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Abstract
Relationships between monthly mean and daily temperatures are assessed for 126 stations in the United States for the 1950–1986 period. A linear regression approach is used throughout. Most of the nation has a decrease in the standard deviation of daily temperatures as the monthly mean temperature increases. Only in the northeast in spring and fall and along the west coast in spring are there extensive areas of increase. Regressions also show that for most of the nation the monthly mean diurnal temperature range increases as monthly mean temperatures increase. The only extended area of opposite trend is in the southeast in the cooler months. These results can be partly explained by changes in cloud amount. A parallel assessment of trends in monthly mean temperatures, standard deviations, and monthly mean maximum temperatures as a function of time indicates only minor areas where weakly significant relationships occur. The implications of these results for using extrapolations of past data for estimates of future conditions resulting from greenhouse warming are considered.