Original Paper

Theoretical and Applied Climatology

, Volume 115, Issue 3, pp 365-373

First online:

Open Access This content is freely available online to anyone, anywhere at any time.

Effect of data homogenization on estimate of temperature trend: a case of Huairou station in Beijing Municipality

  • Lei ZhangAffiliated withCollege of Atmospheric Science, Nanjing University of Information Science & Technology
  • , Guo-Yu RenAffiliated withLaboratory for Climate Studies, National Climate Center, CMA Email author 
  • , Yu-Yu RenAffiliated withLaboratory for Climate Studies, National Climate Center, CMA
  • , Ai-Ying ZhangAffiliated withBeijing Meteorological Bureau, CMA
  • , Zi-Ying ChuAffiliated withBeijing Meteorological Bureau, CMA
  • , Ya-Qing ZhouAffiliated withJinzhong Meteorological Bureau of Shanxi Province, CMA


Daily minimum temperature (Tmin) and maximum temperature (Tmax) data of Huairou station in Beijing from 1960 to 2008 are examined and adjusted for inhomogeneities by applying the data of two nearby reference stations. Urban effects on the linear trends of the original and adjusted temperature series are estimated and compared. Results show that relocations of station cause obvious discontinuities in the data series, and one of the discontinuities for Tmin are highly significant when the station was moved from downtown to suburb in 1996. The daily Tmin and Tmax data are adjusted for the inhomogeneities. The mean annual Tmin and Tmax at Huairou station drop by 1.377°C and 0.271°C respectively after homogenization. The adjustments for Tmin are larger than those for Tmax, especially in winter, and the seasonal differences of the adjustments are generally more obvious for Tmin than for Tmax. Urban effects on annual mean Tmin and Tmax trends are −0.004°C/10 year and −0.035°C/10 year respectively for the original data, but they increase to 0.388°C/10 year and 0.096°C/10 year respectively for the adjusted data. The increase is more significant for the annual mean Tmin series. Urban contributions to the overall trends of annual mean Tmin and Tmax reach 100% and 28.8% respectively for the adjusted data. Our analysis shows that data homogenization for the stations moved from downtowns to suburbs can lead to a significant overestimate of rising trends of surface air temperature, and this necessitates a careful evaluation and adjustment for urban biases before the data are applied in analyses of local and regional climate change.