The original daily Tmin and Tmax data of Huairou, Xiayunling, and Shangdianzi stations from 1960 to 2008 are obtained from the National Meteorological Information Center of the China Meteorological Administration. The locations of the stations are shown in Fig. 1, and the basic information of the stations is listed in Table 1. The station history records are from the Beijing Meteorological Bureau (BMB) (2009), and the population data for the residential areas near the stations are from the China Statistic Bureau (2002).
Table 1 Information of the weather stations used in this study
As a rapidly grown small city, Huairou is located in the northern mountainous areas of the BM, with a population of ~75 thousands in the urban area in 2000 (Fig. 1a). Huairou weather station is a typical urban station (Fig. 1b), and the recent 49-year records from the station are evaluated in this paper for the data inhomogeneities and urbanization effects on the SAT trends as a target observational site. Although Xiayunling and Shangdianzi stations are also located in the mountainous areas, they are both far from larger residential areas, with the former being on a valley in the southwest and the latter on a slope near a small village having a population of no more than a thousand in the northeast (Fig. 1a). The two observational sites are chosen as the reference stations from 20 weather stations with over 30-year records in the BM. In addition to the small population of the residential areas near stations and the similar physiographic characteristics to the target station, the reference stations are also required to have the continuous observation records with as possible as less the missing observational values. The two weather stations were ever used as reference stations in previous studies of urbanization effect on the SAT trends of Beijing station (Chu and Ren 2005; Ren et al. 2007).
Inhomogeneities of SAT data can be caused by such factors as instrumentation, relocation, change in observational time, and modified statistical methods for daily averages. The introduction of the Autonomous Weather Stations (AWS) to operational observations around 2004 in mainland China may in certain extent have resulted in additional inhomogeneities in SAT records. Wang et al. (2007b) indicated, however, that the SAT of AWS has certain difference from that of manual weather stations, but overall the difference is small and not significant. No change in observational time and statistical methods of daily mean SAT occurred during the last 50 years, and these will not cause any detectable inhomogeneities of the SAT data. It has been realized that the most important factor causing the inhomogeneities of SAT data is the frequent relocations of stations in mainland China (Yan et al. 2001; Li et al. 2004; Ren et al. 2005).
Huairou station experienced relocation twice. It was moved for the first time from West Gate of the old town (Site 1 in Fig. 1b) to Beitumenzi (Site 2 in Fig. 1b) at the East Gate Road outside the old town on 1 August 1964. The second move occurred on 1 July 1996, from Beitumenzi to a suburban village called Liugezhang (Site 3 in Fig. 1b), about 5.5 km from the center of the old town (BMB 2009). For the two reference stations, on the other hand, the only move occurred for Shangdianzi station on 1 September 1989, but the horizontal distance of the movement was 750 m, and the observational grounds changed from 255 m above sea level (ASL) to 293 m ASL, increasing by 38 m in altitude.
The data are quality-controlled with the following steps: (1) if the maximum temperature (Tmax) values are lower than the minimum temperature (Tmin) values, they are registered as unreasonable readings. There is no unreasonable record in the SAT dataset of Huairou station; (2) the values beyond four times of standard deviation are marked as outliers. If outliers are detected, the reasonable records are retained, and unreasonable ones are corrected or regarded as missing values, based on the comparison to the records of the neighboring stations. There is only one outlier found in the dataset, but it is not unreasonable; (3) missing values, which account for less than 0.25% of the total records, are filled in by using the means of the same stations for the reference time period 1971–2000.
The monthly mean Tmin and Tmax series T
i,j
are calculated based on the daily records, and the monthly change-in-temperature time series dT/dt for Huairou station are then created referring to Easterling and Peterson (1995a). The i and j indicate number of year and month respectively.
$$ {{\left( {\mathrm{d}T/\mathrm{d}t} \right)}_{i,j }}={T_{i+1,j }}-{T_{i,j }} $$
(1)
The monthly reference change-in-temperature time series \( \left( {\mathrm{d}T/\mathrm{d}t} \right)\prime \) are constructed by averaging the two reference station data with squares of correlation coefficients with Huairou station series as weights. We thus get the reference series \( T\prime \).
$$ T{\prime_{i+1,j }}=T{\prime_{i,j }} + \left( {\mathrm{d}T/\mathrm{d}t} \right){\prime_{i,j }} $$
(2)
Discontinuous points in annual difference series of the target station and the reference stations are detected by using method of moving t test. As mentioned above, Huairou station was moved in 1964 and 1996. In order to effectively identify the discontinuities due to the relocations, the son series length is set as 3 years since the dataset started in 1960. Therefore, the series length n = 49, the son series length n1 = n2 = 3, and the significance level α = 0.01. The metadata are used to validate the existence of the inhomogeneous points, and they are adjusted if proved to be real and caused by relocation. Otherwise, the original records are kept as they were.
The 5-year averages of monthly mean SAT difference between the target station and the reference series is taken as the adjustment values. If the records are less than 5 years before or after discontinuous points, then all the years of record available are used to determine the adjustment values. The adjustments for inhomogeneities are made on basis of daily SAT data. The daily adjustment values are obtained by a linear interpolation method, with the monthly mean adjustment values being assigned to the mid-month days (15th or 14th) of the neighboring months.
The sections of data after the last documented inhomogeneous points are taken as the base series, and they remain unchanged. Before the inhomogeneous points, the adjustment values are added to the original records for every day.
Urban effect (ΔT
ur
) is defined as the SAT trends caused by the changing Urban Heat Island (UHI) intensity and/or other factors (such as aerosols) related to urbanization near the specific locations of urban weather stations (Chu and Ren 2005; Ren et al. 2008). It is estimated by formula:
$$ \varDelta {T_{ur }}={T_u}-{T_r} $$
(3)
where T
u
is the SAT trend of urban station and T
r
is the SAT trend of reference (rural) station (series). ΔT
ur
is larger than 0 if the urbanization raises the SAT trend at urban station, and it is smaller than 0 if the urbanization reduces the SAT trend at urban station.
ΔT
ur
can also be estimated by calculating the annual and monthly mean SAT differences between urban station and reference series and the linear trend of the difference series over the time period analyzed. In this paper, the annual mean SAT difference series of Tmin and Tmax between Huairou station and the average reference series are constructed, and their linear trends for the time period 1960–2008 are estimated by using least-square method and are examined for statistical significance by t test.
Urban contribution (E
u
) is defined as a proportion that the statistically significant urban effect accounts for the total SAT trend at urban station (Chu and Ren 2005; Ren et al. 2008). It can be expressed as:
$$ {E_u}=\left| {\frac{{\Delta {T_{ur }}}}{{{T_u}}}} \right|\times 100\%=\left| {\frac{{{T_u}-{T_r}}}{{{T_u}}}} \right|\times 100\% $$
(4)
Generally, ΔT
ur
/T
u
is a positive value less than 100% (0 ≤ E
u
≤ 100%); absolute value is taken because it, in certain circumstances, assumes negative value due to the effects other than increasing UHI intensity. If E
u
= 100%, then it shows that the SAT trend of the urban station is entirely caused by urbanization; if E
u
is more than 100%, it implies that the extra trend might have been caused by other local factors not yet identified or the errors of data, but it is regarded as 100% in this study. As the definition implies, urban contribution is not calculated if the urban effect is not statistically significant.