河北保定气象站长序列气温资料缺测记录插补和非均一性订正
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摘要
基于多源的气温月值资料,在数据整合和初步质量控制基础上,同时采用标准化序列法和多元线性回归法对河北保定气象站1913—2014年月平均气温、最高气温和最低气温资料进行了插补。通过交叉检验法分析发现,标准化序列法插补得到的气温序列效果较好,并且气候统计特征与同区域周边站的研究结果更具一致性。利用惩罚最大F检验(PMF)对插补后序列的均一性进行了检验,结果表明:通过插补得到的保定站百年气温月值序列的均一性相对较好,仅月平均最低气温序列存在2个显著间断点,分别由同类型仪器的更换和台站迁移导致,研究中采用分位数匹配(QM)对其进行了订正,建立了保定站百年气温月值序列。通过与邻近单站及我国中东部区域均一化百年气温序列的综合对比显示,本文建立的保定站百年气温月值序列与邻近单站的相关性基本达到0.8以上;从增暖趋势来看,保定站与中东部区域平均序列分别达0.121℃/10a、0.204℃/10a,基本在同一量级内:这一定程度上说明建立的保定站百年气温序列相对合理。
Using multi-source of monthly temperature data on the basis of preliminary integration and quality control, monthly mean temperature, maximum temperature, and minimum temperature time series covering 1913-2014 at Baoding station in Hebei province were interpolated by both two approaches of standardized method and multivariate linear regression. The interpolation results were analyzed by cross validation. The standardized method is more suitable for temperature data at Baoding, and the climate statistics characteristics from that are more consistent with those from surrounding stations in the same area. So the interpolated time series were homogenized by the penalized maximal F test(PMF), and results indicate that those monthly hundredyear temperature data are relatively continuous, only two breakpoints have been detected in mean minimum temperature series, which are caused by instrument replacement and station relocation, respectively, and then adjusted by Quantile-Matching(QM). Accordingly, the monthly hundred-year temperature time series at Baoding station were constructed. Moreover, the time series here were compared with the others at Beijing and Tianjin, and regional series in eastern and central China from some aspects of trend change and correlation analysis. The results show that the correlation coefficients of monthly mean temperature between Baoding and adjacent single station almost reached more than 0.8, and the warming amplitude of trends for annual mean hundred-year temperature at Baoding and regional series in eastern and central China were nearly in the same order of magnitude, with 0.121 ℃ /10 a and 0.204 ℃ /10 a, respectively. Therefore, the data we have developed in this paper are relatively logical and errorless, to a certain extent.
Using multi-source of monthly temperature data on the basis of preliminary integration and quality control, monthly mean temperature, maximum temperature, and minimum temperature time series covering 1913-2014 at Baoding station in Hebei province were interpolated by both two approaches of standardized method and multivariate linear regression. The interpolation results were analyzed by cross validation. The standardized method is more suitable for temperature data at Baoding, and the climate statistics characteristics from that are more consistent with those from surrounding stations in the same area. So the interpolated time series were homogenized by the penalized maximal F test(PMF), and results indicate that those monthly hundredyear temperature data are relatively continuous, only two breakpoints have been detected in mean minimum temperature series, which are caused by instrument replacement and station relocation, respectively, and then adjusted by Quantile-Matching(QM). Accordingly, the monthly hundred-year temperature time series at Baoding station were constructed. Moreover, the time series here were compared with the others at Beijing and Tianjin, and regional series in eastern and central China from some aspects of trend change and correlation analysis. The results show that the correlation coefficients of monthly mean temperature between Baoding and adjacent single station almost reached more than 0.8, and the warming amplitude of trends for annual mean hundred-year temperature at Baoding and regional series in eastern and central China were nearly in the same order of magnitude, with 0.121 ℃ /10 a and 0.204 ℃ /10 a, respectively. Therefore, the data we have developed in this paper are relatively logical and errorless, to a certain extent.
引文
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[26]Hegerl G C,Thomas J C,Myles A,et al.Detection of human influence on a new validated 1500-year temperature reconstruction[J].Journal of Climate,2007,20(4):650-667
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[28]杨青,刘新春,霍文,等.塔克拉玛干沙漠腹地1961-1998年逐月平均气温序列的重建[J].气候变化研究进展,2009,5(2):85-89
[29]余予,李俊,任芝花,等.标准序列法在日平均气温缺测数据插补中的应用[J].气象,2012,38(9):1135-1139
[30]王海军,涂诗玉,陈正洪.日气温数据缺测的插补方法试验与误差方法[J].气象,2008,34(7):83-91
[31]陈鹏翔,江远安,刘精.新疆区域逐月缺测气温序列的插补及重建[J].冰川冻土,2014,36(5):1237-1244
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[33]Si P,Zheng Z F,Ren Y,et al.Effects of urbanization on daily temperature extremes in North China[J].Journal of Geographical Sciences,2014,24(2):349-362
[34]Allen R J,Degaetano A T.Estimating missing daily temperature extremes using an optimized regression approach[J].International Journal of Climatology,2001,21(11):1305-1319
[35]司鹏,徐文慧.利用RHtests V4软件包对天津1951-2012年逐日气温序列的均一性分析[J].气候与环境研究,2015,20(6):663-674
[36]Wang X L.Penalized maximal F test for detecting undocumented mean shift without trend change[J].Journal of Atmospheric and Oceanic Technology,2008,25(3):368-384
[37]Xu W H,Li Q X,Wang X L,et al.Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices[J].Journal of Geophysical Research,2013,118(17):9708-9720
[38]司鹏,解以扬.天津太阳总辐射资料的均一性分析[J].气候与环境研究,2015,20(3):269-276
[39]Wang X L,Chen H F,Wu Y H,et al.New techniques for the detection and adjustment of shifts in daily precipitation data series[J].Journal of Applied Meteorology and Climatology,2010,49(12):2416-2436
[40]李庆祥,Matthe W,Menne J,等.利用多模式对中国气温序列中不连续点的检测[J].气候与环境研究,2005,10(4):736-742
[41]张媛,任国玉.无参考序列条件下地面气温观测资料城市化偏差订正方法:以北京站为例[J].地球物理学报,2014,57(7):2197-2207
[42]郭军,任国玉,任雨.近100年天津平均气温与极端气温变化[J].高原气象,2011,30(5):1399-1405
[43]谢庄,曹鸿兴,李慧,等.近百余年北京气候变化的小波特征[J].气象学报,2000,58(3):362-369
[44]司鹏,李庆祥,李伟.城市化进程对中国东北部气温增暖的贡献检测[J].气象,2010,36(2):13-21
[2]施能,陈家其,屠其璞.中国近100年来4个年代际的气候变化特征[J].气象学报,1995,53(4):431-439
[3]李庆祥,黄嘉佑,鞠晓慧.上海地区最高气温资料的恢复试验[J].热带气象学报,2008,24(4):349-353
[4]唐国利,丁一汇,王绍武,等.中国近百年温度曲线的对比分析[J].气候变化研究进展,2009,5(2):71-78
[5]Jones P D.Hemispheric surface air temperature variations:a reanalysis and an update to 1993[J].Journal of Climate,1994,7(11):1794-1802
[6]Jones P D,Lister D H,Osborn T J,et al.Hemispheric and large-scale land-surface air temperature variations:an extensive revision and an update to 2010[J].Journal of Geophysical Research:Atmospheres(1984-2012),2012,117(D5).DOI:10.1029/2011JD017139
[7]Peterson T C,Vose R S.An overview of the global historical climatology network temperature database[J].Bulletin of the American Meteorological Society,1997,78(12):2837-2849
[8]Hansen J,Ruedy R,Sato M,et al.Global surface temperature c h a n g e[J].R e v i e w s o f G e o p h y s i c s,2 0 1 0,4 8(4):1-2 9.D O I:10.1029/2010RG000345 RG4004
[9]王绍武.公元1380年以来我国华北气温序列的重建[J].中国科学:B辑,1990,5:553-560
[10]王绍武,叶瑾琳,龚道溢,等.近百年中国年气温序列的建立[J].应用气象学报,1998,9(4):392-401
[11]郑景云,葛全胜,郝志新,等.1736-1999年西安与汉中地区年冬季平均气温序列重建[J].地理研究,2003,22(3):343-348
[12]汪青春,秦宁生,李栋梁,等.利用多条树轮资料重建青海高原近250年年平均气温序列[J].高原气象,2005,24(3):320-325
[13]靳立亚,秦宁生,勾晓华,等.青海南部高原近450年来春季最高气温序列及其时变特征[J].第四纪研究,2005,25(2):193-201
[14]张同文,袁玉江,喻树龙,等.用树木年轮重建阿勒泰西部5-9月365年来的月平均气温序列[J].干旱区研究,2008,25(2):288-294
[15]陈峰,袁玉江,魏文寿,等.呼图壁河流域过去313年春季平均最高气温序列及其特征分析[J].中国沙漠,2009,29(1):162-167
[16]丁玲玲,葛全胜,郑景云,等.1736-2009年华南地区冬季年平均气温序列重建[J].第四纪研究,2013,33(6):1191-1198
[17]郑景云,刘洋,葛全胜,等.华中地区历史物候记录与1850-2008年的气温变化重建[J].地理学报,2015,70(5):696-704
[18]任永建,陈正洪,肖莺,等.武汉区域百年地表气温变化趋势研究[J].地理科学,2010,30(2):278-282
[19]李正泉,张青,马浩,等.浙江省年平均气温百年序列的构建[J].气象与环境科学,2014,37(4):17-24
[20]彭嘉栋,廖玉芳,刘珺婷,等.洞庭湖区近百年气温序列构建及其变化特征[J].气象与环境学报,2014,30(5):62-68
[21]唐国利,丁一汇,王绍武,等.中国近百年温度曲线的对比分析[J].气候变化研究进展,2009,5(2):71-78
[22]李庆祥,董文杰,李伟,等.近百年中国气温变化中的不确定性估计[J].科学通报,2010,55(16):1544-1554
[23]孟祥林.京津冀一体化背景下“北京-保定”协同发展对策分析[J].北京化工大学学报:社会科学版,2015,3:25-30
[24]Cao L J,Zhao P,Yan Z W,et al.Instrumental temperature series in eastern and central China back to the nineteenth century[J].Journal of Geophysical Research Atmospheres,2013,118(15):8197-8207
[25]黄嘉佑,刘小宁,李庆祥.夏季降水量与气温资料的恢复试验[J].应用气象学报,2004,15(2):200-206
[26]Hegerl G C,Thomas J C,Myles A,et al.Detection of human influence on a new validated 1500-year temperature reconstruction[J].Journal of Climate,2007,20(4):650-667
[27]李庆祥,黄嘉佑,鞠晓慧.上海地区最高气温资料的恢复试验[J].热带气象学报,2008,24(4):349-353
[28]杨青,刘新春,霍文,等.塔克拉玛干沙漠腹地1961-1998年逐月平均气温序列的重建[J].气候变化研究进展,2009,5(2):85-89
[29]余予,李俊,任芝花,等.标准序列法在日平均气温缺测数据插补中的应用[J].气象,2012,38(9):1135-1139
[30]王海军,涂诗玉,陈正洪.日气温数据缺测的插补方法试验与误差方法[J].气象,2008,34(7):83-91
[31]陈鹏翔,江远安,刘精.新疆区域逐月缺测气温序列的插补及重建[J].冰川冻土,2014,36(5):1237-1244
[32]黄嘉佑.气象统计分析与预报方法(第二版)[M].北京:气象出版社,2000:38-51
[33]Si P,Zheng Z F,Ren Y,et al.Effects of urbanization on daily temperature extremes in North China[J].Journal of Geographical Sciences,2014,24(2):349-362
[34]Allen R J,Degaetano A T.Estimating missing daily temperature extremes using an optimized regression approach[J].International Journal of Climatology,2001,21(11):1305-1319
[35]司鹏,徐文慧.利用RHtests V4软件包对天津1951-2012年逐日气温序列的均一性分析[J].气候与环境研究,2015,20(6):663-674
[36]Wang X L.Penalized maximal F test for detecting undocumented mean shift without trend change[J].Journal of Atmospheric and Oceanic Technology,2008,25(3):368-384
[37]Xu W H,Li Q X,Wang X L,et al.Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices[J].Journal of Geophysical Research,2013,118(17):9708-9720
[38]司鹏,解以扬.天津太阳总辐射资料的均一性分析[J].气候与环境研究,2015,20(3):269-276
[39]Wang X L,Chen H F,Wu Y H,et al.New techniques for the detection and adjustment of shifts in daily precipitation data series[J].Journal of Applied Meteorology and Climatology,2010,49(12):2416-2436
[40]李庆祥,Matthe W,Menne J,等.利用多模式对中国气温序列中不连续点的检测[J].气候与环境研究,2005,10(4):736-742
[41]张媛,任国玉.无参考序列条件下地面气温观测资料城市化偏差订正方法:以北京站为例[J].地球物理学报,2014,57(7):2197-2207
[42]郭军,任国玉,任雨.近100年天津平均气温与极端气温变化[J].高原气象,2011,30(5):1399-1405
[43]谢庄,曹鸿兴,李慧,等.近百余年北京气候变化的小波特征[J].气象学报,2000,58(3):362-369
[44]司鹏,李庆祥,李伟.城市化进程对中国东北部气温增暖的贡献检测[J].气象,2010,36(2):13-21