CR 91:47-66 (2023)  -  DOI: https://doi.org/10.3354/cr01724

ABSTRACT: The reliability of climate change detection and research is significantly impacted by the inhomogeneity of surface climate observation data. However, there is an ongoing debate regarding whether comprehensive homogenization has been performed in large-scale homogenized data sets. In this study, we examined the homogeneity of the original maximum and minimum temperature (T_max and T_min) data for 662 meteorological stations in North China by using multiple methods and combining with metadata. The quantile matching method was employed to adjust the daily T_max and T_min series. In order to avoid the potential systematic bias resulting from homogenization, no reference series were introduced during the adjustment process. The adjustment results indicate that T_min in North China is significantly affected by non-climatic factors, particularly station relocations and environmental changes around the stations. The application of homogenization in this study led to a notable increase in the overall temperature trends of the stations, with T_min exhibiting a larger increase and the diurnal temperature range demonstrating a more significant downward trend. Based on the homogenized data, the annual and seasonal mean temperature trends in North China from 1951 to 2020 were re-evaluated. These temperature trends generally surpass those reported in previous research for the same period from 1961 to 2000. The higher estimate of temperature trends may be attributed to the recovered urbanization effect in the newly homogenized data. Thus, the obtained homogenization data still exhibit a significant urbanization bias that requires further assessment and adjustment.

KEY WORDS: Homogenization · Data · Maximum temperature · Minimum temperature · Warming trend · Urbanization effect · North China

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Cite this article as: He J, Ren G, Zhang P, Zheng X, Zhang S (2023) Updated analysis of surface warming trends in North China based on in-depth homogenized data (1951-2020). Clim Res 91:47-66. https://doi.org/10.3354/cr01724 Export citation Share:    Facebook - - linkedIn