Classification of the Earth’s Surface Points Based on the Properties of the Yearly Course of Variables Characterizing the State of the Near-Surface Layer

An approach to assessing seasonality is proposed based on the properties of the yearly course of characteristics—state variables—of the near-surface layer. The factual basis for this assessment is an array of monthly values of any state variable of the near-surface layer (temperature, precipitation amount, etc.) for a climatically significant period of time (e.g., 1, 2, or 3 decades). It is believed that within this period a linear trend of variable under study is possible, but the nature of nondirectional variability does not change. As a result of the analysis of these data for any year within the considered period of time, the values of the yearly course parameters are established, i.e., seasonal systematic deviations from the yearly average value, as well as their standard deviations. Further, in each year, each month is assigned a number R equal to 1 (–1) or 0, depending on whether the corresponding deviation is significantly positive, significantly negative, or uncertain with respect to the sign. This R sequence is used to characterize seasonality. As an example of classifying points of geographic space using seasonality, the year corresponding to the center of the time period under consideration was used in this work. It is considered that, for two points of geographic space, seasonality with respect to the variable under study is identical if their R sequences consisting of 12 numbers are coinsided by a cyclic permutation of months. This approach is applied to the global analysis of seasonal temperature variability. The NOAA-CIRES-DOE 20th Century Reanalysis V3 reanalysis data for 1981–2010 were used. The resulting seasonality classification is presented in the form of schematic maps and provided with corresponding geographical comments.