石家庄市酸雨污染现状研究

利用1992—2005年石家庄气象站酸雨观测资料,计算了近14 a的年平均降水pH值、酸雨发生频率等反映酸雨强度的参数,分析了酸雨的变化特征以及pH平均值与气象条件、电导率、空气污染的关系。结果表明：1992—2005年石家庄年平均酸雨发生频率为8.7%;酸雨主要发生在夏、秋季,占全年的74.4%;月平均降水pH值与电导率具有相似的变化规律,显著相关系数为0.661;SO2浓度的逐年下降是石家庄市酸雨减弱的主要原因;月平均风速和月平均降水pH值相关不显著;不同降水等级酸雨的发生频率差异较大,暴雨的酸雨发生频率最高为41.67%;采用Daniel趋势检验法分析表明,石家庄酸雨污染呈下降趋势。     

Impact of the lateral boundary conditions resolution on dynamical downscaling of precipitation in mediterranean spain

Conclusions on the General Circulation Models (GCMs) horizontal and temporal optimum resolution for dynamical downscaling of rainfall in Mediterranean Spain are derived based on the statistical analysis of mesoscale simulations of past events. These events correspond to the 165 heavy rainfall days during 1984–1993, which are simulated with the HIRLAM mesoscale model. The model is nested within the European Centre for Medium-Range Weather Forecasts atmospheric grid analyses. We represent the spectrum of GCMs resolutions currently applied in climate change research by using varying horizontal and temporal resolutions of these analyses. Three sets of simulations are designed using input data with 1°, 2° and 3° horizontal resolutions (available at 6 h intervals), and three additional sets are designed using 1° horizontal resolution with less frequent boundary conditions updated every 12, 24 and 48 h. The quality of the daily rainfall forecasts is verified against rain-gauge observations using correlation and root mean square error analysis as well as Relative Operating Characteristic curves. Spatial distribution of average precipitation fields are also computed and verified against observations. For the whole Mediterranean Spain, model skill is not appreciably improved when using enhanced spatial input data, suggesting that there is no clear benefit in using high resolution data from General Circulation Model for the regional downscaling of precipitation under the conditions tested. However, significant differences are found in verification scores when boundary conditions are interpolated less frequently than 12 h apart. The analysis is particularized for six major rain bearing flow regimes that affect the region, and differences in model performance are found among the flow types, with slightly better forecasts for Atlantic and cold front passage flows. A remarkable spatial variability in forecast quality is found in the domain, with an overall tendency for higher Relative Operating Characteristic scores in the west and north of the region and over highlands, where the two previous flow regimes are quite influential. The findings of this study could be of help for dynamical downscaling design applied to future precipitation scenarios in the region, as well as to better establish confidence intervals on its results.     

参加CMIP5计划的四个中国模式模拟的东亚地区降水结构特征及未来变化

在全球变暖的背景下, 降水特征的改变体现为降水总量和降水结构的变化。由于缺乏较为长期、覆盖范围广的较高分辨率逐日降水资料, 过去对东亚降水的研究多关注其降水量的长期趋势和时空变率, 较少涉及降水结构的变化。本文利用当前最新且分辨率最高、覆盖范围最广的逐日亚洲陆地降水数据集(简称APHRODITE)以及四个中国参加第五次国际耦合模式比较计划(简称CMIP5)的模式(BCC-CSM1-1, BNU-ESM, FGOALS-g2和FGOALS-s2), 研究了东亚地区降水结构的观测特征及四个模式的模拟能力。基于此, 通过分析四个模式的未来预估试验, 探讨东亚地区降水结构在全球变暖背景下的变化。结果表明:整个东亚地区的累积降水量呈现出随着降水强度的增加先增加后减小的分布形态;降水频率则是随着强度的增加显著减小。小雨(中雨)呈现出南少北多(南多北少)的形态;强降水则较多分布在华南沿海以及日本南部地区。长期趋势上, 整个东亚地区大体上呈现小雨和30 mm/d以上的大雨增加, 而中等强度降水减少的变化趋势。四个模式对东亚降水结构的气候态模拟能力较好。BCC-CSM1-1和FGOALS-g2能够合理再现观测中各个强度降水的变化趋势, 而其他模式模拟不出中雨的减小趋势。四个模式的未来预估表明, 在全球变暖的背景下, 东亚地区30 mm/d以上的强降水会增加。且降水强度越大, 增加越明显。以30°N为界, 小雨(中雨)在变暖背景下呈现南部增加北部减少(南部减少北部增加)的变化趋势。     

An intercomparison of observational precipitation data sets over Northwest India during winter

Development of downscaling models for each calendar month using the data of predictors specifically selected for each calendar month may assists in better capturing the time-varying nature of the predictor-predictand relationships. Such approach will not allow the explicit modelling of the persistence of the predictand (e.g. lag-1 correlation). However, downscaling at an annual time step and subsequent disaggregation to monthly values can explicitly consider the modelling of the persistence of the predictand. This study investigated the potential of annual downscaling of a predictand and subsequent disaggregation of annual values to monthly values, in comparison to the potential of downscaling models separately developed for each calendar month. In the case study, annual and monthly downscaling models were developed for precipitation and evaporation at two stations located in Victoria, Australia. The output of the annual downscaling models was then disaggregated into monthly values using four different methods based on the method of fragments. It was found that the annual to monthly disaggregation methods and monthly downscaling models are able to reproduce the average of monthly observations with relatively higher accuracy in comparison to their ability in reproducing standard deviation, skewness and lag-1 serial correlation. Downscaling models separately developed for each calendar month were able to show relatively smaller root mean square errors for their time series indicating better overall agreement with observations in comparison to their counterpart annual to monthly disaggregation methods. Furthermore, it was found that not only the bias in the output of an annual downscaling model but also the presence of annual totals in the records of observations of a predictand that are very similar in magnitude, but having significantly different sets of fragments, can largely contribute to the poor performance of an annual to monthly disaggregation method.     

Uncertainty analysis of statistical downscaling models using Hadley Centre Coupled Model

Regression-based statistical downscaling is a method broadly used to resolve the coarse spatial resolution of general circulation models. Nevertheless, the assessment of uncertainties linked with climatic variables is essential to climate impact studies. This study presents a procedure to characterize the uncertainty in regression-based statistical downscaling of daily precipitation and temperature over a highly vulnerable area (semiarid catchment) in the west of Iran, based on two downscaling models: a statistical downscaling model (SDSM) and an artificial neural network (ANN) model. Biases in mean, variance, and wet/dry spells are estimated for downscaled data using vigorous statistical tests for 30 years of observed and downscaled daily precipitation and temperature data taken from the National Center for Environmental Prediction reanalysis predictors for the years of 1961 to 1990. In the case of daily temperature, uncertainty is estimated by comparing monthly mean and variance of downscaled and observed daily data at a 95 % confidence level. In daily precipitation, downscaling uncertainties were evaluated from comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and modeled daily precipitation. Results showed that uncertainty in downscaled precipitation is high, but simulation of daily temperature can reproduce extreme events accurately. Finally, this study shows that the SDSM is the most proficient model at reproducing various statistical characteristics of observed data at a 95 % confidence level, while the ANN model is the least capable in this respect. This study attempts to test uncertainties of regression-based statistical downscaling techniques in a semiarid area and therefore contributes to an improvement of the quality of predictions of climate change impact assessment in regions of this type.     

Changes in rainfall regime over Burkina Faso under the climate change conditions simulated by 5 regional climate models

Sahelian rainfall has recorded a high variability during the last century with a significant decrease (more than 20 %) in the annual rainfall amount since 1970. Using a linear regression model, the fluctuations of the annual rainfall from the observations over Burkina Faso during 1961–2009 period are described through the changes in the characteristics of the rainy season. The methodology is then applied to simulated rainfall data produced by five regional climate models under A1B scenario over two periods: 1971–2000 as reference period and 2021–2050 as projection period. As found with other climate models, the projected change in annual rainfall for West Africa is very uncertain. However, the present study shows that some features of the impact of climate change on rainfall regime in the region are robust. The number of the low rainfall events (0.1–5 mm/d) is projected to decrease by 3 % and the number of strong rainfall events (>50 mm/d) is expected to increase by 15 % on average. In addition, the rainy season onset is projected by all models to be delayed by one week on average and a consensus exists on the lengthening of the dry spells at about 20 %. Furthermore, the simulated relationship between changed annual rainfall amounts and the number of rain days or their intensity varies strongly from one model to another and some changes do not correspond to what is observed for the rainfall variability over the last 50 years.     

Multifractal analysis of 1-min summer rainfall time series from a monsoonal watershed in eastern China

Multifractal analysis can provide parameters associated with different scales of rainfall, which may be useful for setting up parsimonious downscaling models of rainfall, or for revealing climate-specific properties. Time series of rain rate with 1-min resolution collected from ten stations over a monsoon watershed in eastern China were used to study the multifractal properties. The power spectra estimated by fast Fourier transform (FFT) and discrete Haar wavelet transform (DWT) showed three scaling regimes: the sub-hourly scaling regime with β?≈?1.2, the scaling regime from 1 h to 1 day with β close to 0.6, and the low-frequency spectra plateau with β?≈?0.1. From the hyperbolic tails of exceeding probability distributions, the estimated values of parameter q _c are in 2–2.5, which were consistent with the critical order of K(q) curves. The statistical moments display two main scaling regimes: the high-frequency regime from 3 min to 5 days and the scaling regime beyond 5 days. The scales of 5–10 days seem a transitional regime. The reason that the regimes, revealed by the power spectra, disagree with the statistical moments may be that both FFT and DWT power spectra have limited abilities of analyzing low-frequency scaling but are sensitive to the properties in high-frequency scales. The H values estimated for the regime of sub-hourly scales are larger than 0.4, and the values for the regime 1 h–1 day are close to 0.1. For the low-frequency scales beyond 1 day, negative H is obtained by DWT power spectra. The parameters of universal multifractal models were also estimated. The values of α for the scaling range of 1 min–5 days are 0.486?±?0.047, and for the low-frequency scaling range, its values are 0.808?±?0.323. For the high- and low-frequency scaling ranges, the values of C ₁ are 0.5 and 0.169, respectively, which is different from the values for daily rainfall series collected at the same rain gages.     

基于BCC_CSM模式的中国东部夏季降水预测检验及订正

基于国家气候中心第二代季节预测模式的历史回报试验数据,检验了模式对我国东部夏季降水的预测能力,探讨了预测误差形成的可能原因,并应用降尺度方法提高了模式的降水预测技巧。分析表明:（1）模式能在一定程度上把握我国东部夏季降水时空变率的两个主要模态（偶极子型模态和全区一致型模态）,但是不同超前时间的预测在刻画模态方差贡献、异常空间分布特征、时间系数的年际变化等方面存在明显误差;（2）模式能够合理预测大尺度环流和海表温度（SST）的变化特征,但是对中国东部夏季降水的总体预测技巧有限,这与模式不能准确刻画西太平洋副热带高压、大陆高压、中高纬阻塞高压等环流系统以及热带太平洋、印度洋SST变率对中国东部降水模态的影响有关;（3）针对1991~2003年回报试验数据中的500 hPa位势高度、850 hPa纬向风和经向风、SST变量,在全球范围内寻找并定位与中国东部站点降水关系最密切的预报因子,进而建立针对降水预测的单因子线性回归、多因子逐步和多元回归模型。采用2004~2013年回报试验对所建立的降水预测模型进行了独立检验,结果表明:所建立的降尺度预测模型能显著提高中国东部地区夏季降水的预报技巧。以6月1日起报试验为例,预测的第一模态（第二模态）与观测的空间相关系数由原始的0.12（0.48）提高到了0.58（0.80）,时间相关系数则从0.47（0.15）提高到0.80（0.67）;其它超前时间的预测试验中,降尺度预测模型的降水预测技巧相比模式原始预测技巧也同样明显提高。     

Future projections of winter rainfall in southeast Australia using a statistical downscaling technique

Much of southeast Australia has experienced rainfall substantially below the long-term average since 1997. This protracted drought is particularly noticeable in those parts of South Australia and Victoria which experience a winter (May through October) rainfall peak. For the most part, the recent meteorological drought has affected the first half of the rainfall season May–June–July (MJJ), while rainfall during the second half August–September–October (ASO) has been much closer to the long term average. The recent multi-year drought is without precedent in the instrumental record, and is qualitatively similar to the abrupt decline in rainfall which was observed in the southwest of Western Australia in the 1960 and 1970s. Using a statistical downscaling technique, the rainfall decline is linked to observed changes in large-scale atmospheric fields (mean sea level pressure and precipitable water). This technique is able to reproduce the statistical properties of rainfall in southeast Australia, including the interannual variability and longer time-scale changes. This has revealed that the rainfall recent decline may be explained by a shift to higher pressures and lower atmospheric precipitable water in the region. To explore the likely future evolution of rainfall in southeast Australia under human induced climate change, the same statistical downscaling technique is applied to five climate models forced with increasing greenhouse gas concentrations. This reveals that average rainfall in the region is likely to decline in the future as greenhouse gas concentrations increase, with the greatest decline occurring during the first half of winter. Projected declines vary amongst models but are generally smaller than the recent early winter rainfall deficits. In contrast, the rainfall decline in late winter–spring is larger in future projections than the recent rainfall deficits have been. We illustrate the consequences of the observed and projected rainfall declines on water supply to the major city of Melbourne, using a simple rainfall run-off relationship. This suggests that the water resources may be dramatically affected by future climate change, with percentage reductions approximately twice as large as corresponding changes in rainfall.     

Downscaling large-scale NCEP CFS to resolve fine-scale seasonal precipitation and extremes for the crop growing seasons over the southeastern United States

Seasonally predicted precipitation at a resolution of 2.5° was statistically downscaled to a fine spatial scale of ~20 km over the southeastern United States. The downscaling was conducted for spring and summer, when the fine-scale prediction of precipitation is typically very challenging in this region. We obtained the global model precipitation for downscaling from the National Center for Environmental Prediction/Climate Forecast System (NCEP/CFS) retrospective forecasts. Ten member integration data with time-lagged initial conditions centered on mid- or late February each year were used for downscaling, covering the period from 1987 to 2005. The primary techniques involved in downscaling are Cyclostationary Empirical Orthogonal Function (CSEOF) analysis, multiple regression, and stochastic time series generation. Trained with observations and CFS data, CSEOF and multiple regression facilitated the identification of the statistical relationship between coarse-scale and fine-scale climate variability, leading to improved prediction of climate at a fine resolution. Downscaled precipitation produced seasonal and annual patterns that closely resemble the fine resolution observations. Prediction of long-term variation within two decades was improved by the downscaling in terms of variance, root mean square error, and correlation. Relative to the coarsely resolved unskillful CFS forecasts, the proposed downscaling drove a significant reduction in wet biases, and correlation increased by 0.1–0.5. Categorical predictability of seasonal precipitation and extremes (frequency of heavy rainfall days), measured with the Heidke skill score (HSS), was also improved by the downscaling. For instance, domain averaged HSS for two category predictability by the downscaling are at least 0.20, while the scores by the CFS are near zero and never exceed 0.1. On the other hand, prediction of the frequency of subseasonal dry spells showed limited improvement over half of the Georgia and Alabama region.     

Projections of Extreme Rainfall in Hong Kong in the 21st Century

The possible changes in the frequency of extreme rainfall events in Hong Kong in the 21st century wereinvestigated by statistically downscaling 30 sets of the daily global climate model projections (involvinga combination of 12 models and 3 greenhouse gas emission scenarios,namely,A2,A1B,and B1) of theFourth Assessment Report of the Intergovernmental Panel on Climate Change.To cater for the intermittentand skewed character of the daily rainfall,multiple stepwise logistic regression and multiple stepwise linearregression were employed to develop the downscaling models for predicting rainfall occurrence and rainfallamount,respectively.Verification of the simulation of the 1971-2000 climate reveals that the models ingeneral have an acceptable skill in reproducing past statistics of extreme rainfall events in Hong Kong.Theprojection results suggest that,in the 21st century,the annual number of rain days in Hong Kong is expectedto decrease while the daily rainfall intensity will increase,concurrent with the expected increase in annualrainfall.Based on the multi-model scenario ensemble mean,the annual number of rain day is expected todrop from 104 days in 1980-1999 to about 77 days in 2090-2099.For extreme rainfall events,about 90% ofthe model-scenario combinations indicate an increase in the annual number of days with daily rainfall 100mm (R100) towards the end of the 21st century.The mean number of R100 is expected to increase from 3.5days in 1980-1999 to about 5.3 days in 2090-2099.The projected changes in other extreme rainfall indicesalso suggest that the rainfall in Hong Kong in the 21st century may also become more extreme with moreuneven distributions of wet and dry periods.While most of the model-emission scenarios in general projectconsistent trends in the change of rainfall extremes in the 21st century,there is a large divergence in theprojections among different model/emission scenarios.This reflects that there are still large uncertainties inmodel simulations of future extreme rainfall events.     

MRI-CGCM模式气候预测回报试验在东亚夏季的检验和降尺度订正

应用1979—2010年MRI-CGCM模式回报、NCEP/NCAR再分析数据和中国东部降水观测资料检验了模式对东亚夏季风的模拟能力,并利用模式500 hPa高度场回报资料建立了中国东部夏季降水的奇异值分解（SVD）降尺度模型。模式较好地模拟了亚洲季风区夏季降水的气候态,但模拟的季风环流偏弱、偏南,导致降水偏弱。模拟降水的方差明显偏小,且模拟降水的外部、内部方差比值低,模拟降水受模式初值影响较大。模式对长江雨型的模拟能力最高,华南雨型次之,华北雨型最低。模式对东亚夏季风第1模态的模拟能力明显高于第2模态。对于东亚夏季风第1模态,模式模拟出了西太平洋异常反气旋,但强度偏弱,且未模拟出中高纬度的日本海气旋、鄂霍次克海反气旋,导致长江中下游至日本南部降水偏弱。各时次模拟环流均能反映但低估了ENSO衰减、印度洋偏暖对西太平洋反气旋的增强作用。对于东亚夏季风第2模态,模式对西太平洋的“气旋-反气旋”结构有一定的模拟能力,但未模拟出贝加尔湖异常反气旋和东亚沿海异常气旋,导致中国东部“北少南多”雨型在模拟中完全遗漏。仅超前时间小于4个月的模拟降水能够反映ENSO发展对降水分布的作用。通过交叉检验选取左场时间系数可以提高降尺度模型的预测技巧,SVD降尺度模型在华南、江南、淮河、华北4个区域平均距平相关系数分别为0.20、0.23、0.18、0.02,明显高于模式直接输出。      

Using the spectral scaling exponent for validation of quantitative precipitation forecasts

This study evaluates the spectral scaling of a heavy rainfall event and assesses the performance of the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) model in terms of the multiscale variability of rainfall in the temporal spectral domain. The event occurred over southern Malay Peninsula on 18 December 2006 and was simulated at high resolutions. 10, 5 and 1?min aggregate rainfall data from rain gauge stations in Singapore and simulated rainfall sampled at different evaluation points on 0.9, 0.3 and 0.1?km grids were utilized. The simulated and observed rain rates were compared via Fourier and wavelet analyses. A scaling regime was noted in the observed rainfall spectra in the timescales between 60?min and 2?min. The scaling exponent obtained from the observed spectra has a value of about 2, which may be indicative of the physics of turbulence and raindrop coalescence and might suggest the predominance of a characteristic raindrop size. At 0.9?km resolution, the model rainfall spectra showed similar scaling to the observed down to about 10?min, below which a fall-off in variance was noted as compared to observations. Higher spatial resolution of up to 0.1?km was crucial to improve the ability of the model to resolve the shorter timescale variability. We suggest that the evaluation of dynamical models in the spectral domain is a crucial step in the validation of quantitative precipitation forecasts and assessing the minimal grid resolution necessary to capture rainfall variability for certain short timescales may be important for hydrological predictions.     

Validation of TRMM Precipitation Radar satellite data over Indonesian region

Research has been conducted to validate monthly and seasonal rain rates derived from the Tropical Rainfall Measuring Mission Precipitation Radar (PR) using rain gauge data analysis from 2004 to 2008. The study area employed 20 gauges across Indonesia to monitor three Indonesian regional rainfall types. The relationship of PR and rain gauge data statistical analysis included the linear correlation coefficient, the mean bias error (MBE), and the root mean square error (RMSE). Data validation was conducted with point-by-point analysis and spatial average analysis. The general results of point-by-point analysis indicated satellite data values of medium correlation, while values of MBE and RMSE tended to indicate underestimations with high square errors. The spatial average analysis indicated the PR data values are lower than gauge values of monsoonal and semi-monsoonal type rainfall, while anti-monsoonal type rainfall was overestimated. The validation analysis showed very good correlation with the gauge data of monsoonal type rainfall, high correlation for anti-monsoonal type rainfall, but medium correlation for semi-monsoonal type rainfall. In general, the statistical error level of monthly seasonal monsoonal type conditions is more stable compared to other rainfall types. Unstable correlations were observed in months of high rainfall for semi-monsoonal and anti-monsoonal type rainfall.     

对基于RegCM4降尺度的中国区域性暴雨事件模拟评估

基于区域气候模式RegCM4对4个全球气候模式动力降尺度模拟（分别记为CdR、EdR、HdR、MdR）以及高分辨率格点观测数据CN05.1的日降水数据,利用“追踪式”客观识别方法,对1981—2005年中国区域性暴雨事件进行了识别,并评估了模式对其气候特征的模拟性能。结果表明：4个动力降尺度模拟以及多模式集合能较好地模拟区域性暴雨事件发生频次、平均持续时间、平均降水量、平均影响范围和综合强度的年内分布特征以及气候平均值。观测的区域性暴雨事件持续时间、平均降水量、平均影响范围和综合强度在不同区间的频率分布特征以及区域性暴雨事件的累计频次、累计持续时间和累计降水量的空间分布特征也能得到很好地再现。模拟值与观测的空间相关系数都在0.9以上,且均方根误差不超过0.4。不过,相对而言,模式模拟的区域性暴雨事件频次略少,主要由对中度区域性暴雨事件低估所致;模拟的平均持续时间和平均降水量略偏高,而平均影响范围略偏小。综合强度方面,除HdR外,其余模拟均有所高估,尤其是MdR。在频率分布特征和空间分布方面,CdR的模拟性能低于其他模拟。多模式集合模拟的平均持续时间、平均降水量、平均影响范围和综合强度的相对误差分别为13%、2%、-11%和3%。     

近56a武汉市降水气候变化特征分析

以武汉市1951—2006年逐日降水资料为基础,采用累积距平、线性趋势、移动T检验、5％分位数、小波分析等方法,分析了近56a来武汉市降水气候变化特征。结果表明：（1）近56年来,武汉市年降水量、降水强度呈增加趋势,而降水日数呈减少趋势;（2）除春季外,其它季节以及汛期、梅雨期、伏旱期等时段的降水量均有所增加;降水日数在春、秋季、汛期呈下降趋势,其余时段则为增加趋势;降水强度在夏季、伏早期呈减小趋势,其余时段均为增大趋势;（3）梅雨期、年的降水量变化较为一致,其周期性变化明显,主要表现为10a年代际周期,突变点约在1979年;（4）1960、1970年代暴雨日数较少,在1979年前后突变增多后,进人多暴雨阶段;（5）历年最大日降水量、5％分位数极端降水强度、暴雨平均强度变化略有减少趋势但不显著,而大暴雨平均强度减弱趋势明显。     

Variability of the TRMM-PR total and convective and stratiform rain fractions over the Indian region during the summer monsoon

Level 3 (3A25) TRMM Precipitation Radar (PR) data are used for 13 years period (1998–2010) to prepare climatology of TRMM PR derived near surface rain (Total rain) and rain fractions for the 4-months duration of Indian Summer Monsoon season (June–September) as well as for individual months. It is found that the total rain is contributed mostly (99 %) by two rain fractions i.e. stratiform and convective rain fractions for the season as well as on the monthly basis. It is also found that total rain estimates by PR are about 65 % of the gauge measured rain over continental India as well as on sub-regional basis. Inter-annual variability of TRMM-PR rain estimates for India mainland and its sub-regions as well as over the neighboring oceanic regions, in terms of coefficient of variability (CV) is discussed. The heaviest rain region over north Bay of Bengal (BoB) is found to have the lowest CV. Another sub-region of low CV lies over the eastern equatorial Indian ocean (EEIO). The CVs of total rain as well as its two major constituents are found to be higher on monthly basis compared to seasonal basis. Existence of a well known dipole between the EEIO and the north BoB is well recognized in PR data also. Significant variation in PR rainfall is found over continental India between excess and deficit monsoon seasons as well as between excess and deficit rainfall months of July and August. Examination of rainfall fractions between the BoB and Central India on year to year basis shows that compensation in rainfall fractions exists on monthly scale on both the regions. Also on the seasonal and monthly scales, compensation is observed in extreme monsoon seasons between the two regions. However, much less compensation is observed between the north BoB and EEIO belts in extreme rain months. This leads to speculation that the deficit and excess seasons over India may result from slight shift of the rainfall from Central India to the neighboring oceanic regions of north BoB. Contribution of stratiform and convective rain fractions have been also examined and the two fractions are found to contribute almost equally to the total rain. Results are further discussed in terms of the possible impact of the two rain fractions on circulation based on possible difference is vertical profiles of latent heat of two types of rain. Substantial differences in the lower and upper tropospheric circulation regimes are noticed in both deficit and excess monsoon months/seasons, emphasizing the interaction between rainfall (latent heat) and circulation.     

The rainfall regime in Lisbon in the last 150 years

The first decades of the rainfall series of Lisbon have been digitized recently allowing a long-term assessment of the rainfall regime for 150 years of uninterrupted, i.e., the first assessment for the longest continuous precipitation time series in western Iberia. This data has been monitored continuously at the D. Luís observatory having started to be published in 1864 in the Observatory's log books (Annals). We use an approach based on different characteristics of rain spells that has been proved to be satisfactory for the analysis of the different parameters related to the rainfall regime in that part of the world. Thus, a rain spell is defined as a series of consecutive days with a measured daily rainfall equal or higher than 1.0 mm. Each rain spell is preceded and followed by at least one dry day. For each rain spell, its duration, its yield (RSY), and its average intensity (RSI) was calculated. Additionally, the total number of rain spells in each year was also considered. Dryness was analyzed using the dry days since last rain approach. Besides the evaluation over the entire 150-year period available, we have also looked into three equally spaced sub-periods. Lisbon reveals large inter-annual and intra-annual variability and both have increased considerably in the last decades. The large intra-annual variability is demonstrated by both; a very large range of annual rainfall percentage accumulated at any given date and by a very large range of dates on which a certain rainfall percentage was accumulated. Again, both metrics increased in the last decades. Parallel to the increase in the uncertainty, a very significant net increase is noticed in the annual totals since the 1960s compared to the first half of the previous century. The increase is mainly due to more intense events which are reflected by higher RSY and RSI values in the last 50 years.     

Past and Future Changes in the Climate of Hong Kong

Over the years, the Hong Kong Observatory has carried out scientific studies to evaluate the observed climate trends and project the future climate in Hong Kong. Analysis of the meteorological observations at the observatory's headquarters in Tsim Sha Tsui since 1885 reveals that the temperature rise in Hong Kong during the past 124 years is in accord with the global rising trend. The accelerated rising trend in the mean temperature in last few decades may be attributed to the anthropogenic influences, especially urbanization. A similar increasing trend is also observed for rainfall. Other observations such as increasing cloud amount and decreasing total global solar radiation are all consistent with the global trend. Studies of past occurrences of extreme temperature and rainfall have also been carried out. The results indicate that cold episodes have become rarer while very hot days and heavy rain events are becoming more frequent. The observatory also makes use of the data from the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC) and employs statistical downscaling techniques to carry out projections of temperature and precipitation in the 21st century. It is found that the rise in temperature in Hong Kong will be slightly higher than the global mean in the 21st century. The annual rainfall in Hong Kong is also expected to rise by the end of the 21st century, so is its year-to-year variability.     

Backcasting long-term climate data: evaluation of hypothesis

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies.