations in WVP series of each observation station. A possible area of representation for each meteorological station was determined by using the Thiessen polygons technique in a geographical information systems media. It was found that 15 seasonal WVP series have a positive trend covering 97% of the GAP area in the summer season; although one WVP series has a negative trend direction. However, in the spring season, 33% of the area had a positive trend, and a negative trend did not appear in any stations. WVP records in the winter season showed an increasing trend over 19% of the GAP area, whereas a decreasing trend prevailed in 9% of the area. The study results led us to conclude that the substantial increase of WVP observations in summer season could be attributed to both the shift from rain-fed agriculture to irrigated agriculture being made increasingly spacious year by year and building large water reservoirs in the GAP located in a semi-arid region. The results also indirectly suggested that the historical trends in the WVP parameters might be related to global climate change phenomenon.     

Climate change and the incidence of a forest pest in Mediterranean ecosystems: can the North Atlantic Oscillation be used as a predictor?

Many forest pest species strongly depend on temperature in their population dynamics, so that rising temperatures worldwide as a consequence of climatic change are leading to increased frequencies and intensities of insect-pest outbreaks. In the Mediterranean area, the climatic conditions are strongly linked to the effects of the North Atlantic Oscillation (NAO). The aim of this work is to analyze the dynamics of the pine processionary moth (Thaumetopoea pityocampa), a severe pest of Pinus species in the Circunmediterranean, throughout a region of southern Spain, in relation to NAO indices. We related the percentage of forest plots with high defoliation by pine processionary moth each year with NAO values for the present and the three previous winters, using generalized linear models with a binomial error distribution. The time series is 16-year long, and we performed analyses for the whole database and for the five main pine species separately. We found a consistent relationship between the response variable and the NAO index. The relationship is stronger with pine species living at medium-high altitudes, such as Aleppo (P. halepensis), black (P. nigra), and Scots (Pinus sylvestris) pine, which show the higher defoliation intensities up to 3?years after a negative NAO phase. The results highlight, for the first time, the usefulness of using global drivers in order to understand the dynamics of pest outbreaks at a regional scale, and they open the window to the development of NAO-based predictive models as an early-warning signal of severe pest outbreaks.     

Anthropogenic and natural causes of climate change

We test for causality between radiative forcing and temperature using multivariate time series models and Granger causality tests that are robust to the non-stationary (trending) nature of global climate data. We find that both natural and anthropogenic forcings cause temperature change and also that temperature causes greenhouse gas concentration changes. Although the effects of greenhouse gases and volcanic forcing are robust across model specifications, we cannot detect any effect of black carbon on temperature, the effect of changes in solar irradiance is weak, and the effect of anthropogenic sulfate aerosols may be only around half that usually attributed to them.     

Trend analysis of precipitation in Jharkhand State,India

Jharkhand is one of the eastern states of India which has an agriculture-based economy. Uncertain and erratic distribution of precipitation as well as a lack of state water resources planning is the major limitation to crop growth in the region. In this study, the spatial and temporal variability in precipitation in the state was examined using a monthly precipitation time series of 111 years (1901–2011) from 18 meteorological stations. Autocorrelation and Mann–Kendall/modified Mann–Kendall tests were utilized to detect possible trends, and the Theil and Sen slope estimator test was used to determine the magnitude of change over the entire time series. The most probable change year (change point) was detected using the Pettitt–Mann–Whitney test, and the entire time series was sub-divided into two parts: before and after the change point. Arc-Map 9.3 software was utilized to assess the spatial patterns of the trends over the entire state. Annual precipitation exhibited a decreasing trend in 5 out of 18 stations during the whole period. For annual, monsoon and winter periods of precipitation, the slope test indicated a decreasing trend for all stations during 1901–2011. The highest variability was observed in post-monsoon precipitation (77.87 %) and the lowest variability was observed in the annual series (15.76 %) over the 111 years. An increasing trend in precipitation in the state was found during the period 1901–1949, which was reversed during the subsequent period (1950–2011).     

In-depth investigation of precipitation-based climate change and cyclic variation in different climatic zones

Climate change and cyclic variation are investigated based on station data of 61 years (1951–2011), representing twelve climatic zones in Iran. Climate change is investigated by applying the non-parametric Mann–Kendall test and the three-dimensional loglinear model to the12-month SPI time series, and by applying the likelihood ratio test to precipitation time series. Cyclic variation is studied by applying the three-dimensional loglinear model to the 12-month SPI time series. Analysis included entire data period, two sub-periods [(1951–1981), (1982–2011)] and three sub-periods [(1951–1971), (1972–1991), (1992–2011)]. The Mann–Kendall test results indicated combinations of different trend behaviors, whereby climate change could not be evaluated. The likelihood ratio test did not confirm climate change (at 95 % confidence level), in most of the studied stations. However, the more in-depth analysis by the three-dimensional loglinear model, i.e., detection of significant differences among drought frequencies, did not confirm climate change (at 95 % confidence level), in any of the studied stations. Cyclic variation was not confirmed by the three-dimensional loglinear model (at 95 % confidence level), in any of the studied stations. The findings of this research illustrate the need for meticulous techniques like the three-dimensional loglinear model, as a necessary tool for climate change and cyclic variation studies.