Quantifying the effect of urbanization on U.S. Historical Climatology Network temperature records. Issue 2 (30th January 2013) - Hausfather, Zeke
Abstract : [1] An assessment quantifying the impact of urbanization on temperature trends from the U.S. Historical Climatology Network (USHCN) is described. Stations were first classified as urban and nonurban (rural) using four different proxy measures of urbanity. Trends from the two station types were then compared using a pairing method that controls for differences in instrument type and via spatial gridding to account for the uneven distribution of stations. The comparisons reveal systematic differences between the raw (unadjusted) urban and rural temperature trends throughout the USHCN period of record according to all four urban classifications. According to these classifications, urbanization accounts for 14–21% of the rise in unadjusted minimum temperatures since 1895 and 6–9% since 1960. The USHCN version 2 homogenization process effectively removes this urban signal such that it becomes insignificant during the last 50–80 years. In contrast, prior to 1930, only about half of the urban signal is removed. Accordingly, the National Aeronautics and Space Administration Goddard Institute for Space Studies urban‐correction procedure has essentially no impact on USHCN version 2 trends since 1930, but effectively removes the residual urban‐rural temperature trend differences for years before 1930 according to all four urban proxy classifications. Finally, an evaluation of the homogenization of USHCN temperature series using subsets of rural‐only and urban‐only reference series from the larger U.S. Cooperative Observer (Coop) Network suggests that the composition of Coop stations surrounding USHCN stations is sufficiently "rural" to limit the aliasing of urban heat island signals onto USHCN version 2 temperature trends during homogenization. Key Points: There are significant urbanity‐correlated biases in USHCN Existing automated homogeneity techniques correct for the majority of bias Urbanity‐correlated biases may be poorly corrected when station density is low