区域气候模式情景预估的统计释用方法研究
|
摘要
对气候变量进行可信的预估是气候变化影响评估的基础和前提。模式情景预估的统计释用方法研究旨在减小气候模式的各类偏差,是提高模式预估水平的一个重要方面。本文利用区域气候模式系统PRECIS对中国区域在SRES AIB'情景下的气候预估进行两种统计释用方法订正的研究分析。两种方法分别基于平均态调整(DC, Delta Change)与基于概率分布调整(DBS, Distribution-Based Scailing)。订正变量选取日降水、日平均温度、日最高温度和日最低温度,控制时段选取1962.12-1992.11,验证时段选取1992.12～2002.11。比较分析验证时段两种方法在这几个气候变量的均值、年内循环、概率分布、极端降水与温度指标几方面的释用效果,发现：DBS方法对气候变量均值的释用效果与DC方法相近,且对多数地区变量年内循环、概率分布以及极端指标的释用效果相对显著得多,同时存在DC方法释用效果更显著的情况。由于DBS方法对变量进行统计订正时不仅仅考虑均值的调整,更考虑了方差、极值等其他统计特征的调整,DBS方法总体上比DC方法优越。
1.两种方法有效改善模拟降水与温度均值的空间分布,特别是对模拟偏差大值的显著减小,且释用效果相近。全国范围绝对偏差减小均达80%,各地区降水(温度)相关性从0.5(0.9)提高到0.95(1.0)。DBS (DC)方法对多数地区日最高(低)温度的改善效果更显著。同时,DC方法对热带地区日最高温度具有明显过订正。
2.两种方法有效改善模拟降水与温度的年内循环。全国范围绝对偏差的减小均以日最低温度最大(42%、53%)而日最高温度最小(18%、33%),各地区降水相关性从0.7提高到0.8,温度相关性与模拟均一致接近1。除华南与西南DC方法对日最高温度的释用效果更显著外,DBS方法对多数地区降水与温度的释用效果更显著,但两种方法对华南降水均具有过订正,且DC方法对华东降水与日最高温度具有过订正。
3.两种方法不同程度改善四季模拟降水与温度的边缘概率分布,且DBS方法的改善强度更大、范围更广。四季平均降水(温度)通过K-S检验的格点比率从29%(8%)提高到49%(21%)和74%(36%)。少数地区DC方法的释用效果更好,例如,冬季华北南部与中部北部地区的日最高温度概率分布；并且存在过订正,例如,DC方法对上海与浙江降水与温度存在明显过小订正与过低订正。夏季降水与冬夏季温度的概率分布改善程度最小,说明极端降水与高/低温是影响概率分布订正的重要原因,是提高统计释用的关键。
4.两种方法可显著改善模拟连续干日数CDD、连续5日最大降水量R5D、极端年较差ETR、霜冻日数FD与日最低温度计算的高温热浪强度HTWI2,并改善极端降水贡献率,但对日最高温度计算的高温热浪指数HTWI1.1和HTWI1.2的改善不明显。FD与HTWI2地区平均相关性可达0.95以上。DBS方法对除FD外的各指标全国范围绝对偏差减小均较DC方法更大,且对除FD与HTWI2外的各指标地区相关性提高更多。少数地区DC方法的释用效果更好,例如,青藏高原的FD与东北的HTWI2。过订正现象的存在,多出现在R95T与HTWI1的统计释用中,且DC方法的过订正略多于DBS方法。
因此,在影响评估研究中,应根据地区、气候变量以及研究侧重点(例如,平均态或极值)的不同,有必要选择最合适的统计释用方法订正的气候情景预估,作为评估模式的气候输入。
Credible estimates of climate variables are the basis and premise of the assessment of the climate change impacts. The elimination of various types from climate models is the important aspect of improving climate projection. Therefore, this paper aims to make a research on the different statistical correction methods based on Delta Change (DC) and Distribution-based Scailing (DBS) used to reduce the bias from PRECIS projection, such as P, Tm, Tmax and Tmin, in China under SRES A1B scenario.1962.12-1992.11is chosen as the control period and1992.12～2002.11is chosen as the validation period. Compare the correction results in terms of average spatial distribution, annual cycle, probability distribution, extreme indices and find that DBS can correct the average biases as well as DC, but it can correct annual cycle, probability distribution and extreme indices over most regions better than DC. There are some circumstances that DC acts better than DBS. In general, DBS is better than DC, since it takes not only mean value but also the standard deviation and extreme values into account when correcting the variables.
1. DC and DBS do a good and comparative job in terms of simulated mean precipitation and temperature, especially decreasing the large biases significantly. The absolute biases decrease up to80%in China and the precipitation (temperature) R increase to0.95(1.0) from0.5(0.9). DBS(DC) can correct Tmax(Tmin) over most regions better. Meanwhile, DC overcorrects Tmax in tropical regions.
2. DC and DBS do a good and comparative job in terms of simulated annual cycle of precipitation and temperature. The absolute biases of Tmin decrease the largest (42%,53%) and Tmax the least (18%,33%). The precipitation R increase to0.8from0.7and the temperature R is similar to the simulated R, which is close to1. Except DC does better at Tmax in HN region and XN region, DBS can do better at precipitation and temperature over most regions. While the two methods both overcorrect P in HN region and DC also overcorrects P and Tmax in HD region.
3. DC and DBS can correct the simulated probability distribution of precipitation and temperature and DBS is better. The grid ratio that pass the K-S test in every season precipitation (temperature) is from29%(8%) up to49%(21%) and74%(36%). There are some circumstances that DC acts better than DBS, e.g. the PDF of Tmax in South HD region and North ZB region. There are some overcorrections, e.g. DC overcorrects precipitation and temperature in Shanghai and Zhejiang. The precipitation in summer and Temperature in winter and summer have been corrected the least, which indicates extreme values are important to PDF correction and are the key to improve the correcting technique.
4. DC and DBS can correct the simulated CDD, R5D, ETR, FD and HTWI2much significantly, and the R95T as well. While they can not correct HTWI1.1or HTWI1.2significantly. The region-averaged R of FD and HTWI2can be larger than0.95. DBS can decrease the absolute biases of all the indices much more than DC and increase the region-averaged R of these indices, except FD and HTWI2, more than DC. DC can do a better job in some regions, e.g. FD in QZGY region and HTWI2in DB region. The overcorrections exist more in R95T and HTWI1and DC overcorrects more than DBS.
As a result, in the impact assessment researches, it is necessary to choose the better statistical correction method to correct the climate projections as the input of assessment models, based on the different regions, climate variables and research goals.
1.两种方法有效改善模拟降水与温度均值的空间分布,特别是对模拟偏差大值的显著减小,且释用效果相近。全国范围绝对偏差减小均达80%,各地区降水(温度)相关性从0.5(0.9)提高到0.95(1.0)。DBS (DC)方法对多数地区日最高(低)温度的改善效果更显著。同时,DC方法对热带地区日最高温度具有明显过订正。
2.两种方法有效改善模拟降水与温度的年内循环。全国范围绝对偏差的减小均以日最低温度最大(42%、53%)而日最高温度最小(18%、33%),各地区降水相关性从0.7提高到0.8,温度相关性与模拟均一致接近1。除华南与西南DC方法对日最高温度的释用效果更显著外,DBS方法对多数地区降水与温度的释用效果更显著,但两种方法对华南降水均具有过订正,且DC方法对华东降水与日最高温度具有过订正。
3.两种方法不同程度改善四季模拟降水与温度的边缘概率分布,且DBS方法的改善强度更大、范围更广。四季平均降水(温度)通过K-S检验的格点比率从29%(8%)提高到49%(21%)和74%(36%)。少数地区DC方法的释用效果更好,例如,冬季华北南部与中部北部地区的日最高温度概率分布；并且存在过订正,例如,DC方法对上海与浙江降水与温度存在明显过小订正与过低订正。夏季降水与冬夏季温度的概率分布改善程度最小,说明极端降水与高/低温是影响概率分布订正的重要原因,是提高统计释用的关键。
4.两种方法可显著改善模拟连续干日数CDD、连续5日最大降水量R5D、极端年较差ETR、霜冻日数FD与日最低温度计算的高温热浪强度HTWI2,并改善极端降水贡献率,但对日最高温度计算的高温热浪指数HTWI1.1和HTWI1.2的改善不明显。FD与HTWI2地区平均相关性可达0.95以上。DBS方法对除FD外的各指标全国范围绝对偏差减小均较DC方法更大,且对除FD与HTWI2外的各指标地区相关性提高更多。少数地区DC方法的释用效果更好,例如,青藏高原的FD与东北的HTWI2。过订正现象的存在,多出现在R95T与HTWI1的统计释用中,且DC方法的过订正略多于DBS方法。
因此,在影响评估研究中,应根据地区、气候变量以及研究侧重点(例如,平均态或极值)的不同,有必要选择最合适的统计释用方法订正的气候情景预估,作为评估模式的气候输入。
Credible estimates of climate variables are the basis and premise of the assessment of the climate change impacts. The elimination of various types from climate models is the important aspect of improving climate projection. Therefore, this paper aims to make a research on the different statistical correction methods based on Delta Change (DC) and Distribution-based Scailing (DBS) used to reduce the bias from PRECIS projection, such as P, Tm, Tmax and Tmin, in China under SRES A1B scenario.1962.12-1992.11is chosen as the control period and1992.12～2002.11is chosen as the validation period. Compare the correction results in terms of average spatial distribution, annual cycle, probability distribution, extreme indices and find that DBS can correct the average biases as well as DC, but it can correct annual cycle, probability distribution and extreme indices over most regions better than DC. There are some circumstances that DC acts better than DBS. In general, DBS is better than DC, since it takes not only mean value but also the standard deviation and extreme values into account when correcting the variables.
1. DC and DBS do a good and comparative job in terms of simulated mean precipitation and temperature, especially decreasing the large biases significantly. The absolute biases decrease up to80%in China and the precipitation (temperature) R increase to0.95(1.0) from0.5(0.9). DBS(DC) can correct Tmax(Tmin) over most regions better. Meanwhile, DC overcorrects Tmax in tropical regions.
2. DC and DBS do a good and comparative job in terms of simulated annual cycle of precipitation and temperature. The absolute biases of Tmin decrease the largest (42%,53%) and Tmax the least (18%,33%). The precipitation R increase to0.8from0.7and the temperature R is similar to the simulated R, which is close to1. Except DC does better at Tmax in HN region and XN region, DBS can do better at precipitation and temperature over most regions. While the two methods both overcorrect P in HN region and DC also overcorrects P and Tmax in HD region.
3. DC and DBS can correct the simulated probability distribution of precipitation and temperature and DBS is better. The grid ratio that pass the K-S test in every season precipitation (temperature) is from29%(8%) up to49%(21%) and74%(36%). There are some circumstances that DC acts better than DBS, e.g. the PDF of Tmax in South HD region and North ZB region. There are some overcorrections, e.g. DC overcorrects precipitation and temperature in Shanghai and Zhejiang. The precipitation in summer and Temperature in winter and summer have been corrected the least, which indicates extreme values are important to PDF correction and are the key to improve the correcting technique.
4. DC and DBS can correct the simulated CDD, R5D, ETR, FD and HTWI2much significantly, and the R95T as well. While they can not correct HTWI1.1or HTWI1.2significantly. The region-averaged R of FD and HTWI2can be larger than0.95. DBS can decrease the absolute biases of all the indices much more than DC and increase the region-averaged R of these indices, except FD and HTWI2, more than DC. DC can do a better job in some regions, e.g. FD in QZGY region and HTWI2in DB region. The overcorrections exist more in R95T and HTWI1and DC overcorrects more than DBS.
As a result, in the impact assessment researches, it is necessary to choose the better statistical correction method to correct the climate projections as the input of assessment models, based on the different regions, climate variables and research goals.
引文
[1]Solomon S, Qin D, Manning M, et al. Climate Change 2007:the physical science basis. Contribution of working group I to the fourth assessment report of the Intergovernmental Panel on Climate Change[M].Cambridge, UK and New York, USA:Cambridge University Press,2007:996.
[2]丁一汇,何建坤,林而达.中国气候变化：科学、影响、适应及对策研究[M].北京：中国环境科学出版社,2009.
[3]丁一汇,任国玉,刘洪滨等.中国气候变化科学概论[M].北京：气象出版社,2008.
[4]林而达,吴绍洪,罗勇等.第二次气候变化国家评估报告[M].北京：科学出版社,2011.
[5]Meehl G A,Stocker T F and Collins W, et al.:Global climate projections. Ln:Climate Change 2007:The physical science basis. Cambridge University Press, Cambridge,UK,2007.
[6]Dickinson R E, Errico R M, Giorgi F,et al.A regional climate model for the western United States[J].Climatic Change,1989,15(3):383-422.
[7]Giorgi F.Simulation of regional climate using a limited area model nested in general circulation model[J].Journal of Climate,1990,3:941-963.
[8]Denis B,Laprise R, Caya D,Cote J.Downscaling ability of one-way nested regional climate models:The big-brother experiemnts[J].Climate Dynamics,18:627-646.
[9]Wang Y, Leung L R,McGregor J L, et al.Regional climate modeling:Progress, challenges,and prospects[J].J.Meteorological Society of Japan Ser 11,2004,82:1599-1628.
[10]Jones R G, Noguer M, Hassell D C,et al.Generating high resolution climate change scenarios using PRECIS[M].Met Office Hadley Centre,Exeter,UK,2004:35.
[11]Met Office.The Hadley Center regional climate modeling system:PRECIS-Updata 2002.Providing Regional Climate for Impacts Studies.2002:P16.
[12]钱云,钱永甫.区域气候模式中云量参数化方案的研究[J].热带气象学报,1994,10(4)：342-348.
[13]Beniston M, Stepehnson D B,Christensen O B,et al.Future extreme events in European climate:An exploration of regional climate model projections[J].Climatic Change,2007, 81(S1):71-95.
[14]Nikulin G, Kjellstrom E, Hansson U,et al.Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of reginal climate simulations[J]. Tellus A,2011,63(1):41-55.
[15]丑纪范.短期气候数值预测的进展和发展前景[J].世界科技研究与发展,2001,23(2)：1-3.
[16]Wood A W, Lung L R, Sridhar V, et al.Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs[J].Climatic Change,2004,62(1-3):189-216.
[17]Baigorria G A, Jones J W, Shin D W,et al.Assessing uncertainties in crop model simulations using daily bias-corrected regional climate model projections[J].Climate Research,2007,34: 211-222.
[18]Ghosh S, Mujumdar P P.Climate change impact assessment:Uncertainty modeling with imprecise probability[J].Journal of Geophysical Research,2009,114,D18113,17PP.
[19]Teutshbein C,Seibert J. Regional climate models for hydrological impact studies at the catchment scale:A review of recent model strategies[J].Geography Compass,2010,4(7): 834-860.
[20]Ho C K, Stephenson D B, Collins M,et al. Calibration strategy:a source of additional uncertainty in climate change projections[J].Bull American Meteorological Society,2012,93: 21-26.
[21]Hawkins E, Osborne T M, Ho C K,et al. Calibration and bias correction of climate projections for crop modelling:An idealised case study over Europe[J].Agricultural and Forest Meteorology,2012, http://dx.doi.org/10.1016/j.agrformet.2012.04.007.
[22]Yang W, Johan A, Phil G L,et al. Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impact studies[J]. Hydrology Research,2010,41(3-4):211-229.
[23]Lettenmaier D P, Wood A W, Palmer R N,et al. Water resources implications of global warming:a US regional perspective[J].Climatic Change,1999,43:537-579.
[24]Hay L E, Wilby R L, Leavesley G H.A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States[J].Journal of the American Water Resources Association,2000,36(2):387-397.
[25]Middelkoop H, Daamen K, Gellens D,et al. Impact of climate change on hydrological regimes and water resources management in the Rhine Basin[J].Climatic Change,2001,49: 105-128.
[26]Ines A V M, Hansen J W. Bias correction of daily GCM rainfall for crop simulation studies[J]. Agriculture and Forest Meteorology,2006,138:44-53.
[27]Li H, Sheffield J, Wood E F.Bias correction of monthly precipitation and temperature fields from Intergovernmental panel on climate change AR4 models using equidistant quantile matching[J].Journal of Geophysical Research,2010,115,D10101.
[28]Piani C, Haerter J O. Coppola E.Statistical bias correction for daily precipitation in regional climate models over Europe[J]. Theoretical and Applied Climatology,2010,99(1-2):187-192.
[29]Piani C,Weedon G P, Best M, et al.Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models[J].Journal of Hydrology,2010,395(3-4):199-215.
[30]Dosio A, Paruolo P. Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models:Evaluation on the present climate[J].Journal of Geophysical Research,2011,116,D16106,22PP.
[31]Holger H, Thomas R. Meteorologically consistent bias correction of climate time series for agricultural models[J]. Theoretical and Applied Climatology,2012, DOI10.1007/s00704-012-0618-x.
[32]Yun W T, Stefanova L,Mitra A K,et al. Multi-model synthetic superensemble algorithm for seasonal climatre prediction using DEMETER forcasts[J].Tellus,2005,57A:280-289.
[33]Zeng Q C, Zhang B L, Yuan C G,et al. A note on some methods suitable for verifying and correcting the prediction of climate anomaly[J]. Advances in Atmospheric Sciences,1994, 11(2):121-127.
[34]Feddersen H, Navarra A, Ward M N.Reduction of model systematic error by statistical correction for dynamical seasonal predictions [J] Journal of Climate,1999,v12:1074-1080.
[35]Kharin V V, Zwiers F W. Skill as a funcion of time scale in ensembles of seasonal hindcasts[J]. Climate Dynamics,2001,17:127-141.
[36]么枕生,丁裕国.气候统计[M].北京：气象出版社,1990：52-58.
[37]李军,黄敬峰,王秀珍.山区月平均气温的短序列订正方法研究[J].浙江大学学报(农业与生 命科学版),2005,31(2)：165-170.
[38]石英,高学杰,吴佳等.华北地区未来气候变化的高分辨率数值模拟[J].应用气象学报,2010,21(5)：580-589.
[39]周邵毅,苏志,黄梅丽.小气候资料订正系统的设计与实现[J].广西气象,2006,27(增刊I)：73-74.
[40]郑祚芳,张秀丽,曹鸿兴等.气候模拟数据的订正与应用——以北京为例[J].气候变化研究进展,2007,3(5)：299-302.
[41]顾节经.沈阳市短期气候统计预测模式的补充订正预测[J].辽宁气象,1998,4：10-12.
[42]Chen H,Lin Z.H..A new correction method suitable for dynamical climate prediction[J]. Advances in Atmospheric Sciences,2006,23(3):425-430.
[43]秦正坤.短期气候数值预测的误差订正和超级集合方法研究[博士论文].南京信息工程大学,2007.
[44]Xu Y,Gao X J, Shen Y,et al.A daily temperature dataset over China and its application in validating a RCM simulation[J].Advances in Atmospheric Sciences,2009:763-772.
[45]Xie P P,Chen M Y,Song Y,et al.A gauge-based analysis of daily precipitation over east Asia[J] Journal of Hydrometeorology,2007,8:607-626.
[46]Bartholy J, Pongracz R, Pieczka I, et al.Computational analysis of expected climate change in the Carpathian Basin using a dynamical climate model[J]. Numerical Analysis and Its Applications,2009,5434:176-183.
[47]Duliere V, Zhang Y X, Salathe E P. Extreme precipitation and temperature over the U.S. Pacific Northwest:A comparison between observations, reanalysis data, and regional models[J]. Journal of Climate,2011,24:1950-1964.
[48]Alves L M, Marengo J. Assessment of regional seasonal predictability using the PRECIS regional climate modeling system over South America[J]. Theoretical and Applied Climatology,2010,100:337-350.
[49]Bhaskaran B, Ramachandran A, Jones R, et al. Regional climate model applications on sub-regional scales over the Indian monsoon region:The role of domain size on downscaling uncertainty [J]. Journal of Geophysical Research,2012,117.
[50]Islam M N, Rafiuddin M, Ahmed A U et al.Calibration of PRECIS in employing future scenarios in Bangladesh[J].International Journal of Climatology,2008,28:617-628.
[51]Islam U S, Rehman N, Sheikh M M. Future change in the frequency of warm and cold spells over Pakistan simulated by the PRECIS regional climate model[J]. Climatic Change,2009, 94:35-45.
[52]Black E, Brayshaw D J, Rambeau C M C.Past, present and future precipitation in the Middle East:insights from models and observations[J]. Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences,2010,368:5173-5184.
[53]Tadross M, Jack C, Hewitson B.On RCM-based projections of change in southern African summer climate[J].Geophysical Research Letters,32.
[54]Moufouma-Okia W, Rowell D P. Impact of soil moisture initialisation and lateral boundary conditions on regional climate model simulations of the West African Monsoon[J]. Climate Dynamics,2010,35:213-229.
[55]熊伟,许吟隆,林而达等.IPCC SRES A2和B2情景下我国玉米产量变化模拟[J].中国农业气象,2005,26(1)：11-15.
[56]居烽,熊伟,许吟隆等.气候变化对我国小麦产量的影响[J].作物学报,2005,31(10)：1340-1343.
[57]张建平,赵艳霞,王春乙等.未来气候变化情景下我国主要粮食作物产量变化模拟[J].干旱地区农业研究,2007,25(5)：208-214.
[58]朱建华,侯振宏,张小全.气候变化对中国林业的影响与应对策略[J].林业经济,2009,(11)：78-83.
[59]李剑泉,李智勇,易浩若.森林与全球气候变化的关系[J].西北林学院学报，2010,25(4)：23-28.
[60]田晓瑞,舒立福,赵风君等.未来情景下西南地区森林火险变化[J].林业科学,2012,48(1)：121-125.
[61]袁飞,谢正辉,任立良等.气候变化对海河流域水文特性的影响[J].水利学报,2005,36(3)：274-279.
[62]刘浏,徐宗学,黄俊雄.气候变化对太湖流域径流的影响[J].北京师范大学学报(自然科学版),2010,46(3)：371-377.
[63]陈豫英,许吟隆,陈楠等SRES A2和B2情景下宁夏可利用降水资源的变化[J].中国沙 漠,2011,31(1)：207-216.
[64]吴建国,吕佳佳.气候变化对青藏高原高寒草甸适宜气候分布范围的潜在影响[J].草地学报,2009,17(6)：699-705.
[65]吴建国.气候变化对7种保护植物分布的潜在影响[J].武汉植物学研究,2010,28(4)：437-452.
[66]赵东升,吴绍洪,尹云鹤.气候变化情景下中国自然植被净初级生产力分布[J].应用生态学报,2011,22(4)：897-904.
[67]杨坤,潘婕,杨国静等.不同气候变化情景下中国血吸虫病传播的范围与强度预估[J].气候变化研究进展,2010,6(4)：248-253.
[68]Cox P M.The impact of new land surface physics on the GCM simulation of climate and climate sensitivity[J]. Cliamate Dynamics,1999,15:183-203.
[69]许吟隆,Jones R利用ECMWF再分析数据验证PRECIS对中国区域气候的模拟能力[J].中国农业气象,2004,25(1)：5-9.
[70]张勇,许吟隆,董文杰等.中国未来极端降水事件的变化——基于气候变化预估结果的分析[J].自然灾害学报,2006,15(6)：228-234.
[71]许吟隆,黄晓莹,张勇等PRECIS对华南地区气候模拟能力的验证[J].中山大学学报(自然科学版).2007,46(5)：93-97.
[72]王芳栋,许吟隆,李涛.区域气候模式PRECIS对中国气候的长期数值模拟试验[J].中国农业气象,2010,31(3)：327-332.
[73]许吟隆,张颖娴,林万涛等.“三江源”地区未来气候变化的模拟分析[J].气候与环境研究,2007,12(5)：667-675.
[74]王芳栋PRECIS和RegCM3对中国区域气候的长期模拟比较[J].中国农业气象,2012,2：159-165.
[75]杨红龙.利用PRECIS进行中国极端气候事件变化的情景分析[博士论文].2010.
[76]Nakicenovic N,Alcamo J, Davis G,et al.Special report on emissions scenarios:a special report of Working Group III of the Intergovenmental Panel on Climate Change[M].Cambridge University Press,2000,P600.
[77]Yu E T, Wang H J, Sun J Q. A quick report on a dynamic downscaling simulation over China using the nested model[J]. Atmospheric and Oceanic Science Letters,2010,3(6):325-329.
[78]Zhang J Y, Wu L Y. Land-atmosphere coupling amplifies hot extremes over China[J]. Chinese Science Bulletin,2011,56(31):3328-3332.
[79]Yang H, Xu Y, Zhang L, Pan J, Li X. Projected change in heat waves over China using the PRECIS climate model[J]. Climate Research,2010,42:79-88.
[80]Wang A, Zeng X. Sensitivities of terrestrial water cycle simulations to the variations of precipitation and air temperature in China[J]. Journal of Geophysical Research,2011,116, D02107, doi:10.1029/2010JD014659.
[81]姜彤.适应行动：数据及风险分析[R].专题研讨一：适应气候变化的共性问题.《适应气候变化——东亚峰会成员国的战略、政策与行动》.科学出版社,2009.
[82]Durbin J. Distribution theory for tests based on the sample distribution function.Society for India and Applied Mathematics,Philadelphia,Pa,1973.
[83]Karl E. Taylor.Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research,2001,106, D7:7183-7192.
[84]Bonsal B R, Zhang X, Vincent L A,et al. Characteristics of daily and extreme temperature over Canada[J].American Metrological Society,2001,5(14):1959-1976.
[85]Frich P, Alexander L V, Della-Marta P, et al. Observed coherent changes in climatic extremes during the second half of the twentieth century[J].Climate Research,2002,19: 193-212.
[86]Karl T R,Knight R W.The 1995 Chicago Heat Wave:How Likely Is a Recourse?[J].Bulletin of the American Metrological Society,1997,78(6):1107-1119.
[87]丁裕国,申红颜,江志红等.气候概率分布理论及其应用新进展[J].气象科技,2009,37(3)：257-262.
[88]江志红,丁裕国,陈威霖.21世纪中国极端降水事件预估[J].气候变化研究进展,2007,3(4)：202-207.
[89]熊安元.冬季极端低温的双正态概率模型及柑桔越冬条件评价[J].气象,1991,17(11)：54-57.
[90]丁裕国,李佳耘,江志红等.极值统计理论的进展及其在气候变化研究中的应用[J].气候变化研究进展,2011,7(4)：248-252.
[91]Haerter J O, Hagemann S, Moseley C,et al.Climate model bias correction and the role of timescales[J].Hydrology and Earth System Sciences,2011,15:1065-1079.
[2]丁一汇,何建坤,林而达.中国气候变化：科学、影响、适应及对策研究[M].北京：中国环境科学出版社,2009.
[3]丁一汇,任国玉,刘洪滨等.中国气候变化科学概论[M].北京：气象出版社,2008.
[4]林而达,吴绍洪,罗勇等.第二次气候变化国家评估报告[M].北京：科学出版社,2011.
[5]Meehl G A,Stocker T F and Collins W, et al.:Global climate projections. Ln:Climate Change 2007:The physical science basis. Cambridge University Press, Cambridge,UK,2007.
[6]Dickinson R E, Errico R M, Giorgi F,et al.A regional climate model for the western United States[J].Climatic Change,1989,15(3):383-422.
[7]Giorgi F.Simulation of regional climate using a limited area model nested in general circulation model[J].Journal of Climate,1990,3:941-963.
[8]Denis B,Laprise R, Caya D,Cote J.Downscaling ability of one-way nested regional climate models:The big-brother experiemnts[J].Climate Dynamics,18:627-646.
[9]Wang Y, Leung L R,McGregor J L, et al.Regional climate modeling:Progress, challenges,and prospects[J].J.Meteorological Society of Japan Ser 11,2004,82:1599-1628.
[10]Jones R G, Noguer M, Hassell D C,et al.Generating high resolution climate change scenarios using PRECIS[M].Met Office Hadley Centre,Exeter,UK,2004:35.
[11]Met Office.The Hadley Center regional climate modeling system:PRECIS-Updata 2002.Providing Regional Climate for Impacts Studies.2002:P16.
[12]钱云,钱永甫.区域气候模式中云量参数化方案的研究[J].热带气象学报,1994,10(4)：342-348.
[13]Beniston M, Stepehnson D B,Christensen O B,et al.Future extreme events in European climate:An exploration of regional climate model projections[J].Climatic Change,2007, 81(S1):71-95.
[14]Nikulin G, Kjellstrom E, Hansson U,et al.Evaluation and future projections of temperature, precipitation and wind extremes over Europe in an ensemble of reginal climate simulations[J]. Tellus A,2011,63(1):41-55.
[15]丑纪范.短期气候数值预测的进展和发展前景[J].世界科技研究与发展,2001,23(2)：1-3.
[16]Wood A W, Lung L R, Sridhar V, et al.Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs[J].Climatic Change,2004,62(1-3):189-216.
[17]Baigorria G A, Jones J W, Shin D W,et al.Assessing uncertainties in crop model simulations using daily bias-corrected regional climate model projections[J].Climate Research,2007,34: 211-222.
[18]Ghosh S, Mujumdar P P.Climate change impact assessment:Uncertainty modeling with imprecise probability[J].Journal of Geophysical Research,2009,114,D18113,17PP.
[19]Teutshbein C,Seibert J. Regional climate models for hydrological impact studies at the catchment scale:A review of recent model strategies[J].Geography Compass,2010,4(7): 834-860.
[20]Ho C K, Stephenson D B, Collins M,et al. Calibration strategy:a source of additional uncertainty in climate change projections[J].Bull American Meteorological Society,2012,93: 21-26.
[21]Hawkins E, Osborne T M, Ho C K,et al. Calibration and bias correction of climate projections for crop modelling:An idealised case study over Europe[J].Agricultural and Forest Meteorology,2012, http://dx.doi.org/10.1016/j.agrformet.2012.04.007.
[22]Yang W, Johan A, Phil G L,et al. Distribution-based scaling to improve usability of regional climate model projections for hydrological climate change impact studies[J]. Hydrology Research,2010,41(3-4):211-229.
[23]Lettenmaier D P, Wood A W, Palmer R N,et al. Water resources implications of global warming:a US regional perspective[J].Climatic Change,1999,43:537-579.
[24]Hay L E, Wilby R L, Leavesley G H.A comparison of delta change and downscaled GCM scenarios for three mountainous basins in the United States[J].Journal of the American Water Resources Association,2000,36(2):387-397.
[25]Middelkoop H, Daamen K, Gellens D,et al. Impact of climate change on hydrological regimes and water resources management in the Rhine Basin[J].Climatic Change,2001,49: 105-128.
[26]Ines A V M, Hansen J W. Bias correction of daily GCM rainfall for crop simulation studies[J]. Agriculture and Forest Meteorology,2006,138:44-53.
[27]Li H, Sheffield J, Wood E F.Bias correction of monthly precipitation and temperature fields from Intergovernmental panel on climate change AR4 models using equidistant quantile matching[J].Journal of Geophysical Research,2010,115,D10101.
[28]Piani C, Haerter J O. Coppola E.Statistical bias correction for daily precipitation in regional climate models over Europe[J]. Theoretical and Applied Climatology,2010,99(1-2):187-192.
[29]Piani C,Weedon G P, Best M, et al.Statistical bias correction of global simulated daily precipitation and temperature for the application of hydrological models[J].Journal of Hydrology,2010,395(3-4):199-215.
[30]Dosio A, Paruolo P. Bias correction of the ENSEMBLES high-resolution climate change projections for use by impact models:Evaluation on the present climate[J].Journal of Geophysical Research,2011,116,D16106,22PP.
[31]Holger H, Thomas R. Meteorologically consistent bias correction of climate time series for agricultural models[J]. Theoretical and Applied Climatology,2012, DOI10.1007/s00704-012-0618-x.
[32]Yun W T, Stefanova L,Mitra A K,et al. Multi-model synthetic superensemble algorithm for seasonal climatre prediction using DEMETER forcasts[J].Tellus,2005,57A:280-289.
[33]Zeng Q C, Zhang B L, Yuan C G,et al. A note on some methods suitable for verifying and correcting the prediction of climate anomaly[J]. Advances in Atmospheric Sciences,1994, 11(2):121-127.
[34]Feddersen H, Navarra A, Ward M N.Reduction of model systematic error by statistical correction for dynamical seasonal predictions [J] Journal of Climate,1999,v12:1074-1080.
[35]Kharin V V, Zwiers F W. Skill as a funcion of time scale in ensembles of seasonal hindcasts[J]. Climate Dynamics,2001,17:127-141.
[36]么枕生,丁裕国.气候统计[M].北京：气象出版社,1990：52-58.
[37]李军,黄敬峰,王秀珍.山区月平均气温的短序列订正方法研究[J].浙江大学学报(农业与生 命科学版),2005,31(2)：165-170.
[38]石英,高学杰,吴佳等.华北地区未来气候变化的高分辨率数值模拟[J].应用气象学报,2010,21(5)：580-589.
[39]周邵毅,苏志,黄梅丽.小气候资料订正系统的设计与实现[J].广西气象,2006,27(增刊I)：73-74.
[40]郑祚芳,张秀丽,曹鸿兴等.气候模拟数据的订正与应用——以北京为例[J].气候变化研究进展,2007,3(5)：299-302.
[41]顾节经.沈阳市短期气候统计预测模式的补充订正预测[J].辽宁气象,1998,4：10-12.
[42]Chen H,Lin Z.H..A new correction method suitable for dynamical climate prediction[J]. Advances in Atmospheric Sciences,2006,23(3):425-430.
[43]秦正坤.短期气候数值预测的误差订正和超级集合方法研究[博士论文].南京信息工程大学,2007.
[44]Xu Y,Gao X J, Shen Y,et al.A daily temperature dataset over China and its application in validating a RCM simulation[J].Advances in Atmospheric Sciences,2009:763-772.
[45]Xie P P,Chen M Y,Song Y,et al.A gauge-based analysis of daily precipitation over east Asia[J] Journal of Hydrometeorology,2007,8:607-626.
[46]Bartholy J, Pongracz R, Pieczka I, et al.Computational analysis of expected climate change in the Carpathian Basin using a dynamical climate model[J]. Numerical Analysis and Its Applications,2009,5434:176-183.
[47]Duliere V, Zhang Y X, Salathe E P. Extreme precipitation and temperature over the U.S. Pacific Northwest:A comparison between observations, reanalysis data, and regional models[J]. Journal of Climate,2011,24:1950-1964.
[48]Alves L M, Marengo J. Assessment of regional seasonal predictability using the PRECIS regional climate modeling system over South America[J]. Theoretical and Applied Climatology,2010,100:337-350.
[49]Bhaskaran B, Ramachandran A, Jones R, et al. Regional climate model applications on sub-regional scales over the Indian monsoon region:The role of domain size on downscaling uncertainty [J]. Journal of Geophysical Research,2012,117.
[50]Islam M N, Rafiuddin M, Ahmed A U et al.Calibration of PRECIS in employing future scenarios in Bangladesh[J].International Journal of Climatology,2008,28:617-628.
[51]Islam U S, Rehman N, Sheikh M M. Future change in the frequency of warm and cold spells over Pakistan simulated by the PRECIS regional climate model[J]. Climatic Change,2009, 94:35-45.
[52]Black E, Brayshaw D J, Rambeau C M C.Past, present and future precipitation in the Middle East:insights from models and observations[J]. Philosophical Transactions of the Royal Society A-Mathematical Physical and Engineering Sciences,2010,368:5173-5184.
[53]Tadross M, Jack C, Hewitson B.On RCM-based projections of change in southern African summer climate[J].Geophysical Research Letters,32.
[54]Moufouma-Okia W, Rowell D P. Impact of soil moisture initialisation and lateral boundary conditions on regional climate model simulations of the West African Monsoon[J]. Climate Dynamics,2010,35:213-229.
[55]熊伟,许吟隆,林而达等.IPCC SRES A2和B2情景下我国玉米产量变化模拟[J].中国农业气象,2005,26(1)：11-15.
[56]居烽,熊伟,许吟隆等.气候变化对我国小麦产量的影响[J].作物学报,2005,31(10)：1340-1343.
[57]张建平,赵艳霞,王春乙等.未来气候变化情景下我国主要粮食作物产量变化模拟[J].干旱地区农业研究,2007,25(5)：208-214.
[58]朱建华,侯振宏,张小全.气候变化对中国林业的影响与应对策略[J].林业经济,2009,(11)：78-83.
[59]李剑泉,李智勇,易浩若.森林与全球气候变化的关系[J].西北林学院学报，2010,25(4)：23-28.
[60]田晓瑞,舒立福,赵风君等.未来情景下西南地区森林火险变化[J].林业科学,2012,48(1)：121-125.
[61]袁飞,谢正辉,任立良等.气候变化对海河流域水文特性的影响[J].水利学报,2005,36(3)：274-279.
[62]刘浏,徐宗学,黄俊雄.气候变化对太湖流域径流的影响[J].北京师范大学学报(自然科学版),2010,46(3)：371-377.
[63]陈豫英,许吟隆,陈楠等SRES A2和B2情景下宁夏可利用降水资源的变化[J].中国沙 漠,2011,31(1)：207-216.
[64]吴建国,吕佳佳.气候变化对青藏高原高寒草甸适宜气候分布范围的潜在影响[J].草地学报,2009,17(6)：699-705.
[65]吴建国.气候变化对7种保护植物分布的潜在影响[J].武汉植物学研究,2010,28(4)：437-452.
[66]赵东升,吴绍洪,尹云鹤.气候变化情景下中国自然植被净初级生产力分布[J].应用生态学报,2011,22(4)：897-904.
[67]杨坤,潘婕,杨国静等.不同气候变化情景下中国血吸虫病传播的范围与强度预估[J].气候变化研究进展,2010,6(4)：248-253.
[68]Cox P M.The impact of new land surface physics on the GCM simulation of climate and climate sensitivity[J]. Cliamate Dynamics,1999,15:183-203.
[69]许吟隆,Jones R利用ECMWF再分析数据验证PRECIS对中国区域气候的模拟能力[J].中国农业气象,2004,25(1)：5-9.
[70]张勇,许吟隆,董文杰等.中国未来极端降水事件的变化——基于气候变化预估结果的分析[J].自然灾害学报,2006,15(6)：228-234.
[71]许吟隆,黄晓莹,张勇等PRECIS对华南地区气候模拟能力的验证[J].中山大学学报(自然科学版).2007,46(5)：93-97.
[72]王芳栋,许吟隆,李涛.区域气候模式PRECIS对中国气候的长期数值模拟试验[J].中国农业气象,2010,31(3)：327-332.
[73]许吟隆,张颖娴,林万涛等.“三江源”地区未来气候变化的模拟分析[J].气候与环境研究,2007,12(5)：667-675.
[74]王芳栋PRECIS和RegCM3对中国区域气候的长期模拟比较[J].中国农业气象,2012,2：159-165.
[75]杨红龙.利用PRECIS进行中国极端气候事件变化的情景分析[博士论文].2010.
[76]Nakicenovic N,Alcamo J, Davis G,et al.Special report on emissions scenarios:a special report of Working Group III of the Intergovenmental Panel on Climate Change[M].Cambridge University Press,2000,P600.
[77]Yu E T, Wang H J, Sun J Q. A quick report on a dynamic downscaling simulation over China using the nested model[J]. Atmospheric and Oceanic Science Letters,2010,3(6):325-329.
[78]Zhang J Y, Wu L Y. Land-atmosphere coupling amplifies hot extremes over China[J]. Chinese Science Bulletin,2011,56(31):3328-3332.
[79]Yang H, Xu Y, Zhang L, Pan J, Li X. Projected change in heat waves over China using the PRECIS climate model[J]. Climate Research,2010,42:79-88.
[80]Wang A, Zeng X. Sensitivities of terrestrial water cycle simulations to the variations of precipitation and air temperature in China[J]. Journal of Geophysical Research,2011,116, D02107, doi:10.1029/2010JD014659.
[81]姜彤.适应行动：数据及风险分析[R].专题研讨一：适应气候变化的共性问题.《适应气候变化——东亚峰会成员国的战略、政策与行动》.科学出版社,2009.
[82]Durbin J. Distribution theory for tests based on the sample distribution function.Society for India and Applied Mathematics,Philadelphia,Pa,1973.
[83]Karl E. Taylor.Summarizing multiple aspects of model performance in a single diagram[J]. Journal of Geophysical Research,2001,106, D7:7183-7192.
[84]Bonsal B R, Zhang X, Vincent L A,et al. Characteristics of daily and extreme temperature over Canada[J].American Metrological Society,2001,5(14):1959-1976.
[85]Frich P, Alexander L V, Della-Marta P, et al. Observed coherent changes in climatic extremes during the second half of the twentieth century[J].Climate Research,2002,19: 193-212.
[86]Karl T R,Knight R W.The 1995 Chicago Heat Wave:How Likely Is a Recourse?[J].Bulletin of the American Metrological Society,1997,78(6):1107-1119.
[87]丁裕国,申红颜,江志红等.气候概率分布理论及其应用新进展[J].气象科技,2009,37(3)：257-262.
[88]江志红,丁裕国,陈威霖.21世纪中国极端降水事件预估[J].气候变化研究进展,2007,3(4)：202-207.
[89]熊安元.冬季极端低温的双正态概率模型及柑桔越冬条件评价[J].气象,1991,17(11)：54-57.
[90]丁裕国,李佳耘,江志红等.极值统计理论的进展及其在气候变化研究中的应用[J].气候变化研究进展,2011,7(4)：248-252.
[91]Haerter J O, Hagemann S, Moseley C,et al.Climate model bias correction and the role of timescales[J].Hydrology and Earth System Sciences,2011,15:1065-1079.