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Urbanization Effects on Estimates of Global Trends in Mean and Extreme Air Temperature

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  • 1 Department of Atmospheric Science, School of Environmental Studies, China University of Geosciences, Wuhan, China
  • | 2 Laboratory for Climate Studies, National Climate Center, China Meteorological Administration, Beijing, China
  • | 3 School of Geography and Information Engineering, China University of Geosciences, Wuhan, China
  • | 4 School of Resource and Environmental Sciences, Wuhan University, Wuhan, China
  • | 5 South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, China
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Abstract

Identifying and separating the signal of urbanization effects in current temperature data series is essential for accurately detecting, attributing, and projecting mean and extreme temperature change on varied spatial scales. This paper proposes a new method based on machine learning to classify the observational stations into rural stations and urban stations. Based on the classification of rural and urban stations, the global and regional land annual mean and extreme temperature indices series over 1951–2018 for all stations and rural stations were calculated, and the urbanization effects and the urbanization contribution of global land annual mean and extreme temperature indices series are quantitatively evaluated using the difference series between all stations and the rural stations. The results showed that the global land annual mean time series for mean temperature and most extreme temperature indices experienced statistically significant urbanization effects. The urbanization effects in the mean and extreme temperature indices series generally occurred after the mid-1980s, and there were significant differences of the magnitudes of urbanization effects among different regions. The urbanization effect on the trends of annual mean and extreme temperature indices series in East Asia is generally the strongest, which is consistent with the rapidly urbanization process in the region over the past decades, but it is generally small in Europe during the recent decades.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0389.s1.

© 2021 American Meteorological Society. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: Guoyu Ren, guoyoo@cma.gov.cn

Abstract

Identifying and separating the signal of urbanization effects in current temperature data series is essential for accurately detecting, attributing, and projecting mean and extreme temperature change on varied spatial scales. This paper proposes a new method based on machine learning to classify the observational stations into rural stations and urban stations. Based on the classification of rural and urban stations, the global and regional land annual mean and extreme temperature indices series over 1951–2018 for all stations and rural stations were calculated, and the urbanization effects and the urbanization contribution of global land annual mean and extreme temperature indices series are quantitatively evaluated using the difference series between all stations and the rural stations. The results showed that the global land annual mean time series for mean temperature and most extreme temperature indices experienced statistically significant urbanization effects. The urbanization effects in the mean and extreme temperature indices series generally occurred after the mid-1980s, and there were significant differences of the magnitudes of urbanization effects among different regions. The urbanization effect on the trends of annual mean and extreme temperature indices series in East Asia is generally the strongest, which is consistent with the rapidly urbanization process in the region over the past decades, but it is generally small in Europe during the recent decades.

Supplemental information related to this paper is available at the Journals Online website: https://doi.org/10.1175/JCLI-D-20-0389.s1.

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Corresponding author: Guoyu Ren, guoyoo@cma.gov.cn

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