资料均一化对气温变化趋势及其城市化偏差估计的影响：以北京地区为例

doi:10.12006/j.issn.1673-1719.2020.246

摘要/Abstract

摘要：

当前的地面气候观测资料普遍存在非气候性因素导致的非均一性,对气候变化监测和研究结论可靠性造成重要影响。结合观测台站的历史沿革数据,使用ACMANT和Pairwise Comparisons方法以及RHtest V4软件,对北京地区20个台站均一化前的月平均气温序列进行了非均一性检验和订正,最后评估了均一化对北京地区气温序列变化趋势及其城市化偏差估算的影响。结果表明：除元数据中记录的断点外,无元数据记录的断点也会对序列的趋势变化造成明显影响,其中乡村站最显著;经过订正,1958—2018年整个北京地区、乡村站以及城市站增温趋势分别为0.27℃/(10 a)、0.10℃/(10 a)和0.32℃/(10 a),较订正前分别上升了0.03℃/(10 a)、0.06℃/(10 a)和0.02℃/(10 a)。利用均一化资料估算,1958—2018年北京观象台的城市化影响为0.24℃/(10 a),城市化贡献率为70.2%,评估结果较前人结论有所降低。可见,在现有的北京地区气温资料序列中,仍可能存在较明显的非均一性和未被记录的断点,对区域平均气温趋势估算具有显著影响。

关键词: 均一化, 气温, 气候变化, 趋势, 城市化影响, 北京

Abstract:

Inhomogeneity caused by non-climate factors is common in current observation data of surface climate, exerting a great impact on the reliability of climate change monitoring and studies. Based on metadata of observational stations, the non-homogenized (only quality-controlled) monthly mean surface air temperature data series of 20 stations in Beijing were first tested and adjusted for inhomogeneities by the methods of ACMANT and Pairwise Comparisons and RHtestV4 software. The effects of homogenization on the estimates of temperature trend and its urbanization bias were then evaluated. The results show that, in addition to the change points recorded in the metadata, undocumented change points will have a significant effect on the trends of temperature series, especially for the rural stations, which were mostly missed in the previous works of homogenization. The warming trends of the homogenized temperature series for the whole area (all stations), rural stations and urban stations from 1958 to 2018 are 0.27℃/(10 a), 0.10℃/(10 a) and 0.32℃/(10 a), respectively, with an increase of 0.03℃/(10 a), 0.06℃/(10 a) and 0.02℃/(10 a), respectively, compared to the non-homogenized data. Based on the homogenized data, it was estimated that the urbanization effects in the Beijing Observatory data series from 1958 to 2018 is 0.24℃/(10 a), and the urbanization contribution is 70.2%, which are lower than the previous studies of our group. Hence, there are still obvious inhomogeneities and undocumented change points in the existing temperature data in Beijing area, which will have a significant impact on the accurate estimates of regional average temperature change and the urbanization-induced systematic bias of the temperature data series.

Key words: Homogenization, Temperature, Climate change, Trend, Urbanization effects, Beijing

何佳骏, 任国玉, 张盼峰. 资料均一化对气温变化趋势及其城市化偏差估计的影响：以北京地区为例[J]. 气候变化研究进展, 2021, 17(5): 503-513.

HE Jia-Jun, REN Guo-Yu, ZHANG Pan-Feng. Effects of data homogenization on the estimates of temperature trend and urbanization bias: taking Beijing area as an example[J]. Climate Change Research, 2021, 17(5): 503-513.

图/表 9

图1 北京地区20个国家级气象站位置分布

Fig. 1 Distributions of 20 national meteorological stations in Beijing

表1 20个台站城市化级别及均一化检测中断点发生年份和相应订正值

Table 1 Urbanization level of 20 stations and year of change points and corresponding magnitude of adjustment

图2 断点订正值的大小分布

Fig. 2 Distribution of adjustment magnitude

图3 斋堂站(a)、霞云岭站(b)、北京观象台(c)、大兴站(d)、乡村站(e)和城市站(f)订正前后的序列及其趋势对比

Fig. 3 Comparison of series and their trends before and after adjustment for Zhaitang station (a), Xiayunling station (b), Beijing Observatory (c), Daxing station (d), rural stations (e) and urban stations (f)

图4 北京地区所有台站有元数据记录和无元数据记录的断点的时间分布

Fig. 4 Time distribution of change points documented and undocumented for all stations in Beijing

图5 北京地区20个台站的本文订正序列、Cao等[21]序列以及原始序列的对比

Fig. 5 Comparison between adjusted series from this paper (red), adjusted series from Cao et al[21] (blue), and raw series (green) of 20 stations in Beijing

表2 不同空间尺度下不同数据的1958—2018年平均气温变化趋势

Table 2 Annual mean surface air temperature trend of 1958-2018 under different spatial scales in multiple data

图6 北京观象台、所有站、乡村站和城市站的年平均气温距平

Fig. 6 Annual mean surface air temperature anomaly of Beijing Observatory, all stations, rural stations and urban stations

表3 不同时期北京观象台的增温趋势、城市化影响和城市化贡献及其与前人研究的对比

Table 3 Warming trend, urbanization effects and urbanization contribution of Beijing observatory in different periods and comparison with previous studies

参考文献 39

[1]	Alexandersson H. A homogeneity test applied to precipitation data[J]. Journal of Climatology, 1986, 6(6):661-675. DOI: 10.1002/joc.3370060607 doi: 10.1002/joc.3370060607 URL
[2]	Easterling D R, Peterson T C. A new method for detecting undocumented discontinuities in climatological time series[J]. International Journal of Climatology, 1995, 15(4):369-377. DOI: 10.1002/joc.3370150403 doi: 10.1002/joc.3370150403 URL
[3]	Vincent L A. A technique for the identification of inhomogeneities in Canadian temperature series[J]. Journal of Climate, 1998, 11(5):1094-1104. DOI: 10.1175/1520-0442(1998)011<1094:ATFTIO>2.0.CO;2 doi: 10.1175/1520-0442(1998)011<1094:ATFTIO>2.0.CO;2 URL
[4]	Peterson T C, Easterling D R, Karl T R, et al. Homogeneity adjustments of in situ atmospheric climate data: a review[J]. International Journal of Climatology, 1998, 18(13):1493-1517. DOI: 10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T;2-T doi: 10.1002/(SICI)1097-0088(19981115)18:13<1493::AID-JOC329>3.0.CO;2-T;2-T URL
[5]	Menne M J, Williams C N. Homogenization of temperature series via pairwise comparisons[J]. Journal of Climate, 2008, 22(7):1700-1717. DOI: 10.1175/2008JCLI2263.1 doi: 10.1175/2008JCLI2263.1 URL
[6]	Li Q X, Liu X N, Zhang H Z, et al. Detecting and adjusting temporal inhomogeneity in Chinese mean surface air temperature data[J]. Advances in Atmospheric Sciences, 2004, 21(2):260-268. DOI: 10.1007/BF02915712 doi: 10.1007/BF02915712 URL
[7]	Vincent L A, Wang X L, Milewska E J, et al. A second generation of homogenized Canadian monthly surface air temperature for climate trend analysis[J]. Journal of Geophysical Research: Atmospheres, 2012, 117(D18). DOI: 10.1029/2012JD017859 doi: 10.1029/2012JD017859
[8]	Wang X L, Feng Y. RHtestsV4 user manual[R/OL]. 2013 [2020-10-23]. http://etccdi.pacificclimate.org/software.shtml
[9]	Trewin B. A daily homogenized temperature data set for Australia[J]. International Journal of Climatology, 2013, 33(6):1510-1529. DOI: 10.1002/joc.3530 doi: 10.1002/joc.3530 URL
[10]	Kuglitsch F G, Auchmannet R, Bleisch R, et al. Break detection of annual Swiss temperature series[J]. Journal of Geophysical Research, 2012, 117(D13). DOI: 10.1029/2012JD017729 doi: 10.1029/2012JD017729
[11]	Squintu A A, Gerard S, Yuri B, et al. Homogenization of daily temperature series in the European climate assessment & dataset[J]. International Journal of Climatology, 2019, 39(3):1243-1261. DOI: 10.1002/joc.5874 doi: 10.1002/joc.5874 URL
[12]	Trewin B, Braganza K, Fawcett R, et al. An updated long-term homogenized daily temperature data set for Australia[J]. Geoscience Data Journal, 2020, 7(2):149-169. DOI: 10.1002/gdj3.95 doi: 10.1002/gdj3.95 URL
[13]	Yan Z W, Yang C, Jones P D. Influence of inhomogeneity on the estimation of mean and extreme temperature trends in Beijing and Shanghai[J]. Advances in Atmospheric Sciences, 2001, 18(3):309-322. DOI: 10.1007/BF02919312 doi: 10.1007/BF02919312 URL
[14]	Li Z, Yan Z W. Homogenized daily mean/maximum/minimum temperature series for China from 1960-2008[J]. Atmospheric and Oceanic Science Letters, 2009, 2(4):237-243. DOI: 10.1080/16742834.2009.11446802 doi: 10.1080/16742834.2009.11446802 URL
[15]	Li Q X, Chen J, Li W, et al. A mainland China homogenized historical temperature dataset of 1951-2004[J]. Bulletin of the American Meteorological Society, 2009, 90(8):1062. DOI: 10.1175/2009BAMS2736.1 doi: 10.1175/2009BAMS2736.1 URL
[16]	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: Atmospheres, 2013, 118(17):9708-9720. DOI: 10.1002/jgrd.50791 doi: 10.1002/jgrd.50791 URL
[17]	郭艳君, 丁一汇. 近50年我国探空温度序列均一化及变化趋势[J]. 应用气象学报, 2008, 19(6):646-654. DOI: 10.3969/j.issn.1001-7313.2008.06.002. doi: 10.3969/j.issn.1001-7313.2008.06.002
	Guo Y J, Ding Y H. Homogeneity and long-term trend analysis on radiosonde temperature time series in China during recent 50 years[J]. Journal of Applied Meteorological Science, 2008, 19(6):646-654. DOI: 10.3969/j.issn.1001-7313.2008.06.002 (in Chinese) doi: 10.3969/j.issn.1001-7313.2008.06.002
[18]	司鹏, 郝立生, 罗传军, 等. 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[J]. 气候变化研究进展, 2017, 13(1):41-51.
	Si P, Hao L S, Luo C J, et al. The interpolation and homogenization of long-term temperature time series at Baoding observation station in Hebei province[J]. Climate Change Research, 2017, 13(1):41-51. DOI: 10.12006/j.issn.1673-1719.2016.043 (in Chinese) doi: 10.12006/j.issn.1673-1719.2016.043 (in Chinese
[19]	王秋香, 周昊楠, 陈晓燕. 单站资料均一性对本地气候资料趋势结果的影响[J]. 沙漠与绿洲气象, 2010, 4(4):1-5.
	Wang Q X, Zhou H N, Chen X Y. The influence of homogeneity of single-station data on trends in local climate data[J]. Desert and Oasis Meteorology, 2010, 4(4):1-5 (in Chinese)
[20]	赵美艳, 余君, 蒋镇, 等. 重庆地区气温序列均一性检验及订正[J]. 气象与环境学报, 2018, 34(6):148-155.
	Zhao M Y, Yu J, Jiang Z, et al. Homogeneity test and correction of temperature series in Chongqing[J]. Journal of Meteorology and Environment, 2018, 34(6):148-155 (in Chinese)
[21]	Cao L J, Zhu Y N, Tang G L, et al. Climatic warming in China according to a homogenized data set from 2419 stations[J]. International Journal of Climatology, 2016, 36(12):4384-4392. DOI: 10.1002/joc.4639 doi: 10.1002/joc.4639 URL
[22]	任芝花, 余予, 邹凤玲, 等. 部分地面要素历史基础气象资料质量检测[J]. 应用气象学报, 2012, 23(6):739-747.
	Ren Z H, Yu Y, Zou F L, et al. Quality detection of surface historical basic meteorological data[J]. Journal of Applied Meteorological Science, 2012, 23(6):739-747 (in Chinese)
[23]	Tysa S K, Ren G Y, Qin Y, et al. Urbanization effect in regional temperature series based on a remote sensing classification scheme of stations[J]. Journal of Geophysical Research: Atmospheres, 2019, 124(20):10646-10661. DOI: 10.1029/2019JD030948 doi: 10.1029/2019JD030948 URL
[24]	Aguilar E, Auer I, Brunet M, et al. Guidelines on climate metadata and homogenization [R/OL]. 2003 [2020-10-23]. http://www.wmo.int/datastat/documents/WCDMP-53_1.pdf
[25]	Domonkos P. Adapted Caussinus-Mestre algorithm for networks of temperature series (ACMANT)[J]. International Journal of Geosciences, 2011, 2(3):293-309. DOI: 10.4236/ijg.2011.23032 doi: 10.4236/ijg.2011.23032 URL
[26]	Hawkins D M. On the choice of segments in piecewise approximation[J]. IMA Journal of Applied Mathematics, 1972, 2(2):250-256. DOI: 10.1093/imamat/9.2.250 doi: 10.1093/imamat/9.2.250
[27]	Caussinus H, Lyazrhi F. Choosing a linear model with a random number of change-points and outliers[J]. Annals of the Institute of Statistical Mathematics, 1997, 49(4):761-775. DOI: 10.1023/a:1003230713770 doi: 10.1023/a:1003230713770 URL
[28]	DeGaetano A T. Attributes of several methods for detecting discontinuities in mean temperature series[J]. Journal of Climate, 2006, 19(5):838-853. DOI: 10.1175/JCLI3662.1 doi: 10.1175/JCLI3662.1 URL
[29]	Wang X L, Wen Q H, Wu Y. Penalized maximal t test for detecting undocumented mean change in climate data series[J]. Journal of Applied Meteorology and Climatology, 2007, 46(6):916-931. DOI: 10.1175/jam2504.1 doi: 10.1175/jam2504.1 URL
[30]	Wang X L. Accounting for autocorrelation in detecting mean shifts in climate data series using the Penalized maximal t or F test[J]. Journal of Applied Meteorology and Climatology, 2008, 47(9):2423-2444. DOI: 10.1175/2008JAMC1741.1 doi: 10.1175/2008JAMC1741.1 URL
[31]	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. DOI: 10.1175/2007JTECHA982.1 doi: 10.1175/2007JTECHA982.1 URL
[32]	Wang X L, Chen H, Wu Y, 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. DOI: 10.1175/2010JAMC2376.1 doi: 10.1175/2010JAMC2376.1 URL
[33]	初子莹, 任国玉. 北京地区城市热岛强度变化对区域温度序列的影响[J]. 气象学报, 2005, 63(4):534-540.
	Chu Z Y, Ren G Y. Change in urban heat island magnitude and its effect on mean air temperature record in Beijing region[J]. Acta Meteorologica Sinica, 2005, 63(4):534-540 (in Chinese)
[34]	Ren G Y, Zhou Y Q, Chu Z Y, et al. Urbanization effects on observed surface air temperature trends in North China[J]. Journal of Climate, 2008, 21(6):1333-1348. DOI: 10.1175/2007JCLI1348.1 doi: 10.1175/2007JCLI1348.1 URL
[35]	Hansen J, Ruedy R, Sato M, et al. Global surface temperature change[J]. Reviews of Geophysics, 2010, 48(4). DOI: 10.1029/2010RG000345 doi: 10.1029/2010RG000345
[36]	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, 2012, 117(D5). DOI: 10.1029/2011JD017139 doi: 10.1029/2011JD017139
[37]	Sun X B, Ren G Y, Xu W H, et al. Global land-surface air temperature change based on the new CMA GLSAT data set[J]. Science Bulletin, 2017, 62(4):236-238. DOI: 10.1016/j.scib.2017.01.017 doi: 10.1016/j.scib.2017.01.017 URL
[38]	Yan Z W, Li Z, Li Q X, et al. Effects of site change and urbanisation in the Beijing temperature series 1977-2006[J]. International Journal of Climatology, 2010, 30(8):1226-1234. DOI: 10.1002/joc.1971 doi: 10.1002/joc.1971 URL
[39]	林学椿, 于淑秋. 北京地区气温的年代际变化和热岛效应[J]. 地球物理学报, 2005, 48(1):39-45. DOI: 10.3321/j.issn:0001-5733.2005.01.007. doi: 10.3321/j.issn:0001-5733.2005.01.007
	Lin X C, Yu S Q. Interdecadal changes of temperature in the Beijing region and its heat island effect[J]. Chinese Journal of Geophysics, 2005, 48(1):39-45. DOI: 10.3321/j.issn:0001-5733.2005.01.007 (in Chinese) doi: 10.3321/j.issn:0001-5733.2005.01.007