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

Abstract

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.

An Integrated Procedure to Determine a Reference Station Network for Evaluating and Adjusting Urban Bias in Surface Air Temperature Data

“…The urbanization effects on the annual mean Tmin and Tmax trends were statistically insignificant [20.018C (10 yr) 21 and 20.048C (10 yr) 21 , respectively] for the original data series, but they reached 0.398C (10 yr) 21 and 0.108C (10 yr) 21 , respectively, for the inhomogeneity-adjusted data series (Zhang et al 2013). When the adjustments are made more thorough, however, the increase in urban warming effect will be substantial.…”

“…Various approaches were utilized for the detection and adjustment of the data inhomogeneities (e.g., Peterson et al 1998;Yan et al 2001;Li et al 2004b;Ren et al 2005;Moberg and Jones 2005). It was also found in studies of SAT change in mainland China using homogenized SAT data Zhou and Ren 2005;Zhang et al 2013;Li et al 2014). The adjustments for inhomogeneities, however, were generally to remedy the ''breakpoints'' in the SAT data series arising from relocations, and to rectify the pre-breakpoint data series against those from the present locations.…”

“…Beijing station and Huairou station in Beijing experienced relocations, for example, with the former being moved 10 times in the twentieth century and 6 times since 1953 , and the latter station moving twice since its establishment in 1959 (Zhang et al 2013) (Fig. Beijing station and Huairou station in Beijing experienced relocations, for example, with the former being moved 10 times in the twentieth century and 6 times since 1953 , and the latter station moving twice since its establishment in 1959 (Zhang et al 2013) (Fig.…”

“…Beijing station and Huairou station in Beijing experienced relocations, for example, with the former being moved 10 times in the twentieth century and 6 times since 1953 , and the latter station moving twice since its establishment in 1959 (Zhang et al 2013) (Fig. The station has been surrounded by buildings and expressways, however, and has become a typical urban station (Ren and Ren 2011;Zhang et al 2013;P. The last relocation of Beijing station occurred in 1997 when it was moved from a site near the western Third Ring Road to its present location, which is 20 km away.…”

Inter-annual variations and trends of the urban warming in Tehran

“…Indeed, previous studies have shown a clear division in temperature trends between large cities and neighboring rural areas (e.g. Stone, 2007;McCarthy et al, 2010;Ren and Zhou, 2014;Zhang et al, 2014). This underlines the need to quantify the degree to which background temperature trends are amplified by urbanization.…”