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

Abstract

Trends in surface air temperature (SAT) are a critical indicator for climate change at varied spatial scales. Because of urbanization effects, however, the current SAT records of many urban stations can hardly meet the demands of the studies. Evaluation and adjustment of the urbanization effects on the SAT trends are needed, which requires an objective selection of reference (rural) stations. Based on the station history information from all meteorological stations with long-term records in mainland China, an integrated procedure for determining the reference SAT stations has been developed and is applied in forming a network of reference SAT stations. Historical data from the network are used to assess the urbanization effects on the long-term SAT trends of the stations of the national Reference Climate Network and Basic Meteorological Network (RCN1BMN or national stations), which had been used most frequently in studies of regional climate change throughout the country. This paper describes in detail the integrated procedure and the assessment results of urbanization effects on the SAT trends of the national stations applying the data from the reference station network determined using the procedure. The results showed a highly significant urbanization effect of 0.0748C (10 yr) 21 and urbanization contribution of 24.9% for the national stations of mainland China during the time period 1961-2004, which compared well to results that were reported in previous studies by the authors using the predecessor of the present reference network and the reference stations selected but when applying other methods. The authors are thus confident that the SAT data from the updated China reference station network as reported in this paper best represented the baseline SAT trends nationwide and could be used for evaluating and adjusting the urban biases in the historical data series of the SAT from different observational networks.

Urbanization Effect in Regional Temperature Series Based on a Remote Sensing Classification Scheme of Stations

“…Sun et al (2016) reported that the linear trend in the observed annual mean temperature of all the national stations in mainland China is 1.44°C over the period 1961-2013, of which 0.49°C (0.12-0.86°C) or about one third can be attributed to urbanization. Ren et al (2015) showed that the contribution of the urbanization effect to the SAT warming is at least 24.9% for the national stations of mainland China during the period 1961-2004, approximately consistent with that reported by Zhang et al (2010).…”

“…The MPULU usually appears in the fourth buffer circle (3–4 km) for all the 11 groups of stations (Figure S2), which can explain the above‐mentioned finding that PULU has the highest correlation with the annual T mean trends series in the 4‐km radius buffer circle (Figure S1). This is also consistent with the fact that most of the stations are located within or near the medium‐sized and small cities of the country, and the level of urbanization effect on the temperature change at the observational sites will be closely related to their distance from the urban centers (Ren et al, ).…”

On trend estimation and significance testing for non-Gaussian and serially dependent data: quantifying the urbanization effect on trends in hot extremes in the megacity of Shanghai

“…Figure 9 shows that after the early 1990s, the UMR differences between the Xujiahui station and Shanghai suburbanaverage tended to decrease, implying that suburban areas had been developing even faster than the downtown area since that time. In addition, since there is an urban heat dome, the bigger a city is the wider the size of the urban heat dome will be and the larger the area affected by the urban heat island effect (Ren et al 2015). Therefore, Pinghu station was chosen as a rural site.…”

“…Besides, it does not belong to Shanghai municipality and therefore the urbanization process is much slower than in Shanghai's suburbs. The selection of this station was based as much as possible on existing observational networks, and it also meets most of the five criteria listed in Ren et al (2015) for selecting a reference station: (1) within the 70,000 limit for eastern plain regions where economies were relatively more developed; (3) This station experienced no relocations during the analysis period. Nevertheless, the urbanization contributions estimated using Pinghu station were similar to those using a relatively less urbanized suburban station (Chongming station) of Shanghai municipality: 38.6 % for annual-mean temperature and 53.1 % for JJA-mean temperature by the OLS-M method.…”

Contribution of urbanization to the increase of extreme heat events in an urban agglomeration in east China

“…However, most previous studies used a static classification of stations throughout an entire analysis period, which may underestimate the UHI effect because some currently urban stations were rural in the past and their contributions to urbanization effects are not included. In addition, most previous studies in China used temperature data from the national reference climatic and basic meteorological stations with a substantial number of stations located within or in close proximity to urbanized areas Ren and Zhou, 2014;Ren et al, 2015]. In addition, most previous studies in China used temperature data from the national reference climatic and basic meteorological stations with a substantial number of stations located within or in close proximity to urbanized areas Ren and Zhou, 2014;Ren et al, 2015].…”