最概然温度背景下不同气候态中国夏冬季极端温度事件时空分布特征

利用1961-2009年中国逐日最高温度资料,分成1961-1990年、1971-2000年和1981-2009年三个气候态(分别称为Ⅰ,Ⅱ,Ⅲ态),基于偏态概率密度函数定义极端温度事件,从频次、强度两个角度研究了不同气候态下中国夏季和冬季极端温度事件的时空变化特征. 空间分布上,夏季极端高温的频次、强度在Ⅰ态黄淮、江淮流域显著减小,在Ⅲ态,干旱半干旱以及经济发达的长江沿岸、长江三角洲以及东南沿海地区显著增加;冬季极端低温频次在Ⅱ态中的北方、长江三角洲以及Ⅲ态中的高原、东北东南部、长江三角洲显著降低. 冬季极端低温强度整体呈降低趋势,但区域特征不明显. 极端温度频次和强度在空间上一致性较好;时间演化上,夏季极端高温频次、强度均在Ⅱ,Ⅲ态显著增加,冬季极端低温频次、强度降低的趋势有所减缓. 在当前气候态(Ⅲ态)夏季极端高温在经济发达地区发生比较频繁且极端性在增强;冬季极端低温的发生维持在一个稳定状态且极端性也相对比较稳定. 极端温度事件的频次与强度在时间变化趋势上存在较好的一致性. 在Ⅰ,Ⅱ,Ⅲ态共同时段内,后一气候态检测出的极端高温频次均比前一气候态少,极端低温频次均比前一气候态多,这与背景温度随气候态逐渐升高是相一致的;后一气候态检测出的极端高温强度均比前一气候态小,极端低温强度均比前一气候态大. 在20世纪70年代末80年代初全球气候系统突变前,极端高温频次具有明显的下降趋势,突变后显著上升;极端低温频次在突变前后变化不明显. 极端高温强度突变前表现出轻微的下降趋势,在突变后上升趋势明显;极端低温强度在突变前后大致相当,突变后的整体强度稍低于突变前. 关键词： 极端高温 极端低温 气候态     

中国夏冬两季最概然温度分布及其增温趋势减缓

利用中国气象局国家气象信息中心1961—2008年夏、冬两季日平均温度资料,基于偏态分布函数提出最概然温度.研究表明,最概然温度比平均温度更能合理地代表气象观测站的背景温度场.就年代际而言,在20世纪90年代中期之前,中国夏季年最概然温度以相对低温为主,随后呈现波动增温趋势,但自2005年增温趋势有所减缓.冬季年最概然温度在1961—1986年这一时段以相对低温为主,1987年到21世纪初显著变暖,但2000年后增温趋势减缓.总体而言,冬季增温幅度较夏季强,且增温时间早于夏季5—10年.本文定义的1961     

最概然分布、微观状态数及其相关问题

通过对一个简单模型的研究可知,最概然分布包含的微观状态数和全部可能微观状态数的比值在热力学极限下为零.当一个热力学体系处于平衡态时,最概然分布及其附近相对于实验可以达到的精度来说的极其狭窄的区域内的分布包括了体系几乎全部可能的微观状态数.     

中国冬季区域性极端低温事件分类及其与气候指数极端性的联系

基于区域性极端低温事件客观识别技术, 对1951–2010年中国冬季的区域性极端低温事件进行客观识别. 根据事件的空间分布特征, 将综合指数前60位的事件划分为全国型、东部型、东北-华北型、华北-华南型、北方型和西北-华南型六类; 通过分析不同区域类型低温事件形成的环流背景场验证了分类的有效性. 在此基础上, 以1971年1月21日开始的典型事件为例, 分析了事件对应的海温场、高度场和风场的异常, 确定与区域性极端低温事件联系较密切的可能气候因子, 进而分析不同类型事件与各气候指数异常的对应关系. 总体而言, 赤道中东太平洋海温指数异常偏小、北太平洋涛动指数异常偏小、北极涛动指数异常偏小和冬季风异常偏强时, 发生区域性极端低温事件的概率较高; 且这四种指数的历年冬季平均值达到15%(或85%)极端阈值的年份中, 发生区域性极端低温事件的百分率分别达到80.0%, 77.8%, 60.0%和62.5%, 从而为区域性极端低温事件的诊断和预测研究等提供了一定的参考. 关键词： 区域性极端低温事件 空间分类 气候指数 极端     

近46年来中国温度破纪录事件的时空分布特征分析

利用概率分布理论和中国近46年逐日最高、最低温度资料,对中国近46年来高温、低温破纪录事件频次和强度的时空分布特征进行了统计分析.结果表明：1976—2005年以来,中国实际检测年均发生高温破纪录事件频次多于理论值2.5次左右,而低温破纪录事件则少于理论值约4.5次,这与全球增暖趋势相一致.高温破纪录事件多发生于中国西北、华北、东北和青藏高原一带,低温破纪录事件则相反,在这些地方明显偏少;在温度破纪录事件年发生频次相对趋势上,中国大部地区高温破纪录事件频次有一定程度的增加而低温破纪录事件呈现普遍减少趋势; 关键词： 破纪录事件 气候变化 极端事件 中国气候     

中国近50年气候破纪录温度事件发生概率分析

随着全球变暖趋势的不断增强,破纪录温度事件（record breaking temperature events, RBTE）发生的频次呈现不断增加的特点.基于已知当前年份的温度记录,未来五年内这个记录被打破的概率是多少？即未来五年内发生新的RBTE的概率是多少？这是目前国内外学术界关注的前沿科学问题之一.文中基于概率统计模型研究在不同情景下RBTE发生概率,并与实际观测资料检测结果作对比分析,研究近50年中国RBTE发生概率的变化与全球增暖的关系.结果显示,RBTE发生概率的大小依赖于温度的增长趋势和相 关键词： 破纪录温度事件 全球增暖 极端事件     

全球变暖对中国极端暖月事件的潜在影响

利用极值理论（extreme value theory, EVT）中的广义帕雷托分布（generalized Pareto distribution, GPD）,结合空间参数估计方案研究了中国1960—2007年740站的月平均温度距平中极端暖月事件（extreme warm month events,EWME）的空间分布特征,给出了历史上典型EWME的重现时间估计,重点分析了区域气候变暖作为强迫因子对EWME分布的潜在影响.分析表明：1）中国EWME的性质的空间分布差异较大,青藏高原西部、中国西南地区、 关键词： 极端气候事件 帕雷托分布 全球变暖     

The characteristics of clusters of weather and extreme climate events in China during the past 50 years

The pick-up algorithm by the k-th order cluster for the closest distance is used in the fields of weather and climactic events, and the technical terms clustered index and high clustered region are defined to investigate their temporal and spatial distribution characteristics in China during the past 50 years. The results show that the contribution of extreme high-temperature event clusters changed in the period from the 1960s to the 1970s, and its strength was enhanced. On the other hand, the decreasing trend in the clusters of low-temperature extremes can be taken as a signal for warmer winters to follow in the decadal time scale. Torrential rain and heavy rainfall clusters have both been lessened in the past 50 years, and have different cluster characteristics because of their definitions. Regions with high clustered indexes are concentrated in southern China. The spatial evolution of the heavy rainfall clusters reveals that clustered heavy rainfall has played an important role in the rain-belt pattern over China during the last 50 years.     

中国近58年温度极端事件的区域特征及其与气候突变的联系

运用动力学自相关因子指数Q分析中国温度的时空变化特征,得到8个不同的动力学温度变化特征区：准噶尔区、 东北区、西北区、西南东区、西南西区、华北区、东南区和中南区.初步讨论了这些特征区的年均温度变化和极端温度年出现天数及其与温度突变的关系,以及不同温度段对中国近58年增暖的可能影响.研究结果表明：（1）准噶尔区、东北区等7个温度特征区近58年的年均温和极端高温的年出现天数均表现为增长趋势,极端低温天数则为降低趋势,极端高温的变化与温度增暖呈正相关,与极端低温则呈负相关.同时,北方涛动、南方涛动 关键词： Q指数')" href="#">Q指数 温度段 极端温度 温度突变     

Long-term memory: a natural mechanism for the clustering of extreme events and anomalous residual times in climate records

We study the statistics of the return intervals between extreme events above a certain threshold in long-term persistent records. We find that the long-term memory leads (i) to a stretched exponential distribution of the return intervals, (ii) to a pronounced clustering of extreme events, and (iii) to an anomalous behavior of the mean residual time to the next event that depends on the history and increases with the elapsed time in a counterintuitive way. We present an analytical scaling approach and demonstrate that all these features can be seen in long climate records. The phenomena should also occur in heartbeat records, Internet traffic, and stock market volatility and have to be taken into account for an efficient risk evaluation.     

A simple study of the correlation effects in the superposition of waves of electric fields: The emergence of extreme events

In this paper, we study the effects of correlated random phases in the intensity of a superposition of N wavefields. Our results suggest that regardless of whether the phase distribution is continuous or discrete if they are random correlated variables, we will observe a denser tail distribution and the emergence of extreme events (amplitudes 30-40 times larger than their average) as the phases correlation increase. Recent results in the literature discuss the role of phase correlations on the emergence of rogue waves both in linear and nonlinear systems, but the mechanisms to generate them are not always straightforward. We show here a simple way to correlate the wavefield that makes it clear that rogue waves or denser tails appear mainly due to wave correlations instead of any particular system property.     

基于气候变化综合指数的中国近50年来气候变化敏感性研究

气候变化指数是目前有关气候变化研究领域的前沿课题, 国内外在气候变化研究领域存在着许多具体指数,但关于综合指数的研究却非常少见. 本文将基于温度和降水的单一要素指数的气候变化信息进行综合, 得到一个综合气候变化指数CCI (climate change index)以评估中国近50年来的气候变化及其区域敏感性.指数大小表征气候突变前后极端气候事件的频数之差, 反映该地区应对气候变化的能力,反映该地区对气候变化的敏感性. 通过该指数可以获得多种气候变化的相关信息,从而为更好地应对极端气候事件提供判断依据. 研究结果显示,内蒙古大部、东北中部、云南以及西北中部等地CCI指数较大, 说明这些地区相对于气候突变之前极端气候事件频发. 中国各个省份中的所有站点的CCI指数平均值表明,中国江南和西南东部对气候变化不敏感; 华北和东北地区极端气候事件频发.气候变化在高纬度地区和热带、 亚热带地区表现明显,北方和西南比较敏感,而黄河以南敏感性较弱, 沿海地区由于受季风及台风带来的强降水的影响, CCI指数相对偏大,敏感性较强.     

On the predictability of extreme events in records with linear and nonlinear long-range memory: Efficiency and noise robustness

We study the predictability of extreme events in records with linear and nonlinear long-range memory in the presence of additive white noise using two different approaches: (i) the precursory pattern recognition technique (PRT) that exploits solely the information about short-term precursors, and (ii) the return interval approach (RIA) that exploits long-range memory incorporated in the elapsed time after the last extreme event. We find that the PRT always performs better when only linear memory is present. In the presence of nonlinear memory, both methods demonstrate comparable efficiency in the absence of white noise. When additional white noise is present in the record (which is the case in most observational records), the efficiency of the PRT decreases monotonously with increasing noise level. In contrast, the RIA shows an abrupt transition between a phase of low level noise where the prediction is as good as in the absence of noise, and a phase of high level noise where the prediction becomes poor. In the phase of low and intermediate noise the RIA predicts considerably better than the PRT, which explains our recent findings in physiological and financial records.     

北京气候中心气候系统模式对中国四季日平均气温的模拟性能评估

利用去趋势波动分析方法对中国四季日平均气温观测资料进行了研究, 发现四季日平均气温均具有很好的长程相关性特征, 中国西部地区尤其是新疆和西藏的长程相关性较强. 基于观测资料中的这种长程相关性特征, 评估了北京气候中心气候系统模式对中国四季日平均气温的模拟性能, 发现该模式能够较好地反映中国四季日平均气温的长程相关性特征. 总体而言, 模式对春季的日平均气温的长程相关性模拟效果最好, 仅对江南地区的长程相关性的模拟较差; 夏季, 模式模拟误差较大的地区包括中国中东部地区及西藏大部, 其中华北南部、黄淮西部、江南大部、华南等地模拟效果最差; 秋季, 模式对东部沿海及东北大部、华北西南部等地模拟的长程相关性偏强, 而在西北大部模拟的长程相关性明显偏弱; 冬季, 除东部沿海地区模拟的长程相关性偏强外, 全国其余大部分地区接近观测或偏弱, 其中西北、西南、华南北部、江南南部、东北北部偏弱明显, 青藏高原西部偏弱最为显著.     

Reductive-annealing-induced changes in Mo valence states on the surfaces of MoO3 single crystals and their high temperature transport

Reduction of MoO₃ in extreme reducing condition is a way to achieve Mo metal. However, effect of less extreme reductive-annealing, where it allows to keep the crystal structure, on physical and chemical properties of MoO₃ has not been well-studied. In this work, we studied the evolution of Mo valence state during reductive annealing and its effect on high temperature transport. We found the formation of oxygen vacancies on surface of MoO₃ single crystals at the low temperature, which is evidenced by increase of Mo⁵⁺ and color change. In addition, formation of Mo⁴⁺ was at the elevated temperature. For understanding the relation between bulk conductivity and Mo valence state, real-time impedance spectroscopy is employed. Use of two different gases makes it possible to distinguish impedance responses of MoO₃ from those of reduced MoO_3-x. Also, from time-dependent impedance measurements, we observed the evolution of transport behaviour by evolution of Mo valence state.     

Early warning signals of abrupt temperature change in different regions of China over the past 50 years

In this paper, the early warning signals of abrupt temperature change in different regions of China are investigated. Seven regions are divided on the basis of different climate temperature patterns, obtained through the rotated empirical orthogonal function, and the signal-to-noise temperature ratios for each region are then calculated. Based on the concept of critical slowing down, the temperature data that contain noise in the different regions of China are preprocessed to study the early warning signals of abrupt climate change. First, the Mann-Kendall method is used to identify the instant of abrupt climate change in the temperature data. Second, autocorrelation coefficients that can identify critical slowing down are calculated. The results show that the critical slowing down phenomenon appeared in temperature data about 5-10 years before abrupt climate change occurred, which indicates that the critical slowing down phenomenon is a possible early warning signal for abrupt climate change, and that noise has less influence on the detection results of the early warning signals. Accordingly, this demonstrates that the model is reliable in identifying the early warning signals of abrupt climate change based on detecting the critical slowing down phenomenon, which provides an experimental basis for the actual application of the method.