Abstract
A subset of stations from the daily U.S. Historical Climatology Network (HCN) is used as a basis for a historical database of temperature extreme occurrence in the United States. The dataset focuses on daily temperature occurrences that exceed (fall below) the 90th (10th) percentiles of daily maximum and minimum temperature. Using a variety of techniques, the temperature extreme occurrence data are homogenized to account for nonclimatic shifts resulting from station relocations, changes in instrument type, and variations in the time of observations. Given the daily resolution of the extreme data, these potential sources of inhomogeneity require testing and adjustment using methods other than those conventionally used with mean temperature data. A data estimation technique, specific to extremes, is also used to produce serially complete exceedence records. Stations are also identified based on their current degree of urbanization using satellite observations. The dataset is intended to provide a research-quality source of temperature extreme data, analogous and complementary to the daily HCN dataset.
Two analyses are presented that illustrate the influence of adjustment. The change in temperature extreme occurrence with time reverses at between 15% and 20% of the HCN stations depending upon whether adjusted or unadjusted series is used. Changes in the distribution of extreme occurrences during drought and nondrought years are also shown to occur.
Corresponding author address: Dr. Art DeGaetano, Northeast Regional Climate Center, Cornell University, 1119 Bradfield Hall, Ithaca, NY 14853. Email: atd2@cornell.edu
