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

Siqi Huang, Guoyu Ren () and Panfeng Zhang
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Siqi Huang: Chinese Academy of Meteorological Sciences, Beijing 100081, China
Guoyu Ren: Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China
Panfeng Zhang: Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan 430074, China

Land, 2024, vol. 13, issue 3, 1-18

Abstract: 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.

Keywords: urbanization effect; surface air temperature; temperature trend; contiguous United States; observation data; machine learning (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2024
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