Wet season Mediterranean precipitation variability: influence of large-scale dynamics and trends

The influence of the large-scale atmospheric circulation at several tropospheric levels on wet season precipitation over 292 sites across the Mediterranean area is assessed. A statistical downscaling model is designed with an objective methodology based on empirical orthogonal functions and canonical correlation analysis (CCA) and tested by means of cross-validation. In all 30% of the total Mediterranean October to March precipitation variability can be accounted for by the combination of four large-scale geopotential height fields and sea level pressure. The Mediterranean sea surface temperatures seem to be less relevant to explain precipitation variability at interannual time scale. It is shown that interdecadal changes in the first CCA mode are related to variations in the North Atlantic Oscillation index and responsible for comparable time scale variations of the Mediterranean precipitation throughout the twentieth century. The analysis reveals that since the mid-nineteenth century precipitation steadily increased with a maximum in the 1960s and decreased since then. The second half of the twentieth century shows a general downward trend of 2.2 mm·month^–1·decade^–1.     

基于广义线性模型和NCEP资料的降水随机发生器

天气发生器可以用来插补历史缺测气象数据或生成未来天气情境, 近年来被普遍应用于对气象变量的降尺度研究, 为陆面的水文、 生态模拟提供外强迫输入。广义线性模型 (GLM) 是近年来用于建立大尺度气象变量与地面气象因子之间的一种有效方法, 基于GLM的天气发生器具有一定的应用前景。本文以NCEP再分析资料中的单格点气温、 500 hPa位势高度、 位温、 相对湿度、 海平面气压等5个变量作为影响降水变化的大尺度因子建立模拟逐日降水量的广义线性模型。模型中对降水概率的描述采用Logistic模型模拟, 而对降水量则分别试用Gamma分布、 指数分布、 正态分布和对数正态分布来模拟, 试图比较和揭示这些基于不同理论分布的模型的能力。模型中待定参数的估计及对研究区逐日降水量的模拟采用了完全相同的实测逐日降水数据和同期NCEP再分析资料。参数的最大似然估计用遗传算法来实现, 对山东省临沂地区10个主要气象观测站降水资料的研究表明, Gamma分布模型的拟合效果最好, 对数正态分布次之, 指数分布再次, 正态分布最差; 参数估计分月获取的拟合效果略好于不分月的。模型逐日降水模拟表明, 对降水发生概率的模拟会低估各月的多年平均值, 基于指数分布的GLM会低估各月总降水量期望 (为月内每日降水量期望之和) 的多年平均值, 而基于对数正态分布的GLM则会在降水量较大时产生高估现象。由对应的天气发生器模型生成的随机模拟降水序列表明, 基于对数正态分布的模型会高估月降水量较大时的多年平均, 而基于指数分布及Gamma分布的模型则模拟效果较好。总体上看, 这种基于NCEP再分析资料和GLM的天气发生器对降水变率具有很强的解释和模拟能力。     

基于DERF的SD方法预测月降水和极端降水日数

针对动力气候模式对区域或更小空间尺度内的日降水预测技巧偏低的问题,应用最优子集回归 (OSR) 方法对国家气候中心业务化的月动力气候模式 (DERF) 输出的高度场、风场和海平面气压场进行降尺度处理用于降水预测,旨在提高预测准确率。1982—2006年交叉检验结果表明:OSR方法能显著提高降水预测技巧,其中11~40 d改善效果最为显著。在此基础上,应用一步法和两步法两种统计降尺度方法预测极端降水日数,交叉检验结果表明:两种方法均优于随机预测,冬季两步法预测技巧略高于一步法,夏季一步法略优于两步法。综合认为OSR,OSR结合随机天气发生器 (WG) 两种统计降尺度方法对月尺度降水或极端降水日数的预测均具有较高的技巧,可作为短期气候预测的重要参考信息。     

Winter precipitation variability and large-scale circulation patterns in Romania

Summary The spatial and temporal variability of winter precipitation and its links to the large-scale atmospheric circulation patterns in Romania are examined. The data set is composed of observed rainfall at 30 meteorological stations during the 1961–1996 period. The large-scale field is represented by the observed geopotential height at 500 hPa (Z500) over the same period, covering the latitudinal belt between 20° N–90° N (resolution 2.5°×2.5°).The Standard Normal Homogeneity Test (SNHT) is applied to detect inhomogeneities in the data, and the Mann-Kendall and Pettitt non-parametric tests are used in order to identify trends and change points in the winter precipitation time series. The empirical orthogonal functions (EOF) technique is used for data reduction in order to highlight the basic patterns of rainfall variability in Romania. The covariance map between precipitation EOF time series (PCs) and the Z500 field, as well as the correlation coefficients between the PCs and circulation indices are calculated in order to identify the influence of large-scale circulation patterns on winter precipitation in Romania.A significant decreasing trend is identified in winter precipitation with a downward shift in winter 1969/1970, most significant from a statistical point of view in the extra-Carpathian region. This change seems to be real since the SNHT test does not reveal any inhomogeneity during the period tested. Significant relationships are found between winter precipitation variability in Romania and the large-scale circulation pattern, such as the North Atlantic Oscillation and the blocking phenomenon in the Atlantic-European sector. The positive phase of the NAO and the reduction in blocking activity could be one of the causes of the decrease in winter precipitation in Romania.     

Intraseasonal variability of winter precipitation over central asia and the western tibetan plateau from 1979 to 2013 and its relationship with the North Atlantic Oscillation

Winter precipitation over Central Asia and the western Tibetan Plateau (CAWTP) is mainly a result of the interaction between the westerly circulation and the high mountains around the plateau. Empirical Orthogonal Functions (EOFs), Singular Value Decomposition (SVD), linear regression and composite analysis were used to analyze winter daily precipitation and other meteorological elements in this region from 1979 to 2013, in order to understand how interactions between the regional circulation and topography affect the intraseasonal variability in precipitation. The SVD analysis shows that the winter daily precipitation variability distribution is characterized by a dipole pattern with opposite signs over the northern Pamir Plateau and over the Karakoram Himalaya, similar to the second mode of EOF analysis. This dipole pattern of precipitation anomaly is associated with local anomalies in both the 700 hPa moisture transport and the 500 hPa geopotential height and is probably caused by oscillations in the regional and large-scale circulations, which can influence the westerly disturbance tracks and water vapor transport. The linear regression shows that the anomalous mid-tropospheric circulation over CAWTP corresponds to an anti-phase variation of the 500 hPa geopotential height anomalies over the southern and northern North Atlantic 10 days earlier (at 95% significance level), that bears a similarity to the North Atlantic Oscillation (NAO). The composite analysis reveals that the NAO impacts the downstream regions including CAWTP by controlling south-north two branches of the middle latitude westerly circulation around the Eurasian border. During the positive phases of the NAO, the northern branch of the westerly circulation goes around the northwest Tibetan Plateau, whereas the southern branch encounters the southwest Tibetan Plateau, which leads to reduced precipitation over the northern Pamir Plateau and increased precipitation over the Karakoram Himalaya, and vice versa.     

An Improved Statistical-Dynamical Downscaling Scheme and its Application to the Alpine Precipitation Climatology

Summary An improved statistical-dynamical downscaling method for the regionalization of large-scale climate analyses or simulations is introduced. The method is based on the disaggregation of a multi-year time-series of large-scale meteorological data into multi-day episodes of quasi-stationary circulation. The episodes are subsequently grouped into a defined number of classes. A regional model is used to simulate the evolution of weather during the most typical episode of each class. These simulations consider the effects of the regional topography. Finally, the regional model results are statistically weighted with the climatological frequencies of the respective circulation classes in order to provide regional climate patterns. The statistical-dynamical downscaling procedure is applied to large-scale analyses for a 12-year climate period 1981–1992. The performance of the new method is demonstrated for winter precipitation in the Alpine region. With the help of daily precipitation analyses it was possible to validate the results and to assess the different sources of errors. It appeared that the main error originates from the regional model, whereas the error of the procedure itself was relatively unimportant. This new statistical-dynamical downscaling method turned out to be an efficient alternative to the commonly used method of nesting a regional model continuously within a general circulation model (dynamical downscaling). Received April 8, 1999 Revised July 30, 1999     

两种统计降尺度方法在秦岭山地的适用性

基于ASD（automated statistical downscaling）统计降尺度模型提供的多元线性回归和岭回归两种统计降尺度方法,采用RCP4.5（representative concentration pathways 4.5）和RCP8.5情景下全球气候模式MPI-ESM-LR输出的预报因子数据、NCEP/NCAR再分析数据和秦岭山地周边10个气象站观测数据,评估两种统计降尺度方法在秦岭山地的适用性及预估秦岭山地未来3个时期（2006-2040年、2041-2070年和2071-2100年）的平均气温和降水。结果表明:率定期和验证期内,两种统计降尺度方法均可以较好地模拟研究区域的平均气温和降水的变化特征,且多元线性回归的模拟效果优于岭回归。在未来气候情景下,两种统计降尺度方法预估的研究区域平均气温均呈明显上升趋势,气温增幅随辐射强迫增加而增大。降水方面,21世纪未来3个时期降水均呈不明显减少趋势,但季节分配发生变化。综合考虑两种统计降尺度方法在秦岭山地对平均气温和降水的模拟效果和情景预估结果,认为多元线性回归降尺度方法更适用于秦岭山地气候变化的降尺度预估研究。     

Circulation patterns, daily precipitation in Portugal and implications for climate change simulated by the second Hadley Centre GCM

Based on principal component analysis (PCA) and a k-means clustering algorithm, daily mean sea level pressure (MSLP) fields over the northeastern Atlantic and Western Europe, simulated by the Hadley Centre's second generation coupled ocean-atmosphere GCM (HADCM2) control run (HADCM2CON), are validated by comparison with the observed daily MSLP fields. It is clear that HADCM2 reproduces daily MSLP fields and its seasonal variability over the region very well, despite suffering from some deficiencies, such as the systematic displacement of the atmospheric centres of action. Four daily circulation patterns, previously identified from the observed daily MSLP fields over the area and well related to daily precipitation in Portugal, were also well classified from the daily MSLP fields simulated by HADCM2. The model can also simulate rather successfully the relationships between the four daily circulation patterns and daily precipitation in southern Portugal. However, compared with observations, daily precipitation intensities simulated by the model are too weak in southern Portugal. Nevertheless, HADCM2 represents a considerable improvement relative to the UKTR experiment. The results described here imply that it is doubtful whether regional precipitation scenarios provided by HADCM2 can be directly applied in impact studies and that a downscaling technique, based on daily circulation patterns, might be successful in reproducing local and regional precipitation characteristics. Moreover, the four circulation patterns can also be clearly identified in the two perturbed experiments, one under greenhouse gases forcing only (HADCM2GHG) and the other under additional forcing of sulphate aerosol (HADCM2SUL), although changes in the frequencies of occurrence of certain circulation patterns are found. Nevertheless, the observed links between regional precipitation in southern Portugal and large-scale atmospheric circulation seem likely to hold in the model's perturbed climate. It is therefore credible to use those links to downscale large-scale atmospheric circulation from GCM simulations to obtain future precipitation scenarios in southern Portugal. Received: 21 August 1998 / Accepted: 28 May 1999     

中国夏季降水的组合统计降尺度模型预测研究

现阶段的动力气候模式尚不能满足东亚区域气候预测的实际需求,这就需要动力和统计相结合的方法,将动力模式中具有较高预测技巧的大尺度环流信息应用到降水等气象要素的统计预测模型当中,以改善后者预测效果。本文中所介绍的组合统计降尺度模型,可将动力气候模式预测的大尺度环流变量和前期观测的外强迫信号作为预测因子来预测中国夏季降水异常。交叉检验结果显示,组合统计降尺度预测模型的距平相关系数较原始模式结果有较大提高。在实时夏季降水预测中,2013～2018年平均的预测技巧相对较高,趋势异常综合检验（PS）评分平均为71.5分,特别是2015～2018年平均的PS评分预测技巧达到72.7分,总体上高于业务模式原始预测和业务发布预测的技巧。该组合统计降尺度模型预测性能稳定,为我国季节预测业务提供了一种有效参考。     

A comparison of statistical downscaling and climate change factor methods: impacts on low flows in the River Thames, United Kingdom

Strategic-scale assessments of climate change impacts are often undertaken using the change factor (CF) methodology whereby future changes in climate projected by General Circulation Models (GCMs) are applied to a baseline climatology. Alternatively, statistical downscaling (SD) methods apply climate variables from GCMs to statistical transfer functions to estimate point-scale meteorological series. This paper explores the relative merits of the CF and SD methods using a case study of low flows in the River Thames under baseline (1961–1990) and climate change conditions (centred on the 2020s, 2050s and 2080s). Archived model outputs for the UK Climate Impacts Programme (UKCIP02) scenarios are used to generate daily precipitation and potential evaporation (PE) for two climate change scenarios via the CF and SD methods. Both signal substantial reductions in summer precipitation accompanied by increased PE throughout the year, leading to reduced flows in the Thames in late summer and autumn. However, changes in flow associated with the SD scenarios are generally more conservative and complex than that arising from CFs. These departures are explained in terms of the different treatment of multidecadal natural variability, temporal structuring of daily climate variables and large-scale forcing of local precipitation and PE by the two downscaling methods.     

动力降尺度CMIP5的2006—2035新疆夏季降水变化

评估了CMIP5 9个模式对新疆夏季降水的模拟效果,从中选取5个较好的模式结果,利用RegCM4动力降尺度再集合平均。通过对CMIP5 9个模式的评估,发现CanCM4、CMCC-CM、CNRM-CM5、HadCM3、MIROC4h对新疆夏季降水的模拟较好。进一步分析动力降尺度和集合平均结果,发现动力降尺度能更好地描述新疆复杂地形造成的降水,同时发现2006—2035年比1976—2005年新疆夏季的总体降水有所减少,存在明显的减少趋势。在空间分布上主要表现为天山山脉和其南侧降水显著减少,昆仑山山脉北缘降水显著增加,这与2006—2035年新疆东北侧500 hPa出现的位势高度正异常和与之相对应的反气旋式环流异常有关。这一环流异常造成了天山山脉上空水汽的显著辐散和昆仑山山脉北缘上空水汽的显著辐合。     

Stochastic Modeling of the Effects of Large-Scale Circulation on Daily Weather in the Southeastern U.S.

Statistical methodology is devised to model time series of daily weather at individual locations in the southeastern U.S. conditional on patterns in large-scale atmosphere–ocean circulation. In this way, weather information on an appropriate temporal and spatial scale for input to crop–climate models can be generated, consistent with the relationship between circulation and temporally and/or spatially aggregated climate data (an exercise sometimes termed `downscaling'). The Bermuda High, a subtropical Atlantic circulation feature, is found to have the strongest contemporaneous correlation with seasonal mean temperature and total precipitation in the Southeast (in particular, stronger than for the El Niño–Southern Oscillation phenomenon). Stochastic models for time series of daily minimum and maximum temperature and precipitation amount are fitted conditional on an index indicating the average position of the Bermuda High. For precipitation, a multi-site approach involving a statistical technique known as `borrowing strength' is applied, constraining the relationship between daily precipitation and the Bermuda High index to be spatially the same. In winter (the time of greatest correlation), higher daily maximum and minimum temperature means and higher daily probability of occurrence of precipitation are found when there is an easterly shift in the average position of the Bermuda High. Methods for determining aggregative properties of these stochastic models for daily weather (e.g., variance and spatial correlation of seasonal total precipitation) are also described, so that their performance in representing low frequency variations can be readily evaluated.     

Constructing Site-Specific Climate Change Scenarios on a Monthly Scale Using Statistical Downscaling

Summary Monthly mean temperature and monthly precipitation totals in two small catchments in the Czech Republic are estimated from large-scale 500 hPa height and 1000/500 hPa thickness fields using statistical downscaling. The method used is multiple linear regression. Whereas precipitation can be determined from large-scale fields with some confidence in only a few months of the year, temperature can be determined successfully. Principal components calculated separately from the height and thickness anomalies are identified as the best predictor set. The method is most accurate if the regression is performed using seasons based on three months. The test on an independent sample, consisting of warm seasons, confirms that the method successfully reproduces the difference in mean temperature between two climatic states, which indicates that this downscaling method is applicable for constructing scenarios of a future climate change. The ECHAM3 GCM is used for scenario construction. The GCM is shown to simulate surface temperature and precipitation with low accuracy, whereas the large-scale atmospheric fields are reproduced well; this justifies the downscaling approach. The observed regression equations are applied to 2xCO₂ GCM output so that the model’s bias is eleminated. This procedure is then discussed and finally, temperature scenarios for the 2xCO₂ climate are constructed for the two catchments. Received December 3, 1998 Revised December 4, 1999     

Multisite statistical downscaling model for daily precipitation combined by multivariate multiple linear regression and stochastic weather generator

This study provides a multi-site hybrid statistical downscaling procedure combining regression-based and stochastic weather generation approaches for multisite simulation of daily precipitation. In the hybrid model, the multivariate multiple linear regression (MMLR) is employed for simultaneous downscaling of deterministic series of daily precipitation occurrence and amount using large-scale reanalysis predictors over nine different observed stations in southern Québec (Canada). The multivariate normal distribution, the first-order Markov chain model, and the probability distribution mapping technique are employed for reproducing temporal variability and spatial dependency on the multisite observations of precipitation series. The regression-based MMLR model explained 16?%?~?22?% of total variance in daily precipitation occurrence series and 13?%?~?25?% of total variance in daily precipitation amount series of the nine observation sites. Moreover, it constantly over-represented the spatial dependency of daily precipitation occurrence and amount. In generating daily precipitation, the hybrid model showed good temporal reproduction ability for number of wet days, cross-site correlation, and probabilities of consecutive wet days, and maximum 3-days precipitation total amount for all observation sites. However, the reproducing ability of the hybrid model for spatio-temporal variations can be improved, i.e. to further increase the explained variance of the observed precipitation series, as for example by using regional-scale predictors in the MMLR model. However, in all downscaling precipitation results, the hybrid model benefits from the stochastic weather generator procedure with respect to the single use of deterministic component in the MMLR model.     

Comparison of regional climate model and statistical downscaling simulations of different winter precipitation change scenarios over Romania

Summary Regional climate model and statistical downscaling procedures are used to generate winter precipitation changes over Romania for the period 2071–2100 (compared to 1961–1990), under the IPCC A2 and B2 emission scenarios. For this purpose, the ICTP regional climate model RegCM is nested within the Hadley Centre global atmospheric model HadAM3H. The statistical downscaling method is based on the use of canonical correlation analysis (CCA) to construct climate change scenarios for winter precipitation over Romania from two predictors, sea level pressure and specific humidity (either used individually or together). A technique to select the most skillful model separately for each station is proposed to optimise the statistical downscaling signal. Climate fields from the A2 and B2 scenario simulations with the HadAM3H and RegCM models are used as input to the statistical downscaling model. First, the capability of the climate models to reproduce the observed link between winter precipitation over Romania and atmospheric circulation at the European scale is analysed, showing that the RegCM is more accurate than HadAM3H in the simulation of Romanian precipitation variability and its connection with large-scale circulations. Both models overestimate winter precipitation in the eastern regions of Romania due to an overestimation of the intensity and frequency of cyclonic systems over Europe. Climate changes derived directly from the RegCM and HadAM3H show an increase of precipitation during the 2071–2100 period compared to 1961–1990, especially over northwest and northeast Romania. Similar climate change patterns are obtained through the statistical downscaling method when the technique of optimum model selected separately for each station is used. This adds confidence to the simulated climate change signal over this region. The uncertainty of results is higher for the eastern and southeastern regions of Romania due to the lower HadAM3H and RegCM performance in simulating winter precipitation variability there as well as the reduced skill of the statistical downscaling model.     

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.     

Current and future atmospheric circulation at 500 hPa over Greenland simulated by the CMIP3 and CMIP5 global models

The Greenland ice sheet is projected to be strongly affected by global warming. These projections are either issued from downscaling methods (such as Regional Climate Models) or they come directly from General Circulation Models (GCMs). In this context, it is necessary to evaluate the accuracy of the daily atmospheric circulation simulated by the GCMs, since it is used as forcing for downscaling methods. Thus, we use an automatic circulation type classification based on two indices (Euclidean distance and Spearman rank correlation using the daily 500 hPa geopotential height) to evaluate the ability of the GCMs from both CMIP3 and CMIP5 databases to simulate the main circulation types over Greenland during summer. For each circulation type, the GCMs are compared to three reanalysis datasets on the basis of their frequency and persistence differences. For the current climate (1961–1990), we show that most of the GCMs do not reproduce the expected frequency and the persistence of the circulation types and that they simulate poorly the observed daily variability of the general circulation. Only a few GCMs can be used as reliable forcings for downscaling methods over Greenland. Finally, when applying the same approach to the future projections of the GCMs, no significant change in the atmospheric circulation over Greenland is detected, besides a generalised increase of the geopotential height due to a uniform warming of the atmosphere.     

A statistical�Cdynamical scheme for reconstructing ocean forcing in the Atlantic. Part I: weather regimes as predictors for ocean surface variables

The links between the observed variability of the surface ocean variables estimated from reanalysis and the overlying atmosphere decomposed in classes of large-scale atmospheric circulation via clustering are investigated over the Atlantic from 1958 to 2002. Daily 500?hPa geopotential height and 1,000?hPa wind anomaly maps are classified following a weather-typing approach to describe the North Atlantic and tropical Atlantic atmospheric dynamics, respectively. The algorithm yields patterns that correspond in the extratropics to the well-known North Atlantic-Europe weather regimes (NAE-WR) accounting for the barotropic dynamics, and in the tropics to wind classes (T-WC) representing the alteration of the trades. 10-m wind and 2-m temperature (T2) anomaly composites derived from regime/wind class occurrence are indicative of strong relationships between daily large-scale atmospheric circulation and ocean surface over the entire Atlantic basin. High temporal correlation values are obtained basin-wide at low frequency between the observed fields and their reconstruction by multiple linear regressions with the frequencies of occurrence of both NAE-WR and T-WC used as sole predictors. Additional multiple linear regressions also emphasize the importance of accounting for the strength of the daily anomalous atmospheric circulation estimated by the combined distances to all regimes centroids in order to reproduce the daily to interannual variability of the Atlantic ocean. We show that for most of the North Atlantic basin the occurrence of NAE-WR generally sets the sign of the ocean surface anomaly for a given day, and that the inter-regime distances are valuable predictors for the magnitude of that anomaly. Finally, we provide evidence that a large fraction of the low-frequency trends in the Atlantic observed at the surface over the last 50?years can be traced back, except for T2, to changes in occurrence of tropical and extratropical weather classes. All together, our findings are encouraging for the prospects of basin-scale ocean dynamical downscaling using a weather-typing approach to reconstruct forcing fields for high resolution ocean models (Part II) from coarse resolution climate models.     

Long-Term Trend of Temperature Derived by Statistical Downscaling Based on EOF Analysis

This study analyzes the ability of statistical downscaling models in simulating the long-term trend of temperature and associated causes at 48 stations in northern China in January and July 1961–2006. The statistical downscaling models are established through multiple stepwise regressions of predictor principal components (PCs). The predictors in this study include temperature at 850 hPa (T850), and the combination of geopotential height and temperature at 850 hPa (H850+T850). For the combined predictors, Empirical Orthogonal Function (EOF) analysis of the two combined fields is conducted. The modeling results from HadCM3 and ECHAM5 under 20C3M and SERS A1B scenarios are applied to the statistical downscaling models to construct local present and future climate change scenarios for each station, during which the projected EOF analysis and the common EOF analysis are utilized to derive EOFs and PCs from the two general circulation models (GCMs). The results show that (1) the trend of temperature in July is associated with the first EOF pattern of the two combined fields, not with the EOF pattern of the regional warming; (2) although HadCM3 and ECHAM5 have simulated a false long-term trend of temperature, the statistical downscaling method is able to well reproduce a correct long-term trend of temperature in northern China due to the successful simulation of the trend of main PCs of the GCM predictors; (3) when the two-field combination and the projected EOF analysis are used, temperature change scenarios have a similar seasonal variation to the observed one; and (4) compared with the results of the common EOF analysis, those of the projected EOF analysis have been much more strongly determined by the observed large-scale atmospheric circulation patterns.     

Mediterranean climate extremes in synoptic downscaling assessments

The behaviour of precipitation and maximum temperature extremes in the Mediterranean area under climate change conditions is analysed in the present study. In this context, the ability of synoptic downscaling techniques in combination with extreme value statistics for dealing with extremes is investigated. Analyses are based upon a set of long-term station time series in the whole Mediterranean area. At first, a station-specific ensemble approach for model validation was developed which includes (1) the downscaling of daily precipitation and maximum temperature values from the large-scale atmospheric circulation via analogue method and (2) the fitting of extremes by generalized Pareto distribution (GPD). Model uncertainties are quantified as confidence intervals derived from the ensemble distributions of GPD-related return values and described by a new metric called “ratio of overlapping”. Model performance for extreme precipitation is highest in winter, whereas the best models for maximum temperature extremes are set up in autumn. Valid models are applied to a 30-year period at the end of the twenty-first century (2070–2099) by means of ECHAM5/MPI-OM general circulation model data for IPCC SRES B1 scenario. The most distinctive future changes are observed in autumn in terms of a strong reduction of precipitation extremes in Northwest Iberia and the Northern Central Mediterranean area as well as a simultaneous distinct increase of maximum temperature extremes in Southwestern Iberia and the Central and Southeastern Mediterranean regions. These signals are checked for changes in the underlying dynamical processes using extreme-related circulation classifications. The most important finding connected to future changes of precipitation extremes in the Northwestern Mediterranean area is a reduction of southerly displaced deep North Atlantic cyclones in 2070–2099 as associated with a strengthened North Atlantic Oscillation. Thus, the here estimated future changes of extreme precipitation are in line with the discourse about the influence of North Atlantic circulation variability on the changing climate in Europe.