Science China Earth Sciences

, Volume 57, Issue 12, pp 2891–2900 | Cite as

Homogenization of climate series: The basis for assessing climate changes

Review

Abstract

Long-term meteorological observation series are fundamental for reflecting climate changes. However, almost all meteorological stations inevitably undergo relocation or changes in observation instruments, rules, and methods, which can result in systematic biases in the observation series for corresponding periods. Homogenization is a technique for adjusting these biases in order to assess the true trends in the time series. In recent years, homogenization has shifted its focus from the adjustments to climate mean status to the adjustments to information about climate extremes or extreme weather. Using case analyses of ideal and actual climate series, here we demonstrate the basic idea of homogenization, introduce new understanding obtained from recent studies of homogenization of climate series in China, and raise issues for further studies in this field, especially with regards to climate extremes, uncertainty of the statistical adjustments, and biased physical relationships among different climate variables due to adjustments in single variable series.

Keywords

climate series inhomogeneity homogenization trends in climate series climate extremes 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alexandersson H. 1986. A homogeneity test applied to precipitation data. J Clim, 6: 661–675CrossRefGoogle Scholar
  2. Alexander L V, Zhang X B, Peterson T C, et al. 2006. Global observed changes in daily climate extremes of temperature and precipitation. J Geophys Res, 111: D05109, doi: 10.1029/2005JD006290Google Scholar
  3. Camuffo D, Jones P D. 2002. Improved understanding of past climatic variability from early daily european instrumental sources. Clim Change, 53: 1–4CrossRefGoogle Scholar
  4. Cao L J, Yan Z W. 2011. Progresses in research of homogenization of climate data (in Chinese). Adv Clim Change Res, 7: 129–135Google Scholar
  5. Cao L J, Zhao P, Yan Z W, et al. 2013. Instrumental temperature series in eastern and central China back to the nineteenth century. J Geophys Res-Atmos, 118: 8197–8209CrossRefGoogle Scholar
  6. Della-Marta P M, Wanner H. 2006. A method of homogenizing the extremes and mean of daily temperature measurements. J Clim, 19: 4179–4197CrossRefGoogle Scholar
  7. Ding T, Qian W H, Yan Z W. 2009. Characteristics and changes of cold surge events over China during 1960–2007. Atmos Ocean Sci Lett, 2: 339–344Google Scholar
  8. Easterling D R, Peterson T C. 1995. A new method for detecting undocumented discontinuities in climatological time series. Int J Climatol, 15: 369–377CrossRefGoogle Scholar
  9. Guo Y J, Li Q X, Ding Y H. 2009. The effect of artificial bias on free air temperature trend derived from historical radiosonde date in China (in Chinese). Chin J Atmos Sci, 33: 1309–1318Google Scholar
  10. Hansen J, Ruedy R, Sato M, et al. 2001. A closer look at United States and global surface temperature change. J Geophys Res-Atmos, 106: 23947–23963CrossRefGoogle Scholar
  11. Jiang Y, Luo Y, Zhao Z C, et al. 2010. Changes in wind speed over China during 1956–2004. Theor Appl Clim, 99: 421–430CrossRefGoogle Scholar
  12. Jiang Z H, Huang Q, Li Q X. 2008. Study of precipitation series homogeneous adjustment and their correction over China in the last 50 years (in Chinese). Clim Environ Res, 13: 67–74Google Scholar
  13. Jones P D, Horton E B, Folland C K, et al. 1999. The use of indices to identify changes in climatic extremes. Clim Change, 42: 131–149CrossRefGoogle Scholar
  14. Li Q X, Liu X N, Zhang H Z, et al. 2004. Detecting and adjusting temporal inhomogeneity in Chinese mean surface air temperature data. Adv Atmos Sci, 21: 260–268CrossRefGoogle Scholar
  15. Li Q X, Zhang H Z, Chen J, et al. 2009. A mainland China homogenized historical temperature dataset of 1951–2004. Bull Am Meteorol Soc, 90: 1062–1065CrossRefGoogle Scholar
  16. Li Q X, Menne M J, Williams Jr C N, et al. 2010. Detection of discontinuities in Chinese temperature series using a multiple test approach (in Chinese). Clim Environ Res, 10: 736–742Google Scholar
  17. Li Z, Yan Z W. 2009. Homogenized China Daily Mean/Maximum/Minimum Temperature Series 1960–2008. Atmos Ocean Sci Lett, 2: 237–243Google Scholar
  18. Li Z, Yan Z W, Tu K, et al. 2011. Changes in wind speed and extremes in Beijing during 1960-2008 based on homogenized observations. Adv Atmos Sci, 28: 408–420CrossRefGoogle Scholar
  19. Li Z, Yan Z W, Cao L J, et al. 2014. Adjusting inhomogeneous daily temperature variability using wavelet analysis. Int J Climatol, 34: 1196–1207CrossRefGoogle Scholar
  20. Liu X F, Jiang Y, Ren G Y, et al. 2009. Effect of urbanization and observation environment change on wind speed trend in Hebei Province, China (in Chinese). Plateau Meteorol, 28: 433–439Google Scholar
  21. Lund R, Reeves J. 2002. Detection of undocumented change points: A revision of the two-phase regression model. J Clim, 15: 2547–2554CrossRefGoogle Scholar
  22. Manton M J, Della-Marta P M, Haylock M R, et al. 2001. Trends in extreme daily rainfall and temperature in Southeast Asia and the South Pacific: 1961–1998. Int J Clim, 21: 269–284CrossRefGoogle Scholar
  23. McVicar T R, Roderick M L, Donohue R J, et al. 2012. Global review and synthesis of trends in observed terrestrial near-surface wind speeds: Implications for evaporation. J Hydrol, 416–417: 182–205CrossRefGoogle Scholar
  24. Mestre O, Gruber C, Prieur C, et al. 2011. SPLIDHOM: A method for homogenization of daily temperature observations. J Appl Meteorol Clim, 50: 2343–2358CrossRefGoogle Scholar
  25. Parker D E, Legg T P, Folland C K. 1992. A new daily Central England temperature series 1772–1991. Int J Clim, 12: 317–342CrossRefGoogle Scholar
  26. Peterson T C. 2003. Assessment of urban versus rural in situ surface temperatures in the contiguous United States: No difference found. J Clim, 16: 2941–2959CrossRefGoogle Scholar
  27. Qian C, Yan Z W, Wu Z H, et al. 2011. Trends in temperature extremes in association with weather-intraseasonal fluctuations in eastern China. Adv Atmos Sci, 28: 284–296CrossRefGoogle Scholar
  28. Ren G Y, Feng G L, Yan Z W. 2010. Progresses in observation studies of climate extremes and changes in mainland China (in Chinese). Clim Environ Res, 15: 337–353Google Scholar
  29. Song C H, Sun A J. 1995. The research on adjusting inhomogeneity temperature series (in Chinese). Plateau Meteorol, 14: 215–220Google Scholar
  30. Szentimrey T. 1999. Multiple Analysis of Series for Homogenization (MASH). Proceedings of the Second Seminar for Homogenization of Surface Climatological Data. Budapest, Hungary, WMO, WCDMP-No. 41: 27–46Google Scholar
  31. Tang G L, Wang S W, Wen X Y, et al. 2011. Comparison of global mean temperature series (in Chinese). Adv Clim Change Res, 7: 85–89Google Scholar
  32. Toreti A, Kuglistch F G, Xoplaki E, et al. 2010. A novel method for homogenization of daily temperature series and its relevance for climate change analysis. J Clim, 23: 5325–5331CrossRefGoogle Scholar
  33. Wang J, Yan Z W, Li Z, et al. 2013. Impact of urbanization on changes in temperature extremes in Beijing during 1978–2008. Chin Sci Bull, 58: 4679–4686CrossRefGoogle Scholar
  34. Wang X L. 2009. A quantile matching adjustment algorithm for Gaussian data series. http://etccdi.pacificclimate.org/RHtest/QMadj_Gaussian.pdf Google Scholar
  35. Wang X L, Chen H, Wu Y, et al. 2010. New techniques for detection and adjustment of shifts in daily precipitation data series. J Appl Meteorol Clim, 49: 2416–2436CrossRefGoogle Scholar
  36. Wu Z H, Huang N E. 2009. Ensemble empirical mode decomposition: A noise-assisted data analysis method. Adv Adaptive Data Anal, 1: 1–41CrossRefGoogle Scholar
  37. Xia J J, Yan Z W, Wu P L. 2013. Multidecadal variability in local growing season during 1901–2009. Clim Dyn, 41: 295–305CrossRefGoogle Scholar
  38. Xu W H, Li Q X, Wang X L, et al. 2013. Homogenization of Chinese daily surface air temperatures and analysis of trends in the extreme temperature indices. J Geophys Res: Atmos, 118: 1–13, doi: 10.1002/jgrd.50791Google Scholar
  39. Yan Z W, Yang C, Jones P D. 2001. Influence of inhomogeneity on the estimation of mean and extreme temperature trends in Beijing and Shanghai. Adv Atmos Sci, 18: 309–322CrossRefGoogle Scholar
  40. Yan Z W, Jones P D, Davies T D, et al. 2002. Trends of extreme temperatures in Europe and China based on daily observations. Clim Change, 53: 355–392CrossRefGoogle Scholar
  41. Yan Z W, Jones P D. 2008. Detecting inhomogeneity in daily climate series using wavelet analysis. Adv Atmos Sci, 25: 157–163CrossRefGoogle Scholar
  42. Yan Z W, Li Z, Li Q X, et al. 2010. Effects of site change and urbanization in the Beijing temperature series 1977–2006. Int J Clim, 30: 1226–1234CrossRefGoogle Scholar
  43. Yan Z W. 2010. Climate extremes. In: Chen Y T, et al., eds. 10000 Difficult Problems of Science (Earth Sciences) (in Chinese). Beijing: Science Press. 828–833Google Scholar
  44. Zhai P M. 1997. Some gross errors and biases in China’s historical radiosonde data (in Chinese). Acta Meteorol Sin, 55: 563–572Google Scholar
  45. Zhang L, Ren G Y, Ren Y Y, et al. 2014. Effect of data homogenization on estimate of temperature trend: A case of Huairou station in Beijing Municipality. Theor Appl Clim, 115: 365–373, doi: 10.1007/s00704-013-0894-0CrossRefGoogle Scholar
  46. Zhao P, Jones P D, Cao L J, et al. 2014. Trend of surface air temperature in eastern China and associated large-scale climate variability over the last 100 years. J Clim, doi: 10.1175/JCLI-D-13-00397.1Google Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Key Laboratory of Regional Climate-Environment for East Asia (RCE-TEA), Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina

Personalised recommendations