Using the quantile regression method to analyze changes in climate characteristics

Possibilities to use the non-parametric regression analysis method, named the quantile regression, for the estimation of changes in climate characteristics are considered. When analyzing the trends of climatic series, the quantile regression method enables to get the information on trends along the whole range of quantile values from 0 to 1 of dependent variable distributions, that is more informative than the use of traditional regression technique, based on the least-squares method (LSM) and enabling to obtain trend estimations for average values of the dependent variable only. Trend estimation errors for various methods are analyzed. The computation of quantile regression parameters for real climatic series is executed. Series of meteorological variables of the diurnal resolution, which characterize the surface climate (minimal, average, and maximal diurnal temperatures) and free atmosphere climate (temperature of isobaric surfaces up to 30 hPa inclusive) are considered. Seasonal peculiarities in trend manifestation at different parts of quantile range of these meteorological values are discussed. Concerning the problem of the analysis of climate trends, the quantile regression method seems to be perspective from the point of view of more detailed understanding of processes in the climate system, such as the surface and tropospheric warming, stratospheric cooling, long-period changes in characteristics of climate variability and extremity.