Multi-Fractal Analysis of Daily Air Temperature Time Series in Coastal Areas of China

In this article, the Multi-Fractal Detrended Fluctuation Analysis(MF-DFA) method is adopted to study the temperature, i. e., the maximum temperature(Tmax), mean temperature(Tavg) and minimum(Tmin) air temperature,multifractal characteristics and their formation mechanism, in the typical temperature zones in the coastal regions in Guangdong, Jiangsu and Liaoning Provinces. Following are some terms and concepts used in the present study.Multifractality is defined as a term that characterizes the complexity and self-similarity of objects, and fractal characteristics depict the distribution of probability over the whole set caused by different local conditions or different levels in the process of evolution. Fractality strength denotes the fluctuation range of the data set, and long-range correlation(LRC) measures the stability of the climate system and the trend of climate change in the future. In this research, it is found that the internal stability and feedback mechanism of climate systems in different regions show regional differences. Furthermore, the research also proves that the Tavg, Tmaxand Tminof the above three provinces are highly multifractal. The temperature series multifractality of each province decreases in the order of temperature series multifractality of Liaoning temperature series multifractality of Guangdong temperature series multifractality of Jiangsu, and the corresponding long-range correlations follow the same order. It reveals that the most stable temperature series is that of Liaoning, followed by the temperature series of Guangdong, and the most unstable one is that of Jiangsu.Liaoning has the most stable climate system, and it will thus be less responsive to the future climate warming. The stability of the climate system in Jiangsu is the weakest, and its temperature fluctuation will continue to increase in the future, which will probably result in the meteorological disasters of high temperature and heat wave there. Guangdong possesses the strongest degree of multifractal strength, which indicates that its internal temperature series fluctuation is the largest among the three regions. The Tmaxmultifractal strength of Jiangsu is stronger than that of Liaoning, while the Tavgand Tminmultifractal strength of Jiangsu is weaker than that of Liaoning, showing that Jiangsu has a larger internal Tmaxfluctuation than Liaoning does, while it has a smaller fluctuation of Tavgand Tminthan Liaoning does. Guangdong and Liaoning both show the strongest Tminmultifractal strength, followed by Tavgmultifractal strength, and the weakest Tmax multifractal strength. However, Jiangsu has the strongest Tmax, followed by Tavg, and the weakest Tmin. The research findings show that these phenomena are closely related to solar radiation, monsoon strength, topography and some other factors. In addition, the multifractality of the temperature time series results from the negative power-law distribution and long-range correlation, in which the long-range correlation influence of temperature series itself plays the dominant role. With the backdrop of global climate change, this research can provide a theoretical basis for the prediction of the spatial-temporal air temperature variation in the eastern coastal areas of China and help us understand its characteristics and causes, and thus the present study will be significant for the environmental protection of coastal areas.     

标准序列法在日平均气温缺测数据插补中的应用

利用标准序列法,对1971—2000年我国2000多个国家级地面气象站日平均气温进行了插补试验,并用交叉检验方法进行验证,对比了相关性最好和距离最近两种邻近站选取方案的插补结果。试验表明,相关性最好方案的插补精度优于距离最近方案,利用前一方案进行插补时,只需要选择与待插补站日平均气温序列相关性最高的4个邻近站参与计算即可。插补试验结果表明,平均绝对误差约为0.42℃。插补值与实际观测值之间的绝对误差、均方根误差、两者之差在±0.5℃范围内的样本比例,均与邻近站平均距离呈较好的指数关系。     

武汉市周年逐日电力指标对气温的非线性响应

为了建立电力因子与气象要素的关系,利用2005-2007年武汉市电网日电量、日最大负荷、日最小负荷及相应的气温资料,分析气温对各电力指标的影响,建立了各电力指标与气温的非线性统计模型,并与线性模型进行比较.结果表明,非线性模型比线性模型能更好地反映电力指标与气温的关系,日最大负荷、日电量、日最小负荷最不敏感的平均温度临界点分别是15～16℃、14～15℃、13～14℃,低于该温度时气温下降或高于该温度时气温上升,电力指标均呈非线性增加,与最不敏感的平均气温偏离越大,电力指标增加越快.     

Application of Multiple Analysis of Series for Homogenization to Beijing Daily Temperature Series （1960-2006）

Homogenization of climate observations remains a challenge to climate change researchers, especially in cases where metadata (e.g., probable dates of break points) are not always available. To examine the influence of metadata on homogenizing climate data, the authors applied the recently developed Multiple Analysis of Series for Homogenization (MASH) method to the Beijing (BJ) daily temperature series for 1960--2006 in three cases with different references: (1) 13M---considering metadata at BJ and 12 nearby stations; (2) 13NOM---considering the same 13 stations without metadata; and (3) 21NOM---considering 20 further stations and BJ without metadata. The estimated mean annual, seasonal, and monthly inhomogeneities are similar between the 13M and 13NOM cases, while those in the 21NOM case are slightly different. The detected biases in the BJ series corresponding to the documented relocation dates are as low as -0.71^oC, -0.79^oC, and -0.5^oC for the annual mean in the 3 cases, respectively. Other biases, including those undocumented in metadata, are minor. The results suggest that any major inhomogeneity could be detected via MASH, albeit with minor differences in estimating inhomogeneities based on the different references. The adjusted annual series showed a warming trend of 0.337, 0.316, and 0.365^oC (10 yr)^-1 for the three cases, respectively, smaller than the estimate of 0.453^oC (10 yr)^-1 in the original series, mainly due to the relocation-induced biases. The impact of the MASH-type homogenization on estimates of climate extremes in the daily temperature series is also discussed.     

中国均一化日平均温、最高温和最低温序列1960-2008

Inhomogeneities in the daily mean/maximum/ minimum temperature （Tm/Tmax/Tmin） series from 1960- 2008 at 549 National Standard Stations （NSSs） in China were analyzed by using the Multiple Analysis of Series for Homogenization （MASH） software package. Typical biases in the dataset were illustrated via the cases of Beijing （B J）, Wutaishan （WT）, Urumqi （UR） and Henan （HN） stations. The homogenized dataset shows a mean warming trend of 0.261/0.193/0.344℃/decade for the annual series of Tm/Tmax/Tmin, slightly smaller than that of the original dataset by 0.006/0.009/0.007℃/decade. However, considerable differences between the adjusted and original datasets were found at the local scale. The adjusted Tmin series shows a significant warming trend almost everywhere for all seasons, while there are a number of stations with an insignificant trend in the original dataset. The adjusted Tm data exhibit significant warming trends annually as well as for the autumn and winter seasons in northern China, and cooling trends only for the summer in the middle reaches of the Yangtze River and parts of central China and for the spring in southwestern China, while the original data show cooling trends at several stations for the annual and seasonal scales in the Qinghai, Shanxi, Hebei, and Xinjiang provinces. The adjusted Tmax data exhibit cooling trends for summers at a number of stations in the mid-lower reaches of the Yangtze and Yellow Rivers and for springs and winters at a few stations in southwestern China, while the original data show cooling trends at three/four stations for the annual/autumn periods in the Qinghai and Yunnan provinces. In general, the number of stations with a cooling trend was much smaller in the adjusted Tm and Tmax dataset than in the original dataset. The cooling trend for summers is mainly due to cooling in August. The results of homogenization using MASH appear to be robust; in particular, different groups of stations with consideration of elevation led to minor effects i     

我国新极端最高气温的考察研究

根据在吐鲁番盆地组织的对比观测,得出了我国两个极端最高气温新极值,和吐鲁番盆地底部3个与最高气温有关的结论.两个新极值是,2008年8月3日艾丁湖底出现了我国最新的极端最高气温纪录:49.7℃;艾丁湖底历史上还曾出现过51℃左右的更高记录.三点结论是,(1) 艾丁湖底夏季气温日较差可高达24℃左右,比我国东部地区大得多;(2) 吐鲁番盆地底部最高气温的垂直温差梯度1.7～1.9℃/100m,比东部地区夏季月平均最高气温梯度0.7～0.8℃/100m要大得多;(3) 吐鲁番盆地底部日最高气温出现时间(地方时16-17时)比东部地区(地方时14-15时)要晚1～2小时.     

基于全连接神经网络方法的日最高气温预报

为了考察辅助变量、时间滞后变量设置的重要性和神经网络中嵌入层对分类变量处理的有效性,利用2015年1月15日—2020年12月31日欧洲中期天气预报中心（European Centre for Medium-Range Weather Forecasts,ECMWF）高分辨率模式（high resolution,HRES）输出产品及中国2238个国家级地面气象站基本气象要素数据集,在全连接神经网络基础上设计4个试验,构建24 h最高气温预报神经网络模型。结果表明：加入辅助变量、时间滞后变量的特征和带有嵌入层的全连接神经网络结构的深度学习神经网络模型对HRES日最高气温预报误差均有订正效果,均方根误差降低29.72%~47.82%,温度预报准确率提高16.67%~38.89%。加入经过嵌入层处理的辅助变量后,可显著提高青藏高原中南部和西南地区东部的平均绝对偏差不超过2℃的正技巧站点比例（比仅用HRES预报因子建模分别提高21.74%和14.17%）,在此基础上加入时间滞后变量显著提高上述两个地区的平均绝对偏差不超过2℃的正技巧站点比例（比仅用HRES预报因子建模分别提高40.98%和20.33%）,且预报性能更加稳定。     

日气温数据缺测的插补方法试验与误差分析

对缺测气象观测记录进行插补是建立连续气象数据集的基础.将孤立1日或数日缺测资料进行插补的线性回归模型法应用于连续缺测数月的逐日最高、最低和平均气温的插补,并进行了一系列改进,包括:(1)用滑动选优法确定邻近参考气象站站数和数据样本时间窗的最佳值;(2)在记录缺测站与邻近参考站之间建立逐日气温的线性回归模型,并选取以最小绝对误差(Least Absolute Deviation,LAD)为目标函数求取模型参数的方法,取代以最小均方根误差为目标函数的最小二乘法(Least Squares Estimate,LAD)求解模型参数的方法,可提高计算效率和参数的稳定性;(3)进一步提出将LAD法与DeGaetano标准化序列法插补结果平均的综合插补方法,以减少极端误差.通过对湖北蔡甸气象站1961-2006年插补试验表明:(1)以4个邻近站和年数为8年、日数为15天时间窗的样本资料建模进行插补误差达到最小;(2)逐日最高、最低和平均气温的平均绝对误差分别为0.32℃、0.45℃、0.28℃,误差在±0.8℃以内的频次分别占总数的94.1%、84.8%、96.1%,观测值与插补值月相关系数在0.886以上.插补与观测资料平均值和相关系数分别通过了显著水平为0.05和0.01的检验.     

最高最低气温出现时间分析

对新乡气象站2012-2013年每目的最高最低气温出现时间进行分析发现,在正常出现时间之外,有许多异常情况。剔除异常时间点,对正常时间进行统计得到：日最高气温冬半年平均出现在14～15时,夏半年在15～16时;最低气温冬季在07时左右,夏季在05：30前后。两年中有22％的温度极值在异常时间点出现。异常极值的出现主要由冷锋、强冷空气、云、降水、风、雾、霾等因素引起。当气温日较差比较小时,气温极值出现的时间和地点存在随机性。     

日平均计算方法对气温统计值的影响

为了弄清不同日平均计算方法对气温统计值的影响,利用陕西6个基准站的定时气温资料分别进行24次观测与4次观测,以及4次与3次观测值计算所得日平均气温计算值的差异分析,并对1961-2010年的3次、4次、24次计算的年平均气温序列进行均一性的惩罚最大F检验(PMFT).结果表明:24次气温定时值计算的日平均气温均值高于4次值,平均差值为0.13℃,标准差为0.39℃,两者差值在秋季较大.4次比3次日平均气温值平均偏低0.14℃,标准差为0.85℃,一年中,夏季差值最大.不同次数的日平均气温计算方法可引起月、年平均气温值0.2℃甚至以上的升降.24次气温值的使用可以使单站的气温增暖速率提高0.03～0.04℃/10a.但日平均气温计算方法的改变不会造成气温序列的非均一.     

中国近百年温度曲线的对比分析

 温度变化是全球变化研究中非常重要的基础性问题,在中国近百年温度变化方面,十几年来已取得了明显进展,这首先表现在建立了若干条全国平均气温序列,而几条主要序列间的相关系数在0.73～0.97之间。同时,这些进展也表现在基础资料质量提高、空间覆盖面扩大、序列均一性改善以及结果可靠性提高等方面。对多序列进行综合分析得到的新结果显示,1906-2005年中国的年平均气温上升了（0.78±0.27）℃,2007年是我国近百年来最暖的一年;代表性分析显示,现有的几条中国温度序列中除覆盖完整的序列外,其他序列在1920或1930年代以前可能主要反映中国东部变化情况,但在此之后则能较好地代表全国大部分地区的气候变化特征。     

日较差分级的新疆逐时气温预报

利用1994-2005年新疆12个基准站逐时气温资料,统计了不同日较差等级内的逐时气温和逐时气温变量的气候平均值,并在此基础上自动地输出新疆逐站逐时气温预报值,由此得到与气温变化有关的专业气象预报产品,应用于专业气象业务。     

我国太阳日总辐射计算方法的研究

对全国23个站点的日照时数、日最高气温、日最低气温、太阳日总辐射量等气象要素实测资料进行统计分析,利用回归分析法建立了以日照百分率和气温日较差为主要相关因子的各地日总辐射估算模型。结果表明：除了高原站拉萨以外,推算模型的复相关系数均介于0．80～0．93之间,拟合效果较好。在春、夏季使用独立的季节模型有一定的必要性,该方法适用于我国各地太阳日总辐射的推算,     

利用MSU序列研究高空大气温度变化的进展

美国NOAA卫星上搭载的微波大气探测仪MSU以及后续继承改进仪器AMSU,自1978年投入运行以来,已经积累了超过30年的全球大气温度的观测资料。近年来,国外学者通过仪器定标误差订正、卫星轨道衰减订正、观测时间差异导致的温度日变化订正、仪器间的相互定标处理等技术,发现并剔除了一些影响较大的非气候因素,提高了该序列资料的质量,并用于全球气候变化研究,特别是对流层和平流层近30年的温度变化研究,研究结果是对常规地面和探空资料分析结果的重要补充。本文对目前国际上3种常用的MSU序列资料集的定标误差、偏差订正和不同卫星上仪器观测序列的一致化处理方法进行了综合介绍,并比较分析了3种资料用于30年高空大气温度变化趋势分析的异同。     

中国近百年温度序列

我们收集了711个站近3×105个数据的温度记录，将全国分成10个区，先计算出每个区的平均温度序列，最后得到近百年的全国温度序列。进而，讨论了全国温度序列的气候变化，结果指出中国近百年温度变化与北半球的变化很相似，都有两个增暖时段即40年代和80年代的增温。北半球平均温度80年代要比40年代高，而中国平均温度80年代要比40年代低。     

南北极区和青藏高原臭氧变化与中国降水和温度的联系

利用 1 978年 1 1月～ 1 993年 4月TOMS全球臭氧资料和中国地面资料 ,研究了南北极区春季和青藏高原大气臭氧变化与中国降水和温度变化的联系。结果显示 ,南北极区春季和青藏高原冬季臭氧变化与中国的降水和温度变化具有较好的相关性 ,因此可利用大气臭氧变化预测中国部分地区降水和温度变化。     

南宁市气温日较差特征及天气系统影响

运用常规气象资料，对南宁市气温日较差的时空变化进行了分析，结果表明：南宁市气温日较差有逐年减小的趋势，夏秋季气温日较差大于冬春两季气温日较差，不同下垫面对气温日较差影响不同。对气温日较差较大的个例分析表明，引起气温日较差较大的主要天气系统为冷高压控制型、锋面影响型、暖低压影响型、副高影响型。     

Changes in Canada's Climate: Trends in Indices Based on Daily Temperature and Precipitation Data

ABSTRACT

Trends in indices based on daily temperature and precipitation are examined for two periods: 1948–2016 for all stations in Canada and 1900–2016 for stations in the south of Canada. These indices, a number of which reflect extreme events, are considered to be impact relevant. The results show changes consistent with warming, with larger trends associated with cold temperatures. The number of summer days (when daily maximum temperature >25°C) has increased at most locations south of 65°N, and the number of hot days (daily maximum temperature >30°C) and hot nights (daily minimum temperature >22°C) have increased at a few stations in the most southerly regions. Very warm temperatures in both summer and winter (represented by the 95th percentile of their daily maximum and minimum temperatures, respectively) have increased across the country, with stronger trends in winter. Warming is more pronounced for cold temperatures. The frost-free season has become longer with fewer frost days, consecutive frost days, and ice days. Very cold temperatures in both winter and summer (represented by the 5th percentile of their daily maximum and minimum temperatures, respectively) have increased substantially across the country, again with stronger trends in the winter. Changes in other temperature indices are consistent with warming. The growing season is now longer, and the number of growing degree-days has increased. The number of heating degree-days has decreased across the country, while the number of cooling degree-days has increased at many stations south of 55°N. The frequency of annual and spring freeze–thaw days shows an increase in the interior provinces and a decrease in the remainder of the country. Changes in precipitation indices are less spatially coherent. An increase in the number of days with rainfall and heavy rainfall is found at several locations in the south. A decrease in the number of days with snowfall and heavy snowfall is observed in the western provinces, while an increase is found in the north. There is no evidence of significant changes in the annual highest 1-day rainfall and 1-day snowfall. The maximum number of consecutive dry days has decreased, mainly in the south.     

近45年哈密地区温度变化特征

利用1961-2005年近45年哈密地区6个站点的观测资料,分析了该地区的平均气温、平均最高气温、平均最低气温和平均日较差、炎热日以及寒冷日的年际、年代际的变化特征,同时对降水、云量等要素也进行了分析,揭示其与哈密地区温度变化的可能关系。结果表明,近45年来哈密地区气候显著增暖,平均气温在夏季增暖幅度最大,春季最弱。1990年代以后增暖趋势表现最明显,21世纪以来增幅最大。与平均气温变化趋势相一致,最高温度和最低温度也呈显著升高趋势,其中最低温度的升高幅度远大于最高温度和平均气温的升高幅度。哈密地区近45年平均日较差显著减小,这主要是因为最低气温的升高幅度大于最高气温的升高幅度。在全球增暖背景下,哈密地区的炎热日数显著增加,而寒冷日数显著减少。个别站的气温增温不明显,这与局地的降水、云量增加,日照减少有一定关系。此外,哈密地区冬季平均气温在1980年代中后期有一次明显的突变,突变时间晚于新疆其他地区5~6年左右,表明气候突变在不同地区会有不同的表现。     

内蒙古气温序列均一性检验

通过构建参考序列、找出间断点、结果集成等步骤对内蒙古地区119个地面气象站逐日平均气温、逐日最高气温、逐日最低气温进行均一性检验。结果表明:内蒙古地区大部分地面气象站气温资料均一性状况良好;部分台站气温资料不均一的原因主要是受迁站影响,其次是观测站周围环境变化、观测仪器的更换以及观测标准的变更。不均一现象对最低气温影响较大,其次是平均气温,最高气温受影响相对最小。