Influence of Changes in Meteorological Observational Environment on Urbanization Bias in Surface Air Temperature: A Review

Tao, Shi; Yang, Yibo; Sun, Dabing; Huang, Yong; Shi, Chong

doi:10.3389/fclim.2021.781999

Cited by 5 publications

(3 citation statements)

References 101 publications

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“…In the traditional researches on the CUHI effect, the study areas were simply divided into urban and suburban types, that is, the CUHII was calculated by the average temperature difference between urban stations and reference stations. However, this single division calculation ignored the differences of meteorological elements within the city, thus affecting the accurate assessment of CUHI (Ren and Ren, 2011;Yang et al, 2013;Li et al, 2015;Shi et al, 2022).…”

Section: The Influence Of Climate Background and Local Circulation On...mentioning

confidence: 99%

Research progress on the synergies between heat waves and canopy urban heat island and their driving factors

Shi,

Liu,

Wen

et al. 2024

Front. Environ. Sci.

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Under the background of global warming and accelerating urbanization, the interaction between heat waves (HWs) and canopy urban heat island (CUHI) has become one of the focuses in the field of global climate change research. This paper comprehensively reviewed and summarized the research process on the synergies of HWs and CUHI and their influencing mechanism. The coupling effect between HWs and CUHI remains debated, which may be related to the use of different standards to define heat wave events. The spatiotemporal differences in the synergies between HWs and CUHI was also influenced by climate background and local circulation. For instance, scholars have reached different conclusions regarding the stronger synergistic effect between daytime and nighttime in cities with different climate backgrounds. In addition, the modulation of urban morphological structure to the synergies between HWs and CUHI cannot be ignored. In the future, it is necessary to adopt different definitions of HWs to systematically study the formation mechanism of urban excess warming from different climatic backgrounds, local circulation conditions, and urban morphologies.

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Section: The Influence Of Climate Background and Local Circulation On...mentioning

confidence: 99%

Research progress on the synergies between heat waves and canopy urban heat island and their driving factors

Shi,

Liu,

Wen

et al. 2024

Front. Environ. Sci.

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“…Firstly, following previous studies [24,27], a circular buffer zone with a radius of 2 km was constructed around each station. Most of the stations were 2-3 km away from areas of city or county town when they were initially built, and 2 km was chosen because this scale can represent well the local effects of urban expansion, as suggested by many climate studies [13,49,50]. The proportion of the built-up area was calculated based on the corresponding land cover within the buffer zone [24,41].…”

Section: Division Of Station Typesmentioning

confidence: 99%

Long-Term Variations in Warm and Cold Events in Nanjing, China: Roles of Synoptic Weather Patterns and Urbanization

Tian

Zong

Dong

et al. 2023

Land

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Studying the long-term variations in warm and cold events and their causes under global warming is important for understanding urban climate change, planning, and green development, as well as for disaster prevention and mitigation. In this study, taking the megacity of Nanjing in China as an example, we analyzed the trends and characteristics of the daily average temperature, daily maximum temperature, daily minimum temperature, and warm and cold events from 1960 to 2021, and their association with synoptic weather patterns (SWPs) and urbanization. The results showed that, over the past 62 years, the maximum/average/minimum temperatures in Nanjing have trended upward significantly (at the urban station they increased at rates of 0.17, 0.34, and 0.67 °C/decade), with the minimum temperatures being the most significant. In spring, the warming rate of the average temperature was the greatest, reaching 0.45 °C/decade. All other seasons had their highest warming rate in their minimum temperatures, reaching 0.38 °C/decade, 0.73 °C/decade, and 0.67 °C/decade in the summer, autumn, and winter, respectively. The extreme high temperatures showed a decreasing trend until the mid-1980s, closely related to the decrease in the two SWPs with prevailing southwesterly winds (Types 1 and 2), while a significant increasing trend was apparent thereafter, mainly related to the increase in the SWPs with prevailing southeasterly winds (Types 3 and 4). The number of warm days was strongly positively correlated with extreme high temperatures during the study period, and about 91% of the warm day interannual variation can be explained by extreme high temperature variation. The extreme low temperatures showed a significant decreasing trend. The number of cold nights was strongly and positively correlated with extreme low temperatures, and about 85% of the cold night interannual variation can be explained by extreme low temperature variation. The effect of urbanization was basically positive, contributing the most to the average temperatures and second-most to the minimum temperatures, with warming contributions of 26.5% and 20.9%, respectively, and an insignificant contribution to the maximum temperatures. The effect of urbanization on extreme high temperatures was not significant, but the contribution of warming to extreme low temperatures reached 27.9%. Our results have important implications for future urban climate prediction, as well as for impact assessment and decision making in urban planning.

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“…As a result, the temperature information recorded by observation stations also contains enhanced UHI signals, resulting in higher SAT observation values that affect the accuracy of trend estimates based on these data [12]. Therefore, the urbanization effects on SAT data series or systematic urbanization biases contained in SAT data cannot be ignored [13][14][15][16] when studying long time series of global or regional climate change. Only by scientifically evaluating the urbanization bias in SAT data series over the last century can we provide a further understanding of the characteristics and mechanisms of long-term trends in large-scale SAT data from the past.…”

Section: Introductionmentioning

confidence: 99%

Urbanization Effects in Estimating Surface Air Temperature Trends in the Contiguous United States

Huang,

Ren,

Zhang

2024

Land

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In the past century, local-scale warming caused by a strengthening urban heat island effect has brought inevitable systematic bias to observational data from surface weather stations located in or near urban areas. In this study, the land use situation around U.S. Climate Reference Network (USCRN) stations was used as a reference for rural station selection; stations with similar environmental conditions in the U.S. Historical Climatology Network (USHCN) were selected as reference stations using a machine learning method, and then the maximum surface air temperature (Tmax) series, minimum surface air temperature (Tmin) series and mean surface air temperature (Tmean) series of rural stations during 1921–2020 were compared with those for all nearby stations (including both rural and urban stations) to evaluate urbanization effects in the USHCN observation data series of the contiguous United States, which can be regarded as urbanization bias contained in the latest homogenized USHCN observation data. The results showed that the urbanization effect on the Tmean trend of USHCN stations is 0.002 °C dec−1, and the urbanization contribution is 35%, indicating that urbanization around USHCN stations has led to at least one-third of the overall warming recorded at USHCN stations over the last one hundred years. The urbanization effects on Tmax and Tmin trends of USHCN stations are −0.015 °C dec−1 and 0.013 °C dec−1, respectively, and the urbanization contribution for Tmin is 34%. These results have significance for understanding the systematic bias in USHCN temperature data, and they provide a reference for subsequent studies on data correction and climate change monitoring.

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Influence of Changes in Meteorological Observational Environment on Urbanization Bias in Surface Air Temperature: A Review

Cited by 5 publications

References 101 publications

Research progress on the synergies between heat waves and canopy urban heat island and their driving factors

Research progress on the synergies between heat waves and canopy urban heat island and their driving factors

Long-Term Variations in Warm and Cold Events in Nanjing, China: Roles of Synoptic Weather Patterns and Urbanization

Urbanization Effects in Estimating Surface Air Temperature Trends in the Contiguous United States

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