基于年代际突变分量的东亚夏季降水动力-统计预报方案研究

基于1983~2011年CMAP降水数据分析,揭示东亚及其近海地区(简称东亚)的夏季降水距平在1999年前后由"+-+"型分布(由北至南)调整为"-+-"型分布,且这种年代际变化主要体现在EOF第3模态中;而在国家气候中心海气耦合模式(BCC_CGCM)回报和预报资料中,1999年前后则由"+-+-"分布型(由北至南)调整为"-+-+"型,即BCC_CGCM对降水年代际分布的预报与实况有较大差别.同时,结合EOF年代际突变分量给出了全球海温场与东亚夏季降水年代际变化有显著联系的关键海区,分析了BCC_CGCM对关键区海温的预报能力及对应的东亚夏季降水预报效果的差异,验证了基于关键区海温指数对改进模式预报结果的可能性.在此基础上,给出了基于年代际突变分量的东亚夏季降水动力-统计预报方案研究,并进行独立样本回报.结果表明,基于V区海温指数订正结果的距平相关系数(ACC)达到了0.25,距平符号一致率(ACR)达到61%,7个区域订正结果平均的ACC为0.03和ACR为51%,较BCC_CGCM模式结果的-0.01和49%均有一定的改进;且订正后1999年前后两个时段降水距平的空间分布和纬圈平均演变均体现了降水型由"+-+"向"-+-"调整的特征.因此,基于海温关键区指数的动力-统计相结合的预报方案能够增加模式对东亚夏季降水的季节预报结果中的年代际变化信息.     

用地形变资料测定通海地震的地震断层参数

本文提出了用大地测量资料在最小二乘意义下确定通海地震断层参数的一个方法.使用这一方法求得如下结果:断层走向N60°W;倾向S30……W;倾角84°;断层面长52公里;宽22公里;断面出露地表;平均走向滑距-2.24米;平均倾向滑距0.14米;地震矩8.5×10²⁶达因·厘米;应力降34巴;释放总应变能量下限为6.5×10²²尔格. 为了计算方便,我们简化了曼辛哈和斯迈里断层位移场的解析表达式.     

长波辐射资料整理与内蒙古地区震例分析

将NASA官方网站下载的OLR原始数据资料,处理为二维空间数据和单点时序数据,通过地震目录核实对比,利用OLR月平均场演变法和月平均场距平法分析1974年以来内蒙古及周边地区发生的337次地震事件（Ms≥5）前,长波辐射的时空变化特征。结果发现：在震前约3—5个月,在许多地震前长波辐射（OLR）出现明显的异常反映,许多点位在震前异常比较明显。     

TMI反演海温与Hadley中心海温资料的气候尺度比较分析

本文分析研究了1998~2006年热带及副热带TMI反演获得的海温资料（TMISST）和Hadley中心海温资料（HadISST1）在气候尺度上的异同.研究结果表明,TMISST和HadISST1均能反映热带及副热带海温的气候态分布特征,也都能指示其海温的异常变化.但是,两种海温资料之间仍然存在一定的差异,主要表现为TMISST较HadISST1偏高.此外,两者差异不仅存在明显的地域性特征,而且存在明显的季节变化：夏季两种海温资料的差异最大,秋季最小,但各季均有30%以上格点的差值在0.3℃以上.对两种海温资料距平场的EOF分析亦明确显示HadISST1和TMISST存在差异,HadISST1所反映的统计特征较TMISST更加细致.由于TMISST的反演基于一套综合性算法,其反演结果受到其他诸如风速、水汽、云水或降水等大气参数的干扰,进而造成HadISST1和TMISST的差异,且这些因素还造成了两者偏差水平分布的地域性特征.同时,也正是这些因素和海温垂直结构的季节变化,导致了两者偏差存在明显的季节变化.此外,在大陆近海区域,由于TMI信号受到污染,海温反演存在一定的缺陷,导致两者的差别较大,因此,在近海区域TMISST要慎用.     

基于MODIS数据的干旱遥感监测研究

干旱是影响社会发展和农业生产的重要因素之一。本文基于EOS/MODIS卫星遥感资料,选取江西省2001-2006年的NDVI时间序列数据,分析了NDVI对干旱的响应规律。计算了NDVI与气温、降水之间的关系。并提取植被状态指数(VCI),分析VCI与气温距平、降水距平的空间分布规律。结果表明:2003年江西夏季旱灾以高温少雨天气为主。这一时期的NDVI数值明显低于其他年份同一时期的NDVI值。气温温度越高,NDVI值越大;日照时数时间越长,NDVI值越大;降水量越高,NDVI值越大;降水距平百分率越高,VCI值越高;平均温度距平越小,VCI值越高。说明气候因素对NDVI指数和VCI指数有很大影响。研究表明,基于MODIS的植被指数可以反映旱灾的时空分布规律。     

初值协调性对模式数值积分结果的影响

利用国家气候中心新一代全球大气环流模式BCC_AGCM2.0.1,考虑了初值协调性对模式数值积分结果的影响,进行了两组数值回报试验(简称S1,S2),对27年(1980～2006年)的夏季基本气候态进行了对比分析,并考察了该模式对夏季气候的回报技巧。使用交叉检验的方法,计算了对模式结果的评估参数值,包括时间和空间距平相关系数,对该模式性能进行了评估和检验。结果表明,BCC_AGCM2.0.1对季节尺度的大气环流场具有良好的模拟性能,模式基本上再现了观测位势高度场、温度场、流场的分布特征以及大尺度降水分布特征。500 hPa位势高度、温度空间距平相关系数对比表明,平均而言,500 hPa位势高度、温度的空间距平相关性,热带区域(30°S～30°N)高于东亚区域(0°～60°N,60°E～150°E)和全球区域。回报与观测的降水距平百分率相关系数分布对比表明,试验S2在我国江淮地区及南方地区的回报技巧要明显优于S1。     

中国东部夏季降水准两年振荡空间分布及背景场特征

本文采用经验正交函数展开(EOF)及相关分析等方法,使用中国气象局整编的160站1951~2005年月平均降水资料和NCEP/NCAR再分析资料研究了中国东部夏季降水准两年周期振荡的空间模态及其大气环流背景场.结果表明:(1)中国地区降水季节性差异明显,夏季是主要的降水期并具有明显的准两年周期振荡(TBO)特征,中国东部地区是降水TBO方差变化最大的区域.(2)中国东部夏季降水TBO存在两个主要的空间模态,第1模态以27°N为界南北成反位相的变化关系,降水振幅较大;第2模态降水振幅相对较小,大值中心位于河套-华北地区.(3)形成中国东部夏季降水TBO的两个主要空间模态环流背景场明显不同.第1模态与西太平洋海温成正相关,与东太平洋海温成负相关.第2模态则主要与日本海附近的海温成正相关.当夏季降水TBO以江淮偏多时(第1模态),西太平洋海温偏高,东太平洋海温偏低,中国东部及沿海上空850 hPa有异常反气旋,500 hPa高度相关场东亚上空呈"正负正"波列特征,200 hPa南亚高压加强,西风急流位置偏南.当夏季降水TBO降水位置偏北时(第2模态),中国东部及沿海上空有异常气旋,200 hPa南亚高压偏弱,西风急流位置偏北.     

一种优化地震前兆观测点布设的方法

使用基于主成分分析的优化地震前兆观测点方法, 用1998年数据对上海地区的9个水氡观测台站进行优化。 结果表明, 可以撤消3个信息量较小的台站观测, 剩下的6个台所含信息量占全部9个台站信息量的比例为93.0%。 表明在地震前兆观测中, 如果局部地区的地震前兆观测台站数据具有一定的相关性, 那么即使大量增加前兆观测台站的数量, 也并不意味着所获取的信息量会同步增多。 文中所用的优化方法对于指导地震前兆观测台站的优化具有一定意义。     

中国电离层TEC同化现报系统

数据同化是在基于物理机制的背景模型上,融合时空不规则分布的观测数据的一种现报方法.同化能够有效弥补数据的时空局限和模型的精度偏差,使二者相互匹配从而获得更加合理可信的模拟效果.本研究利用电离层数据同化方法,针对中国及周边区域（15°N-55°N,70°E-140°E）构建了电离层总电子含量（TEC）同化现报系统.系统使用国际参考电离层（IRI）作为背景场,利用中国科学院空间环境监测网和国际GNSS服务组织（IGS）的部分地基GNSS台站数据作为观测值,并采用三维变分与Gauss-Markov卡尔曼滤波相结合的算法进行背景场和观测值的数据同化,生成覆盖中国及周边区域的电离层TEC和GPS单频接收机延迟误差的格点化准实时现报地图,并在中国科学院空间环境预报中心（http：//sepc.ac.cn/TEC_chn.php）网上发布,每15 min进行更新.该系统是我国基于同化算法的电离层现报系统之一,已用于中国及周边区域的电离层环境实时监测,可为卫星导航、雷达成像、短波通信等科学研究和工程应用提供相对及时、准确、有效的电离层TEC和误差修正信息.     

基于植被光合有效辐射资料研究中国地区植被大气反馈作用

本文采用中国地区基于卫星观测的植被光合有效辐射资料(FPAR)和月平均气候数据(1982-2000年)来分析中国区域陆面植被与气温、降水的反馈作用.通过计算和分析超前滞后相关系数和反馈系数发现:春、夏季FPAR超前气温一个月相关系数在全国大部分地区为负值,反映出植被生长旺盛,可以降低局地气温.春、秋两季气温与FPAR的同期相关系数较大.夏季降水超前FPAR一个月的正相关性反映出夏季降水对于植被生长存在促进作用.在中国长江流域以南区域,植被对于气温的反馈系数为一致正值,可达0.5 ℃(0.1FPAR)^-1;在30°N以北区域显示出一致的负反馈,可达-0.42 ℃(0.1FPAR)^-1.FPAR对降水全年反馈系数全国区域平均可达-2.12 cm month^-1(0.1FPAR)^-1.不同植被类型、不同季节的植被反馈效应也存在差异.植被反馈系数可以用来验证动态植被模式计算的植被大气反馈作用.     

A Gridded Daily Min/Max Temperature Dataset With 0.1° Resolution for the Yangtze River Valley and its Error Estimation

The minimum/maximum (Min/Max) temperature in the Yangtze River valley is decomposed into the climatic mean and anomaly component. A spatial interpolation is developed which combines the 3D thin-plate spline scheme for climatological mean and the 2D Barnes scheme for the anomaly component to create a daily Min/Max temperature dataset. The climatic mean field is obtained by the 3D thin-plate spline scheme because the relationship between the decreases in Min/Max temperature with elevation is robust and reliable on a long time-scale. The characteristics of the anomaly field tend to be related to elevation variation weakly, and the anomaly component is adequately analyzed by the 2D Barnes procedure, which is computationally efficient and readily tunable. With this hybridized interpolation method, a daily Min/Max temperature dataset that covers the domain from 99°E to 123°E and from 24°N to 36°N with 0.1° longitudinal and latitudinal resolution is obtained by utilizing daily Min/Max temperature data from three kinds of station observations, which are national reference climatological stations, the basic meteorological observing stations and the ordinary meteorological observing stations in 15 provinces and municipalities in the Yangtze River valley from 1971 to 2005. The error estimation of the gridded dataset is assessed by examining cross-validation statistics. The results show that the statistics of daily Min/Max temperature interpolation not only have high correlation coefficient (0.99) and interpolation efficiency (0.98), but also the mean bias error is 0.00 °C. For the maximum temperature, the root mean square error is 1.1 °C and the mean absolute error is 0.85 °C. For the minimum temperature, the root mean square error is 0.89 °C and the mean absolute error is 0.67 °C. Thus, the new dataset provides the distribution of Min/Max temperature over the Yangtze River valley with realistic, successive gridded data with 0.1° × 0.1° spatial resolution and daily temporal scale. The primary factors influencing the dataset precision are elevation and terrain complexity. In general, the gridded dataset has a relatively high precision in plains and flatlands and a relatively low precision in mountainous areas.     

Trends in extreme temperature indices in the Poyang Lake Basin,China

Based on daily maximum and minimum temperature records at 78 meteorological stations in the Basin of China’s largest fresh water lake (Poyang Lake Basin), the temporal and spatial variability of 11 extreme temperature indices are investigated for the period 1959–2010. The analysis indicates that the annual mean of daily minimum temperature (T_min) has increased significantly, while no significant trends were observed in the annual mean of daily maximum temperature (T_max), resulting in a significant decrease in the diurnal temperature range. Trends and percentages of stations with significant trends in T_min-related indices are generally stronger and higher than those in T_max-related indices; however, no significant trends can be found in T_max-related indices (TXMean, TX90p, TXx and TX10p) at both seasonal and annual time scale. Low correlations with Global-SST ENSO index are also detected in T_max-related indices. Significant positive relationships can be found in T_min-related indices (TNMean, TNx, TNn and TN90p), however, the most significant negative coefficient was also found in cold nights (TN10p) with the Global-SST ENSO index. Singular value decomposition (SVD) correlating extreme temperatures over the Poyang Lake Basin and the North Pacific SST indicates the East China Sea, Western Pacific and Bering Sea to be stronger linked with T_min than T_max with the first mode (SVD-1) explaining 90 and 94 % of annual T_max and T_min respectively.     

Hydrological modelling using proxies for gauged precipitation and temperature

Precipitation and temperature time series suffer from many problems, such as short time, inadequate spatial coverage, missing data, and biases from various causes, which are particularly critical in remote areas such as Northern Canada. The development of alternative datasets for using as proxies for inadequate/missing weather data represents a key research area. In this paper, the performance of 6 alternative datasets is evaluated for hydrological modelling over 12 watersheds located across Canada and the contiguous United States. The datasets can be classified into 3 distinct categories: (a) interpolated gridded data, (b) reanalysis data, and (c) climate model outputs. Hydrological simulations were carried out using a lumped conceptual hydrological model calibrated using standard weather data and compared against results using a calibration specific to each alternative dataset. Prior to the hydrological simulations, the alternative datasets were all evaluated with respect to their ability to reproduce gridded daily precipitation and temperature characteristics over North America. The results show that both the reanalysis data and climate model data adequately represent the spatial pattern of daily precipitation and temperature over North America. The North American Regional Reanalysis (NARR) dataset consistently shows the best performance. With respect to hydrological modelling, the observed discharges are accurately represented by both the gridded and NARR datasets, and more so for the NARR data. The National Centers for Environmental Prediction dataset consistently performs worst as it is unable to even capture the seasonal pattern of observed streamflow for 3 out of the 12 watersheds. These results indicate that the NARR dataset could be used as a proxy for gauged precipitation and temperature for hydrological modelling over watersheds where observational datasets are deficient. The results also illustrate the ability of climate model data to be used for performing hydrological modelling when driven by reanalysis data at their boundaries, and especially so for high‐resolution models.     

Evaluation of gridded precipitation data in the Hindu Kush–Karakoram–Himalaya mountainous area

The accurate measurement of precipitation is crucial for hydrological studies. This is especially true for the Hindu Kush–Karakoram–Himalaya (HKKH) mountain region, which is characterized by high spatiotemporal precipitation variability. The paucity of raingauges makes it difficult to measure precipitation in this region precisely. We conducted evaluation of TMPA 3B42V7 and APHRO 1101 in the HKKH area on a daily basis at a spatial resolution of 0.25°?×?0.25°, using 27 raingauges. Statistically, the largest error in the gridded data arose mainly from elevation, followed by volumetric error and Nash–Sutcliffe efficiency. Overall, the TMPA data have a poor correlation with ground observations in the HKKH area, especially for higher altitudes. The western areas are relatively more underestimated and the Karakoram shows higher frequency of bias in the TMPA retrievals. This method could help improve the satellite precipitation estimation algorithm as it considers local physiography and climatic factors.     

Spatio-temporal evaluation of global gridded precipitation datasets across Iran

ABSTRACT

In this work, the accuracy of four gridded precipitation datasets – Climatic Research Unit (CRU), Global Precipitation Climatology Centre (GPCC), PERSIANN-Climate Data Record (PCDR) and University of Delaware (UDEL) – is evaluated across Iran to find an alternative source of precipitation data. Monthly, seasonal and annual precipitation data from 85 synoptic stations for the period 1984–2013 were used as the basis for the evaluations. Our results indicate that all datasets underestimate and overestimate precipitation in stations with annual precipitation greater than 600 and less than 100 mm, respectively. However, all datasets correctly recognize regimes of precipitation, but with a bias in amount of precipitation. Our spatio-temporal assessments show that GPCC is the most suitable dataset to be used over Iran. Both UDEL and CRU can be considered as the second and third most suitable datasets, while PCDR showed the weakest performance among the studied datasets.     

Changes of temperature extremes for 1960–2004 in Far-West China

The spatial and temporal patterns of the temperature extremes defined by 5th and 95th percentiles based on daily maximum/minimum temperature dataset were analyzed using Mann–Kendall test and linear regression method. The research results indicate that: (1) the seasonal minimum temperature is in stronger increasing trend than the seasonal maximum temperature; (2) in comparison with the changes of the maximum temperature, more stations display significantly increasing trends of minimum temperature in frequency and intensity; (3) comparatively, more stations have significantly decreasing trends in the intra-seasonal extreme temperature anomaly in summer and winter than in spring and autumn. The areal mean minimum temperature is in stronger increasing trend than areal mean maximum temperature; (4) the warming process in the Far-West (FW) China is characterized mainly by significantly increasing minimum temperature. The research will be helpful for local human mitigation to alterations in water resource and ecological environment in FW China due to changes of temperature extremes, as the ecologically fragile region of China.     

Gridded climate data products are an alternative to instrumental measurements as inputs to rainfall–runoff models

Rainfall–runoff models are widely used to predict flows using observed (instrumental) time series of air temperature and precipitation as inputs. Poor model performance is often associated with difficulties in estimating catchment‐scale meteorological variables from point observations. Readily available gridded climate products are an underutilized source of temperature and precipitation time series for rainfall–runoff modelling, which may overcome some of the performance issues associated with poor‐quality instrumental data in small headwater monitoring catchments. Here we compare the performance of instrumental measured and E‐OBS gridded temperature and precipitation time series as inputs in the rainfall–runoff models “PERSiST” and “HBV” for flow prediction in six small Swedish catchments. For both models and most catchments, the gridded data produced statistically better simulations than did those obtained using instrumental measurements. Despite the high correspondence between instrumental and gridded temperature, both temperature and precipitation were responsible for the difference. We conclude that (a) gridded climate products such as the E‐OBS dataset could be more widely used as alternative input to rainfall–runoff models, even when instrumental measurements are available, and (b) the processing applied to gridded climate products appears to provide a more realistic approximation of small catchment‐scale temperature and precipitation patterns needed for flow simulations. Further research on this issue is needed and encouraged.     

Daily extreme precipitation and trends over China

Based on daily precipitation data of more than 2000 Chinese stations and more than 50 yr, we constructed time series of extreme precipitation based on six different indices for each station: annual and summer maximum(top-1) precipitation,accumulated amount of 10 precipitation maxima(annual, summer; top-10), and total annual and summer precipitation.Furthermore, we constructed the time series of the total number of stations based on the total number of stations with top-1 and top-10 annual extreme precipitation for the whole data period, the whole country, and six subregions, respectively. Analysis of these time series indicate three regions with distinct trends of extreme precipitation:(1) a positive trend region in Southeast China,(2) a positive trend region in Northwest China, and(3) a negative trend region in North China. Increasing(decreasing)ratios of 10–30% or even 30% were observed in these three regions. The national total number of stations with top-1 and top-10 precipitation extremes increased respectively by 2.4 and 15 stations per decade on average but with great inter-annual variations.There have been three periods with highly frequent precipitation extremes since 1960:(1) early 1960 s,(2) middle and late 1990 s,and(3) early 21 st century. There are significant regional differences in trends of regional total number of stations with top-1 and top-10 precipitation. The most significant increase was observed over Northwest China. During the same period, there are significant changes in the atmospheric variables that favor the decrease of extreme precipitation over North China: an increase in the geopotential height over North China and its upstream regions, a decrease in the low-level meridional wind from South China coast to North China, and the corresponding low moisture content in North China. The extreme precipitation values with a50-year empirical return period are 400–600 mm at the South China coastal regions and gradually decrease to less than 50 mm in Northwest China. The mean increase rate in comparison with 20-year empirical return levels is 6.8%. The historical maximum precipitation is more than twice the 50-year return levels.     

MM5V3模式对IPCCA1B情景下中国地区极端事件的模拟和预估

利用MM5V3区域气候模式单向嵌套ECHAM5全球环流模式,对中国地区1978-2000年及IPCC A1B情景下2038-2070年气候分别进行了水平分辨率为50 km的模拟试验.文章首先检验了模式模拟的当代极端气候结果,在此基础上对6个极端温度指数和6个极端降水指数的未来变化进行了预估.检验结果表明:MM5V3模式对中国地区当代日最高、最低温度及强降水(大雨和暴雨)日数的空间分布和概率特征均具有一定的模拟能力,但模拟的日最高温度在大部分地区偏低,日最低温度在南方地区偏低、西北地区偏高.概率统计结果显示日最高温度向低值频段偏移,日最低温度在0℃的峰值附近明显偏高.模式对大雨和暴雨年平均日数的模拟在东部地区偏多,概率统计结果则为一致偏大.未来中国地区极端气候预估结果表明:极端高温、极端低温和相对高温在全国范围内都将升高,且线性趋势均为上升;霜日日数则为减少,并具有下降趋势;暖日日数和相对低温在青藏高原和新疆部分地区有所减少、其它地区均为增加,且线性趋势暖日日数为上升,相对低温不明显.极端降水指数的变化具有区域特征,其中单日最大降水、连续五日最大降水、最长无雨期、强降水日数、简单降水强度和极端降水总量均在江淮、华南及西南地区有所增多,而在东北及内蒙古地区有所减少,未来中国南方地区降水的极端化趋势将更加显著.极端降水指数的线性趋势除最长无雨期外其它均为上升.     

中国平均降水和极端降水对气候变暖的响应:CMIP5模式模拟评估和预估

基于24个CMIP5全球耦合模式模拟结果,分析了中国区域年平均降水和ETCCDI强降水量(R95p)、极端强降水量(R99p)对增暖的响应.定量分析结果显示,CMIP5集合模拟的当代中国区域平均降水对增温的响应较观测偏弱,而极端降水的响应则偏强.对各子区域气温与平均降水、极端降水的关系均有一定的模拟能力,并且极端降水的模拟好于平均降水.RCP4.5和RCP8.5情景下,随着气温的升高,中国区域平均降水和极端降水均呈现一致增加的趋势,中国区域平均气温每升高1℃,平均降水增加的百分率分别为3.5%和2.4%,R95p增加百分率为11.9%和11.0%,R99p更加敏感,分别增加21.6%和22.4%.就各分区来看,当代的区域性差异较大,未来则普遍增强,并且区域性差异减小,在持续增暖背景下,中国及各分区极端降水对增暖的响应比平均降水更强,并且越强的极端降水敏感性越大.未来北方地区平均降水对增暖的响应比南方地区的要大,青藏高原和西南地区的R95p和R99p增加最显著,表明未来这些区域发生暴雨和洪涝的风险将增大.