Seasonal and Diurnal Cycles in Climate Change and Variability

Long-term climatic variations and trends often have seasonal and diurnal cycles, which have not been studied systematically due to the lack of an adequate technique and computational resources. Here we discuss a recently developed statistical technique for describing climatic processes with seasonal and diurnal cycles in statistical moments and trends. This technique has been successfully used to study routine observations of meteorological stations and satellite observations of sea ice, snow cover, and tropospheric temperature (MSU). It has proven to be extremely efficient for analyzing climatic records with arbitrary observation times. By using adequate approximations of non-stationary cyclic components in expected values of climatic variables, we are able to compute time series of residuals which may be considered as stationary random processes for which lag and cross-correlations can be studied without any danger of false results. This technique also provides new opportunities to analyze long term changes in climate variability.