阿尔泰山降水氢氧稳定同位素特征及水汽来源分析

阿尔泰山横亘于亚欧大陆中部,是中纬度西风带气候研究的重点区域之一。利用阿尔泰山地区4个站点的监测数据,研究了该区域降水氢氧稳定同位素的年内变化特征及大气降水线方程,分析了降水同位素的温度效应,并利用后向轨迹探讨了水汽来源。结果表明：（1） 阿尔泰山各站点降水同位素比率在季节上表现为夏高冬低,且南侧站点的季节差异比北侧大,除Novosibirsk外大多数站点的降水氘盈余值为夏低冬高。（2） 除Novosibirsk外,研究区大多数站点大气降水线方程的斜率和截距都低于全球平均值。（3） 各站点降水同位素存在明显的温度效应,体现在季节变化和空间分布上。（4） 后向轨迹表明,研究区受到西风水汽、极地水汽和近源水汽路径的影响,且偏北站点可能受极地水汽路径的影响更大。上述认识有助于明确阿尔泰山不同区域降水同位素时空变化反映的水文气候信息,并为该区域大气水循环及气候变化研究提供参考。     

1854-2010年川西高原北部初夏气温变化与全球海表温度关系初探

本文利用主成分分析法提取了川西高原北部5个样点反映气温变化的树轮宽度年表的主成分年表,其中第一主成分年表(PC1)的方差贡献率为59.52%,能够较好地代表川西高原北部地区树轮宽度变化的共同特征。在器测时段内(1961-2010年),PC1年表、6-7月区域平均气温观测序列与东亚气温场的相关分布较为一致,均与中国的西北-青藏高原一带以及西伯利亚东部呈现显著的正相关(r > 0.443,p < 0.001),表明PC1年表能够反映较大空间范围的气温变化特征。器测时期,PC1年表和观测序列与全球海温场的相关分析表明,两者均与西太平洋、印度洋及北大西洋海域显著正相关,反映了在海气相互作用过程中多个海区海表温度变化可能对研究区气温变化有一定影响;而利用PC1年表在更长时间尺度(1854-2010年)及分阶段分析则发现不同阶段对区域气温起主导作用的海区有比较明显的差异,甚至在比较大的空间范围内在不同阶段出现相反的分布型,且由相关系数反映出的海气相互作用强度也具有显著的差异,气温波动较大时,海气相互作用也比较强烈。异常年分析则反映出西北太平洋和北大西洋海温异常对研究区气温异常具有持续稳定的影响。     

基于广义Pareto分布的广东省近50年极端降水事件研究

利用广东省平均和5个代表站点1961-2010年近50年的逐日降水量资料,采用基于广义Pareto分布的平均剩余寿命图法计算极端降水阈值,并与传统的百分位法进行比较,对广东省极端降水的时间变化和空间分布特征进行了分析。结果表明,平均剩余寿命图法是计算极端降水阈值的一种有效方法,计算结果更能反映极端降水事件的区域差异。近50年来,广东省及各区域极端降水事件的强度和发生频率年际变化较大,均没有明显的上升趋势;热带、南亚热带和中亚热带3个自然分区的变化特征差异明显,空间上表现为南高北低的分布形式。     

过去2000年亚洲气候变化(PAGES-Asia2k)集成研究进展及展望

介绍了国际过去全球变化研究计划(PAGES)过去2000年气候变化研究网络的设立背景、研究目的及亚洲在其中的独特性;总结了过去2000年亚洲气候变化(PAGES-Asia2k)集成研究的主要进展及中国的贡献,分析了当前在亚洲区域开展该项研究的主要问题及难点,并对未来研究进行了展望。亚洲历史悠久,高分辨率的代用资料丰富,是开展过去2000年气候变化研究的理想区域,但目前尚处于起步阶段。已有进展表明:在过去1200年中,亚洲830-1220年相对温暖,1340-1880年明显寒冷,20世纪则快速增暖;但亚洲气候类型多、变化过程复杂、区域差异大。因此,发展气候序列重建的新技术方法,提高重建序列的时、空分辨率、空间覆盖度和重建结果的精度,建立亚洲过去2000年气候代用资料数据库,集成重建过去2000年区域温度变化序列及干湿变化空间型,进而开展区域气候变化驱动机制与气候变化影响及适应研究,是该计划下一步的研究重点。     

基于温度作为辅助变量的中国降水δ^18O空间分布特征

本研究利用从WorldClim获得的高分辨率气象数据分析稳定同位素温度效应,研究发现最热季度的平均气温与中国62个站点同位素年平均值之间表现出很好的相关性（R2=0．79）。在此基础上利用地统计学方法,也就是采用温度作为辅助变量的空间插值算法——局部平均的简单克里金法（Simple Kriging with locally varying mean,SKlm）,对中国降水中稳定同位素年平均值空间分布进行了模拟,得到了较高分辨率的中国降水中年均δ^18O空间分布图,该图能够较好地表现区域同位素变化模式,能够反映如降水汽团的来源、性质等的气候环境背景,也能在一定程度上反映包括当地的纬度、地形、海拔高度等在内的局部地理因素。     

利用树轮重建秦岭地区历史时期 初春温度变化

采用树木年轮气候学方法,利用秦岭地区树轮长年表资料重建秦岭地区历史时期初春温度,表明各气象站点重建序列与器测序列均呈显著的正相关关系,其中重建序列与实测记录的相关系数最高可达0.7以上,其他站的相关系数取值也均接近或超过0.6,说明重建序列能较好地反映秦岭地区的初春温度变化。对重建序列的分析结果表明,近300年来秦岭地区的初春温度变化存在明显的冷暖时段：1715~1740年、1773~1804年和1893~1958年三个时段的初春温度相对较高,持续时间分别为26年、32年和66年;而1741~1772年、1805~1892年、1959~1992年三个时段的初春温度相对较低,持续时间分别为32年、88年和34年,在整体上具有升温快速降温缓慢的特征。重建结果的变化趋势与其他相关研究结果极为一致。但是在20世纪后期,无论是在重建序列还是在器测序列中至少到1992年均未反映出明显的增温趋势。秦岭西部地区初春季节变冷与增暖的幅度均大于秦岭中东部地区。此外,秦岭地区初春温度变化具有100年左右、50~60年、7~8年以及2~3年等准周期变化特征。     

北极地区20世纪温度变化趋势的不确定性

北极作为地球的冷源之一,对于地球气候系统起着重要的调节作用.本研究通过分析和总结北极地区器测数据和模拟集成结果,以及最近2 000 a来的温度记录,得出如下结果：1）格陵兰冰盖表层大气温度记录显示,20世纪存在1923—1965年和1994年—至今2个相对温暖时段,且前者更为温暖,温度变化幅度远大于北半球的平均温度.2）北极地区20世纪温度上升是客观存在的,其夏季温度和年平均温度呈现一致变化,未发现明显的温度异常信号.3）定量重建的古气候记录显示了北极地区20世纪升温的特征,但不同记录揭示的升温幅度存在差异.与器测结果不同,多条重建记录未能揭示自1994年至今的升温阶段,反映了古气候载体对气候响应的复杂性,揭示出北极地区未来气候变化趋势存在不确定性.在全球变化备受关注的背景下,北极地区对气候变化研究的重要性日益显现,在做出明确结论之前,需要进一步加大研究力度.     

近百年来全球不同冷、暖期温度分布及环流特征

本文用全球格点气温、海面温度、海平面气压等资料分析了本世纪以来四个不同冷暖时段（１９０６－１９１５，１９３６－１９４５，１９６６－１９７５及１９７９－１９８８年）的温度场和地面气压场。着重讨论了不同冷、暖期之间温度距平场的分布特点、区域和季节差异以及海、陆变化的不同步性等。在温度的纬向谐波分析中发现冬季暖期，中、高纬度超长波活动盛行，表明１０年尺度冷、暖的环流机制与天气尺度是一致的。     

西藏自治区植被与气候变化的关系

气候变化下植被的时空响应是近年来的研究热点。高海拔西藏地区气候独特多变,研究该区域植被与气候变化的关系具有重要意义。西藏地区的气象站少,利用站点观测资料插值分析误差相对较大,难以准确获得空间连续的数据。本文采用2001~2013年MODIS卫星16天时间序列数据和同期的降雨卫星TRMM数据,利用线性回归和相关性分析法研究西藏地区植被、地表温度和降雨量的时空特征及相关性。研究表明:在2001~2013年间,西藏地区植被与地表温度、降雨量在时间波动和空间分布上具有一致性。植被NDVI逐年增大,植被状况逐渐改善,地表温度总体呈上升趋势,降雨量整体无明显变化,三者年际变化率主要集中在-0.005~0.005/a,-0.05℃~0.15℃/a,-30~40 mm/a。近13年来植被NDVI、地表温度和降雨量的变化区域差异性较大,在西藏中部和东部变化明显。植被NDVI的变化与气候变化(尤其是地表温度上升)密切相关,受降雨明显影响的区域分布在西藏中部,受地表温度明显影响的区域分布在西藏东部和西部。     

基于地铁客流的广州地铁站点类型识别

使用地铁刷卡数据对地铁客流进行研究时,面临数据量大、数据维度高、信息冗余度高等问题。传统方法主要通过专家经验建立指标进行分析,不能有效地利用原始数据,也容易受主观因素的影响。文章使用主成分分析方法(PCA)对广州地铁站点客流数据进行特征提取,用以提高特征选取过程的科学性,避免主观因素的影响,降低数据维度、消除冗余信息的过程中能够尽量保留原始数据的信息。研究表明:PCA提取出6个主成分,保留了原始数据91.41%的信息,前2个主成分代表了工作日地铁客流最重要的居住型站点客流特征和就业型站点客流特征。在此基础上利用K-均值聚类算法对广州地铁站点进行类型识别,识别出居住导向型、就业导向型、职住错位型等7类站点。通过分析不同站点的空间分布,发现广州地铁站点呈圈层式分布特征,站点类型随着到城市中心距离的增加而减少,反映了广州市城市功能的空间分布,城市中心区域站点类型多样,说明这一区域城市发展成熟、功能齐全;城市中心区域周围主要分布着职住错位型站点,城市发展较为成熟;而城市外围主要分布着居住导向型站点,承担城市的居住职能。     

Mapping Air Temperature in the Lancang River Basin Using the Reconstructed MODIS LST Data

Air temperature is an important climatological variable and is usually measured in meteorological stations. Accurate mapping of its spatial and temporal distribution is of great interest for various scientific disciplines, but low station density and complexity of the terrain usually lead to significant errors and unrepresentative spatial patterns over large areas. Fortunately the current studies have shown that the regression models can help overcome the problem with the help of time series remote sensing data. However, noise induced by cloud contamination and other atmospheric disturbances variability impedes the application of LST data. An improved Savizky-Golay (SG) algorithm based on the LST background library is used in this paper to reconstruct MODIS LST product. Data statistical analysis included 12 meteorological stations and 120 reconstructed MODIS LST images of the period from 2001 to 2010. The coeffificient of correlations (R²) for 80% of the stations was higher than 0.5 (below 0.5 for only 2 stations) which illustrated that there is a considerably close agreement between monthly mean TA (air temperature) and the reconstructed LST in the Lancang River basin. Comparing to the regression model for every month with only LST data, the regression model with LST and NDVI had higher R² and RMSE. Finally, the LST-NDVI regression method was applied as an estimate model to produce distributed maps of air temperature with month intervals and 1 km spatial in the Lancang River basin of 2010.     

自适应的IDW插值方法及其在气温场中的应用

反距离权重(Inverse Distance Weighting,IDW)插值通常采用距离搜索策略选择插值参考点,当采样点集分布不均匀时,距离搜索策略使得参考点聚集一侧影响插值精度。自然邻近关系具有良好的自适应分布特性,可有效地解决参考点分布不均匀问题。结合自然邻近关系,提出自适应的反距离权重(Adaptive-IDW,AIDW)插值方法。首先对采样数据构建初始Delaunay三角网,然后采用逐点插入法,将待插值点插入初始Delaunay三角网中,局部调整得到新的Delaunay三角网,以待插值点的一阶邻近点作为IDW插值的参考点,使参考点自适应均匀地分布在待插值点周围,再进行IDW插值计算。利用AIDW插值方法对Franke函数、全国气温观测数据进行插值实验,结果表明此方法具有较高的精度,且减少了“牛眼”现象。     

积温数据栅格化方法的实验

根据中国 4 0 0多个气象站 1995年的积温数据分析 ,≥ 0℃积温和≥ 10℃积温与经纬度、海拔高度的相关性分别为r2 =0 96 5 6和r2 =0 94 0 2。以≥ 0℃积温数据为例 ,利用聚类分析法 ,将全国分为 7个积温计算区 ,每个区分别构建模型 ,通过“回归方程计算 +空间残差”的方法对全国积温数据进行栅格化。验证结果为 :计算值与实测值之间的线性相关系数r2 =0 9889,平均相对误差 3 5 6 % ,相对误差在 5 %以内的气象站个数占验证气象站总数的 86 %。     

Two centuries of temperature variation and volcanic forcing reconstructed for the northern Tibetan Plateau

Abstract

The northern Tibetan Plateau has been subject to recent warming far above the global average. With few instrumental climate records available for this region before the 1950s, paleoclimatic reconstructions must be used to understand annual-to-centennial-scale climate variations and local climate response to large-scale forcing mechanisms. We developed a maximum latewood density chronology of Qinghai spruce (Picea crassifolia) from the southern slope of the Qilian Mountains, northern Tibetan Plateau. Based on the chronology, we reconstructed August–September temperature for 1780–2008. The temperature reconstruction model accounts for 39.7% of instrumental temperature variance from 1957 to 2008, successfully capturing the most recent warming. Superposed epoch analysis indicated a volcanic forcing for temperature, resulting in pulses of cooler conditions that can persist for 2–4 years. Tree-ring data indicated that warm-dry and cold-wet climate combinations mainly occurred in northern Tibetan Plateau before CE 1900, and revealed a clear wetting and warming trend since the 1980s. Our study provides long-term perspective on recent climate change in northern Tibetan Plateau to guide expectations of future climate variability and aid sustainable development, and provides scenarios for climate change adaptation and inputs for climate models representing a broader range of conditions than those of historical climate records.     

Reconstructing air temperature at eleven remote alpine and arctic lakes in Europe from 1781 to 1997 AD

Pristine and sensitive environments, such as remote alpine and arctic lakes, are particularly susceptible to the effects of climate change. However, these remote environments do not have sufficiently long instrumental climate records to support studies on contemporary climate change. The issue of the scarcity of instrumental climate data at remote regions is addressed by reconstructing monthly mean air temperatures from 1781 to 1997 AD at eleven remote alpine and arctic lakes in Europe, as part of the MOuntain LAke Research (MOLAR) project. Stepwise multiple regression is applied to establish linear transfer functions of temperatures between each of eleven upland records and twenty homogenised long lowland records. Twelve monthly transfer functions are obtained for each lake. The skill of these transfer functions is found to range typically between 60 and 99%. The lower skill values generally correspond to winter months. The temperature reconstructions obtained using the transfer functions need to be corrected with vertical temperature gradients. Air-temperature lapse rates were obtained for each lake region by spatial interpolation of radiosonde air-temperature data (1990–1997). The resulting reconstructions at each lake were checked using air-temperature data (1996–1997) from automatic weather stations installed at the lakes during the MOLAR project. We estimate the typical reconstruction errors to be about 1.3 °C for low-sun months and about 0.98 °C for high-sun months. Trend analyses on the reconstructed annual mean air temperatures at the lakes show two distinct types of trends for the 19th and 20th centuries. During the period 1801–1900, the western European lakes show no significant trend whereas annual mean air temperatures at the eastern European lakes decrease significantly. The period 1901–1997 presents a warming trend at all but the Fennoscandian lakes. Our results are in good agreement with previous studies on the spatial distribution and magnitude of temperature change in Europe. Principal component analysis performed on the reconstructed annual mean air temperature reveals two different regimes of trends for the past two centuries. It also allows a regional clustering of the inter-annual variability of air temperature at the lakes to be identified.     

基于树轮地貌学的山洪研究进展

山洪是中国主要的自然灾害之一,严重威胁山区人民生命财产与工程建设安全。针对山洪已发展了多种多样的研究手段,但多集中于过去几十年的时间段内。树轮地貌学方法作为研究历史山洪事件的有效手段之一,在世界范围内被广泛应用。利用树轮中的生长干扰信息,可以对山洪事件进行精确定年,重建无记录或少记录地区内山洪发生的频率、大小和空间分布特征等,根据伤疤的高度或应用水力模型则可以定量重建山洪的流量大小。随着树轮地貌学方法和技术的逐渐成熟,研究趋向于探讨山洪的驱动机制、更大空间尺度山洪的规律性等,具有广阔的应用前景,但是基于树轮的山洪研究工作在国内还未见报道。论文对树轮地貌学应用于山洪研究的发展过程进行了系统回顾,对研究进展进行了简要概述,最后讨论该研究领域的潜力及局限性,以期为在国内进一步开展相关工作提供参考信息。     

Reconstructing the Palaeoclimate of Jæren, Southwestern Norway, for the Period 1821–1850, from Historical Documentary Records

Documentary data provide long time series and sometimes high-resolution, detailed data from historical times to the present and can give valuable information about palaeoclimate, and for the prediction of future climates. In this paper, documentary data containing qualitative information on climate, in the form of a diary written by the farmer I.G. Grude, and two newspapers, were used for the reconstruction of the palaeoclimate at Jæren, in the county of Rogaland, in southwestern Norway, during the period 1821–50. An index method to quantify the qualitative climate data was developed and used for a low-resolution reconstruction of winter, summer and annual temperatures. A high-resolution climate reconstruction of temperature and precipitation for the winter of 1837/38 is also presented, making use of a method that keeps the climate data in a qualitative form. The climate reconstructions are compared to an instrumental temperature series from Bergen, for the same period. The two data sets are in good agreement except for summer temperature (annual temperature: r = 0.75, winter temperature: r = 0.77, summer temperature: r = 0.44). Compared to average temperatures during 1961–90, the instrumental data from Bergen during the 1821–50 period show slightly different temperatures: annual average was 0.3°C lower, winter 0.4°C lower, and summer 0.1°C lower than at present, implying conditions consistent with the "Little Ice Age" climate.     

Selection of prior distributions and multiscale analysis in Bayesian temperature reconstructions based on fossil assemblages

It is possible to reconstruct the past variation of an environmental variable from measured historical indicators when the modern values of the variable and the indicators are known. In a Bayesian statistical approach, the selection of a prior probability distribution for the past values of the environmental variable can then be crucial and the selection therefore should be made carefully. This is particularly the case when the data are noisy and the statistical model used is complex since the influence of the prior on the results can then be especially strong. It can be difficult to elicit the prior probability distribution from the available information, since usually there are no measured data on the past values of the variable one wants to reconstruct and different reconstructions are typically consistent with each other only at a coarse level. To overcome these difficulties we propose to use a non-informative smoothing prior, possibly in combination with an informative prior, that simply penalizes for roughness of the reconstruction as measured by the variability of its values. We believe that it can sometimes be easier to set an overall prior distribution on the roughness than to agree on a prior for the actual values of the reconstructed variable. Note that by using a smoothing prior one incorporates into the model itself the smoothing step usually done before or after the actual numerical reconstruction. Another idea proposed in this paper is to integrate the reconstruction model with a multiscale feature analysis technique known as SiZer. Multiscale analysis of the posterior distribution of the reconstructed variable makes it possible to infer its statistically significant features such as trends, maxima and minima at several different time scales. While only temperature is considered in this paper, the technique can be applied to other environmental variables.     

CO2 seasonal variation and global change: Test global warming from another point of view

CO₂ and temperature records at Mauna Loa, Hawaii, and other observation stations show that the correlation between CO₂ and temperature is not significant. These stations are located away from big cities, and in various latitudes and hemispheres. But the correlation is significant in global mean data. Over the last five decades, CO₂ has grown at an accelerating rate with no corresponding rise in temperature in the stations. This discrepancy indicates that CO₂ probably is not the driving force of temperature change globally but only locally (mainly in big cities). We suggest that the Earth''s atmospheric concentration of CO₂ is too low to drive global temperature change. Our empirical perception of the global warming record is due to the urban heat island effect:temperature rises in areas with rising population density and rising industrial activity. This effect mainly occurs in the areas with high population and intense human activities, and is not representative of global warming. Regions far from cities, such as the Mauna Loa highland, show no evident warming trend. The global monthly mean temperature calculated by record data, widely used by academic researchers, shows R²=0.765, a high degree of correlation with CO₂. However, the R² shows much less significance (mean R²=0.024) if calculated by each record for 188 selected stations over the world. This test suggests that the inflated high correlation between CO₂ and temperature (mean R²=0.765-0.024=0.741) used in reports from the Intergovernmental Panel on Climate Change (IPCC) was very likely produced during data correction and processing. This untrue global monthly mean temperature has created a picture:human emission drives global warming.     

Maximum entropy regularization of the geomagnetic core field inverse problem

The maximum entropy technique is an accepted method of image reconstruction when the image is made up of pixels of unknown positive intensity (e.g. a grey-scale image). The problem of reconstructing the magnetic field at the core–mantle boundary from surface data is a problem where the target image, the value of the radial field B_r , can be of either sign. We adopt a known extension of the usual maximum entropy method that can be applied to images consisting of pixels of unconstrained sign. We find that we are able to construct images which have high dynamic ranges, but which still have very simple structure. In the spherical harmonic domain they have smoothly decreasing power spectra. It is also noteworthy that these models have far less complex null flux curve topology (lines on which the radial field vanishes) than do models which are quadratically regularized. Problems such as the one addressed are ubiquitous in geophysics, and it is suggested that the applications of the method could be much more widespread than is currently the case.