Evidence of urban blending in homogenized temperature records in Japan and in the United States: implications for the reliability of global land surface air temperature data

In order to reduce the amount of non-climatic biases of air temperature in each weather station’s record by comparing it to neighboring stations, global land surface air temperature datasets are routinely adjusted using statistical homogenization to minimize such biases. However, homogenization can unintentionally introduce new non-climatic biases due to an often-overlooked statistical problem known as “urban blending” or “aliasing of trend biases”. This issue arises when the homogenization process inadvertently mixes urbanization biases of neighboring stations into the adjustments applied to each station record. As a result, urbanization biases of the original unhomogenized temperature records are spread throughout the homogenized data. To evaluate the extent of this phenomenon, the homogenized temperature data for two countries (Japan and United States) are analyzed. Using the Japanese stations in the widely used Global Historical Climatology Network (GHCN) dataset, it is first confirmed that the unhomogenized Japanese temperature data are strongly affected by urbanization bias (possibly ~60% of the long-term warming). The United States Historical Climatology Network dataset (USHCN) contains a relatively large amount of long, rural station records and therefore is less affected by urbanization bias. Nonetheless, even for this relatively rural dataset, urbanization bias could account for ~20% of the long-term warming. It is then shown that urban blending is a major problem for the homogenized data for both countries. The IPCC’s low estimate of urbanization bias in the global temperature data based on homogenized temperature records may have been biased low due to urban blending. Recommendations on how future homogenization efforts could be modified to reduce urban blending are discussed.