Monthly Mean Diurnal Cycles in Surface Temperatures over Land for Global Climate Studies

Abstract

Monthly mean diurnal cycles (MDCs) of surface temperatures over land, represented in 3-h universal time intervals, have been analyzed. Satellite near-global data from the International Satellite Cloud Climatology Project (ISCCP) with a (280 km)2 resolution (C-2 product) are available for seven individual years and as a climatology derived thereof. Surface 19-yr climatologies on ground and air temperatures, separately for all-sky and clear-sky conditions, matched with the ISCCP data, are employed to better understand satellite-derived MDCs. The MDCs have been converted to local solar time, refined to a regular 1-h time grid using cubic splines, and subjected to principal component analysis. The first two modes approximate MDCs in air and ground?satellite temperatures with rmse?s of about σ = 0.5° and 1°C, respectively, and these accuracies are improved by 20%?35% if the third mode is added. This suggests that two to three temperature measurements during the day allow reconstruction of the full MDC. In the case of two modes, optimal observation times are close to the occurrence of minimum and maximum temperatures, Tmin and Tmax. The authors provide an empirical algorithm for reconstructing the full MDC using Tmin and Tmax, and estimate its accuracy. In the analyzed match-up dataset, the statistical structure of ground temperature for all-sky conditions most closely resembles that of the ISCCP derived temperature. The results are potentially useful for climate- and global-scale studies and applications.