Statistical downscaling of summer temperature extremes in northern China

Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downscale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross-validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downscaling to Hadley Centre Coupled Model Version 3 (HadCM3) simulations under the Representative Concentration Pathways 4.5 (RCP 4.5) scenario of the Fifth Coupled Model Inter-comparison Project (CMIP5). The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5℃, and the 10th of daily minimum temperatures will increase by about 2℃ during the period 2011-35 relative to 1980-99.     

Recent trends in winter temperature extremes in eastern China and their relationship with the Arctic Oscillation and ENSO

Interannual variations in the number of winter extreme warm and cold days over eastern China （EC） and their relationship with the Arctic Oscillation （AO） and E1 Nifio-Southern Oscillation （ENSO） were investigated using an updated temperature dataset comprising 542 Chinese stations during the period 1961- 2011. Results showed that the number of winter extreme warm （cold） days across EC experienced a significant increase （decrease） around the mid-1980s, which could be attributed to interdecadal variation of the East Asian Winter Monsoon （EAWM）. Probability distribution functions （PDFs） of winter temperature extremes in different phases of the AO and ENSO were estimated based on Generalized Extreme Value Distribution theory. Correlation analysis and the PDF technique consistently demonstrated that interannual variation of winter extreme cold days in the northern part of EC （NEC） is closely linked to the AO, while it is most strongly related to the ENSO in the southern part （SEC）. However, the number of winter extreme warm days across EC has little correlation with both AO and ENSO. Furthermore, results indicated that, whether before or after the mid-1980s shift, a significant connection existed between winter extreme cold days in NEC and the AO. However, a significant connection between winter extreme cold days in SEC and the ENSO was only found after the mid-1980s shift. These results highlight the different roles of the AO and ENSO in influencing winter temperature extremes in different parts of EC and in different periods, thus providing important clues for improving short-term climate prediction for winter temperature extremes.     

Changing Trends of Daily Temperature Extremes with Different Intensities in China

By comparing two sets of quality-controlled daily temperature observation data with and without the inhomogeneity test and adjustment from 654 stations in China during 1956-2004 and 1956-2010, impacts of inhomogeneity on changing trends of four percentile temperature extreme indices, including occurrences of cold days, cold nights, warm days, and warm nights with varying intensities, were discussed. It is found that the inhomogeneity affected the long-term trends averaged over extensive regions limitedly. In order to minimize the inhomogeneity impact, the 83 stations identified with obvious inhomogeneity impacts were removed, and an updated analysis of changing trends of the four temperature extreme indices with varying intensities during 1956-2010 was conducted. The results show that annual occurrences of both cold nights and cold days decreased greatly while those of warm nights and warm days increased significantly during the recent 20 years. The more extreme the event is, the greater the magnitude of changing trends for the temperature extreme index is. An obvious increasing trend was observed in annual occurrences of cold days and cold nights in the recent four years. The magnitude of changing trends of warm extreme indices was greater than that of cold extreme indices, and it was greater in northern China than in southern China. Trends for summer occurrence of cold days were not significant. Decreasing trends of occurrences of both cold nights and cold days were the greatest in December, January, and February (DJF) but the least in June, July, and August (JJA), while increasing trends of warm nights were the greatest in JJA. Cold nights significantly decreased from 1956 to 1990, and then the decreasing trend considerably weakened. The decreasing trend also showed an obvious slowdown in recent years for occurrence of cold days. However, increasing trends of warm nights and warm days both have been accelerated continuously since the recent decades. Further analysis presents that the evolution of the trends for occurrences of the four temperature extreme indices was dominated by the changes in northern China.     

基于统计降尺度模型的江淮流域极端气候的模拟与预估

利用江淮流域29个代表站点1961--2000年逐日最高温度、最低温度和逐日降水资料,以及NCEP逐日大尺度环流场资料,引入基于多元线性回归与随机天气发生器相结合的统计降尺度模型SDSM（statistical downscalingmodel）,通过对每个站点建模,确立SDSM参数,并将该模型应用于SRESA2排放情景下HadCM3和cGcM3模式,得到了江淮流域各代表台站21世纪的逐日最高、最低温度和降水序列以及热浪、霜冻、强降水等极端气候指数。结果表明,当前气候下,统计降尺度方法模拟的极端温度指数与观测值有很好的一致性,能有效纠正耦合模式的“冷偏差”,如SDSM对江淮平均的冬季最高、最低温度的模拟偏差较CGCM3模式分别减少3℃和4．5℃。对于极端降水则能显著纠正耦合模式模拟的降水强度偏低的问题,如CGCM3对江淮流域夏季降水强度的模拟偏差为-60．6％,但降尺度后SDSM—CGCM3的偏差仅为-6％,说明降尺度模型SDSM的确有“增加值”的作用。21世纪末期在未来SRESA2情景下,对于极端温度,无论Had．CM3还是CGCM3模式驱动统计模型,江淮流域所有代表台站,各个季节的最高、最低温度都显著增加,且以夏季最为显著,增幅在2—4℃;与之相应霜冻天数将大幅减少,热浪天数大幅增多,各站点冬季霜冻天数减少幅度为5—25d,夏季热浪天数增加幅度为4～14d;对于极端降水指数,在两个不同耦合模式HadCM3和CGCM3驱动下的变化尤其是变化幅度的一致性比温度差,但大部分站点各个季节极端强降水事件将增多,强度增强,SDSM—HadCM3和SDSM-CGCM3预估的夏季极端降水贡献率将分别增加26％和27％。     

近50a华东地区夏季极端降水事件的年代际变化

利用中国华东地区90站点1960--2009年夏季（6—8月）逐日降水资料,分析了近50a来华东地区各类极端降水事件的强度和发生频次的年代际变化。结果表明：华东地区极端降水事件年代际变化特征明显。近20a来,不论是极端降水事件的平均强度还是发生次数都要明显高于前30a;1990年代是极端事件多发且强度较强的年代;华东区域极端强降水过程事件的连续降水日数多在9d以下,而极端连续降水日数事件基本在9d以上;较之华东地区其他区域,福建地区存在更多的强度大、持续久的降水过程;华东地区最大极端降水量出现在江西北部与安徽南部的交界区域。极端降水事件频发带存在南北摆动的年代际变化,这一特征在极端日降水事件和极端强降水过程事件上表现得更为明显。同时,存在两个极端事件频发带,分别位于长江流域附近。在后3个年代,这两个频发带呈现出分一合一分的年代际变化特征。     

中国极端气候变化观测研究回顾与展望

评述、总结了近年有关中国极端气候变化的观测研究成果,讨论了尚未解决的科学问题和今后应重点加强的工作方向。已有研究表明,1951年以来中国大陆地区极端气候事件频率和强度发生了一定变化,但不同类型和不同区域极端气候变化存在明显差异。从全国范围看,与异常偏冷相关的极端事件如寒潮、冷夜和冷昼天数、霜冻日数等,显著减少减弱,偏冷的气候极值减轻;与异常偏暖相关的暖夜、暖昼日数明显增多,暖夜日数增多尤其明显,但高温事件频数和偏热的气候极值未见显著长期趋势;全国平均暴雨和极端强降水事件频率和强度有所增长,特别是长江中下游和东南地区、西部特别是西北地区有较明显增长,而华北、东北中南部和西南部分地区减少减弱;多数地区小雨频数明显下降,偏轻和偏强降水的强度似有增加;全国遭受气象干旱的范围呈较明显增加趋势,其中华北和东北地区增加更为显著;登陆和影响我国的热带气旋、台风频数有所下降,其造成的降水总量有较明显减少;北方地区的沙尘暴事件从总体上看有显著减少减弱趋势;我国东部部分地区夏季雷暴发生频率也存在较明显下降趋势。现有工作表明,在涉及极端气候变化研究的资料处理和分析方法方面还有改进余地。观测资料的非均一性,以及观测环境改变和城市化对地面气候要素变化趋势的影响偏差,需要进行深入评价和客观订正。此外,目前对于区域极端气候变化的综合分析还较薄弱,在极端气候变化机理的研究方面有待加强。     

华南地区汛期极端降水的概率分布特征

利用1960-2005年华南地区71个测站的逐日降水量资料和NCEP/NCAR南半球月平均海平面气压场再分析资料,采用Le Page榆验、广义极值分布等统计诊断方法,研究了华南地区近46 a前汛期(4-6月)和后汛期(7-9月)极端降水的时空变化及概率分布特征.并讨论了南半球澳大利亚高压和马斯克林高压强度指数与华南汛期暴雨日数间年代际变化的关系.结果表明:(1)1992年华南地区降水发生了由减少趋势到增多趋势的突变,降水趋势发生突变后前汛期极端降水量和日极端降水强度有所下降,而后汛期则是显著增强.(2)华南汛期年平均日最大降水量、50 a一遇日最大降水量极值和暴雨日数的空间分布特征相似,即前汛期的空间分布自南向北呈现"低-高-低"的分布趋势,后汛期呈现由沿海到内陆的"高-低"的分布趋势.(3)1992年发生突变后,前、后汛期年平均日最大降水量和年平均暴雨日数显著增加和减少的空间分布基本一致.(4)就年代际变化而言,南半球澳大利亚高压和马斯克林高压的强度变化是华南汛期降水异常的重要气候背景,即当两高压处在同时增强时期时,华南前汛期极端降水处于偏少阶段,后汛期则处于偏多阶段.     

气候与土地利用变化对水文水资源的影响研究

水资源短缺和水患灾害已成为全球关心的重大问题。气候与土地利用变化对流域水资源和旱涝的影响以及由此产生的社会经济后果已引起人类社会的广泛关注。深入综合地开展这方面的研究对国民经济建设和可持续发展规划决策有重要的意义。通过分析总结已进行的有关研究工作 ,对该领域的研究进展作了简要回顾 ,讨论了现有工作的不足和今后的研究内容和方法。     

Comparison of temperature extremes between Zhongshan Station and Great Wall Station in Antarctica

Although temperature extremes have led to more and more disasters, there are as yet few studies on the extremes and many disagreements on temperature changes in Antarctica. Based on daily minimum, maximum, and mean air temperatures(Tmin, Tmax, Tmean) at Great Wall Station(GW) and Zhongshan Station(ZS), we compared the temperature extremes and revealed a strong warming trend in Tmin, a slight warming trend in Tmean, cooling in Tmax, a decreasing trend in the daily temperature range, and the typical characteristic of coreless winter temperature. There are different seasonal variabilities, with the least in summer. The continentality index and seasonality show that the marine air mass has more effect on GW than ZS. Following the terminology of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change(IPCC AR5), we defined nine indices of temperature extremes, based on the Antarctic geographical environment. Extreme-warm days have decreased, while extreme-warm nights have shown a nonsignificant trend. The number of melting days has increased at GW, while little change at ZS. More importantly, we have found inverse variations in temperature patterns between the two stations, which need further investigation into the dynamics of climate change in Antarctica.     

Spatio-temporal downscaling of projected precipitation in the 21st century: indication of a wetter monsoon over the Upper Mahanadi Basin,India

An attempt is made to assess the future trend of spatio-temporal variation of precipitation over a medium-sized river basin. The Statistical Downscaling Model (SDSM, version 4.2) is used to downscale the outputs from two general circulation models (GCMs) for three future epochs: epoch-1 (2011–2040), epoch-2 (2041–2070) and epoch-3 (2071–2100). Considering the Upper Mahanadi Basin as a test bed, the study results indicate a “wetter” monsoon (June–September) and the annual increase in precipitation is 12% during epoch-3, which is consistent for both GCMs. Monthly analyses indicate that the precipitation totals are likely to increase and the magnitude of increase is greater during monsoon months than non-monsoon months. The number of month-wise daily extremes increases in most months in the year. However, the maximum percentage increase (with respect to baseline period, 1971–2000) in the number of extreme events is found in the non-monsoon months (specifically before and after the monsoon).